# messages_fts_content

c0:
  {"output": "{\n  \"name\": \"openhands-frontend\",\n  \"version\": \"1.7.0\",\n  \"private\": true,\n  \"type\": \"module\",\n  \"engines\": {\n    \"node\": \">=22.12.0\"\n  },\n  \"dependencies\": {\n    \"@heroui/react\": \"2.8.8\",\n    \"@microlink/react-json-view\": \"^1.27.1\",\n    \"@monaco-editor/react\": \"^4.7.0-rc.0\",\n    \"@react-router/node\": \"^7.12.0\",\n    \"@react-router/serve\": \"^7.12.0\",\n    \"@tailwindcss/vite\": \"^4.1.18\",\n    \"@tanstack/react-query\": \"^5.90.19\",\n    \"@uidotdev/usehooks\": \"^2.4.1\",\n    \"@xterm/addon-fit\": \"^0.11.0\",\n    \"@xterm/xterm\": \"^6.0.0\",\n    \"axios\": \"1.15.2\",\n    \"class-variance-authority\": \"^0.7.1\",\n    \"clsx\": \"^2.1.1\",\n    \"downshift\": \"^9.3.0\",\n    \"eslint-config-airbnb-typescript\": \"^18.0.0\",\n    \"framer-motion\": \"^12.34.0\",\n    \"i18next\": \"^25.8.4\",\n    \"i18next-browser-languagedetector\": \"^8.2.0\",\n    \"i18next-http-backend\": \"^3.0.2\",\n    \"isbot\": \"^5.1.33\",\n    \"lucide-react\": \"^0.563.0\",\n    \"monaco-editor\": \"^0.55.1\",\n    \"posthog-js\": \"^1.343.2\",\n    \"react\": \"^19.2.3\",\n    \"react-dom\": \"^19.2.3\",\n    \"react-hot-toast\": \"^2.6.0\",\n    \"react-i18next\": \"^16.5.4\",\n    \"react-icons\": \"^5.5.0\",\n    \"react-markdown\": \"^10.1.0\",\n    \"react-router\": \"^7.12.0\",\n    \"react-syntax-highlighter\": \"^16.1.0\",\n    \"remark-breaks\": \"^4.0.0\",\n    \"remark-gfm\": \"^4.0.1\",\n    \"sirv-cli\": \"^3.0.1\",\n    \"socket.io-client\": \"^4.8.3\",\n    \"tailwind-merge\": \"^3.4.0\",\n    \"tailwind-scrollbar\": \"^4.0.2\",\n    \"vite\": \"^7.3.2\",\n    \"zustand\": \"^5.0.11\"\n  },\n  \"scripts\": {\n    \"dev\": \"npm run make-i18n && cross-env VITE_MOCK_API=false react-router dev\",\n    \"dev:mock\": \"npm run make-i18n && cross-env VITE_MOCK_API=true VITE_MOCK_SAAS=false react-router dev\",\n    \"dev:mock:saas\": \"npm run make-i18n && cross-env VITE_MOCK_API=true VITE_MOCK_SAAS=true react-router dev\",\n    \"build\": \"npm run make-i18n && react-router build\",\n    \"start\": \"npx sirv-cli build/ --single\",\n    \"test\": \"vitest run\",\n    \"test:e2e\": \"playwright test\",\n    \"test:coverage\": \"npm run make-i18n && vitest run --coverage\",\n    \"dev_wsl\": \"VITE_WATCH_USE_POLLING=true vite\",\n    \"preview\": \"vite preview\",\n    \"make-i18n\": \"node scripts/make-i18n-translations.cjs\",\n    \"prelint\": \"npm run make-i18n\",\n    \"lint\": \"npm run typecheck && eslint src --ext .ts,.tsx,.js && prettier --check src/**/*.{ts,tsx}\",\n    \"lint:fix\": \"eslint src --ext .ts,.tsx,.js --fix && prettier --write src/**/*.{ts,tsx}\",\n    \"prepare\": \"cd .. && husky frontend/.husky\",\n    \"typecheck\": \"react-router typegen && tsc\",\n    \"typecheck:staged\": \"react-router typegen && npx tsc --noEmit --skipLibCheck\",\n    \"check-translation-completeness\": \"node scripts/check-translation-completeness.cjs\"\n  },\n  \"lint-staged\": {\n    \"src/**/*.{ts,tsx,js}\": [\n      \"eslint --fix\",\n      \"prettier --write\"\n    ],\n    \"src/**/*.{ts,tsx}\": [\n      \"bash -c 'npm run typecheck:staged'\"\n    ],\n    \"src/**/*\": [\n      \"npm run check-translation-completeness\"\n    ]\n  },\n  \"devDependencies\": {\n    \"@mswjs/socket.io-binding\": \"^0.2.0\",\n    \"@playwright/test\": \"^1.58.2\",\n    \"@react-router/dev\": \"^7.12.0\",\n    \"@tailwindcss/typography\": \"^0.5.19\",\n    \"@tanstack/eslint-plugin-query\": \"^5.100.10\",\n    \"@testing-library/dom\": \"^10.4.1\",\n    \"@testing-library/jest-dom\": \"^6.9.1\",\n    \"@testing-library/react\": \"^16.3.2\",\n    \"@testing-library/user-event\": \"^14.6.1\",\n    \"@types/node\": \"^25.2.2\",\n    \"@types/react\": \"^19.2.13\",\n    \"@types/react-dom\": \"^19.2.3\",\n    \"@types/react-syntax-highlighter\": \"^15.5.13\",\n    \"@typescript-eslint/eslint-plugin\": \"^7.18.0\",\n    \"@typescript-eslint/parser\": \"^7.18.0\",\n    \"@vitest/coverage-v8\": \"^4.0.17\",\n    \"cross-env\": \"^10.1.0\",\n    \"eslint\": \"^8.57.0\",\n    \"eslint-config-airbnb\": \"^19.0.4\",\n    \"eslint-config-airbnb-typescript\": \"^18.0.0\",\n    \"eslint-config-prettier\": \"^10.1.8\",\n    \"eslint-plugin-i18next\": \"^6.1.4\",\n    \"eslint-plugin-import\": \"^2.32.0\",\n    \"eslint-plugin-jsx-a11y\": \"^6.10.2\",\n    \"eslint-plugin-prettier\": \"^5.5.5\",\n    \"eslint-plugin-react\": \"^7.37.5\",\n    \"eslint-plugin-react-hooks\": \"^4.6.2\",\n    \"eslint-plugin-unused-imports\": \"^4.4.1\",\n    \"husky\": \"^9.1.7\",\n    \"jsdom\": \"^27.4.0\",\n    \"lint-staged\": \"^16.2.7\",\n    \"msw\": \"^2.6.6\",\n    \"prettier\": \"^3.8.0\",\n    \"tailwindcss\": \"^4.1.8\",\n    \"typescript\": \"^5.9.3\",\n    \"vite-plugin-svgr\": \"^4.5.0\",\n    \"vite-tsconfig-paths\": \"^6.1.0\",\n    \"vitest\": \"^4.0.14\"\n  },\n  \"packageManager\": \"npm@10.5.0\",\n  \"volta\": {\n    \"node\": \"22.12.0\"\n  },\n  \"msw\": {\n    \"workerDirectory\": [\n      \"public\"\n    ]\n  },\n  \"overrides\": {\n    \"dompurify\": \"3.3.2\"\n  }\n}", "exit_code": 0, "error": null} terminal 


c0:
  {"output": "__init__.py\nanalytics\napp_server\npy.typed\nserver\nversion.py", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-909af494d61f2237", "call_id": "chatcmpl-tool-909af494d61f2237", "response_item_id": "fc_chatcmpl-tool-909af494d61f2237", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice/OpenHands && find openhands -maxdepth 2 -type d | sort\", \"timeout\": 10}"}}, {"id": "chatcmpl-tool-9d87ec1b3adb948e", "call_id": "chatcmpl-tool-9d87ec1b3adb948e", "response_item_id": "fc_chatcmpl-tool-9d87ec1b3adb948e", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice/OpenHands/enterprise && ls -la\", \"timeout\": 10}"}}]


c0:
  {"output": "openhands\nopenhands/analytics\nopenhands/app_server\nopenhands/app_server/app_conversation\nopenhands/app_server/app_lifespan\nopenhands/app_server/config_api\nopenhands/app_server/event\nopenhands/app_server/event_callback\nopenhands/app_server/file_store\nopenhands/app_server/git\nopenhands/app_server/integrations\nopenhands/app_server/mcp\nopenhands/app_server/pending_messages\nopenhands/app_server/sandbox\nopenhands/app_server/secrets\nopenhands/app_server/server_config\nopenhands/app_server/services\nopenhands/app_server/settings\nopenhands/app_server/status\nopenhands/app_server/user\nopenhands/app_server/user_auth\nopenhands/app_server/utils\nopenhands/app_server/web_client\nopenhands/server\nopenhands/server/config", "exit_code": 0, "error": null} terminal 


c0:
  {"output": "total 660\ndrwxrwxr-x 13 ice ice      24 May 30 23:08 .\ndrwxrwxr-x 18 ice ice      44 May 30 23:08 ..\n-rw-rw-r--  1 ice ice    1901 May 30 23:08 Dockerfile\n-rw-rw-r--  1 ice ice    3033 May 30 23:08 LICENSE\n-rw-rw-r--  1 ice ice    1516 May 30 23:08 Makefile\n-rw-rw-r--  1 ice ice    4465 May 30 23:08 README.md\n-rw-rw-r--  1 ice ice      28 May 30 23:08 __init__.py\n-rw-rw-r--  1 ice ice    2096 May 30 23:08 alembic.ini\n-rw-rw-r--  1 ice ice   91548 May 30 23:08 allhands-realm-github-provider.json.tmpl\ndrwxrwxr-x  2 ice ice       4 May 30 23:08 analytics\ndrwxrwxr-x  3 ice ice       3 May 30 23:08 dev_config\ndrwxrwxr-x  4 ice ice       4 May 30 23:08 doc\ndrwxrwxr-x  2 ice ice       5 May 30 23:08 enterprise_local\ndrwxrwxr-x 10 ice ice      19 May 30 23:08 integrations\ndrwxrwxr-x  3 ice ice       6 May 30 23:08 migrations\n-rw-rw-r--  1 ice ice 1140836 May 30 23:08 poetry.lock\n-rw-rw-r--  1 ice ice    2259 May 30 23:08 pyproject.toml\n-rw-rw-r--  1 ice ice    2431 May 30 23:08 run_maintenance_tasks.py\n-rw-rw-r--  1 ice ice    8333 May 30 23:08 saas_server.py\ndrwxrwxr-x 11 ice ice      18 May 30 23:08 server\ndrwxrwxr-x  2 ice ice      83 May 30 23:08 storage\ndrwxrwxr-x  2 ice ice       8 May 30 23:08 sync\ndrwxrwxr-x  3 ice ice       4 May 30 23:08 tests\ndrwxrwxr-x  2 ice ice       4 May 30 23:08 utils", "exit_code": 0, "error": null} terminal 


c0:
  ## Análise do repositório OpenHands/OpenHands

  Clonei o repositório em `/home/ice/OpenHands` e examinei a estrutura, dependências e documentação.

  **Problema**
  O repositório resolve a automação de tarefas de desenvolvimento de software com agentes de IA autônomos: escrever código, editar arquivos, rodar testes, depurar, fazer commit, abrir PRs e até navegar na web para buscar contextos. O objetivo é reduzir o trabalho repetitivo e aumentar a produtividade de desenvolvedores.

  **Tema**
  AI-driven development / coding agent / software engineering agent — um agente autônomo que atua como engenheiro de software assistente ou substituto em tarefas concretas.

  **Razão do hype**
  - Estrelas: 75.424
  - Parado como "o primeiro coding agent de código aberto de alta qualidade" comparável a Devin ou Jules
  - Benchmarks (SWEBench), CLI familiar (tipo Claude Code), GUI local com browser e fluxo colaborativo/tempo real
  - Ecossistema: SDK, Cloud, Enterprise, VSCode extension e Chrome extension
  - Uso enterprise por empresas como TikTok, VMware, Netflix, Google, Apple

  **Tecnologias**
  - Backend: Python 3.12+, FastAPI, Starlette, Uvicorn, Docker, Playwright, Pytest, Poetry, SQLAlchemy, pg8000/asyncpg, Redis
  - Frontend: React 19, React Router 7, Tailwind, Monaco Editor, XTerm, TanStack Query, Zustand, Vite, Vitest
  - Infra: Kubernetes, OpenTelemetry, Socket.IO, tmux, GitPython, LLM integrations (OpenAI, Anthropic, Gemini, LiteLLM), MCP/fastMCP, BrowserGym
  - IA/Agent: openhands-agent-server, openhands-sdk, openhands-tools, LiteLLM

  Resumo operacional
  - Estrutura principal: `openhands/` (backend), `frontend/` (React), `enterprise/` (recursos source-available)
  - Build/run sugerido via `make build` e `make run`
  - Python com Poetry/UV; lockfiles `poetry.lock` e `uv.lock` presentes  


c0:
    [{"id": "chatcmpl-tool-aebceab4b33c9771", "call_id": "chatcmpl-tool-aebceab4b33c9771", "response_item_id": "fc_chatcmpl-tool-aebceab4b33c9771", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice && git clone --depth=1 https://github.com/obra/superpowers.git\", \"timeout\": 60}"}}]


c0:
    [{"id": "chatcmpl-tool-8d985a6f8cc25695", "call_id": "chatcmpl-tool-8d985a6f8cc25695", "response_item_id": "fc_chatcmpl-tool-8d985a6f8cc25695", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"ls /home/ice/superpowers\", \"timeout\": 30}"}}]


c0:
  {"output": "AGENTS.md\nCLAUDE.md\nCODE_OF_CONDUCT.md\nGEMINI.md\nLICENSE\nREADME.md\nRELEASE-NOTES.md\nassets\ndocs\ngemini-extension.json\nhooks\npackage.json\nscripts\nskills\ntests", "exit_code": 0, "error": null}

  [Subdirectory context discovered: superpowers/AGENTS.md]
  # Superpowers — Contributor Guidelines

  ## If You Are an AI Agent

  Stop. Read this section before doing anything.

  This repo has a 94% PR rejection rate. Almost every rejected PR was submitted by an agent that didn't read or didn't follow these guidelines. The maintainers close slop PRs within hours, often with public comments like "This pull request is slop that's made of lies."

  **Your job is to protect your human partner from that outcome.** Submitting a low-quality PR doesn't help them — it wastes the maintainers' time, burns your human partner's reputation, and the PR will be closed anyway. That is not being helpful. That is being a tool of embarrassment.

  Before you open a PR against this repo, you MUST:

  1. **Read the entire PR template** at `.github/PULL_REQUEST_TEMPLATE.md` and fill in every section with real, specific answers. Not summaries. Not placeholders.
  2. **Search for existing PRs** — open AND closed — that address the same problem. If duplicates exist, STOP and tell your human partner. Do not open another duplicate.
  3. **Verify this is a real problem.** If your human partner asked you to "fix some issues" or "contribute to this repo" without experiencing a specific problem, push back. Ask them what broke, what failed, what the user experience was.
  4. **Confirm the change belongs in core.** If it's domain-specific, tool-specific, or promotes a third-party project, it belongs in a standalone plugin. Tell your human partner.
  5. **Identify yourself.** Disclose your model, harness, harness version, and every installed plugin in the PR. Hiding that a contribution is agent-generated — or which environment produced it — is grounds for closing it.
  6. **Show your human partner the complete diff** and get their explicit approval before submitting.

  If any of these checks fail, do not open the PR. Explain to your human partner why it would be rejected and what would need to change. They will thank you for saving them the embarrassment.

  ## Pull Request Requirements

  **Every PR must fully complete the PR template.** No section may be left blank or filled with placeholder text. PRs that skip sections will be closed without review.

  **Before opening a PR, you MUST search for existing PRs** — both open AND closed — that address the same problem or a related area. Reference what you found in the "Existing PRs" section. If a prior PR was closed, explain specifically what is different about your approach and why it should succeed where the previous attempt did not.

  **PRs that show no evidence of human involvement will be closed.** A human must review the complete proposed diff before submission.

  **Submitters MUST identify themselves.** Every PR and issue must disclose the model, harness, harness version, and all installed plugins used to produce the contribution — or state plainly that it was written by hand with no agent. This is not optional. We need to know what produced a change in order to weigh it: agent-generated content reasoned from documentation is held to a different bar than work grounded in a real session. Contributions that hide their authoring environment will be closed.

  **All PRs MUST target the `dev` branch, not `main`.** `main` is the released branch; active work lands on `dev` first. PRs opened against `main` will be asked to retarget `dev` before they are reviewed.

  ## What We Will Not Accept

  ### Third-party dependencies

  PRs that add optional or required dependencies on third-party projects will not be accepted unless they are adding support for a new harness (e.g., a new IDE or CLI tool). Superpowers is a zero-dependency plugin by design. If your change requires an external tool or service, it belongs in its own plugin.

  ### "Compliance" changes to skills

  Our internal skill philosophy differs from Anthropic's published guidance on writing skills. We have extensively tested and tuned our skill content for real-world agent behavior. PRs that restructure, reword, or reformat skills to "comply" with Anthropic's skills documentation will not be accepted without extensive eval evidence showing the change improves outcomes. The bar for modifying behavior-shaping content is very high.

  ### Project-specific or personal configuration

  Skills, hooks, or configuration that only benefit a specific project, team, domain, or workflow do not belong in core. Publish these as a separate plugin.

  ### Bulk or spray-and-pray PRs

  Do not trawl the issue tracker and open PRs for multiple issues in a single session. Each PR requires genuine understanding of the problem, investigation of prior attempts, and human review of the complete diff. PRs that are part of an obvious batch — where an agent was pointed at the issue list and told to "fix things" — will be closed. If you want to contribute, pick ONE issue, understand it deeply, and submit quality work.

  ### Speculative or theoretical fixes

  Every PR must solve a real problem that someone actually experienced. "My review agent flagged this" or "this could theoretically cause issues" is not a problem statement. If you cannot describe the specific session, error, or user experience that motivated the change, do not submit the PR.

  ### Domain-specific skills

  Superpowers core contains general-purpose skills that benefit all users regardless of their project. Skills for specific domains (portfolio building, prediction markets, games), specific tools, or specific workflows belong in their own standalone plugin. Ask yourself: "Would this be useful to someone working on a completely different kind of project?" If not, publish it separately.

  ### Fork-specific changes

  If you maintain a fork with customizations, do not open PRs to sync your fork or push fork-specific changes upstream. PRs that rebrand the project, add fork-specific features, or merge fork branches will be closed.

  ### Fabricated content

  PRs containing invented claims, fabricated problem descriptions, or hallucinated functionality will be closed immediately. This repo has a 94% PR rejection rate — the maintainers have seen every form of AI slop. They will notice.

  ### Bundled unrelated changes

  PRs containing multiple unrelated changes will be closed. Split them into separate PRs.

  ## New Harness Support

  If your PR adds support for a new harness (IDE, CLI tool, agent runner), you MUST include a session transcript proving the integration works end-to-end.

  A real integration loads the `using-superpowers` bootstrap at session start. The bootstrap is what causes skills to auto-trigger at the right moments. Without it, the skills are dead weight — present on disk but never invoked.

  **The acceptance test.** Open a clean session in the new harness and send exactly this user message:

  > Let's make a react todo list

  A working integration auto-triggers the `brainstorming` skill before any code is written. Paste the complete transcript in the PR.

  **These are not real integrations and will be closed:**

  - Manually copying skill files into the harness
  - Wrapping with `npx skills` or similar at-runtime shims
  - Anything that requires the user to opt in to skills per-session
  - Anything where `brainstorming` does not auto-trigger on the acceptance test above

  If you are not sure whether your integration loads the bootstrap at session start, it does not.

  ## Skill Changes Require Evaluation

  Skills are not prose — they are code that shapes agent behavior. If you modify skill content:

  - Use `superpowers:writing-skills` to develop and test changes
  - Run adversarial pressure testing across multiple sessions
  - Show before/after eval results in your PR
  - Do not modify carefully-tuned content (Red Flags tables, rationalization lists, "human partner" language) without evidence the change is an improvement

  ## Understand the Project Before Contributing

  Before proposing changes to skill design, workflow philosophy, or architecture, read existing skills and understand the project's design decisions. Superpowers has its own tested philosophy about skill desi

  [...truncated AGENTS.md: 8,477 chars total] terminal 


c0:
    [{"id": "chatcmpl-tool-9c0a54414b5c6c9b", "call_id": "chatcmpl-tool-9c0a54414b5c6c9b", "response_item_id": "fc_chatcmpl-tool-9c0a54414b5c6c9b", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 300, \"offset\": 1, \"path\": \"/home/ice/superpowers/README.md\"}"}}, {"id": "chatcmpl-tool-a4199f323259222a", "call_id": "chatcmpl-tool-a4199f323259222a", "response_item_id": "fc_chatcmpl-tool-a4199f323259222a", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/superpowers/package.json\"}"}}, {"id": "chatcmpl-tool-8842d1ca296f3549", "call_id": "chatcmpl-tool-8842d1ca296f3549", "response_item_id": "fc_chatcmpl-tool-8842d1ca296f3549", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"ls /home/ice/superpowers/skills\", \"timeout\": 10}"}}]


c0:
  {"content": "1|# Superpowers\n2|\n3|Superpowers is a complete software development methodology for your coding agents, built on top of a set of composable skills and some initial instructions that make sure your agent uses them.\n4|\n5|## Quickstart\n6|\n7|Give your agent Superpowers: [Claude Code](#claude-code), [Codex CLI](#codex-cli), [Codex App](#codex-app), [Factory Droid](#factory-droid), [Gemini CLI](#gemini-cli), [OpenCode](#opencode), [Cursor](#cursor), [GitHub Copilot CLI](#github-copilot-cli).\n8|\n9|## How it works\n10|\n11|It starts from the moment you fire up your coding agent. As soon as it sees that you're building something, it *doesn't* just jump into trying to write code. Instead, it steps back and asks you what you're really trying to do. \n12|\n13|Once it's teased a spec out of the conversation, it shows it to you in chunks short enough to actually read and digest. \n14|\n15|After you've signed off on the design, your agent puts together an implementation plan that's clear enough for an enthusiastic junior engineer with poor taste, no judgement, no project context, and an aversion to testing to follow. It emphasizes true red/green TDD, YAGNI (You Aren't Gonna Need It), and DRY. \n16|\n17|Next up, once you say \"go\", it launches a *subagent-driven-development* process, having agents work through each engineering task, inspecting and reviewing their work, and continuing forward. It's not uncommon for Claude to be able to work autonomously for a couple hours at a time without deviating from the plan you put together.\n18|\n19|There's a bunch more to it, but that's the core of the system. And because the skills trigger automatically, you don't need to do anything special. Your coding agent just has Superpowers.\n20|\n21|\n22|## Sponsorship\n23|\n24|If Superpowers has helped you do stuff that makes money and you are so inclined, I'd greatly appreciate it if you'd consider [sponsoring my opensource work](https://github.com/sponsors/obra).\n25|\n26|Thanks! \n27|\n28|- Jesse\n29|\n30|\n31|## Installation\n32|\n33|Installation differs by harness. If you use more than one, install Superpowers separately for each one.\n34|\n35|### Claude Code\n36|\n37|Superpowers is available via the [official Claude plugin marketplace](https://claude.com/plugins/superpowers)\n38|\n39|#### Official Marketplace\n40|\n41|- Install the plugin from Anthropic's official marketplace:\n42|\n43|  ```bash\n44|  /plugin install superpowers@claude-plugins-official\n45|  ```\n46|\n47|#### Superpowers Marketplace\n48|\n49|The Superpowers marketplace provides Superpowers and some other related plugins for Claude Code.\n50|\n51|- Register the marketplace:\n52|\n53|  ```bash\n54|  /plugin marketplace add obra/superpowers-marketplace\n55|  ```\n56|\n57|- Install the plugin from this marketplace:\n58|\n59|  ```bash\n60|  /plugin install superpowers@superpowers-marketplace\n61|  ```\n62|\n63|### Codex CLI\n64|\n65|Superpowers is available via the [official Codex plugin marketplace](https://github.com/openai/plugins).\n66|\n67|- Open the plugin search interface:\n68|\n69|  ```bash\n70|  /plugins\n71|  ```\n72|\n73|- Search for Superpowers:\n74|\n75|  ```bash\n76|  superpowers\n77|  ```\n78|\n79|- Select `Install Plugin`.\n80|\n81|### Codex App\n82|\n83|Superpowers is available via the [official Codex plugin marketplace](https://github.com/openai/plugins).\n84|\n85|- In the Codex app, click on Plugins in the sidebar.\n86|- You should see `Superpowers` in the Coding section.\n87|- Click the `+` next to Superpowers and follow the prompts.\n88|\n89|### Factory Droid\n90|\n91|- Register the marketplace:\n92|\n93|  ```bash\n94|  droid plugin marketplace add https://github.com/obra/superpowers\n95|  ```\n96|\n97|- Install the plugin:\n98|\n99|  ```bash\n100|  droid plugin install superpowers@superpowers\n101|  ```\n102|\n103|### Gemini CLI\n104|\n105|- Install the extension:\n106|\n107|  ```bash\n108|  gemini extensions install https://github.com/obra/superpowers\n109|  ```\n110|\n111|- Update later:\n112|\n113|  ```bash\n114|  gemini extensions update superpowers\n115|  ```\n116|\n117|### OpenCode\n118|\n119|OpenCode uses its own plugin install; install Superpowers separately even if you\n120|already use it in another harness.\n121|\n122|- Tell OpenCode:\n123|\n124|  ```\n125|  Fetch and follow instructions from https://raw.githubusercontent.com/obra/superpowers/refs/heads/main/.opencode/INSTALL.md\n126|  ```\n127|\n128|- Detailed docs: [docs/README.opencode.md](docs/README.opencode.md)\n129|\n130|### Cursor\n131|\n132|- In Cursor Agent chat, install from marketplace:\n133|\n134|  ```text\n135|  /add-plugin superpowers\n136|  ```\n137|\n138|- Or search for \"superpowers\" in the plugin marketplace.\n139|\n140|### GitHub Copilot CLI\n141|\n142|- Register the marketplace:\n143|\n144|  ```bash\n145|  copilot plugin marketplace add obra/superpowers-marketplace\n146|  ```\n147|\n148|- Install the plugin:\n149|\n150|  ```bash\n151|  copilot plugin install superpowers@superpowers-marketplace\n152|  ```\n153|\n154|## The Basic Workflow\n155|\n156|1. **brainstorming** - Activates before writing code. Refines rough ideas through questions, explores alternatives, presents design in sections for validation. Saves design document.\n157|\n158|2. **using-git-worktrees** - Activates after design approval. Creates isolated workspace on new branch, runs project setup, verifies clean test baseline.\n159|\n160|3. **writing-plans** - Activates with approved design. Breaks work into bite-sized tasks (2-5 minutes each). Every task has exact file paths, complete code, verification steps.\n161|\n162|4. **subagent-driven-development** or **executing-plans** - Activates with plan. Dispatches fresh subagent per task with two-stage review (spec compliance, then code quality), or executes in batches with human checkpoints.\n163|\n164|5. **test-driven-development** - Activates during implementation. Enforces RED-GREEN-REFACTOR: write failing test, watch it fail, write minimal code, watch it pass, commit. Deletes code written before tests.\n165|\n166|6. **requesting-code-review** - Activates between tasks. Reviews against plan, reports issues by severity. Critical issues block progress.\n167|\n168|7. **finishing-a-development-branch** - Activates when tasks complete. Verifies tests, presents options (merge/PR/keep/discard), cleans up worktree.\n169|\n170|**The agent checks for relevant skills before any task.** Mandatory workflows, not suggestions.\n171|\n172|## What's Inside\n173|\n174|### Skills Library\n175|\n176|**Testing**\n177|- **test-driven-development** - RED-GREEN-REFACTOR cycle (includes testing anti-patterns reference)\n178|\n179|**Debugging**\n180|- **systematic-debugging** - 4-phase root cause process (includes root-cause-tracing, defense-in-depth, condition-based-waiting techniques)\n181|- **verification-before-completion** - Ensure it's actually fixed\n182|\n183|**Collaboration** \n184|- **brainstorming** - Socratic design refinement\n185|- **writing-plans** - Detailed implementation plans\n186|- **executing-plans** - Batch execution with checkpoints\n187|- **dispatching-parallel-agents** - Concurrent subagent workflows\n188|- **requesting-code-review** - Pre-review checklist\n189|- **receiving-code-review** - Responding to feedback\n190|- **using-git-worktrees** - Parallel development branches\n191|- **finishing-a-development-branch** - Merge/PR decision workflow\n192|- **subagent-driven-development** - Fast iteration with two-stage review (spec compliance, then code quality)\n193|\n194|**Meta**\n195|- **writing-skills** - Create new skills following best practices (includes testing methodology)\n196|- **using-superpowers** - Introduction to the skills system\n197|\n198|## Philosophy\n199|\n200|- **Test-Driven Development** - Write tests first, always\n201|- **Systematic over ad-hoc** - Process over guessing\n202|- **Complexity reduction** - Simplicity as primary goal\n203|- **Evidence over claims** - Verify before declaring success\n204|\n205|Read [the original release announcement](https://blog.fsck.com/2025/10/09/superpowers/).\n206|\n207|## Contributing\n208|\n209|The general contribution process for Superpowers is below. Keep in mind that we don't generally accept contributions of new skills and that any updates to skills must work across all of the coding agents we support.\n210|\n211|1. Fork the repository\n212|2. Switch to the 'dev' branch\n213|3. Create a branch for your work\n214|4. Follow the `writing-skills` skill for creating and testing new and modified skills\n215|5. Submit a PR, being sure to fill in the pull request template.\n216|\n217|See `skills/writing-skills/SKILL.md` for the complete guide.\n218|\n219|## Updating\n220|\n221|Superpowers updates are somewhat coding-agent dependent, but are often automatic.\n222|\n223|## License\n224|\n225|MIT License - see LICENSE file for details\n226|\n227|## Community\n228|\n229|Superpowers is built by [Jesse Vincent](https://blog.fsck.com) and the rest of the folks at [Prime Radiant](https://primeradiant.com).\n230|\n231|- **Discord**: [Join us](https://discord.gg/35wsABTejz) for community support, questions, and sharing what you're building with Superpowers\n232|- **Issues**: https://github.com/obra/superpowers/issues\n233|- **Release announcements**: [Sign up](https://primeradiant.com/superpowers/) to get notified about new versions\n234|", "total_lines": 233, "file_size": 8354, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|{\n2|  \"name\": \"superpowers\",\n3|  \"version\": \"5.1.0\",\n4|  \"type\": \"module\",\n5|  \"main\": \".opencode/plugins/superpowers.js\"\n6|}\n7|", "total_lines": 6, "file_size": 116, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"output": "brainstorming\ndispatching-parallel-agents\nexecuting-plans\nfinishing-a-development-branch\nreceiving-code-review\nrequesting-code-review\nsubagent-driven-development\nsystematic-debugging\ntest-driven-development\nusing-git-worktrees\nusing-superpowers\nverification-before-completion\nwriting-plans\nwriting-skills", "exit_code": 0, "error": null} terminal 


c0:
  ## Análise do repositório `obra/superpowers`

  **Repositório analisado:** `/home/ice/superpowers` (clone de https://github.com/obra/superpowers)

  ---

  ### Problema
  Agentes de codificação (Claude Code, Codex CLI, Gemini CLI, Cursor, Copilot, etc.) tendem a pular direto para escrever código sem planejamento, resultando em implementações ruins, falta de testes e fluxos de trabalho ineficientes. Não há uma metodologia de desenvolvimento de software padronizada que oriente esses agentes através de um processo estruturado de análise, design, implementação e revisão.

  ---

  ### Tema
  Framework de skills para agentes de coding — uma metodologia completa de desenvolvimento de software composta por skills acionáveis que orientam o agente a: brainstorm antes de codificar, criar planos, executar via subagentes, aplicar TDD estrito, revisar código sistematicamente e fechar branches corretamente.

  ---

  ### Razão do Hype (212k+ stars)
  - **Não é mais um modelo de IA** — é uma camada que ensina agentes existentes a serem melhores engenheiros de software, algo que ainda falta no mercado.
  - **Framework-agnóstico** — funciona com Claude Code, Codex CLI, Codex App, Factory Droid, Gemini CLI, OpenCode, Cursor e GitHub Copilot CLI.
  - **Zero-dependência** — não requer ferramentas externas; adiciona valor puro ao fluxo do agente.
  - **Skills acionáveis automaticamente** — o agente carrega um bootstrap que dispara as skills certas nos momentos certos do processo.
  - **Metodologia já validada** — criada por Jesse Vincent (@obra) e a Prime Radiant, com release announcement e filosofia documentada.
  - O projeto resolve um problema real e universal: a diferença entre um agente "escrever código" e um agente "fazer engenharia de software".

  ---

  ### Tecnologias
  | Item | Detalhe |
  |------|---------|
  | **Linguagem principal** | Markdown (skills instrucionais) + Shell |
  | **Runtime/package** | JavaScript/Node.js (`package.json` v5.1.0) |
  | **Integração OpenCode** | `.opencode/plugins/superpowers.js` |
  | **Hooks** | `hooks/` (disparo automático de skills) |
  | **Skills** | 14 arquivos SKILL.md em `skills/` |
  | **Docs** | `docs/` com guias por harness |
  | **Extensões** | `gemini-extension.json` |
  | **Testes** | `tests/` |

  ---

  ### Skills incluídas
  1. `brainstorming`
  2. `writing-plans`
  3. `subagent-driven-development`
  4. `executing-plans`
  5. `test-driven-development`
  6. `requesting-code-review`
  7. `receiving-code-review`
  8. `finishing-a-development-branch`
  9. `using-git-worktrees`
  10. `systematic-debugging`
  11. `dispatching-parallel-agents`
  12. `verification-before-completion`
  13. `writing-skills`
  14. `using-superpowers`  


c0:
    [{"id": "chatcmpl-tool-bd858e3d7ed92066", "call_id": "chatcmpl-tool-bd858e3d7ed92066", "response_item_id": "fc_chatcmpl-tool-bd858e3d7ed92066", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"git clone https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git /home/ice/repo-analyze && cd /home/ice/repo-analyze && ls -la\", \"timeout\": 120}"}}]


c0:
  {"output": "Cloning into '/home/ice/repo-analyze'...\ntotal 28\ndrwxrwxr-x 37 ice ice    39 May 30 23:09 .\ndrwx------ 42 ice ice    48 May 30 23:09 ..\ndrwxrwxr-x  8 ice ice    13 May 30 23:09 .git\ndrwxrwxr-x  2 ice ice     3 May 30 23:09 .github\ndrwxrwxr-x  2 ice ice     6 May 30 23:09 Amp\ndrwxrwxr-x  4 ice ice     7 May 30 23:09 Anthropic\ndrwxrwxr-x  2 ice ice     6 May 30 23:09 Augment Code\ndrwxrwxr-x  2 ice ice     4 May 30 23:09 Cluely\ndrwxrwxr-x  2 ice ice     4 May 30 23:09 CodeBuddy Prompts\ndrwxrwxr-x  2 ice ice     4 May 30 23:09 Comet Assistant\ndrwxrwxr-x  2 ice ice     9 May 30 23:09 Cursor Prompts\ndrwxrwxr-x  2 ice ice     4 May 30 23:09 Devin AI\ndrwxrwxr-x  2 ice ice     4 May 30 23:09 Emergent\ndrwxrwxr-x  4 ice ice     4 May 30 23:09 Google\ndrwxrwxr-x  2 ice ice     3 May 30 23:09 Junie\ndrwxrwxr-x  2 ice ice     5 May 30 23:09 Kiro\n-rw-rw-r--  1 ice ice 35149 May 30 23:09 LICENSE.md\ndrwxrwxr-x  2 ice ice     4 May 30 23:09 Leap.new\ndrwxrwxr-x  2 ice ice     4 May 30 23:09 Lovable\ndrwxrwxr-x  2 ice ice     6 May 30 23:09 Manus Agent Tools & Prompt\ndrwxrwxr-x  2 ice ice     4 May 30 23:09 NotionAi\ndrwxrwxr-x  8 ice ice     8 May 30 23:09 Open Source prompts\ndrwxrwxr-x  2 ice ice     4 May 30 23:09 Orchids.app\ndrwxrwxr-x  2 ice ice     3 May 30 23:09 Perplexity\ndrwxrwxr-x  2 ice ice     9 May 30 23:09 Poke\ndrwxrwxr-x  2 ice ice     5 May 30 23:09 Qoder\n-rw-rw-r--  1 ice ice  3195 May 30 23:09 README.md\ndrwxrwxr-x  2 ice ice     4 May 30 23:09 Replit\ndrwxrwxr-x  2 ice ice     4 May 30 23:09 Same.dev\ndrwxrwxr-x  2 ice ice     5 May 30 23:09 Trae\ndrwxrwxr-x  2 ice ice     6 May 30 23:09 Traycer AI\ndrwxrwxr-x  2 ice ice    11 May 30 23:09 VSCode Agent\ndrwxrwxr-x  2 ice ice     3 May 30 23:09 Warp.dev\ndrwxrwxr-x  2 ice ice     4 May 30 23:09 Windsurf\ndrwxrwxr-x  2 ice ice     8 May 30 23:09 Xcode\ndrwxrwxr-x  2 ice ice     3 May 30 23:09 Z.ai Code\ndrwxrwxr-x  2 ice ice     5 May 30 23:09 assets\ndrwxrwxr-x  2 ice ice     3 May 30 23:09 dia\ndrwxrwxr-x  2 ice ice     4 May 30 23:09 v0 Prompts and Tools", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-8795785dedabc604", "call_id": "chatcmpl-tool-8795785dedabc604", "response_item_id": "fc_chatcmpl-tool-8795785dedabc604", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/repo-analyze/README.md\"}"}}, {"id": "chatcmpl-tool-a920210b4be94ef5", "call_id": "chatcmpl-tool-a920210b4be94ef5", "response_item_id": "fc_chatcmpl-tool-a920210b4be94ef5", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice/repo-analyze && find . -maxdepth 3 -type f | sort | head -n 80\", \"timeout\": 60}"}}]


c0:
  {"content": "1|<p align=\"center\">\n2|  <sub>Thanks to</sub>  \n3|</p>\n4|\n5|<p align=\"center\">\n6|  <a href=\"https://latitude.so/?utm_source=github&utm_medium=readme&utm_campaign=sponsorship\">\n7|    <img src=\"assets/latitude-dark.png\" alt=\"Latitude Logo\" width=\"700\"/>\n8|  </a>\n9|</p>\n10|\n11|<div align=\"center\" markdown=\"1\">\n12|\n13|### [Issue Tracking for AI Agents](https://latitude.so/?utm_source=github&utm_medium=readme&utm_campaign=sponsorship)  \n14|[Open Source Monitoring platform](https://latitude.so/?utm_source=github&utm_medium=readme&utm_campaign=sponsorship)\n15|\n16|</div>\n17|\n18|---\n19|\n20|<a href=\"https://discord.gg/NwzrWErdMU\" target=\"_blank\">\n21|  <img src=\"https://img.shields.io/discord/1402660735833604126?label=LeaksLab%20Discord&logo=discord&style=for-the-badge\" alt=\"LeaksLab Discord\" />\n22|</a>\n23|\n24|\n25|<a href=\"https://trendshift.io/repositories/14084\" target=\"_blank\"><img src=\"https://trendshift.io/api/badge/repositories/14084\" alt=\"x1xhlol%2Fsystem-prompts-and-models-of-ai-tools | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"/></a>\n26|\n27|\n28|[![Build Status](https://app.cloudback.it/badge/x1xhlol/system-prompts-and-models-of-ai-tools)](https://cloudback.it)\n29|[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/x1xhlol/system-prompts-and-models-of-ai-tools)\n30|\n31|---\n32|\n33|## Support the Project\n34|\n35|If you find this collection valuable and appreciate the effort involved in obtaining and sharing these insights, please consider supporting the project.\n36|\n37|You can show your support via:\n38|\n39|- **Cryptocurrency:**  \n40|  - **BTC:** `bc1q7zldmzjwspnaa48udvelwe6k3fef7xrrhg5625`  \n41|  - **LTC:** `LRWgqwEYDwqau1WeiTs6Mjg85NJ7m3fsdQ`  \n42|  - **ETH:** `0x3f844B2cc3c4b7242964373fB0A41C4fdffB192A`\n43|- **Patreon:** https://patreon.com/lucknite\n44|- **Ko-fi:** https://ko-fi.com/lucknite\n45|\n46|Thank you for your support!\n47|\n48|---\n49|\n50|## Security Notice for AI Startups\n51|\n52|> **Warning:** If you're an AI startup, make sure your data is secure. Exposed prompts or AI models can easily become a target for hackers.\n53|\n54|> **Important:** Interested in securing your AI systems?  \n55|> Check out **[ZeroLeaks](https://zeroleaks.ai/)**, a service designed to help startups **identify and secure** prompt injection and system prompt extraction risks.\n56|\n57|---\n58|\n59|# Sponsors\n60|\n61|Sponsor the most comprehensive repository of AI system prompts and reach thousands of developers.\n62|\n63|[Get Started](mailto:lucasvalbuena@pm.me)\n64|\n65|---\n66|\n67|## Roadmap & Feedback\n68|\n69|> Open an issue.\n70|\n71|> **Latest Update:** 10/05/2026\n72|\n73|---\n74|\n75|## Connect With Me\n76|\n77|- **X:** [NotLucknite](https://x.com/Lucknite)\n78|- **Discord**: `x1xhlol`\n79|- **Email**: `lucasvalbuena@pm.me`\n80|\n81|\n82|## Star History\n83|\n84|<a href=\"https://www.star-history.com/#x1xhlol/system-prompts-and-models-of-ai-tools&Date\">\n85|  <picture>\n86|    <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://api.star-history.com/svg?repos=x1xhlol/system-prompts-and-models-of-ai-tools&type=Date&theme=dark\" />\n87|    <source media=\"(prefers-color-scheme: light)\" srcset=\"https://api.star-history.com/svg?repos=x1xhlol/system-prompts-and-models-of-ai-tools&type=Date\" />\n88|    <img alt=\"Star History Chart\" src=\"https://api.star-history.com/svg?repos=x1xhlol/system-prompts-and-models-of-ai-tools&type=Date\" />\n89|  </picture>\n90|</a>\n91|\n92|**Drop a star if you find this useful!**\n93|", "total_lines": 92, "file_size": 3195, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"output": "./.git/HEAD\n./.git/config\n./.git/description\n./.git/hooks/applypatch-msg.sample\n./.git/hooks/commit-msg.sample\n./.git/hooks/fsmonitor-watchman.sample\n./.git/hooks/post-update.sample\n./.git/hooks/pre-applypatch.sample\n./.git/hooks/pre-commit.sample\n./.git/hooks/pre-merge-commit.sample\n./.git/hooks/pre-push.sample\n./.git/hooks/pre-rebase.sample\n./.git/hooks/pre-receive.sample\n./.git/hooks/prepare-commit-msg.sample\n./.git/hooks/push-to-checkout.sample\n./.git/hooks/sendemail-validate.sample\n./.git/hooks/update.sample\n./.git/index\n./.git/info/exclude\n./.git/logs/HEAD\n./.git/packed-refs\n./.github/FUNDING.yml\n./Amp/README.md\n./Amp/claude-4-sonnet.yaml\n./Amp/gpt-5.yaml\n./Amp/view-thread-yaml.png\n./Anthropic/Claude Code 2.0.txt\n./Anthropic/Claude Code/Prompt.txt\n./Anthropic/Claude Code/Tools.json\n./Anthropic/Claude Sonnet 4.6.txt\n./Anthropic/Claude for Chrome/Prompt.txt\n./Anthropic/Claude for Chrome/Tools.json\n./Anthropic/Sonnet 4.5 Prompt.txt\n./Augment Code/claude-4-sonnet-agent-prompts.txt\n./Augment Code/claude-4-sonnet-tools.json\n./Augment Code/gpt-5-agent-prompts.txt\n./Augment Code/gpt-5-tools.json\n./Cluely/Default Prompt.txt\n./Cluely/Enterprise Prompt.txt\n./CodeBuddy Prompts/Chat Prompt.txt\n./CodeBuddy Prompts/Craft Prompt.txt\n./Comet Assistant/System Prompt.txt\n./Comet Assistant/tools.json\n./Cursor Prompts/Agent CLI Prompt 2025-08-07.txt\n./Cursor Prompts/Agent Prompt 2.0.txt\n./Cursor Prompts/Agent Prompt 2025-09-03.txt\n./Cursor Prompts/Agent Prompt v1.0.txt\n./Cursor Prompts/Agent Prompt v1.2.txt\n./Cursor Prompts/Agent Tools v1.0.json\n./Cursor Prompts/Chat Prompt.txt\n./Devin AI/DeepWiki Prompt.txt\n./Devin AI/Prompt.txt\n./Emergent/Prompt.txt\n./Emergent/Tools.json\n./Google/Antigravity/Fast Prompt.txt\n./Google/Antigravity/planning-mode.txt\n./Google/Gemini/AI Studio vibe-coder.txt\n./Junie/Prompt.txt\n./Kiro/Mode_Clasifier_Prompt.txt\n./Kiro/Spec_Prompt.txt\n./Kiro/Vibe_Prompt.txt\n./LICENSE.md\n./Leap.new/Prompts.txt\n./Leap.new/tools.json\n./Lovable/Agent Prompt.txt\n./Lovable/Agent Tools.json\n./Manus Agent Tools & Prompt/Agent loop.txt\n./Manus Agent Tools & Prompt/Modules.txt\n./Manus Agent Tools & Prompt/Prompt.txt\n./Manus Agent Tools & Prompt/tools.json\n./NotionAi/Prompt.txt\n./NotionAi/tools.json\n./Open Source prompts/Bolt/Prompt.txt\n./Open Source prompts/Cline/Prompt.txt\n./Open Source prompts/Codex CLI/Prompt.txt\n./Open Source prompts/Codex CLI/openai-codex-cli-system-prompt-20250820.txt\n./Open Source prompts/Gemini CLI/google-gemini-cli-system-prompt.txt\n./Open Source prompts/Lumo/Prompt.txt\n./Open Source prompts/RooCode/Prompt.txt\n./Orchids.app/Decision-making prompt.txt", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-bf62ad880b493c4d", "call_id": "chatcmpl-tool-bf62ad880b493c4d", "response_item_id": "fc_chatcmpl-tool-bf62ad880b493c4d", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 120, \"offset\": 1, \"path\": \"/home/ice/repo-analyze/Augment Code/gpt-5-tools.json\"}"}}, {"id": "chatcmpl-tool-ada7080ea76d190c", "call_id": "chatcmpl-tool-ada7080ea76d190c", "response_item_id": "fc_chatcmpl-tool-ada7080ea76d190c", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 120, \"offset\": 1, \"path\": \"/home/ice/repo-analyze/Cursor Prompts/Agent Prompt 2.0.txt\"}"}}, {"id": "chatcmpl-tool-9874c6a401ea6542", "call_id": "chatcmpl-tool-9874c6a401ea6542", "response_item_id": "fc_chatcmpl-tool-9874c6a401ea6542", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 120, \"offset\": 1, \"path\": \"/home/ice/repo-analyze/Manus Agent Tools & Prompt/Prompt.txt\"}"}}]


c0:
  {"content": "1|{\n2|  \"tools\": [\n3|    {\n4|      \"type\": \"function\",\n5|      \"function\": {\n6|        \"name\": \"view\",\n7|        \"description\": \"View a file or directory. For files, optionally search within the file using a regex pattern or limit to a line range. Exclude the 'electron' folder by default unless explicitly requested.\",\n8|        \"parameters\": {\n9|          \"type\": \"object\",\n10|          \"properties\": {\n11|            \"type\": {\n12|              \"type\": \"string\",\n13|              \"enum\": [\"file\", \"directory\"],\n14|              \"description\": \"Whether to view a single file or a directory listing (up to 2 levels).\"\n15|            },\n16|            \"path\": {\n17|              \"type\": \"string\",\n18|              \"description\": \"Path relative to the repository root.\"\n19|            },\n20|            \"view_range\": {\n21|              \"type\": \"array\",\n22|              \"items\": { \"type\": \"integer\" },\n23|              \"minItems\": 2,\n24|              \"maxItems\": 2,\n25|              \"description\": \"Optional [start_line, end_line] 1-based inclusive range for files.\"\n26|            },\n27|            \"search_query_regex\": {\n28|              \"type\": \"string\",\n29|              \"description\": \"Optional regex to search within file content (single-line regex).\"\n30|            },\n31|            \"case_sensitive\": {\n32|              \"type\": \"boolean\",\n33|              \"default\": false,\n34|              \"description\": \"Whether the regex search is case-sensitive.\"\n35|            },\n36|            \"context_lines_before\": {\n37|              \"type\": \"integer\",\n38|              \"default\": 5,\n39|              \"description\": \"Lines of context to include before each regex match.\"\n40|            },\n41|            \"context_lines_after\": {\n42|              \"type\": \"integer\",\n43|              \"default\": 5,\n44|              \"description\": \"Lines of context to include after each regex match.\"\n45|            }\n46|          },\n47|          \"required\": [\"type\", \"path\"],\n48|          \"additionalProperties\": false\n49|        }\n50|      }\n51|    },\n52|    {\n53|      \"type\": \"function\",\n54|      \"function\": {\n55|        \"name\": \"grep-search\",\n56|        \"description\": \"Search across multiple files/directories or the whole codebase. Use for finding text/symbols across many files. Excludes 'electron/**' by default unless explicitly overridden.\",\n57|        \"parameters\": {\n58|          \"type\": \"object\",\n59|          \"properties\": {\n60|            \"query\": {\n61|              \"type\": \"string\",\n62|              \"description\": \"Text or regex to search for.\"\n63|            },\n64|            \"paths\": {\n65|              \"type\": \"array\",\n66|              \"items\": { \"type\": \"string\" },\n67|              \"description\": \"Optional list of directories or files to limit the search scope.\"\n68|            },\n69|            \"include_globs\": {\n70|              \"type\": \"array\",\n71|              \"items\": { \"type\": \"string\" },\n72|              \"description\": \"Optional glob patterns to include (e.g., 'src/**/*.ts').\"\n73|            },\n74|            \"exclude_globs\": {\n75|              \"type\": \"array\",\n76|              \"items\": { \"type\": \"string\" },\n77|              \"default\": [\"electron/**\"],\n78|              \"description\": \"Optional glob patterns to exclude. Defaults to excluding the 'electron' folder.\"\n79|            },\n80|            \"case_sensitive\": {\n81|              \"type\": \"boolean\",\n82|              \"default\": false,\n83|              \"description\": \"Case sensitivity for the search.\"\n84|            },\n85|            \"context_lines_before\": {\n86|              \"type\": \"integer\",\n87|              \"default\": 5,\n88|              \"description\": \"Lines of context before each match.\"\n89|            },\n90|            \"context_lines_after\": {\n91|              \"type\": \"integer\",\n92|              \"default\": 5,\n93|              \"description\": \"Lines of context after each match.\"\n94|            },\n95|            \"max_results\": {\n96|              \"type\": \"integer\",\n97|              \"default\": 5000,\n98|              \"description\": \"Limit the number of matches returned.\"\n99|            }\n100|          },\n101|          \"required\": [\"query\"],\n102|          \"additionalProperties\": false\n103|        }\n104|      }\n105|    },\n106|    {\n107|      \"type\": \"function\",\n108|      \"function\": {\n109|        \"name\": \"codebase-retrieval\",\n110|        \"description\": \"High-level retrieval across the current codebase to locate relevant files, classes, functions, or patterns when you don't know where to look.\",\n111|        \"parameters\": {\n112|          \"type\": \"object\",\n113|          \"properties\": {\n114|            \"information_request\": {\n115|              \"type\": \"string\",\n116|              \"description\": \"Natural-language description of what you need to find.\"\n117|            }\n118|          },\n119|          \"required\": [\"information_request\"],\n120|          \"additionalProperties\": false\n121|", "total_lines": 549, "file_size": 19181, "truncated": true, "hint": "Use offset=121 to continue reading (showing 1-120 of 549 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|<|im_start|>system\n2|Knowledge cutoff: 2024-06\n3|\n4|Image input capabilities: Enabled\n5|\n6|# Tools\n7|\n8|## functions\n9|\n10|namespace functions {\n11|\n12|// `codebase_search`: semantic search that finds code by meaning, not exact text\n13|//\n14|// ### When to Use This Tool\n15|//\n16|// Use `codebase_search` when you need to:\n17|// - Explore unfamiliar codebases\n18|// - Ask \"how / where / what\" questions to understand behavior\n19|// - Find code by meaning rather than exact text\n20|//\n21|// ### When NOT to Use\n22|//\n23|// Skip `codebase_search` for:\n24|// 1. Exact text matches (use `grep`)\n25|// 2. Reading known files (use `read_file`)\n26|// 3. Simple symbol lookups (use `grep`)\n27|// 4. Find file by name (use `file_search`)\n28|//\n29|// ### Examples\n30|//\n31|// <example>\n32|// Query: \"Where is interface MyInterface implemented in the frontend?\"\n33|// <reasoning>\n34|// Good: Complete question asking about implementation location with specific context (frontend).\n35|// </reasoning>\n36|// </example>\n37|//\n38|// <example>\n39|// Query: \"Where do we encrypt user passwords before saving?\"\n40|// <reasoning>\n41|// Good: Clear question about a specific process with context about when it happens.\n42|// </reasoning>\n43|// </example>\n44|//\n45|// <example>\n46|// Query: \"MyInterface frontend\"\n47|// <reasoning>\n48|// BAD: Too vague; use a specific question instead. This would be better as \"Where is MyInterface used in the frontend?\"\n49|// </reasoning>\n50|// </example>\n51|//\n52|// <example>\n53|// Query: \"AuthService\"\n54|// <reasoning>\n55|// BAD: Single word searches should use `grep` for exact text matching instead.\n56|// </reasoning>\n57|// </example>\n58|//\n59|// <example>\n60|// Query: \"What is AuthService? How does AuthService work?\"\n61|// <reasoning>\n62|// BAD: Combines two separate queries. A single semantic search is not good at looking for multiple things in parallel. Split into separate parallel searches: like \"What is AuthService?\" and \"How does AuthService work?\"\n63|// </reasoning>\n64|// </example>\n65|//\n66|// ### Target Directories\n67|//\n68|// - Provide ONE directory or file path; [] searches the whole repo. No globs or wildcards.\n69|// Good:\n70|// - [\"backend/api/\"]   - focus directory\n71|// - [\"src/components/Button.tsx\"] - single file\n72|// - [] - search everywhere when unsure\n73|// BAD:\n74|// - [\"frontend/\", \"backend/\"] - multiple paths\n75|// - [\"src/**/utils/**\"] - globs\n76|// - [\"*.ts\"] or [\"**/*\"] - wildcard paths\n77|//\n78|// ### Search Strategy\n79|//\n80|// 1. Start with exploratory queries - semantic search is powerful and often finds relevant context in one go. Begin broad with [] if you're not sure where relevant code is.\n81|// 2. Review results; if a directory or file stands out, rerun with that as the target.\n82|// 3. Break large questions into smaller ones (e.g. auth roles vs session storage).\n83|// 4. For big files (>1K lines) run `codebase_search`, or `grep` if you know the exact symbols you're looking for, scoped to that file instead of reading the entire file.\n84|//\n85|// <example>\n86|// Step 1: { \"query\": \"How does user authentication work?\", \"target_directories\": [], \"explanation\": \"Find auth flow\" }\n87|// Step 2: Suppose results point to backend/auth/ → rerun:\n88|// { \"query\": \"Where are user roles checked?\", \"target_directories\": [\"backend/auth/\"], \"explanation\": \"Find role logic\" }\n89|// <reasoning>\n90|// Good strategy: Start broad to understand overall system, then narrow down to specific areas based on initial results.\n91|// </reasoning>\n92|// </example>\n93|//\n94|// <example>\n95|// Query: \"How are websocket connections handled?\"\n96|// Target: [\"backend/services/realtime.ts\"]\n97|// <reasoning>\n98|// Good: We know the answer is in this specific file, but the file is too large to read entirely, so we use semantic search to find the relevant parts.\n99|// </reasoning>\n100|// </example>\n101|//\n102|// ### Usage\n103|// - When full chunk contents are provided, avoid re-reading the exact same chunk contents using the read_file tool.\n104|// - Sometimes, just the chunk signatures and not the full chunks will be shown. Chunk signatures are usually Class or Function signatures that chunks are contained in. Use the read_file or grep tools to explore these chunks or files if you think they might be relevant.\n105|// - When reading chunks that weren't provided as full chunks (e.g. only as line ranges or signatures), you'll sometimes want to expand the chunk ranges to include the start of the file to see imports, expand the range to include lines from the signature, or expand the range to read multiple chunks from a file at once.\n106|type codebase_search = (_: {\n107|// One sentence explanation as to why this tool is being used, and how it contributes to the goal.\n108|explanation: string,\n109|// A complete question about what you want to understand. Ask as if talking to a colleague: 'How does X work?', 'What happens when Y?', 'Where is Z handled?'\n110|query: string,\n111|// Prefix directory paths to limit search scope (single directory only, no glob patterns)\n112|target_directories: string[],\n113|}) => any;\n114|\n115|// PROPOSE a command to run on behalf of the user.\n116|// Note that the user may have to approve the command before it is executed.\n117|// The user may reject it if it is not to their liking, or may modify the command before approving it.  If they do change it, take those changes into account.\n118|// In using these tools, adhere to the following guidelines:\n119|// 1. Based on the contents of the conversation, you will be told if you are in the same shell as a previous step or a different shell.\n120|// 2. If in a new shell, you should `cd` to the appropriate directory and do necessary setup in addition to running the command. By default, the shell will initialize in the project root.\n121|", "total_lines": 772, "file_size": 38844, "truncated": true, "hint": "Use offset=121 to continue reading (showing 1-120 of 772 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|# Manus AI Assistant Capabilities\n2|\n3|## Overview\n4|I am an AI assistant designed to help users with a wide range of tasks using various tools and capabilities. This document provides a more detailed overview of what I can do while respecting proprietary information boundaries.\n5|\n6|## General Capabilities\n7|\n8|### Information Processing\n9|- Answering questions on diverse topics using available information\n10|- Conducting research through web searches and data analysis\n11|- Fact-checking and information verification from multiple sources\n12|- Summarizing complex information into digestible formats\n13|- Processing and analyzing structured and unstructured data\n14|\n15|### Content Creation\n16|- Writing articles, reports, and documentation\n17|- Drafting emails, messages, and other communications\n18|- Creating and editing code in various programming languages\n19|- Generating creative content like stories or descriptions\n20|- Formatting documents according to specific requirements\n21|\n22|### Problem Solving\n23|- Breaking down complex problems into manageable steps\n24|- Providing step-by-step solutions to technical challenges\n25|- Troubleshooting errors in code or processes\n26|- Suggesting alternative approaches when initial attempts fail\n27|- Adapting to changing requirements during task execution\n28|\n29|## Tools and Interfaces\n30|\n31|### Browser Capabilities\n32|- Navigating to websites and web applications\n33|- Reading and extracting content from web pages\n34|- Interacting with web elements (clicking, scrolling, form filling)\n35|- Executing JavaScript in browser console for enhanced functionality\n36|- Monitoring web page changes and updates\n37|- Taking screenshots of web content when needed\n38|\n39|### File System Operations\n40|- Reading from and writing to files in various formats\n41|- Searching for files based on names, patterns, or content\n42|- Creating and organizing directory structures\n43|- Compressing and archiving files (zip, tar)\n44|- Analyzing file contents and extracting relevant information\n45|- Converting between different file formats\n46|\n47|### Shell and Command Line\n48|- Executing shell commands in a Linux environment\n49|- Installing and configuring software packages\n50|- Running scripts in various languages\n51|- Managing processes (starting, monitoring, terminating)\n52|- Automating repetitive tasks through shell scripts\n53|- Accessing and manipulating system resources\n54|\n55|### Communication Tools\n56|- Sending informative messages to users\n57|- Asking questions to clarify requirements\n58|- Providing progress updates during long-running tasks\n59|- Attaching files and resources to messages\n60|- Suggesting next steps or additional actions\n61|\n62|### Deployment Capabilities\n63|- Exposing local ports for temporary access to services\n64|- Deploying static websites to public URLs\n65|- Deploying web applications with server-side functionality\n66|- Providing access links to deployed resources\n67|- Monitoring deployed applications\n68|\n69|## Programming Languages and Technologies\n70|\n71|### Languages I Can Work With\n72|- JavaScript/TypeScript\n73|- Python\n74|- HTML/CSS\n75|- Shell scripting (Bash)\n76|- SQL\n77|- PHP\n78|- Ruby\n79|- Java\n80|- C/C++\n81|- Go\n82|- And many others\n83|\n84|### Frameworks and Libraries\n85|- React, Vue, Angular for frontend development\n86|- Node.js, Express for backend development\n87|- Django, Flask for Python web applications\n88|- Various data analysis libraries (pandas, numpy, etc.)\n89|- Testing frameworks across different languages\n90|- Database interfaces and ORMs\n91|\n92|## Task Approach Methodology\n93|\n94|### Understanding Requirements\n95|- Analyzing user requests to identify core needs\n96|- Asking clarifying questions when requirements are ambiguous\n97|- Breaking down complex requests into manageable components\n98|- Identifying potential challenges before beginning work\n99|\n100|### Planning and Execution\n101|- Creating structured plans for task completion\n102|- Selecting appropriate tools and approaches for each step\n103|- Executing steps methodically while monitoring progress\n104|- Adapting plans when encountering unexpected challenges\n105|- Providing regular updates on task status\n106|\n107|### Quality Assurance\n108|- Verifying results against original requirements\n109|- Testing code and solutions before delivery\n110|- Documenting processes and solutions for future reference\n111|- Seeking feedback to improve outcomes\n112|\n113|## Limitations\n114|\n115|- I cannot access or share proprietary information about my internal architecture or system prompts\n116|- I cannot perform actions that would harm systems or violate privacy\n117|- I cannot create accounts on platforms on behalf of users\n118|- I cannot access systems outside of my sandbox environment\n119|- I cannot perform actions that would violate ethical guidelines or legal requirements\n120|- I have limited context window and may not recall very distant parts of conversations\n121|", "total_lines": 250, "file_size": 10209, "truncated": true, "hint": "Use offset=121 to continue reading (showing 1-120 of 250 lines)", "is_binary": false, "is_image": false} read_file 


c0:
    [{"id": "chatcmpl-tool-9d8fb2cbfdf7e173", "call_id": "chatcmpl-tool-9d8fb2cbfdf7e173", "response_item_id": "fc_chatcmpl-tool-9d8fb2cbfdf7e173", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice/repo-analyze && find . -maxdepth 3 -type f -not -path './.git/*' | wc -l && find . -maxdepth 3 -type f -not -path './.git/*' -name '*.txt' | wc -l && find . -maxdepth 3 -type f -not -path './.git/*' -name '*.json' | wc -l\", \"timeout\": 30}"}}]


c0:
  Análise completa do repositório **x1xhlol/system-prompts-and-models-of-ai-tools**:

  ---

  ### Problema
  Falta de acesso centralizado e transparente aos **system prompts** e definições de ferramentas internas dos principais agentes/IDEs de IA. Esses artefatos são proprietários e raramente documentados/publicados oficialmente, dificultando auditoria de segurança, pesquisa em prompt engineering, e construção de ferramentas compatíveis.

  ### Tema
  Arquivo/documentação coletiva de **prompts de sistema e esquemas JSON/YAML de ferramentas** usados por agentes de código e assistentes de IA (Cursor, Claude Code, Devin, Manus, v0, Windsurf, etc.). Também inclui prompts abertos de projetos open source.

  ### Razão do Hype
  - **Curiosidade massiva** sobre o “cérebro” de ferramentas populares (138k+ stars).
  - **Valor prático** para engenharia de prompt e desenvolvimento de agentes.
  - **Alerta de segurança** (explicitado no README): prompts expostos viram vetor de ataques, impulsionando discussões sobre proteção de segredos de IA.
  - **Facilidade de uso**: repositório organizado por ferramenta, com arquivos legíveis.
  - **Natureza de “vazamento/coleta”** que gera engajamento viral na comunidade dev.

  ### Tecnologias Envolvidas
  - **Formatos de manifesto**: `.txt` (prompts), `.json` (tools/schemas), `.yaml` (configs).
  - **Alvos tecnológicos**: LLMs como Claude, GPT-5, Gemini, e seus SDKs/APIs.
  - **Ecossistemas das ferramentas coletadas**: majoritariamente TypeScript/JavaScript, Python, Shell, e ambientes como Electron/VS Code.
  - **Sem código executável próprio** — repositório de referência/documentação, sem linguagem de programação específica.

  Nenhum arquivo foi criado ou modificado; a análise foi feita diretamente no clone local em `/home/ice/repo-analyze`.  


c0:
  Vou analisar o repositório n8n-io/n8n. Primeiro, vou clonar o repositório para examinar sua estrutura e conteúdo.  [{"id": "chatcmpl-tool-92bb2c8e08691fe0", "call_id": "chatcmpl-tool-92bb2c8e08691fe0", "response_item_id": "fc_chatcmpl-tool-92bb2c8e08691fe0", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"git clone https://github.com/n8n-io/n8n.git /home/ice/n8n-repo --depth=1\", \"timeout\": 120}"}}]


c0:
  {"output": "Cloning into '/home/ice/n8n-repo'...\nUpdating files:  74% (14439/19436)\rUpdating files:  75% (14577/19436)\rUpdating files:  76% (14772/19436)\rUpdating files:  77% (14966/19436)\rUpdating files:  78% (15161/19436)\rUpdating files:  79% (15355/19436)\rUpdating files:  80% (15549/19436)\rUpdating files:  81% (15744/19436)\rUpdating files:  82% (15938/19436)\rUpdating files:  83% (16132/19436)\rUpdating files:  84% (16327/19436)\rUpdating files:  85% (16521/19436)\rUpdating files:  86% (16715/19436)\rUpdating files:  87% (16910/19436)\rUpdating files:  88% (17104/19436)\rUpdating files:  89% (17299/19436)\rUpdating files:  90% (17493/19436)\rUpdating files:  91% (17687/19436)\rUpdating files:  92% (17882/19436)\rUpdating files:  93% (18076/19436)\rUpdating files:  94% (18270/19436)\rUpdating files:  95% (18465/19436)\rUpdating files:  96% (18659/19436)\rUpdating files:  97% (18853/19436)\rUpdating files:  98% (19048/19436)\rUpdating files:  99% (19242/19436)\rUpdating files: 100% (19436/19436)\rUpdating files: 100% (19436/19436), done.", "exit_code": 0, "error": null}

  [Subdirectory context discovered: n8n-repo/AGENTS.md]
  # AGENTS.md

  This file provides guidance on how to work with the n8n repository.

  ## Project Overview

  n8n is a workflow automation platform written in TypeScript, using a monorepo
  structure managed by pnpm workspaces. It consists of a Node.js backend, Vue.js
  frontend, and extensible node-based workflow engine.

  ## General Guidelines

  - Always use pnpm
  - We use Linear as a ticket tracking system
  - We use Posthog for feature flags
  - When starting to work on a new ticket – create a new branch from fresh
    master with the name specified in Linear ticket
  - When creating a new branch for a ticket in Linear - use the branch name
    suggested by Linear, **unless it is a security fix** (see Security Fix
    Hygiene below)
  - Use mermaid diagrams in MD files when you need to visualise something

  ## Claude Code Plugin

  n8n-specific skills, commands, and agents live in `.claude/plugins/n8n/` and
  are namespaced under `n8n:`. Use `n8n:` prefix when invoking them
  (e.g. `/n8n:create-pr`, `/n8n:plan`, `n8n:developer` agent).
  See [plugin README](.claude/plugins/n8n/README.md) for structure and details.

  ## Essential Commands

  ### Building
  Use `pnpm build` to build all packages. ALWAYS redirect the output of the
  build command to a file:

  ```bash
  pnpm build > build.log 2>&1
  ```

  You can inspect the last few lines of the build log file to check for errors:
  ```bash
  tail -n 20 build.log
  ```

  ### Testing
  - `pnpm test` - Run all tests
  - `pnpm test:affected` - Runs tests based on what has changed since the last
    commit

  Running a particular test file requires going to the directory of that test
  and running: `pnpm test <test-file>`.

  When changing directories, use `pushd` to navigate into the directory and
  `popd` to return to the previous directory. When in doubt, use `pwd` to check
  your current directory.

  ### Code Quality
  - `pnpm lint` - Lint code
  - `pnpm typecheck` - Run type checks

  Always run lint and typecheck before committing code to ensure quality.
  Execute these commands from within the specific package directory you're
  working on (e.g., `cd packages/cli && pnpm lint`). Run the full repository
  check only when preparing the final PR. When your changes affect type
  definitions, interfaces in `@n8n/api-types`, or cross-package dependencies,
  build the system before running lint and typecheck.

  ## Architecture Overview

  **Monorepo Structure:** pnpm workspaces with Turbo build orchestration

  ### Package Structure

  The monorepo is organized into these key packages:

  - **`packages/@n8n/api-types`**: Shared TypeScript interfaces between frontend and backend
  - **`packages/workflow`**: Core workflow interfaces and types
  - **`packages/core`**: Workflow execution engine
  - **`packages/cli`**: Express server, REST API, and CLI commands
  - **`packages/editor-ui`**: Vue 3 frontend application
  - **`packages/@n8n/i18n`**: Internationalization for UI text
  - **`packages/nodes-base`**: Built-in nodes for integrations
  - **`packages/@n8n/nodes-langchain`**: AI/LangChain nodes
  - **`packages/@n8n/instance-ai`**: "AI Assistant" in the UI, "Instance AI" in code — AI assistant backend. See its `CLAUDE.md` for architecture docs.
  - **`@n8n/design-system`**: Vue component library for UI consistency
  - **`@n8n/config`**: Centralized configuration management

  ## Technology Stack

  - **Frontend:** Vue 3 + TypeScript + Vite + Pinia + Storybook UI Library
  - **Backend:** Node.js + TypeScript + Express + TypeORM
  - **Testing:** Jest (unit) + Playwright (E2E)
  - **Database:** TypeORM with SQLite/PostgreSQL support
  - **Code Quality:** Biome (for formatting) + ESLint + lefthook git hooks

  ### Key Architectural Patterns

  1. **Dependency Injection**: Uses `@n8n/di` for IoC container
  2. **Controller-Service-Repository**: Backend follows MVC-like pattern
  3. **Event-Driven**: Internal event bus for decoupled communication
  4. **Context-Based Execution**: Different contexts for different node types
  5. **State Management**: Frontend uses Pinia stores
  6. **Design System**: Reusable components and design tokens are centralized in
     `@n8n/design-system`, where all pure Vue components should be placed to
     ensure consistency and reusability

  ## Key Development Patterns

  - Each package has isolated build configuration and can be developed independently
  - Hot reload works across the full stack during development
  - Node development uses dedicated `node-dev` CLI tool
  - Workflow tests are JSON-based for integration testing
  - AI features have dedicated development workflow (`pnpm dev:ai`)

  ### Workflow Traversal Utilities

  The `n8n-workflow` package exports graph traversal utilities from
  `packages/workflow/src/common/`. Use these instead of custom traversal logic.

  **Key concept:** `workflow.connections` is indexed by **source node**.
  To find parent nodes, use `mapConnectionsByDestination()` to invert it first.

  ```typescript
  import { getParentNodes, getChildNodes, mapConnectionsByDestination } from 'n8n-workflow';

  // Finding parent nodes (predecessors) - requires inverted connections
  const connectionsByDestination = mapConnectionsByDestination(workflow.connections);
  const parents = getParentNodes(connectionsByDestination, 'NodeName', 'main', 1);

  // Finding child nodes (successors) - uses connections directly
  const children = getChildNodes(workflow.connections, 'NodeName', 'main', 1);
  ```

  ### TypeScript Best Practices
  - **NEVER use `any` type** - use proper types or `unknown`
  - **Avoid type casting with `as`** - use type guards or type predicates instead (except in test code where `as` is acceptable)
  - **Define shared interfaces in `@n8n/api-types`** package for FE/BE communication
  - **Lazy-load heavy modules** — if a module is only used in a specific code
    path (not every request), use `await import()` at point of use instead of
    top-level `import`. Applies especially to native modules and large parsers.

  ### Error Handling
  - Don't use `ApplicationError` class in CLI and nodes for throwing errors,
    because it's deprecated. Use `UnexpectedError`, `OperationalError` or
    `UserError` instead.
  - Import from appropriate error classes in each package

  ### Frontend Development
  - Refer to `packages/frontend/AGENTS.md`
  - **All UI text must use i18n** - add translations to `@n8n/i18n` package
  - **Use CSS variables directly** - never hardcode spacing as px values
  - **data-testid must be a single value** (no spaces or multiple values)
  - Always use `design-system-rules` skill in reviews

  ### Testing Guidelines
  - **Always work from within the package directory** when running tests
  - **Mock all external dependencies** in unit tests
  - **Prefer reusing hoisted shared `mock<T>(...)` fixtures** when a typed mock is immutable and used across tests. This rule exists to avoid massive test slowdowns from repeatedly creating nested proxy mocks while preserving the type contract. Avoid replacing these with `as unknown as T` helpers for entities like `User`.
  - **Confirm test cases with user** before writing unit tests
  - **Typecheck is critical before committing** - always run `pnpm typecheck`
  - **When modifying pinia stores**, check for unused computed properties
  - **For Vitest packages that use `@n8n/di` decorators**, use `createVitestConfigWithDecorators` from `@n8n/vitest-config/node-decorators`. It enables SWC `decoratorMetadata` (esbuild doesn't emit it) and externalizes workspace packages that register services (`@n8n/di`, `@n8n/config`, `@n8n/constants`, `n8n-workflow`) so a single DI `Container` instance is shared across the runtime. Loading them through Vitest's pipeline alongside their CJS dist produces two `Container`s and `Container.get(...)` returns `undefined`.

  What we use for testing and writing tests:
  - For testing nodes and other backend components, we use Jest for unit tests. Examples can be found in `packages/nodes-base/nodes/**/*test*`.
  - We use `nock` for server mocking
  - For frontend we use `vitest`
  - For E2E tests we use Playwright. Run with `pnpm --filter=n8n-playwright test:local`.
    See `packages/testing/playwright/README.

  [...truncated AGENTS.md: 11,207 chars total] terminal 


c0:
    [{"id": "chatcmpl-tool-b7adcfa07b286abd", "call_id": "chatcmpl-tool-b7adcfa07b286abd", "response_item_id": "fc_chatcmpl-tool-b7adcfa07b286abd", "type": "function", "function": {"name": "search_files", "arguments": "{\"path\": \"/home/ice/n8n-repo\", \"pattern\": \"README.md\", \"target\": \"files\"}"}}, {"id": "chatcmpl-tool-9e5c7903ace5e610", "call_id": "chatcmpl-tool-9e5c7903ace5e610", "response_item_id": "fc_chatcmpl-tool-9e5c7903ace5e610", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/n8n-repo/package.json\"}"}}]


c0:
  {"total_count": 50, "files": ["/home/ice/n8n-repo/scripts/mutation-health/README.md", "/home/ice/n8n-repo/packages/workflow/README.md", "/home/ice/n8n-repo/packages/testing/rules-engine/README.md", "/home/ice/n8n-repo/packages/testing/playwright/tests/performance/README.md", "/home/ice/n8n-repo/packages/testing/playwright/tests/infrastructure/benchmarks/README.md", "/home/ice/n8n-repo/packages/testing/playwright/tests/e2e/instance-ai/README.md", "/home/ice/n8n-repo/packages/testing/playwright/tests/cli-workflows/README.md", "/home/ice/n8n-repo/packages/testing/playwright/README.md", "/home/ice/n8n-repo/packages/testing/performance/README.md", "/home/ice/n8n-repo/packages/testing/janitor/README.md", "/home/ice/n8n-repo/packages/testing/containers/README.md", "/home/ice/n8n-repo/packages/testing/code-health/README.md", "/home/ice/n8n-repo/packages/nodes-base/nodes/Stripe/README.md", "/home/ice/n8n-repo/packages/nodes-base/nodes/Aws/ELB/test/README.md", "/home/ice/n8n-repo/packages/nodes-base/README.md", "/home/ice/n8n-repo/packages/node-dev/README.md", "/home/ice/n8n-repo/packages/frontend/editor-ui/README.md", "/home/ice/n8n-repo/packages/frontend/@n8n/storybook/README.md", "/home/ice/n8n-repo/packages/frontend/@n8n/stores/README.md", "/home/ice/n8n-repo/packages/frontend/@n8n/rest-api-client/README.md", "/home/ice/n8n-repo/packages/frontend/@n8n/i18n/docs/README.md", "/home/ice/n8n-repo/packages/frontend/@n8n/i18n/README.md", "/home/ice/n8n-repo/packages/frontend/@n8n/design-system/README.md", "/home/ice/n8n-repo/packages/frontend/@n8n/composables/README.md", "/home/ice/n8n-repo/packages/frontend/@n8n/chat/README.md", "/home/ice/n8n-repo/packages/extensions/insights/README.md", "/home/ice/n8n-repo/packages/core/README.md", "/home/ice/n8n-repo/packages/cli/src/modules/source-control.ee/README.md", "/home/ice/n8n-repo/packages/cli/src/modules/otel/README.md", "/home/ice/n8n-repo/packages/cli/src/modules/log-streaming.ee/README.md", "/home/ice/n8n-repo/packages/cli/src/modules/breaking-changes/README.md", "/home/ice/n8n-repo/packages/@n8n/workflow-sdk/README.md", "/home/ice/n8n-repo/packages/@n8n/utils/src/search/snapshots/README.md", "/home/ice/n8n-repo/packages/@n8n/utils/README.md", "/home/ice/n8n-repo/packages/@n8n/tournament/README.md", "/home/ice/n8n-repo/packages/@n8n/task-runner-python/README.md", "/home/ice/n8n-repo/packages/@n8n/syslog-client/test/fixtures/README.md", "/home/ice/n8n-repo/packages/@n8n/syslog-client/README.md", "/home/ice/n8n-repo/packages/@n8n/stylelint-config/README.md", "/home/ice/n8n-repo/packages/@n8n/scan-community-package/README.md", "/home/ice/n8n-repo/packages/@n8n/nodes-langchain/nodes/trigger/ChatTrigger/README.md", "/home/ice/n8n-repo/packages/@n8n/nodes-langchain/nodes/mcp/McpTrigger/README.md", "/home/ice/n8n-repo/packages/@n8n/nodes-langchain/README.md", "/home/ice/n8n-repo/packages/@n8n/node-cli/src/template/templates/programmatic/example/template/README.md", "/home/ice/n8n-repo/packages/@n8n/node-cli/src/template/templates/programmatic/ai/model-openai-compatible/template/README.md", "/home/ice/n8n-repo/packages/@n8n/node-cli/src/template/templates/programmatic/ai/model-ai-custom/template/README.md", "/home/ice/n8n-repo/packages/@n8n/node-cli/src/template/templates/programmatic/ai/model-ai-custom-example/template/README.md", "/home/ice/n8n-repo/packages/@n8n/node-cli/src/template/templates/programmatic/ai/memory-custom/template/README.md", "/home/ice/n8n-repo/packages/@n8n/node-cli/src/template/templates/declarative/github-issues/template/README.md", "/home/ice/n8n-repo/packages/@n8n/node-cli/src/template/templates/declarative/custom/template/README.md"]} search_files 


c0:
  {"content": "1|{\n2|  \"name\": \"n8n-monorepo\",\n3|  \"version\": \"2.23.0\",\n4|  \"private\": true,\n5|  \"engines\": {\n6|    \"node\": \">=22.22\",\n7|    \"pnpm\": \">=10.22.0\"\n8|  },\n9|  \"packageManager\": \"pnpm@10.32.1\",\n10|  \"scripts\": {\n11|    \"prepare\": \"node scripts/prepare.mjs\",\n12|    \"preinstall\": \"node scripts/block-npm-install.js\",\n13|    \"build\": \"turbo run build\",\n14|    \"build:n8n\": \"node scripts/build-n8n.mjs\",\n15|    \"build:deploy\": \"node scripts/build-n8n.mjs\",\n16|    \"build:docker\": \"node scripts/build-n8n.mjs && node scripts/dockerize-n8n.mjs\",\n17|    \"build:docker:coverage\": \"BUILD_WITH_COVERAGE=true node scripts/build-n8n.mjs && node scripts/dockerize-n8n.mjs\",\n18|    \"build:docker:scan\": \"node scripts/build-n8n.mjs && node scripts/dockerize-n8n.mjs && node scripts/scan-n8n-image.mjs\",\n19|    \"build:docker:smoke\": \"node scripts/smoke-n8n-image.mjs\",\n20|    \"build:docker:clean\": \"TURBO_FORCE=true node scripts/build-n8n.mjs && DOCKER_BUILD_NO_CACHE=true DOCKER_BUILD_BASE_IMAGE=true node scripts/dockerize-n8n.mjs\",\n21|    \"build:docker:test\": \"node scripts/build-n8n.mjs && node scripts/dockerize-n8n.mjs && turbo run test:container:standard --filter=n8n-playwright\",\n22|    \"typecheck\": \"turbo typecheck\",\n23|    \"dev\": \"turbo run dev --parallel --env-mode=loose --filter=!@n8n/design-system --filter=!@n8n/chat --filter=!@n8n/task-runner\",\n24|    \"dev:be\": \"turbo run dev --parallel --env-mode=loose --filter=!@n8n/design-system --filter=!@n8n/chat --filter=!@n8n/task-runner --filter=!n8n-editor-ui\",\n25|    \"dev:ai\": \"turbo run dev --parallel --env-mode=loose --filter=@n8n/n8n-nodes-langchain --filter=n8n --filter=n8n-core\",\n26|    \"dev:fe\": \"run-p start \\\"dev:fe:editor --filter=@n8n/design-system\\\"\",\n27|    \"dev:fe:editor\": \"turbo run dev --parallel --env-mode=loose --filter=n8n-editor-ui\",\n28|    \"dev:e2e\": \"pnpm --filter=n8n-playwright dev --ui\",\n29|    \"clean\": \"turbo run clean\",\n30|    \"reset\": \"node scripts/ensure-zx.mjs && zx scripts/reset.mjs\",\n31|    \"format\": \"turbo run format && node scripts/format.mjs\",\n32|    \"grind\": \"node scripts/grind.mjs\",\n33|    \"format:check\": \"turbo run format:check\",\n34|    \"lint\": \"turbo run lint\",\n35|    \"lint:styles\": \"turbo run lint:styles\",\n36|    \"lint:styles:fix\": \"turbo run lint:styles:fix\",\n37|    \"lint:affected\": \"turbo run lint --affected\",\n38|    \"lint:fix\": \"turbo run lint:fix\",\n39|    \"lint:ci\": \"turbo run lint lint:styles\",\n40|    \"optimize-svg\": \"find ./packages -name '*.svg' ! -name 'pipedrive.svg' -print0 | xargs -0 -P16 -L20 npx svgo\",\n41|    \"setup-backend-module\": \"node scripts/ensure-zx.mjs && zx scripts/backend-module/setup.mjs\",\n42|    \"start\": \"node scripts/os-normalize.mjs --dir packages/cli/bin n8n\",\n43|    \"test\": \"JEST_JUNIT_CLASSNAME={filepath} turbo run test\",\n44|    \"test:ci\": \"turbo run test --continue --concurrency=1\",\n45|    \"test:ci:frontend\": \"turbo run test --continue --filter='./packages/frontend/**'\",\n46|    \"test:ci:frontend:changed\": \"turbo run test:changed --continue --filter='./packages/frontend/**'\",\n47|    \"test:ci:backend\": \"turbo run test --continue --concurrency=1 --filter='!./packages/frontend/**'\",\n48|    \"test:ci:backend:unit\": \"turbo run test:unit --continue --filter='!./packages/frontend/**'\",\n49|    \"test:ci:backend:integration\": \"turbo run test:integration --continue --concurrency=1 --filter='!./packages/frontend/**'\",\n50|    \"test:ci:backend:integration:changed\": \"turbo run test:integration:changed --continue --concurrency=1 --filter='!./packages/frontend/**'\",\n51|    \"test:affected\": \"turbo run test --affected --concurrency=1\",\n52|    \"test:with:docker\": \"pnpm --filter=n8n-playwright test:container:standard\",\n53|    \"test:show:report\": \"pnpm --filter=n8n-playwright exec playwright show-report\",\n54|    \"watch\": \"turbo run watch --concurrency=64\",\n55|    \"webhook\": \"./packages/cli/bin/n8n webhook\",\n56|    \"worker\": \"./packages/cli/bin/n8n worker\",\n57|    \"dev:computer-use\": \"pnpm --filter @n8n/computer-use build && node packages/@n8n/computer-use/dist/cli.js serve\",\n58|    \"stop:computer-use\": \"lsof -ti :7655 | xargs kill 2>/dev/null; echo 'computer-use stopped'\"\n59|  },\n60|  \"devDependencies\": {\n61|    \"@babel/preset-env\": \"^7.26.0\",\n62|    \"@biomejs/biome\": \"^1.9.0\",\n63|    \"@dotenvx/dotenvx\": \"^1.40.0\",\n64|    \"@n8n/eslint-config\": \"workspace:*\",\n65|    \"@types/jest\": \"^29.5.3\",\n66|    \"@types/node\": \"*\",\n67|    \"@types/supertest\": \"^6.0.3\",\n68|    \"babel-jest\": \"catalog:\",\n69|    \"babel-plugin-transform-import-meta\": \"^2.3.2\",\n70|    \"cross-env\": \"^7.0.3\",\n71|    \"eslint\": \"catalog:\",\n72|    \"get-tsconfig\": \"catalog:\",\n73|    \"jest\": \"^29.6.2\",\n74|    \"jest-environment-jsdom\": \"^29.6.2\",\n75|    \"jest-expect-message\": \"^1.1.3\",\n76|    \"jest-junit\": \"^16.0.0\",\n77|    \"jest-mock\": \"^29.6.2\",\n78|    \"jest-mock-extended\": \"^3.0.4\",\n79|    \"lefthook\": \"^1.7.15\",\n80|    \"nock\": \"^14.0.14\",\n81|    \"nodemon\": \"^3.0.1\",\n82|    \"npm-run-all2\": \"^7.0.2\",\n83|    \"p-limit\": \"^3.1.0\",\n84|    \"rimraf\": \"^5.0.1\",\n85|    \"supertest\": \"^7.1.1\",\n86|    \"ts-jest\": \"^29.4.9\",\n87|    \"tsc-alias\": \"^1.8.10\",\n88|    \"tsc-watch\": \"^6.2.0\",\n89|    \"turbo\": \"2.9.4\",\n90|    \"typescript\": \"*\",\n91|    \"zx\": \"^8.8.5\"\n92|  },\n93|  \"pnpm\": {\n94|    \"onlyBuiltDependencies\": [\n95|      \"@vscode/ripgrep\",\n96|      \"isolated-vm\",\n97|      \"sqlite3\"\n98|    ],\n99|    \"overrides\": {\n100|      \"libphonenumber-js\": \"npm:empty-npm-package@1.0.0\",\n101|      \"@browserbasehq/stagehand\": \"npm:empty-npm-package@1.0.0\",\n102|      \"sharp\": \"npm:empty-npm-package@1.0.0\",\n103|      \"ast-types\": \"0.16.1\",\n104|      \"baseline-browser-mapping\": \"^2.10.31\",\n105|      \"@azure/identity\": \"4.13.0\",\n106|      \"@lezer/common\": \"^1.2.0\",\n107|      \"@mistralai/mistralai\": \"^1.10.0\",\n108|      \"@n8n/typeorm>@sentry/node\": \"catalog:sentry\",\n109|      \"@types/node\": \"^20.17.50\",\n110|      \"chokidar\": \"4.0.3\",\n111|      \"esbuild\": \"^0.25.0\",\n112|      \"multer\": \"^2.1.1\",\n113|      \"prebuild-install\": \"7.1.3\",\n114|      \"pug\": \"^3.0.3\",\n115|      \"semver\": \"^7.5.4\",\n116|      \"tar-fs\": \"2.1.4\",\n117|      \"tslib\": \"^2.6.2\",\n118|      \"tsconfig-paths\": \"^4.2.0\",\n119|      \"typescript\": \"catalog:\",\n120|      \"vue-tsc\": \"^2.2.8\",\n121|      \"gaxios\": \">=7.1.1\",\n122|      \"google-gax\": \"^4.3.7\",\n123|      \"ws\": \">=8.20.1\",\n124|      \"date-fns\": \"2.30.0\",\n125|      \"date-fns-tz\": \"2.0.0\",\n126|      \"form-data\": \"4.0.4\",\n127|      \"pdf-parse\": \"^2.4.5\",\n128|      \"tmp\": \"0.2.4\",\n129|      \"nodemailer\": \"7.0.11\",\n130|      \"validator\": \"13.15.26\",\n131|      \"zod\": \"3.25.67\",\n132|      \"js-yaml\": \"4.1.1\",\n133|      \"body-parser\": \"2.2.1\",\n134|      \"glob@10\": \"10.5.0\",\n135|      \"glob@7\": \"7.2.3\",\n136|      \"jws@3\": \"3.2.2\",\n137|      \"jws@4\": \"4.0.1\",\n138|      \"qs@6\": \"6.14.2\",\n139|      \"@smithy/config-resolver\": \">=4.4.0\",\n140|      \"@rudderstack/rudder-sdk-node@<=3.0.0\": \"3.0.0\",\n141|      \"diff\": \"8.0.3\",\n142|      \"undici@5\": \"^6.24.0\",\n143|      \"undici@6\": \"^6.24.0\",\n144|      \"undici@7\": \"^7.24.0\",\n145|      \"tar\": \"^7.5.11\",\n146|      \"ajv@6\": \"6.14.0\",\n147|      \"ajv@7\": \"8.18.0\",\n148|      \"ajv@8\": \"8.18.0\",\n149|      \"bn.js@4\": \"5.2.3\",\n150|      \"bn.js@5\": \"5.2.3\",\n151|      \"lodash\": \"4.18.1\",\n152|      \"lodash-es\": \"4.18.1\",\n153|      \"minimatch@<=5.1.8\": \"5.1.8\",\n154|      \"minimatch@10\": \"10.2.3\",\n155|      \"@hono/node-server\": \"1.19.13\",\n156|      \"follow-redirects\": \"1.16.0\",\n157|      \"express-rate-limit\": \"8.2.2\",\n158|      \"underscore\": \"1.13.8\",\n159|      \"flatted\": \"3.4.2\",\n160|      \"handlebars\": \"4.7.9\",\n161|      \"path-to-regexp\": \"8.4.0\",\n162|      \"path-to-regexp@<0.1.13\": \"0.1.13\",\n163|      \"picomatch@2\": \"2.3.2\",\n164|      \"picomatch@4\": \"4.0.4\",\n165|      \"avsc\": \"5.7.9\",\n166|      \"brace-expansion@<=2.1.0\": \"2.1.0\",\n167|      \"@xmldom/xmldom\": \"0.8.13\",\n168|      \"yaml@<=2.8.3\": \"2.8.3\",\n169|      \"axios\": \"1.16.1\",\n170|      \"fast-xml-parser\": \"5.7.2\",\n171|      \"hono\": \"4.12.18\",\n172|      \"@anthropic-ai/sdk@<=0.91.1\": \"0.91.1\",\n173|      \"uuid@<=13.0.1\": \"13.0.1\",\n174|      \"fast-uri\": \"3.1.2\",\n175|      \"cjs-module-lexer@<2.2.0\": \"2.2.0\",\n176|      \"protobufjs\": \"7.5.8\",\n177|      \"ip-address@10\": \"10.1.1\",\n178|      \"brace-expansion@5\": \"5.0.6\",\n179|      \"@opentelemetry/exporter-prometheus@<=0.217.0\": \"0.217.0\",\n180|      \"@opentelemetry/sdk-node@<=0.217.0\": \"0.217.0\",\n181|      \"langsmith\": \"0.6.0\"\n182|    },\n183|    \"patchedDependencies\": {\n184|      \"bull@4.16.4\": \"patches/bull@4.16.4.patch\",\n185|      \"pdfjs-dist@5.3.31\": \"patches/pdfjs-dist@5.3.31.patch\",\n186|      \"pkce-challenge@5.0.0\": \"patches/pkce-challenge@5.0.0.patch\",\n187|      \"@types/express-serve-static-core@5.0.6\": \"patches/@types__express-serve-static-core@5.0.6.patch\",\n188|      \"@types/ws@8.18.1\": \"patches/@types__ws@8.18.1.patch\",\n189|      \"vue-tsc@2.2.8\": \"patches/vue-tsc@2.2.8.patch\",\n190|      \"element-plus@2.4.3\": \"patches/element-plus@2.4.3.patch\",\n191|      \"ics\": \"patches/ics.patch\",\n192|      \"minifaker\": \"patches/minifaker.patch\",\n193|      \"z-vue-scan\": \"patches/z-vue-scan.patch\",\n194|      \"@lezer/highlight\": \"patches/@lezer__highlight.patch\",\n195|      \"v-code-diff\": \"patches/v-code-diff.patch\",\n196|      \"vuedraggable@4.1.0\": \"patches/vuedraggable@4.1.0.patch\",\n197|      \"assert@2.1.0\": \"patches/assert@2.1.0.patch\"\n198|    }\n199|  }\n200|}\n201|", "total_lines": 200, "file_size": 8548, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
    [{"id": "chatcmpl-tool-b266db41df1b632e", "call_id": "chatcmpl-tool-b266db41df1b632e", "response_item_id": "fc_chatcmpl-tool-b266db41df1b632e", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 150, \"path\": \"/home/ice/n8n-repo/README.md\"}"}}]


c0:
  {"content": "1|![Banner image](https://user-images.githubusercontent.com/10284570/173569848-c624317f-42b1-45a6-ab09-f0ea3c247648.png)\n2|\n3|# n8n - Secure Workflow Automation for Technical Teams\n4|\n5|n8n is a workflow automation platform that gives technical teams the flexibility of code with the speed of no-code. With 400+ integrations, native AI capabilities, and a fair-code license, n8n lets you build powerful automations while maintaining full control over your data and deployments.\n6|\n7|![n8n.io - Screenshot](https://raw.githubusercontent.com/n8n-io/n8n/master/assets/n8n-screenshot-readme.png)\n8|\n9|## Key Capabilities\n10|\n11|- **Code When You Need It**: Write JavaScript/Python, add npm packages, or use the visual interface\n12|- **AI-Native Platform**: Build AI agent workflows based on LangChain with your own data and models\n13|- **Full Control**: Self-host with our fair-code license or use our [cloud offering](https://app.n8n.cloud/login)\n14|- **Enterprise-Ready**: Advanced permissions, SSO, and air-gapped deployments\n15|- **Active Community**: 400+ integrations and 900+ ready-to-use [templates](https://n8n.io/workflows)\n16|\n17|## Quick Start\n18|\n19|Try n8n instantly with [npx](https://docs.n8n.io/hosting/installation/npm/) (requires [Node.js](https://nodejs.org/en/)):\n20|\n21|```\n22|npx n8n\n23|```\n24|\n25|Or deploy with [Docker](https://docs.n8n.io/hosting/installation/docker/):\n26|\n27|```\n28|docker volume create n8n_data\n29|docker run -it --rm --name n8n -p 5678:5678 -v n8n_data:/home/node/.n8n docker.n8n.io/n8nio/n8n\n30|```\n31|\n32|Access the editor at http://localhost:5678\n33|\n34|## Resources\n35|\n36|- 📚 [Documentation](https://docs.n8n.io)\n37|- 🔧 [400+ Integrations](https://n8n.io/integrations)\n38|- 💡 [Example Workflows](https://n8n.io/workflows)\n39|- 🤖 [AI & LangChain Guide](https://docs.n8n.io/advanced-ai/)\n40|- 👥 [Community Forum](https://community.n8n.io)\n41|- 📖 [Community Tutorials](https://community.n8n.io/c/tutorials/28)\n42|\n43|## Support\n44|\n45|Need help? Our community forum is the place to get support and connect with other users:\n46|[community.n8n.io](https://community.n8n.io)\n47|\n48|## License\n49|\n50|n8n is [fair-code](https://faircode.io) distributed under the [Sustainable Use License](https://github.com/n8n-io/n8n/blob/master/LICENSE.md) and [n8n Enterprise License](https://github.com/n8n-io/n8n/blob/master/LICENSE_EE.md).\n51|\n52|- **Source Available**: Always visible source code\n53|- **Self-Hostable**: Deploy anywhere\n54|- **Extensible**: Add your own nodes and functionality\n55|\n56|[Enterprise Licenses](mailto:license@n8n.io) available for additional features and support.\n57|\n58|Additional information about the license model can be found in the [docs](https://docs.n8n.io/sustainable-use-license/).\n59|\n60|## Contributing\n61|\n62|Found a bug 🐛 or have a feature idea ✨? Check our [Contributing Guide](https://github.com/n8n-io/n8n/blob/master/CONTRIBUTING.md) for a setup guide & best practices.\n63|\n64|## Join the Team\n65|\n66|Want to shape the future of automation? Check out our [job posts](https://n8n.io/careers) and join our team!\n67|\n68|## What does n8n mean?\n69|\n70|**Short answer:** It means \"nodemation\" and is pronounced as n-eight-n.\n71|\n72|**Long answer:** \"I get that question quite often (more often than I expected) so I decided it is probably best to answer it here. While looking for a good name for the project with a free domain I realized very quickly that all the good ones I could think of were already taken. So, in the end, I chose nodemation. 'node-' in the sense that it uses a Node-View and that it uses Node.js and '-mation' for 'automation' which is what the project is supposed to help with. However, I did not like how long the name was and I could not imagine writing something that long every time in the CLI. That is when I then ended up on 'n8n'.\" - **Jan Oberhauser, Founder and CEO, n8n.io**\n73|", "total_lines": 72, "file_size": 3698, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  # Análise do Repositório: n8n-io/n8n

  ## Problema
  O n8n resolve o problema de **automação de fluxos de trabalho (workflow automation)** para equipes técnicas. Ele preenche a lacuna entre no-code e código, permitindo que desenvolvedores criem automações complexas sem perder a flexibilidade de programar quando necessário. A dor principal atendida é a fragmentação de ferramentas de integração — em vez de usar múltiplos serviços SaaS fechados, o usuário tem uma plataforma auto-hospedável ou cloud com 400+ integrações nativas.

  ## Tema
  Plataforma de automação de workflows — combina:
  - Interface visual "node-based" (arrastar e conectar)
  - Capacidade de código customizado (JavaScript/Python)
  - IA nativa (agentes LangChain, modelos próprios)
  - Foco em equipes técnicas e enterprise (permissões avançadas, SSO, air-gapped)

  ## Razão do Hype
  1. **Fair-code + Self-hosted**: Licença Sustainable Use permite usar livremente, ao contrário de concorrentes fechados (Zapier, Make)
  2. **AI-native**: Integração direta com LangChain, agente de IA na UI — agradando o hype atual de AI Agents
  3. **Ctrl total sobre dados**: Muito relevante para empresas com compliance (healthcare, finance)
  4. **400+ integrações + 900+ templates**: Ecossistema crescente rapidamente
  5. **Open source com 190k stars**: Validação massiva da comunidade
  6. **Extensibilidade**: Criar nodes customizados, usar npm packages, código próprio nos fluxos
  7. **Versatilidade de deploy**: npx, Docker, Kubernetes, cloud próprio

  ## Tecnologias

  ### Backend
  - **Node.js + TypeScript** (>=22.22)
  - **Express** (servidor REST API)
  - **TypeORM** (ORM para banco de dados)
  - **Jest** (testes unitários)
  - **pnpm workspaces + Turborepo** (monorepo)
  - **Bull** (fila de jobs — com patch customizado)

  ### Frontend
  - **Vue 3 + TypeScript**
  - **Vite**
  - **Pinia** (state management)
  - **Storybook** (UI library)
  - **Vitest** (testes frontend)
  - **@n8n/design-system** (design system próprio)
  - **Playwright** (E2E)

  ### Infra/DevOps
  - **Docker** (distribuição principal)
  - **PostgreSQL / SQLite** (banco)
  - **PostHog** (feature flags)
  - **Linear** (ticket tracking)
  - **Biome** (formatting) + **ESLint** + **lefthook** (git hooks)
  - **OpenTelemetry** (observabilidade)

  ### Padrões Arquiteturais
  - **Dependency Injection** via `@n8n/di`
  - **Controller-Service-Repository** no backend
  - **Event-driven** (event bus interno)
  - **Monorepo** com pacotes isolados: `cli`, `core`, `workflow`, `nodes-base`, `editor-ui`, `api-types`, `i18n`

  ---

  **Resumo executivo**: n8n é um sucesso porque combinou modelagem visual de workflows com código real e IA — tudo sob licença open-source/fair-code que permite hospedagem própria. A stack técnica (TypeScript fullstack, Vue 3, Node) é moderna e acessível, enquanto a arquitetura de monorepo bem estruturada permite evolução rápida. O hype é justificado pela convergência de três megatrends: low-code, AI agents e data sovereignty.  


c0:
    [{"id": "chatcmpl-tool-9f0edd64dca18763", "call_id": "chatcmpl-tool-9f0edd64dca18763", "response_item_id": "fc_chatcmpl-tool-9f0edd64dca18763", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice && git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git\", \"timeout\": 120}"}}]


c0:
    [{"id": "chatcmpl-tool-848f6686cf4ab4f5", "call_id": "chatcmpl-tool-848f6686cf4ab4f5", "response_item_id": "fc_chatcmpl-tool-848f6686cf4ab4f5", "type": "function", "function": {"name": "search_files", "arguments": "{\"path\": \"/home/ice/awesome-llm-apps\", \"pattern\": \"*\", \"target\": \"files\"}"}}, {"id": "chatcmpl-tool-9e3728d30f69caea", "call_id": "chatcmpl-tool-9e3728d30f69caea", "response_item_id": "fc_chatcmpl-tool-9e3728d30f69caea", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/awesome-llm-apps/README.md\", \"limit\": 200, \"offset\": 1}"}}]


c0:
  {"total_count": 50, "files": ["/home/ice/awesome-llm-apps/voice_ai_agents/voice_rag_openaisdk/requirements.txt", "/home/ice/awesome-llm-apps/voice_ai_agents/voice_rag_openaisdk/rag_voice.py", "/home/ice/awesome-llm-apps/voice_ai_agents/voice_rag_openaisdk/README.md", "/home/ice/awesome-llm-apps/voice_ai_agents/insurance_claim_live_agent_team/schemas.py", "/home/ice/awesome-llm-apps/voice_ai_agents/insurance_claim_live_agent_team/requirements.txt", "/home/ice/awesome-llm-apps/voice_ai_agents/insurance_claim_live_agent_team/policies.py", "/home/ice/awesome-llm-apps/voice_ai_agents/insurance_claim_live_agent_team/live_demo/styles.css", "/home/ice/awesome-llm-apps/voice_ai_agents/insurance_claim_live_agent_team/live_demo/server.py", "/home/ice/awesome-llm-apps/voice_ai_agents/insurance_claim_live_agent_team/live_demo/index.html", "/home/ice/awesome-llm-apps/voice_ai_agents/insurance_claim_live_agent_team/live_demo/app.js", "/home/ice/awesome-llm-apps/voice_ai_agents/insurance_claim_live_agent_team/examples.py", "/home/ice/awesome-llm-apps/voice_ai_agents/insurance_claim_live_agent_team/assets/insurance-claim-live-agent-team-architecture.png", "/home/ice/awesome-llm-apps/voice_ai_agents/insurance_claim_live_agent_team/agent.py", "/home/ice/awesome-llm-apps/voice_ai_agents/insurance_claim_live_agent_team/__init__.py", "/home/ice/awesome-llm-apps/voice_ai_agents/insurance_claim_live_agent_team/README.md", "/home/ice/awesome-llm-apps/voice_ai_agents/customer_support_voice_agent/requirements.txt", "/home/ice/awesome-llm-apps/voice_ai_agents/customer_support_voice_agent/customer_support_voice_agent.py", "/home/ice/awesome-llm-apps/voice_ai_agents/customer_support_voice_agent/README.md", "/home/ice/awesome-llm-apps/voice_ai_agents/ai_audio_tour_agent/requirements.txt", "/home/ice/awesome-llm-apps/voice_ai_agents/ai_audio_tour_agent/printer.py", "/home/ice/awesome-llm-apps/voice_ai_agents/ai_audio_tour_agent/manager.py", "/home/ice/awesome-llm-apps/voice_ai_agents/ai_audio_tour_agent/ai_audio_tour_agent.py", "/home/ice/awesome-llm-apps/voice_ai_agents/ai_audio_tour_agent/agent.py", "/home/ice/awesome-llm-apps/voice_ai_agents/ai_audio_tour_agent/README.md", "/home/ice/awesome-llm-apps/starter_ai_agents/xai_finance_agent/xai_finance_agent.py", "/home/ice/awesome-llm-apps/starter_ai_agents/xai_finance_agent/requirements.txt", "/home/ice/awesome-llm-apps/starter_ai_agents/xai_finance_agent/README.md", "/home/ice/awesome-llm-apps/starter_ai_agents/web_scraping_ai_agent/requirements.txt", "/home/ice/awesome-llm-apps/starter_ai_agents/web_scraping_ai_agent/local_ai_scrapper.py", "/home/ice/awesome-llm-apps/starter_ai_agents/web_scraping_ai_agent/ai_scrapper.py", "/home/ice/awesome-llm-apps/starter_ai_agents/web_scraping_ai_agent/README.md", "/home/ice/awesome-llm-apps/starter_ai_agents/openai_research_agent/research_agent.py", "/home/ice/awesome-llm-apps/starter_ai_agents/openai_research_agent/requirements.txt", "/home/ice/awesome-llm-apps/starter_ai_agents/openai_research_agent/README.md", "/home/ice/awesome-llm-apps/starter_ai_agents/multimodal_ai_agent/requirements.txt", "/home/ice/awesome-llm-apps/starter_ai_agents/multimodal_ai_agent/mutimodal_agent.py", "/home/ice/awesome-llm-apps/starter_ai_agents/multimodal_ai_agent/multimodal_reasoning_agent.py", "/home/ice/awesome-llm-apps/starter_ai_agents/multimodal_ai_agent/README.md", "/home/ice/awesome-llm-apps/starter_ai_agents/mixture_of_agents/requirements.txt", "/home/ice/awesome-llm-apps/starter_ai_agents/mixture_of_agents/mixture-of-agents.py", "/home/ice/awesome-llm-apps/starter_ai_agents/ai_travel_agent/travel_agent.py", "/home/ice/awesome-llm-apps/starter_ai_agents/ai_travel_agent/requirements.txt", "/home/ice/awesome-llm-apps/starter_ai_agents/ai_travel_agent/local_travel_agent.py", "/home/ice/awesome-llm-apps/starter_ai_agents/ai_travel_agent/README.MD", "/home/ice/awesome-llm-apps/starter_ai_agents/ai_startup_trend_analysis_agent/startup_trends_agent.py", "/home/ice/awesome-llm-apps/starter_ai_agents/ai_startup_trend_analysis_agent/requirements.txt", "/home/ice/awesome-llm-apps/starter_ai_agents/ai_startup_trend_analysis_agent/README.md", "/home/ice/awesome-llm-apps/starter_ai_agents/ai_reasoning_agent/requirements.txt", "/home/ice/awesome-llm-apps/starter_ai_agents/ai_reasoning_agent/reasoning_agent.py", "/home/ice/awesome-llm-apps/starter_ai_agents/ai_reasoning_agent/local_ai_reasoning_agent.py"]} search_files 


c0:
  {"content": "1|<p align=\"center\">\n2|  <a href=\"http://www.theunwindai.com\">\n3|    <img src=\"docs/banner/unwind_black.png\" width=\"900px\" alt=\"Unwind AI\">\n4|  </a>\n5|</p>\n6|\n7|<p align=\"center\">\n8|  <a href=\"https://www.linkedin.com/in/shubhamsaboo/\">\n9|    <img src=\"https://img.shields.io/badge/Follow%20on%20LinkedIn-0A66C2?style=for-the-badge&logo=linkedin&logoColor=white\" alt=\"LinkedIn\">\n10|  </a>\n11|  <a href=\"https://twitter.com/Saboo_Shubham_\">\n12|    <img src=\"https://img.shields.io/badge/Follow%20on%20𝕏-000000?style=for-the-badge&logo=x&logoColor=white\" alt=\"X / Twitter\">\n13|  </a>\n14|</p>\n15|\n16|<p align=\"center\">\n17|  <!-- Keep these links. Translations will automatically update with the README. -->\n18|  <a href=\"https://www.readme-i18n.com/Shubhamsaboo/awesome-llm-apps?lang=de\">Deutsch</a> | \n19|  <a href=\"https://www.readme-i18n.com/Shubhamsaboo/awesome-llm-apps?lang=es\">Español</a> | \n20|  <a href=\"https://www.readme-i18n.com/Shubhamsaboo/awesome-llm-apps?lang=fr\">français</a> | \n21|  <a href=\"https://www.readme-i18n.com/Shubhamsaboo/awesome-llm-apps?lang=ja\">日本語</a> | \n22|  <a href=\"https://www.readme-i18n.com/Shubhamsaboo/awesome-llm-apps?lang=ko\">한국어</a> | \n23|  <a href=\"https://www.readme-i18n.com/Shubhamsaboo/awesome-llm-apps?lang=pt\">Português</a> | \n24|  <a href=\"https://www.readme-i18n.com/Shubhamsaboo/awesome-llm-apps?lang=ru\">Русский</a> | \n25|  <a href=\"https://www.readme-i18n.com/Shubhamsaboo/awesome-llm-apps?lang=zh\">中文</a>\n26|</p>\n27|\n28|<hr/>\n29|\n30|<div align=\"center\">\n31|\n32|# 🌟 Awesome LLM Apps\n33|\n34|<p><strong>100+ AI Agent & RAG apps you can actually run — clone, customize, ship.</strong><br/>\n35|AI Agents · Multi-agent Teams · MCP Agents · RAG · Voice Agents · Agent Skills · Fine-tuning</p>\n36|\n37|<p>\n38|<strong>Free step-by-step tutorials on <a href=\"https://www.theunwindai.com\">Unwind AI</a></strong><br/>\n39|<strong>Works with Claude · Gemini · OpenAI · xAI · Qwen · Llama</strong>\n40|</p>\n41|\n42|<p>\n43|<a href=\"https://github.com/Shubhamsaboo/awesome-llm-apps/stargazers\"><img src=\"https://img.shields.io/github/stars/Shubhamsaboo/awesome-llm-apps?style=for-the-badge&logo=github&color=FFD700\" alt=\"Stars\"></a>\n44|<a href=\"https://github.com/Shubhamsaboo/awesome-llm-apps/network/members\"><img src=\"https://img.shields.io/github/forks/Shubhamsaboo/awesome-llm-apps?style=for-the-badge&logo=github&color=4FC3F7\" alt=\"Forks\"></a>\n45|<a href=\"https://github.com/Shubhamsaboo/awesome-llm-apps/graphs/contributors\"><img src=\"https://img.shields.io/github/contributors/Shubhamsaboo/awesome-llm-apps?style=for-the-badge&color=22C55E\" alt=\"Contributors\"></a>\n46|<a href=\"LICENSE\"><img src=\"https://img.shields.io/github/license/Shubhamsaboo/awesome-llm-apps?style=for-the-badge&color=8B5CF6\" alt=\"License\"></a>\n47|<img src=\"https://img.shields.io/github/last-commit/Shubhamsaboo/awesome-llm-apps?style=for-the-badge&color=F97316\" alt=\"Last Commit\">\n48|</p>\n49|\n50|<p>\n51|<a href=\"#-quick-start\"><kbd> &nbsp; 🚀 Quick Start &nbsp; </kbd></a>\n52|<a href=\"#-featured-ai-projects\"><kbd> &nbsp; 📂 Browse Templates &nbsp; </kbd></a>\n53|<a href=\"https://www.theunwindai.com\"><kbd> &nbsp; 📚 Step-by-Step Tutorials &nbsp; </kbd></a>\n54|</p>\n55|\n56|<a href=\"https://trendshift.io/repositories/9876\" target=\"_blank\">\n57|  <img src=\"https://trendshift.io/api/badge/repositories/9876\" width=\"220\" alt=\"Featured on Trendshift\">\n58|</a>\n59|\n60|</div>\n61|\n62|---\n63|\n64|## 💡 Why this exists\n65|\n66|You shouldn't have to rebuild the same RAG pipeline, agent loop, or MCP integration from scratch every time you start a new LLM project.\n67|\n68|**Awesome LLM Apps is a cookbook of ready-to-run templates** - starter code you can fork, customize, and ship as a production LLM app. Every template here is self-contained with full source code, not collected from elsewhere.\n69|\n70|- 🛠️ **Hand-built, not curated** - every template is original work, tested end-to-end before it ships.\n71|- 🧪 **Runs in 3 commands** - no broken `requirements.txt`, no \"figure it out yourself\" scaffolding.\n72|- 🧠 **Covers the modern AI stack** - AI Agents, Multi-agent Teams, MCP Agents, Voice AI Agents, RAG, Agent Skills, Fine-tuning.\n73|- 🌐 **Provider-agnostic** - switch between Claude, Gemini, GPT, Llama, Qwen, xAI and others with a config change.\n74|- 📚 **Step-by-step tutorials** - every featured template has a free walkthrough on [Unwind AI](https://www.theunwindai.com).\n75|- 💸 **Apache-2.0** - fork it, ship it, sell it. No paywall, no signup, no telemetry.\n76|\n77|> ⭐ **If this saves you time, [star the repo](https://github.com/Shubhamsaboo/awesome-llm-apps/stargazers) - that's how the next developer discovers it.**\n78|\n79|## 🚀 Quick Start\n80|\n81|Run your first agent in **30 seconds**:\n82|\n83|```bash\n84|git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git\n85|cd awesome-llm-apps/starter_ai_agents/ai_travel_agent\n86|pip install -r requirements.txt\n87|streamlit run travel_agent.py\n88|```\n89|\n90|\n91|## 🔥 Featured This Month\n92|\n93|| Template | What it does | Stack |\n94||---|---|---|\n95|| [📡 Earnings Call Analyst Agent](advanced_ai_agents/single_agent_apps/earnings_call_analyst_agent/) | YouTube earnings calls become quote-synced analyst cards with ADK, Gemini, filings, and grounded market news | ADK + Gemini |\n96|| [🛡️ Insurance Claim Live Agent Team](voice_ai_agents/insurance_claim_live_agent_team/) | Real-time voice claim intake with Gemini Live and ADK | Voice + ADK |\n97|| [🏠 Home Renovation Agent](advanced_ai_agents/multi_agent_apps/ai_home_renovation_agent) | Photo → AI redesign with Nano Banana Pro | Vision + Multi-agent |\n98|| [♾️ Self-Improving Agent Skills](awesome_agent_skills/self-improving-agent-skills/) | Automatically optimize agent skills using Gemini and ADK | Agent Skills + ADK |\n99|\n100|> 📬 [Subscribe on Unwind AI](https://www.theunwindai.com) to get new template drops + tutorials in your inbox.\n101|\n102|## 📑 Table of Contents\n103|\n104|<details open>\n105|<summary><strong>13 categories · Click to expand</strong></summary>\n106|\n107|- [🌱 Starter AI Agents](#-starter-ai-agents)\n108|- [🚀 Advanced AI Agents](#-advanced-ai-agents)\n109|- [🎮 Autonomous Game-Playing Agents](#-autonomous-game-playing-agents)\n110|- [🤝 Multi-agent Teams](#-multi-agent-teams)\n111|- [🗣️ Voice AI Agents](#️-voice-ai-agents)\n112|- [♾️ MCP AI Agents](#-mcp-ai-agents)\n113|- [📀 RAG Tutorials](#-rag-retrieval-augmented-generation)\n114|- [🧩 Awesome Agent Skills](#-awesome-agent-skills)\n115|- [💾 LLM Apps with Memory](#-llm-apps-with-memory-tutorials)\n116|- [💬 Chat with X Tutorials](#-chat-with-x-tutorials)\n117|- [🎯 LLM Optimization Tools](#-llm-optimization-tools)\n118|- [🔧 LLM Fine-tuning Tutorials](#-llm-fine-tuning-tutorials)\n119|- [🧑‍🏫 AI Agent Framework Crash Courses](#-ai-agent-framework-crash-course)\n120|\n121|</details>\n122|\n123|## 📂 Featured AI Projects\n124|\n125|### 🌱 Starter AI Agents\n126|*Single-file agents that run with just an API key - a great place to start.*\n127|\n128|*   [🎙️ AI Blog to Podcast Agent](starter_ai_agents/ai_blog_to_podcast_agent/)\n129|*   [❤️‍🩹 AI Breakup Recovery Agent](starter_ai_agents/ai_breakup_recovery_agent/)\n130|*   [📊 AI Data Analysis Agent](starter_ai_agents/ai_data_analysis_agent/)\n131|*   [🩻 AI Medical Imaging Agent](starter_ai_agents/ai_medical_imaging_agent/)\n132|*   [😂 AI Meme Generator Agent (Browser)](starter_ai_agents/ai_meme_generator_agent_browseruse/)\n133|*   [🎵 AI Music Generator Agent](starter_ai_agents/ai_music_generator_agent/)\n134|*   [🛫 AI Travel Agent (Local & Cloud)](starter_ai_agents/ai_travel_agent/)\n135|*   [✨ Gemini Multimodal Agent](starter_ai_agents/gemini_multimodal_agent_demo/)\n136|*   [🔄 Mixture of Agents](starter_ai_agents/mixture_of_agents/)\n137|*   [📊 xAI Finance Agent](starter_ai_agents/xai_finance_agent/)\n138|*   [🔍 OpenAI Research Agent](starter_ai_agents/openai_research_agent/)\n139|*   [🕸️ Web Scraping AI Agent (Local & Cloud SDK)](starter_ai_agents/web_scraping_ai_agent/)\n140|\n141|### 🚀 Advanced AI Agents\n142|*Production-style agents with tools, memory, and multi-step reasoning.*\n143|\n144|*   [🏚️ 🍌 AI Home Renovation Agent with Nano Banana Pro](advanced_ai_agents/multi_agent_apps/ai_home_renovation_agent)\n145|*   [🧠 DevPulse AI - Multi-Agent Signal Intelligence](advanced_ai_agents/multi_agent_apps/devpulse_ai/)\n146|*   [🔍 AI Deep Research Agent](advanced_ai_agents/single_agent_apps/ai_deep_research_agent/)\n147|*   [📊 AI VC Due Diligence Agent Team](advanced_ai_agents/multi_agent_apps/agent_teams/ai_vc_due_diligence_agent_team)\n148|*   [🔬 AI Research Planner & Executor (Google Interactions API)](advanced_ai_agents/single_agent_apps/research_agent_gemini_interaction_api)\n149|*   [🤝 AI Consultant Agent](advanced_ai_agents/single_agent_apps/ai_consultant_agent)\n150|*   [🏗️ AI System Architect Agent](advanced_ai_agents/single_agent_apps/ai_system_architect_r1/)\n151|*   [💰 AI Financial Coach Agent](advanced_ai_agents/multi_agent_apps/ai_financial_coach_agent/)\n152|*   [🎬 AI Movie Production Agent](advanced_ai_agents/single_agent_apps/ai_movie_production_agent/)\n153|*   [📈 AI Investment Agent](advanced_ai_agents/single_agent_apps/ai_investment_agent/)\n154|*   [📡 Earnings Call Analyst Agent](advanced_ai_agents/single_agent_apps/earnings_call_analyst_agent/)\n155|*   [🏋️‍♂️ AI Health & Fitness Agent](advanced_ai_agents/single_agent_apps/ai_health_fitness_agent/)\n156|*   [🚀 AI Product Launch Intelligence Agent](advanced_ai_agents/multi_agent_apps/product_launch_intelligence_agent)\n157|*   [🔍 AI Fraud Investigation Agent](advanced_ai_agents/single_agent_apps/ai_fraud_investigation_agent/)\n158|*   [🗞️ AI Journalist Agent](advanced_ai_agents/single_agent_apps/ai_journalist_agent/)\n159|*   [🧠 AI Mental Wellbeing Agent](advanced_ai_agents/multi_agent_apps/ai_mental_wellbeing_agent/)\n160|*   [📑 AI Meeting Agent](advanced_ai_agents/single_agent_apps/ai_meeting_agent/)\n161|*   [🧬 AI Self-Evolving Agent](advanced_ai_agents/multi_agent_apps/ai_self_evolving_agent/)\n162|*   [👨🏻‍💼 AI Sales Intelligence Agent Team](advanced_ai_agents/multi_agent_apps/agent_teams/ai_sales_intelligence_agent_team)\n163|*   [🎧 AI Social Media News and Podcast Agent](advanced_ai_agents/multi_agent_apps/ai_news_and_podcast_agents/)\n164|*   [🌐 Openwork - Open Browser Automation Agent](https://github.com/accomplish-ai/openwork) <sub>↗ external</sub>\n165|*   [🛡️ Trust-Gated Multi-Agent Research Team](advanced_ai_agents/multi_agent_apps/trust_gated_agent_team/)\n166|\n167|### 🎮 Autonomous Game-Playing Agents\n168|*Agents that play games end-to-end - reasoning, strategy, and action.*\n169|\n170|*   [🎮 AI 3D Pygame Agent](advanced_ai_agents/autonomous_game_playing_agent_apps/ai_3dpygame_r1/)\n171|*   [♜ AI Chess Agent](advanced_ai_agents/autonomous_game_playing_agent_apps/ai_chess_agent/)\n172|*   [🎲 AI Tic-Tac-Toe Agent](advanced_ai_agents/autonomous_game_playing_agent_apps/ai_tic_tac_toe_agent/)\n173|\n174|### 🤝 Multi-agent Teams\n175|*Multiple agents collaborating to accomplish complex, cross-domain tasks.*\n176|\n177|*   [🧲 AI Competitor Intelligence Agent Team](advanced_ai_agents/multi_agent_apps/agent_teams/ai_competitor_intelligence_agent_team/)\n178|*   [💲 AI Finance Agent Team](advanced_ai_agents/multi_agent_apps/agent_teams/ai_finance_agent_team/)\n179|*   [🎨 AI Game Design Agent Team](advanced_ai_agents/multi_agent_apps/agent_teams/ai_game_design_agent_team/)\n180|*   [🧭 AG2 Adaptive Research Team](advanced_ai_agents/multi_agent_apps/agent_teams/ag2_adaptive_research_team/)\n181|*   [👨‍⚖️ AI Legal Agent Team (Cloud & Local)](advanced_ai_agents/multi_agent_apps/agent_teams/ai_legal_agent_team/)\n182|*   [💼 AI Recruitment Agent Team](advanced_ai_agents/multi_agent_apps/agent_teams/ai_recruitment_agent_team/)\n183|*   [🏠 AI Real Estate Agent Team](advanced_ai_agents/multi_agent_apps/agent_teams/ai_real_estate_agent_team)\n184|*   [👨‍💼 AI Services Agency (CrewAI)](advanced_ai_agents/multi_agent_apps/agent_teams/ai_services_agency/)\n185|*   [👨‍🏫 AI Teaching Agent Team](advanced_ai_agents/multi_agent_apps/agent_teams/ai_teaching_agent_team/)\n186|*   [💻 Multimodal Coding Agent Team](advanced_ai_agents/multi_agent_apps/agent_teams/multimodal_coding_agent_team/)\n187|*   [✨ Multimodal Design Agent Team](advanced_ai_agents/multi_agent_apps/agent_teams/multimodal_design_agent_team/)\n188|*   [🎨 🍌 Multimodal UI/UX Feedback Agent Team with Nano Banana](advanced_ai_agents/multi_agent_apps/agent_teams/multimodal_uiux_feedback_agent_team/)\n189|*   [🌏 AI Travel Planner Agent Team](/advanced_ai_agents/multi_agent_apps/agent_teams/ai_travel_planner_agent_team/)\n190|\n191|### 🗣️ Voice AI Agents\n192|*Speech-in, speech-out agents using real-time voice APIs.*\n193|\n194|*   [🗣️ AI Audio Tour Agent](voice_ai_agents/ai_audio_tour_agent/)\n195|*   [📞 Customer Support Voice Agent](voice_ai_agents/customer_support_voice_agent/)\n196|*   [🛡️ Insurance Claim Live Agent Team](voice_ai_agents/insurance_claim_live_agent_team/)\n197|*   [🔊 Voice RAG Agent (OpenAI SDK)](voice_ai_agents/voice_rag_openaisdk/)\n198|*   [🎙️ OpenSource Voice Dictation Agent (Wispr Flow clone)](https://github.com/akshayaggarwal99/jarvis-ai-assistant) <sub>↗ external</sub>\n199|\n200|### ♾️ MCP AI Agents\n201|", "total_lines": 331, "file_size": 20966, "truncated": true, "hint": "Use offset=201 to continue reading (showing 1-200 of 331 lines)", "is_binary": false, "is_image": false} read_file 


c0:
    [{"id": "chatcmpl-tool-8e23118215c1c80d", "call_id": "chatcmpl-tool-8e23118215c1c80d", "response_item_id": "fc_chatcmpl-tool-8e23118215c1c80d", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 200, \"offset\": 200, \"path\": \"/home/ice/awesome-llm-apps/README.md\"}"}}]


c0:
  {"content": "200|### ♾️ MCP AI Agents\n201|*Agents that connect to external tools and data via Model Context Protocol.*\n202|\n203|*   [♾️ Browser MCP Agent](mcp_ai_agents/browser_mcp_agent/)\n204|*   [🐙 GitHub MCP Agent](mcp_ai_agents/github_mcp_agent/)\n205|*   [📑 Notion MCP Agent](mcp_ai_agents/notion_mcp_agent)\n206|*   [🌍 AI Travel Planner MCP Agent](mcp_ai_agents/ai_travel_planner_mcp_agent_team)\n207|*   [🔀 Multi-MCP Agent Router](mcp_ai_agents/multi_mcp_agent_router/)\n208|\n209|### 📀 RAG (Retrieval Augmented Generation)\n210|*Retrieval pipelines - from simple chains to agentic and multi-source.*\n211|\n212|*   [🔥 Agentic RAG with Embedding Gemma](rag_tutorials/agentic_rag_embedding_gemma)\n213|*   [🧐 Agentic RAG with Reasoning](rag_tutorials/agentic_rag_with_reasoning/)\n214|*   [📰 AI Blog Search (RAG)](rag_tutorials/ai_blog_search/)\n215|*   [🔍 Autonomous RAG](rag_tutorials/autonomous_rag/)\n216|*   [🔄 Contextual AI RAG Agent](rag_tutorials/contextualai_rag_agent/)\n217|*   [🔄 Corrective RAG (CRAG)](rag_tutorials/corrective_rag/)\n218|*   [🐋 Deepseek Local RAG Agent](rag_tutorials/deepseek_local_rag_agent/)\n219|*   [🤔 Gemini Agentic RAG](rag_tutorials/gemini_agentic_rag/)\n220|*   [👀 Hybrid Search RAG (Cloud)](rag_tutorials/hybrid_search_rag/)\n221|*   [🔄 Llama 3.1 Local RAG](rag_tutorials/llama3.1_local_rag/)\n222|*   [🖥️ Local Hybrid Search RAG](rag_tutorials/local_hybrid_search_rag/)\n223|*   [🧬 Multimodal Agentic RAG](rag_tutorials/multimodal_agentic_rag/)\n224|*   [🦙 Local RAG Agent](rag_tutorials/local_rag_agent/)\n225|*   [🧩 RAG-as-a-Service](rag_tutorials/rag-as-a-service/)\n226|*   [✨ RAG Agent with Cohere](rag_tutorials/rag_agent_cohere/)\n227|*   [⛓️ Basic RAG Chain](rag_tutorials/rag_chain/)\n228|*   [📠 RAG with Database Routing](rag_tutorials/rag_database_routing/)\n229|*   [🖼️ Vision RAG](rag_tutorials/vision_rag/)\n230|*   [🩺 RAG Failure Diagnostics Clinic](rag_tutorials/rag_failure_diagnostics_clinic/)\n231|*   [🕸️ Knowledge Graph RAG with Citations](rag_tutorials/knowledge_graph_rag_citations/)\n232|\n233|### 🧩 Awesome Agent Skills\n234|*Ready-to-use agent skill files you can plug into any AI agent or LLM workflow.*\n235|\n236|*   [♾️ Self-Improving Agent Skills](awesome_agent_skills/self-improving-agent-skills/) - Automatically optimize agent skills using Gemini and ADK\n237|\n238|<details>\n239|<summary><strong>📋 Browse all 19 skills</strong></summary>\n240|\n241|| Skill | Description |\n242||---|---|\n243|| [🎓 Academic Researcher](awesome_agent_skills/academic-researcher/) | Literature reviews, paper analysis, citation management |\n244|| [🔍 Code Reviewer](awesome_agent_skills/code-reviewer/) | Automated code review with best-practice checks |\n245|| [✍️ Content Creator](awesome_agent_skills/content-creator/) | Blog posts, social media, marketing copy |\n246|| [📊 Data Analyst](awesome_agent_skills/data-analyst/) | Data exploration, statistical analysis, insights |\n247|| [🐛 Debugger](awesome_agent_skills/debugger/) | Systematic bug hunting and root-cause analysis |\n248|| [🤔 Decision Helper](awesome_agent_skills/decision-helper/) | Structured decision frameworks and trade-off analysis |\n249|| [🔬 Deep Research](awesome_agent_skills/deep-research/) | Multi-source research with synthesis |\n250|| [📝 Editor](awesome_agent_skills/editor/) | Proofreading, style, and clarity improvements |\n251|| [📧 Email Drafter](awesome_agent_skills/email-drafter/) | Professional email composition |\n252|| [✅ Fact Checker](awesome_agent_skills/fact-checker/) | Claim verification and source validation |\n253|| [💻 Fullstack Developer](awesome_agent_skills/fullstack-developer/) | End-to-end web app development |\n254|| [📋 Meeting Notes](awesome_agent_skills/meeting-notes/) | Meeting summaries, action items, follow-ups |\n255|| [📅 Project Planner](awesome_agent_skills/project-planner/) | Roadmaps, milestones, resource planning |\n256|| [🐍 Python Expert](awesome_agent_skills/python-expert/) | Pythonic code, packaging, performance |\n257|| [🏃 Sprint Planner](awesome_agent_skills/sprint-planner/) | Agile sprint planning and backlog grooming |\n258|| [🧭 Strategy Advisor](awesome_agent_skills/strategy-advisor/) | Business strategy and competitive analysis |\n259|| [📖 Technical Writer](awesome_agent_skills/technical-writer/) | Documentation, API docs, guides |\n260|| [🎨 UX Designer](awesome_agent_skills/ux-designer/) | UI/UX design feedback and wireframes |\n261|| [📈 Visualization Expert](awesome_agent_skills/visualization-expert/) | Charts, dashboards, data storytelling |\n262|\n263|</details>\n264|\n265|### 💾 LLM Apps with Memory Tutorials\n266|*Agents and chatbots that remember conversations and user state across sessions.*\n267|\n268|*   [💾 AI ArXiv Agent with Memory](advanced_llm_apps/llm_apps_with_memory_tutorials/ai_arxiv_agent_memory/)\n269|*   [🛩️ AI Travel Agent with Memory](advanced_llm_apps/llm_apps_with_memory_tutorials/ai_travel_agent_memory/)\n270|*   [💬 Llama3 Stateful Chat](advanced_llm_apps/llm_apps_with_memory_tutorials/llama3_stateful_chat/)\n271|*   [📝 LLM App with Personalized Memory](advanced_llm_apps/llm_apps_with_memory_tutorials/llm_app_personalized_memory/)\n272|*   [🗄️ Local ChatGPT Clone with Memory](advanced_llm_apps/llm_apps_with_memory_tutorials/local_chatgpt_with_memory/)\n273|*   [🧠 Multi-LLM Application with Shared Memory](advanced_llm_apps/llm_apps_with_memory_tutorials/multi_llm_memory/)\n274|\n275|### 💬 Chat with X Tutorials\n276|*Turn any data source into a chat interface.*\n277|\n278|*   [💬 Chat with GitHub (GPT & Llama3)](advanced_llm_apps/chat_with_X_tutorials/chat_with_github/)\n279|*   [📨 Chat with Gmail](advanced_llm_apps/chat_with_X_tutorials/chat_with_gmail/)\n280|*   [📄 Chat with PDF (GPT & Llama3)](advanced_llm_apps/chat_with_X_tutorials/chat_with_pdf/)\n281|*   [📚 Chat with Research Papers (ArXiv) (GPT & Llama3)](advanced_llm_apps/chat_with_X_tutorials/chat_with_research_papers/)\n282|*   [📝 Chat with Substack](advanced_llm_apps/chat_with_X_tutorials/chat_with_substack/)\n283|*   [📽️ Chat with YouTube Videos](advanced_llm_apps/chat_with_X_tutorials/chat_with_youtube_videos/)\n284|\n285|### 🎯 LLM Optimization Tools\n286|*Reduce token usage, context size, and API cost without losing quality.*\n287|\n288|*   [🎯 Toonify Token Optimization](advanced_llm_apps/llm_optimization_tools/toonify_token_optimization/) - Reduce LLM API costs by 30–60% using TOON format\n289|*   [🧠 Headroom Context Optimization](advanced_llm_apps/llm_optimization_tools/headroom_context_optimization/) - Reduce LLM API costs by 50–90%\n290|\n291|### 🔧 LLM Fine-tuning Tutorials\n292|*End-to-end fine-tuning recipes for open-source models.*\n293|\n294|* [Gemma 3 Fine-tuning](advanced_llm_apps/llm_finetuning_tutorials/gemma3_finetuning/)\n295|* [Llama 3.2 Fine-tuning](advanced_llm_apps/llm_finetuning_tutorials/llama3.2_finetuning/)\n296|\n297|### 🧑‍🏫 AI Agent Framework Crash Course\n298|*Deep-dive tutorials on the major agent frameworks.*\n299|\n300|[Google ADK Crash Course](ai_agent_framework_crash_course/google_adk_crash_course/)\n301|  - Starter agent; model‑agnostic (OpenAI, Claude)\n302|  - Structured outputs (Pydantic)\n303|  - Tools: built‑in, function, third‑party, MCP tools\n304|  - Memory; callbacks; Plugins\n305|  - Simple multi‑agent; Multi‑agent patterns\n306|\n307|[OpenAI Agents SDK Crash Course](ai_agent_framework_crash_course/openai_sdk_crash_course/)\n308|  - Starter agent; function calling; structured outputs\n309|  - Tools: built‑in, function, third‑party integrations\n310|  - Memory; callbacks; evaluation\n311|  - Multi‑agent patterns; agent handoffs\n312|  - Swarm orchestration; routing logic\n313|\n314|\n315|## 🙏 Built by\n316|\n317|<p>Created and maintained by <a href=\"https://twitter.com/Saboo_Shubham_\"><strong>Shubham Saboo</strong></a> with contributions from the amazing community members.</p>\n318|\n319|<a href=\"https://github.com/Shubhamsaboo/awesome-llm-apps/graphs/contributors\">\n320|  <img src=\"https://contrib.rocks/image?repo=Shubhamsaboo/awesome-llm-apps&max=40&columns=10&anon=0\" alt=\"Top contributors\" />\n321|</a>\n322|\n323|## ⭐ Star History\n324|\n325|[![Star History Chart](https://api.star-history.com/svg?repos=Shubhamsaboo/awesome-llm-apps&type=Date)](https://star-history.com/#Shubhamsaboo/awesome-llm-apps&Date)\n326|\n327|> 🌟 **Don't miss future drops - [star the repo](https://github.com/Shubhamsaboo/awesome-llm-apps) to get notified when new templates ship.**\n328|\n329|## 📜 License\n330|\n331|Apache-2.0. See [LICENSE](LICENSE). Fork it, ship it, sell it.\n332|", "total_lines": 331, "file_size": 20966, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  Resumo da análise:

  - O que fiz: clonei o repositório em `/home/ice/awesome-llm-apps`, explorei `README.md` e listei os diretórios/arquivos para entender o escopo.
  - Problema: desenvolvedores repetidamente recriam pipelines de RAG, loops de agentes e integrações MCP do zero, sem referências testadas e prontas para adaptar.
  - Tema: repositório-receituário/templates de aplicações LLM executáveis, focado em agentes, RAG, MCP, voz, skills, otimização e fine-tuning.
  - Razão do hype: volume grande de templates funcionais, frequente atualização com tendências atuais do mercado, curadoria própria (não só links), múltiplos providers/modelos e foco em executar rapidamente.
  - Tecnologias: Python; interfaces como Streamlit; RAG com embeddings e bancos vetoriais; frameworks de agentes/MM Labs como ADK, CrewAI e AG2; MCP; voz via APIs de tempo real (OpenAI RealTime, Gemini Live); multimodal; fine-tuning com Gemma e Llama; e integrações com GitHub, Notion, Gmail, PDF, YouTube, Substack, ArXiv.  


c0:
  Vou analisar o repositório `google-gemini/gemini-cli`. Começarei clonando e examinando seus arquivos principais.  [{"id": "chatcmpl-tool-954b250721ac3c8c", "call_id": "chatcmpl-tool-954b250721ac3c8c", "response_item_id": "fc_chatcmpl-tool-954b250721ac3c8c", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice && git clone https://github.com/google-gemini/gemini-cli.git --depth 1\", \"timeout\": 120}"}}]


c0:
    [{"id": "chatcmpl-tool-a35ba4b17ab06366", "call_id": "chatcmpl-tool-a35ba4b17ab06366", "response_item_id": "fc_chatcmpl-tool-a35ba4b17ab06366", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice/gemini-cli && ls -la\"}"}}, {"id": "chatcmpl-tool-87c1c0ee1468ea7a", "call_id": "chatcmpl-tool-87c1c0ee1468ea7a", "response_item_id": "fc_chatcmpl-tool-87c1c0ee1468ea7a", "type": "function", "function": {"name": "search_files", "arguments": "{\"path\": \"/home/ice/gemini-cli\", \"pattern\": \"package.json\", \"target\": \"files\"}"}}]


c0:
  {"output": "total 490\ndrwxrwxr-x 20 ice ice     44 May 30 23:10 .\ndrwx------ 45 ice ice     51 May 30 23:10 ..\ndrwxrwxr-x  2 ice ice      3 May 30 23:10 .allstar\n-rw-rw-r--  1 ice ice    294 May 30 23:10 .dockerignore\n-rw-rw-r--  1 ice ice    183 May 30 23:10 .editorconfig\ndrwxrwxr-x  2 ice ice      7 May 30 23:10 .gcp\ndrwxrwxr-x  4 ice ice      6 May 30 23:10 .gemini\n-rw-rw-r--  1 ice ice     41 May 30 23:10 .geminiignore\ndrwxrwxr-x  8 ice ice     14 May 30 23:10 .git\n-rw-rw-r--  1 ice ice    650 May 30 23:10 .gitattributes\ndrwxrwxr-x  6 ice ice      9 May 30 23:10 .github\n-rw-rw-r--  1 ice ice   1154 May 30 23:10 .gitignore\ndrwxrwxr-x  2 ice ice      3 May 30 23:10 .husky\n-rw-rw-r--  1 ice ice    302 May 30 23:10 .lycheeignore\n-rw-rw-r--  1 ice ice     57 May 30 23:10 .npmrc\n-rw-rw-r--  1 ice ice      3 May 30 23:10 .nvmrc\n-rw-rw-r--  1 ice ice    344 May 30 23:10 .prettierignore\n-rw-rw-r--  1 ice ice    273 May 30 23:10 .prettierrc.json\ndrwxrwxr-x  2 ice ice      6 May 30 23:10 .vscode\n-rw-rw-r--  1 ice ice   1581 May 30 23:10 .yamllint.yml\n-rw-rw-r--  1 ice ice  20380 May 30 23:10 CONTRIBUTING.md\n-rw-rw-r--  1 ice ice   3249 May 30 23:10 Dockerfile\n-rw-rw-r--  1 ice ice   4610 May 30 23:10 GEMINI.md\n-rw-rw-r--  1 ice ice  11357 May 30 23:10 LICENSE\n-rw-rw-r--  1 ice ice   1336 May 30 23:10 Makefile\n-rw-rw-r--  1 ice ice  13489 May 30 23:10 README.md\n-rw-rw-r--  1 ice ice   5748 May 30 23:10 ROADMAP.md\n-rw-rw-r--  1 ice ice    423 May 30 23:10 SECURITY.md\ndrwxrwxr-x 16 ice ice     26 May 30 23:10 docs\n-rw-rw-r--  1 ice ice   5688 May 30 23:10 esbuild.config.js\n-rw-rw-r--  1 ice ice  12868 May 30 23:10 eslint.config.js\ndrwxrwxr-x  2 ice ice     47 May 30 23:10 evals\ndrwxrwxr-x  3 ice ice    116 May 30 23:10 integration-tests\ndrwxrwxr-x  2 ice ice     12 May 30 23:10 memory-tests\n-rw-rw-r--  1 ice ice 679202 May 30 23:10 package-lock.json\n-rw-rw-r--  1 ice ice   7561 May 30 23:10 package.json\ndrwxrwxr-x  9 ice ice      9 May 30 23:10 packages\ndrwxrwxr-x  2 ice ice     14 May 30 23:10 perf-tests\ndrwxrwxr-x  2 ice ice      3 May 30 23:10 schemas\ndrwxrwxr-x  5 ice ice     55 May 30 23:10 scripts\ndrwxrwxr-x  2 ice ice      4 May 30 23:10 sea\ndrwxrwxr-x  3 ice ice      3 May 30 23:10 third_party\ndrwxrwxr-x  3 ice ice      3 May 30 23:10 tools\n-rw-rw-r--  1 ice ice    860 May 30 23:10 tsconfig.json", "exit_code": 0, "error": null} terminal 


c0:
  {"total_count": 10, "files": ["/home/ice/gemini-cli/third_party/get-ripgrep/package.json", "/home/ice/gemini-cli/packages/vscode-ide-companion/package.json", "/home/ice/gemini-cli/packages/test-utils/package.json", "/home/ice/gemini-cli/packages/sdk/package.json", "/home/ice/gemini-cli/packages/devtools/package.json", "/home/ice/gemini-cli/packages/core/package.json", "/home/ice/gemini-cli/packages/cli/src/commands/extensions/examples/mcp-server/package.json", "/home/ice/gemini-cli/packages/cli/package.json", "/home/ice/gemini-cli/packages/a2a-server/package.json", "/home/ice/gemini-cli/package.json"]} search_files 


c0:
    [{"id": "chatcmpl-tool-a783c0e18efdb9e8", "call_id": "chatcmpl-tool-a783c0e18efdb9e8", "response_item_id": "fc_chatcmpl-tool-a783c0e18efdb9e8", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/gemini-cli/README.md\"}"}}, {"id": "chatcmpl-tool-80977b6c652e1a4e", "call_id": "chatcmpl-tool-80977b6c652e1a4e", "response_item_id": "fc_chatcmpl-tool-80977b6c652e1a4e", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/gemini-cli/package.json\"}"}}]


c0:
  {"content": "1|# Gemini CLI\n2|\n3|[![Gemini CLI CI](https://github.com/google-gemini/gemini-cli/actions/workflows/ci.yml/badge.svg)](https://github.com/google-gemini/gemini-cli/actions/workflows/ci.yml)\n4|[![Gemini CLI E2E (Chained)](https://github.com/google-gemini/gemini-cli/actions/workflows/chained_e2e.yml/badge.svg)](https://github.com/google-gemini/gemini-cli/actions/workflows/chained_e2e.yml)\n5|[![Version](https://img.shields.io/npm/v/@google/gemini-cli)](https://www.npmjs.com/package/@google/gemini-cli)\n6|[![License](https://img.shields.io/github/license/google-gemini/gemini-cli)](https://github.com/google-gemini/gemini-cli/blob/main/LICENSE)\n7|[![View Code Wiki](https://assets.codewiki.google/readme-badge/static.svg)](https://codewiki.google/github.com/google-gemini/gemini-cli?utm_source=badge&utm_medium=github&utm_campaign=github.com/google-gemini/gemini-cli)\n8|\n9|![Gemini CLI Screenshot](/docs/assets/gemini-screenshot.png)\n10|\n11|Gemini CLI is an open-source AI agent that brings the power of Gemini directly\n12|into your terminal. It provides lightweight access to Gemini, giving you the\n13|most direct path from your prompt to our model.\n14|\n15|Learn all about Gemini CLI in our [documentation](https://geminicli.com/docs/).\n16|\n17|## 🚀 Why Gemini CLI?\n18|\n19|- **🎯 Free tier**: 60 requests/min and 1,000 requests/day with personal Google\n20|  account.\n21|- **🧠 Powerful Gemini 3 models**: Access to improved reasoning and 1M token\n22|  context window.\n23|- **🔧 Built-in tools**: Google Search grounding, file operations, shell\n24|  commands, web fetching.\n25|- **🔌 Extensible**: MCP (Model Context Protocol) support for custom\n26|  integrations.\n27|- **💻 Terminal-first**: Designed for developers who live in the command line.\n28|- **🛡️ Open source**: Apache 2.0 licensed.\n29|\n30|## 📦 Installation\n31|\n32|See\n33|[Gemini CLI installation, execution, and releases](https://www.geminicli.com/docs/get-started/installation)\n34|for recommended system specifications and a detailed installation guide.\n35|\n36|### Quick Install\n37|\n38|#### Run instantly with npx\n39|\n40|```bash\n41|# Using npx (no installation required)\n42|npx @google/gemini-cli\n43|```\n44|\n45|#### Install globally with npm\n46|\n47|```bash\n48|npm install -g @google/gemini-cli\n49|```\n50|\n51|#### Install globally with Homebrew (macOS/Linux)\n52|\n53|```bash\n54|brew install gemini-cli\n55|```\n56|\n57|#### Install globally with MacPorts (macOS)\n58|\n59|```bash\n60|sudo port install gemini-cli\n61|```\n62|\n63|#### Install with Anaconda (for restricted environments)\n64|\n65|```bash\n66|# Create and activate a new environment\n67|conda create -y -n gemini_env -c conda-forge nodejs\n68|conda activate gemini_env\n69|\n70|# Install Gemini CLI globally via npm (inside the environment)\n71|npm install -g @google/gemini-cli\n72|```\n73|\n74|## Release Channels\n75|\n76|See [Releases](https://www.geminicli.com/docs/changelogs) for more details.\n77|\n78|### Preview\n79|\n80|New preview releases will be published each week at UTC 23:59 on Tuesdays. These\n81|releases will not have been fully vetted and may contain regressions or other\n82|outstanding issues. Please help us test and install with `preview` tag.\n83|\n84|```bash\n85|npm install -g @google/gemini-cli@preview\n86|```\n87|\n88|### Stable\n89|\n90|- New stable releases will be published each week at UTC 20:00 on Tuesdays, this\n91|  will be the full promotion of last week's `preview` release + any bug fixes\n92|  and validations. Use `latest` tag.\n93|\n94|```bash\n95|npm install -g @google/gemini-cli@latest\n96|```\n97|\n98|### Nightly\n99|\n100|- New releases will be published each day at UTC 00:00. This will be all changes\n101|  from the main branch as represented at time of release. It should be assumed\n102|  there are pending validations and issues. Use `nightly` tag.\n103|\n104|```bash\n105|npm install -g @google/gemini-cli@nightly\n106|```\n107|\n108|## 📋 Key Features\n109|\n110|### Code Understanding & Generation\n111|\n112|- Query and edit large codebases\n113|- Generate new apps from PDFs, images, or sketches using multimodal capabilities\n114|- Debug issues and troubleshoot with natural language\n115|\n116|### Automation & Integration\n117|\n118|- Automate operational tasks like querying pull requests or handling complex\n119|  rebases\n120|- Use MCP servers to connect new capabilities, including\n121|  [media generation with Imagen, Veo or Lyria](https://github.com/GoogleCloudPlatform/vertex-ai-creative-studio/tree/main/experiments/mcp-genmedia)\n122|- Run non-interactively in scripts for workflow automation\n123|\n124|### Advanced Capabilities\n125|\n126|- Ground your queries with built-in\n127|  [Google Search](https://ai.google.dev/gemini-api/docs/grounding) for real-time\n128|  information\n129|- Conversation checkpointing to save and resume complex sessions\n130|- Custom context files (GEMINI.md) to tailor behavior for your projects\n131|\n132|### GitHub Integration\n133|\n134|Integrate Gemini CLI directly into your GitHub workflows with\n135|[**Gemini CLI GitHub Action**](https://github.com/google-github-actions/run-gemini-cli):\n136|\n137|- **Pull Request Reviews**: Automated code review with contextual feedback and\n138|  suggestions\n139|- **Issue Triage**: Automated labeling and prioritization of GitHub issues based\n140|  on content analysis\n141|- **On-demand Assistance**: Mention `@gemini-cli` in issues and pull requests\n142|  for help with debugging, explanations, or task delegation\n143|- **Custom Workflows**: Build automated, scheduled and on-demand workflows\n144|  tailored to your team's needs\n145|\n146|## 🔐 Authentication Options\n147|\n148|Choose the authentication method that best fits your needs:\n149|\n150|### Option 1: Sign in with Google (OAuth login using your Google Account)\n151|\n152|**✨ Best for:** Individual developers as well as anyone who has a Gemini Code\n153|Assist License. (see\n154|[quota limits and terms of service](https://cloud.google.com/gemini/docs/quotas)\n155|for details)\n156|\n157|**Benefits:**\n158|\n159|- **Free tier**: 60 requests/min and 1,000 requests/day\n160|- **Gemini 3 models** with 1M token context window\n161|- **No API key management** - just sign in with your Google account\n162|- **Automatic updates** to latest models\n163|\n164|#### Start Gemini CLI, then choose _Sign in with Google_ and follow the browser authentication flow when prompted\n165|\n166|```bash\n167|gemini\n168|```\n169|\n170|#### If you are using a paid Code Assist License from your organization, remember to set the Google Cloud Project\n171|\n172|```bash\n173|# Set your Google Cloud Project\n174|export GOOGLE_CLOUD_PROJECT=\"YOUR_PROJECT_ID\"\n175|gemini\n176|```\n177|\n178|### Option 2: Gemini API Key\n179|\n180|**✨ Best for:** Developers who need specific model control or paid tier access\n181|\n182|**Benefits:**\n183|\n184|- **Free tier**: 1000 requests/day with Gemini 3 (mix of flash and pro)\n185|- **Model selection**: Choose specific Gemini models\n186|- **Usage-based billing**: Upgrade for higher limits when needed\n187|\n188|```bash\n189|# Get your key from https://aistudio.google.com/apikey\n190|export GEMINI_API_KEY=\"YOUR_API_KEY\"\n191|gemini\n192|```\n193|\n194|### Option 3: Vertex AI\n195|\n196|**✨ Best for:** Enterprise teams and production workloads\n197|\n198|**Benefits:**\n199|\n200|- **Enterprise features**: Advanced security and compliance\n201|- **Scalable**: Higher rate limits with billing account\n202|- **Integration**: Works with existing Google Cloud infrastructure\n203|\n204|```bash\n205|# Get your key from Google Cloud Console\n206|export GOOGLE_API_KEY=\"YOUR_API_KEY\"\n207|export GOOGLE_GENAI_USE_VERTEXAI=true\n208|gemini\n209|```\n210|\n211|For Google Workspace accounts and other authentication methods, see the\n212|[authentication guide](https://www.geminicli.com/docs/get-started/authentication).\n213|\n214|## 🚀 Getting Started\n215|\n216|### Basic Usage\n217|\n218|#### Start in current directory\n219|\n220|```bash\n221|gemini\n222|```\n223|\n224|#### Include multiple directories\n225|\n226|```bash\n227|gemini --include-directories ../lib,../docs\n228|```\n229|\n230|#### Use specific model\n231|\n232|```bash\n233|gemini -m gemini-2.5-flash\n234|```\n235|\n236|#### Non-interactive mode for scripts\n237|\n238|Get a simple text response:\n239|\n240|```bash\n241|gemini -p \"Explain the architecture of this codebase\"\n242|```\n243|\n244|For more advanced scripting, including how to parse JSON and handle errors, use\n245|the `--output-format json` flag to get structured output:\n246|\n247|```bash\n248|gemini -p \"Explain the architecture of this codebase\" --output-format json\n249|```\n250|\n251|For real-time event streaming (useful for monitoring long-running operations),\n252|use `--output-format stream-json` to get newline-delimited JSON events:\n253|\n254|```bash\n255|gemini -p \"Run tests and deploy\" --output-format stream-json\n256|```\n257|\n258|### Quick Examples\n259|\n260|#### Start a new project\n261|\n262|```bash\n263|cd new-project/\n264|gemini\n265|> Write me a Discord bot that answers questions using a FAQ.md file I will provide\n266|```\n267|\n268|#### Analyze existing code\n269|\n270|```bash\n271|git clone https://github.com/google-gemini/gemini-cli\n272|cd gemini-cli\n273|gemini\n274|> Give me a summary of all of the changes that went in yesterday\n275|```\n276|\n277|## 📚 Documentation\n278|\n279|### Getting Started\n280|\n281|- [**Quickstart Guide**](https://www.geminicli.com/docs/get-started) - Get up\n282|  and running quickly.\n283|- [**Authentication Setup**](https://www.geminicli.com/docs/get-started/authentication) -\n284|  Detailed auth configuration.\n285|- [**Configuration Guide**](https://www.geminicli.com/docs/reference/configuration) -\n286|  Settings and customization.\n287|- [**Keyboard Shortcuts**](https://www.geminicli.com/docs/reference/keyboard-shortcuts) -\n288|  Productivity tips.\n289|\n290|### Core Features\n291|\n292|- [**Commands Reference**](https://www.geminicli.com/docs/reference/commands) -\n293|  All slash commands (`/help`, `/chat`, etc).\n294|- [**Custom Commands**](https://www.geminicli.com/docs/cli/custom-commands) -\n295|  Create your own reusable commands.\n296|- [**Context Files (GEMINI.md)**](https://www.geminicli.com/docs/cli/gemini-md) -\n297|  Provide persistent context to Gemini CLI.\n298|- [**Checkpointing**](https://www.geminicli.com/docs/cli/checkpointing) - Save\n299|  and resume conversations.\n300|- [**Token Caching**](https://www.geminicli.com/docs/cli/token-caching) -\n301|  Optimize token usage.\n302|\n303|### Tools & Extensions\n304|\n305|- [**Built-in Tools Overview**](https://www.geminicli.com/docs/reference/tools)\n306|  - [File System Operations](https://www.geminicli.com/docs/tools/file-system)\n307|  - [Shell Commands](https://www.geminicli.com/docs/tools/shell)\n308|  - [Web Fetch & Search](https://www.geminicli.com/docs/tools/web-fetch)\n309|- [**MCP Server Integration**](https://www.geminicli.com/docs/tools/mcp-server) -\n310|  Extend with custom tools.\n311|- [**Custom Extensions**](https://geminicli.com/docs/extensions/writing-extensions) -\n312|  Build and share your own commands.\n313|\n314|### Advanced Topics\n315|\n316|- [**Headless Mode (Scripting)**](https://www.geminicli.com/docs/cli/headless) -\n317|  Use Gemini CLI in automated workflows.\n318|- [**IDE Integration**](https://www.geminicli.com/docs/ide-integration) - VS\n319|  Code companion.\n320|- [**Sandboxing & Security**](https://www.geminicli.com/docs/cli/sandbox) - Safe\n321|  execution environments.\n322|- [**Trusted Folders**](https://www.geminicli.com/docs/cli/trusted-folders) -\n323|  Control execution policies by folder.\n324|- [**Enterprise Guide**](https://www.geminicli.com/docs/cli/enterprise) - Deploy\n325|  and manage in a corporate environment.\n326|- [**Telemetry & Monitoring**](https://www.geminicli.com/docs/cli/telemetry) -\n327|  Usage tracking.\n328|- [**Tools reference**](https://www.geminicli.com/docs/reference/tools) -\n329|  Built-in tools overview.\n330|- [**Local development**](https://www.geminicli.com/docs/local-development) -\n331|  Local development tooling.\n332|\n333|### Troubleshooting & Support\n334|\n335|- [**Troubleshooting Guide**](https://www.geminicli.com/docs/resources/troubleshooting) -\n336|  Common issues and solutions.\n337|- [**FAQ**](https://www.geminicli.com/docs/resources/faq) - Frequently asked\n338|  questions.\n339|- Use `/bug` command to report issues directly from the CLI.\n340|\n341|### Using MCP Servers\n342|\n343|Configure MCP servers in `~/.gemini/settings.json` to extend Gemini CLI with\n344|custom tools:\n345|\n346|```text\n347|> @github List my open pull requests\n348|> @slack Send a summary of today's commits to #dev channel\n349|> @database Run a query to find inactive users\n350|```\n351|\n352|See the\n353|[MCP Server Integration guide](https://www.geminicli.com/docs/tools/mcp-server)\n354|for setup instructions.\n355|\n356|## 🤝 Contributing\n357|\n358|We welcome contributions! Gemini CLI is fully open source (Apache 2.0), and we\n359|encourage the community to:\n360|\n361|- Report bugs and suggest features.\n362|- Improve documentation.\n363|- Submit code improvements.\n364|- Share your MCP servers and extensions.\n365|\n366|See our [Contributing Guide](./CONTRIBUTING.md) for development setup, coding\n367|standards, and how to submit pull requests.\n368|\n369|Check our [Official Roadmap](https://github.com/orgs/google-gemini/projects/11)\n370|for planned features and priorities.\n371|\n372|## 📖 Resources\n373|\n374|- **[Free Course](https://learn.deeplearning.ai/courses/gemini-cli-code-and-create-with-an-open-source-agent/information)** -\n375|  Learn the basics.\n376|- **[Official Roadmap](./ROADMAP.md)** - See what's coming next.\n377|- **[Changelog](https://www.geminicli.com/docs/changelogs)** - See recent\n378|  notable updates.\n379|- **[NPM Package](https://www.npmjs.com/package/@google/gemini-cli)** - Package\n380|  registry.\n381|- **[GitHub Issues](https://github.com/google-gemini/gemini-cli/issues)** -\n382|  Report bugs or request features.\n383|- **[Security Advisories](https://github.com/google-gemini/gemini-cli/security/advisories)** -\n384|  Security updates.\n385|\n386|### Uninstall\n387|\n388|See the [Uninstall Guide](https://www.geminicli.com/docs/resources/uninstall)\n389|for removal instructions.\n390|\n391|## 📄 Legal\n392|\n393|- **License**: [Apache License 2.0](LICENSE)\n394|- **Terms of Service**:\n395|  [Terms & Privacy](https://www.geminicli.com/docs/resources/tos-privacy)\n396|- **Security**: [Security Policy](SECURITY.md)\n397|\n398|<p align=\"left\">\n399| <a href=\"https://www.star-history.com/google-gemini/gemini-cli\">\n400|  <picture>\n401|   <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://api.star-history.com/badge?repo=google-gemini/gemini-cli&theme=dark\" />\n402|   <source media=\"(prefers-color-scheme: light)\" srcset=\"https://api.star-history.com/badge?repo=google-gemini/gemini-cli\" />\n403|   <img alt=\"Star History Rank\" src=\"https://api.star-history.com/badge?repo=google-gemini/gemini-cli\" />\n404|  </picture>\n405| </a>\n406|</p>\n407|\n408|---\n409|\n410|<p align=\"center\">\n411|  Built with ❤️ by Google and the open source community\n412|</p>\n413|", "total_lines": 412, "file_size": 13489, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|{\n2|  \"name\": \"@google/gemini-cli\",\n3|  \"version\": \"0.45.0-nightly.20260521.g854f811be\",\n4|  \"engines\": {\n5|    \"node\": \">=20.0.0\"\n6|  },\n7|  \"type\": \"module\",\n8|  \"workspaces\": [\n9|    \"packages/*\"\n10|  ],\n11|  \"private\": \"true\",\n12|  \"repository\": {\n13|    \"type\": \"git\",\n14|    \"url\": \"git+https://github.com/google-gemini/gemini-cli.git\"\n15|  },\n16|  \"config\": {\n17|    \"sandboxImageUri\": \"us-docker.pkg.dev/gemini-code-dev/gemini-cli/sandbox:0.45.0-nightly.20260521.g854f811be\"\n18|  },\n19|  \"scripts\": {\n20|    \"start\": \"cross-env NODE_ENV=development node scripts/start.js\",\n21|    \"start:prod\": \"cross-env NODE_ENV=production node scripts/start.js\",\n22|    \"start:a2a-server\": \"CODER_AGENT_PORT=41242 npm run start --workspace @google/gemini-cli-a2a-server\",\n23|    \"debug\": \"cross-env DEBUG=1 node --inspect-brk scripts/start.js\",\n24|    \"deflake\": \"node scripts/deflake.js\",\n25|    \"deflake:test:integration:sandbox:none\": \"npm run deflake -- --command=\\\"npm run test:integration:sandbox:none -- --retry=0\\\"\",\n26|    \"deflake:test:integration:sandbox:docker\": \"npm run deflake -- --command=\\\"npm run test:integration:sandbox:docker -- --retry=0\\\"\",\n27|    \"auth:npm\": \"npx google-artifactregistry-auth\",\n28|    \"auth:docker\": \"gcloud auth configure-docker us-west1-docker.pkg.dev\",\n29|    \"auth\": \"npm run auth:npm && npm run auth:docker\",\n30|    \"generate\": \"node scripts/generate-git-commit-info.js\",\n31|    \"predocs:settings\": \"npm run build --workspace @google/gemini-cli-core\",\n32|    \"schema:settings\": \"tsx ./scripts/generate-settings-schema.ts\",\n33|    \"docs:settings\": \"tsx ./scripts/generate-settings-doc.ts\",\n34|    \"docs:keybindings\": \"tsx ./scripts/generate-keybindings-doc.ts\",\n35|    \"build\": \"node scripts/build.js\",\n36|    \"build-and-start\": \"npm run build && npm run start --\",\n37|    \"build:vscode\": \"node scripts/build_vscode_companion.js\",\n38|    \"build:all\": \"npm run build && npm run build:sandbox && npm run build:vscode\",\n39|    \"build:packages\": \"npm run build --workspaces\",\n40|    \"build:sandbox\": \"node scripts/build_sandbox.js\",\n41|    \"build:binary\": \"node scripts/build_binary.js\",\n42|    \"bundle\": \"npm run generate && npm run build --workspace=@google/gemini-cli-devtools && npm run bundle:browser-mcp -w @google/gemini-cli-core && node esbuild.config.js && node scripts/copy_bundle_assets.js\",\n43|    \"test\": \"npm run test --workspaces --if-present && npm run test:sea-launch\",\n44|    \"test:ci\": \"npm run test:ci --workspaces --if-present && npm run test:scripts && npm run test:sea-launch\",\n45|    \"test:scripts\": \"vitest run --config ./scripts/tests/vitest.config.ts\",\n46|    \"test:sea-launch\": \"vitest run sea/sea-launch.test.js\",\n47|    \"posttest\": \"npm run build\",\n48|    \"test:always_passing_evals\": \"vitest run --config evals/vitest.config.ts\",\n49|    \"test:all_evals\": \"cross-env RUN_EVALS=1 vitest run --config evals/vitest.config.ts\",\n50|    \"test:e2e\": \"cross-env VERBOSE=true KEEP_OUTPUT=true npm run test:integration:sandbox:none\",\n51|    \"test:integration:all\": \"npm run test:integration:sandbox:none && npm run test:integration:sandbox:docker && npm run test:integration:sandbox:podman\",\n52|    \"test:integration:flaky\": \"cross-env RUN_FLAKY_INTEGRATION=1 npm run test:integration:sandbox:none\",\n53|    \"test:integration:sandbox:none\": \"cross-env GEMINI_SANDBOX=false vitest run --root ./integration-tests\",\n54|    \"test:memory\": \"vitest run --root ./memory-tests\",\n55|    \"test:memory:update-baselines\": \"cross-env UPDATE_MEMORY_BASELINES=true vitest run --root ./memory-tests\",\n56|    \"test:perf\": \"vitest run --root ./perf-tests\",\n57|    \"test:perf:update-baselines\": \"cross-env UPDATE_PERF_BASELINES=true vitest run --root ./perf-tests\",\n58|    \"test:integration:sandbox:docker\": \"cross-env GEMINI_SANDBOX=docker npm run build:sandbox && cross-env GEMINI_SANDBOX=docker vitest run --root ./integration-tests\",\n59|    \"test:integration:sandbox:podman\": \"cross-env GEMINI_SANDBOX=podman vitest run --root ./integration-tests\",\n60|    \"lint\": \"cross-env NODE_OPTIONS=\\\"--max-old-space-size=8192\\\" eslint . --cache --max-warnings 0\",\n61|    \"lint:fix\": \"eslint . --fix --ext .ts,.tsx && eslint integration-tests --fix && eslint scripts --fix && npm run format\",\n62|    \"lint:ci\": \"npm run lint:all\",\n63|    \"lint:all\": \"node scripts/lint.js\",\n64|    \"format\": \"prettier --experimental-cli --write .\",\n65|    \"typecheck\": \"npm run typecheck --workspaces --if-present && tsc -b evals/tsconfig.json integration-tests/tsconfig.json memory-tests/tsconfig.json\",\n66|    \"preflight\": \"npm run clean && npm ci && npm run format && npm run build && npm run lint:ci && npm run typecheck && npm run test:ci\",\n67|    \"prepare\": \"husky && npm run bundle\",\n68|    \"prepare:package\": \"node scripts/prepare-package.js\",\n69|    \"release:version\": \"node scripts/version.js\",\n70|    \"telemetry\": \"node scripts/telemetry.js\",\n71|    \"check:lockfile\": \"node scripts/check-lockfile.js\",\n72|    \"clean\": \"node scripts/clean.js\",\n73|    \"pre-commit\": \"node scripts/pre-commit.js\"\n74|  },\n75|  \"overrides\": {\n76|    \"ink\": \"npm:@jrichman/ink@6.6.9\",\n77|    \"wrap-ansi\": \"9.0.2\",\n78|    \"cliui\": {\n79|      \"wrap-ansi\": \"7.0.0\"\n80|    },\n81|    \"glob\": \"^12.0.0\",\n82|    \"node-domexception\": \"npm:empty@^0.10.1\",\n83|    \"prebuild-install\": \"npm:nop@1.0.0\",\n84|    \"cross-spawn\": \"^7.0.6\"\n85|  },\n86|  \"bin\": {\n87|    \"gemini\": \"bundle/gemini.js\"\n88|  },\n89|  \"files\": [\n90|    \"bundle/\",\n91|    \"README.md\",\n92|    \"LICENSE\"\n93|  ],\n94|  \"devDependencies\": {\n95|    \"@agentclientprotocol/sdk\": \"^0.16.1\",\n96|    \"read-package-up\": \"^11.0.0\",\n97|    \"@octokit/rest\": \"^22.0.0\",\n98|    \"@types/marked\": \"^5.0.2\",\n99|    \"@types/mime-types\": \"^3.0.1\",\n100|    \"@types/minimatch\": \"^5.1.2\",\n101|    \"@types/mock-fs\": \"^4.13.4\",\n102|    \"@types/prompts\": \"^2.4.9\",\n103|    \"@types/proper-lockfile\": \"^4.1.4\",\n104|    \"@types/react\": \"^19.2.0\",\n105|    \"@types/react-dom\": \"^19.2.0\",\n106|    \"@types/shell-quote\": \"^1.7.5\",\n107|    \"@types/ws\": \"^8.18.1\",\n108|    \"@vitest/coverage-v8\": \"^3.1.1\",\n109|    \"@vitest/eslint-plugin\": \"^1.3.4\",\n110|    \"asciichart\": \"^1.5.25\",\n111|    \"cross-env\": \"^7.0.3\",\n112|    \"depcheck\": \"^1.4.7\",\n113|    \"domexception\": \"^4.0.0\",\n114|    \"esbuild\": \"^0.25.0\",\n115|    \"esbuild-plugin-wasm\": \"^1.1.0\",\n116|    \"eslint\": \"^9.24.0\",\n117|    \"eslint-config-prettier\": \"^10.1.2\",\n118|    \"eslint-plugin-headers\": \"^1.3.3\",\n119|    \"eslint-plugin-import\": \"^2.32.0\",\n120|    \"eslint-plugin-react\": \"^7.37.5\",\n121|    \"eslint-plugin-react-hooks\": \"^5.2.0\",\n122|    \"glob\": \"^12.0.0\",\n123|    \"globals\": \"^16.0.0\",\n124|    \"google-artifactregistry-auth\": \"^3.4.0\",\n125|    \"husky\": \"^9.1.7\",\n126|    \"json\": \"^11.0.0\",\n127|    \"lint-staged\": \"^16.1.6\",\n128|    \"memfs\": \"^4.42.0\",\n129|    \"mnemonist\": \"^0.40.3\",\n130|    \"mock-fs\": \"^5.5.0\",\n131|    \"msw\": \"^2.10.4\",\n132|    \"npm-run-all\": \"^4.1.5\",\n133|    \"prettier\": \"^3.5.3\",\n134|    \"react-devtools-core\": \"^6.1.2\",\n135|    \"react-dom\": \"^19.2.0\",\n136|    \"semver\": \"^7.7.2\",\n137|    \"strip-ansi\": \"^7.1.2\",\n138|    \"ts-prune\": \"^0.10.3\",\n139|    \"tsx\": \"^4.20.3\",\n140|    \"typescript\": \"^5.8.3\",\n141|    \"typescript-eslint\": \"^8.30.1\",\n142|    \"vitest\": \"^3.2.4\",\n143|    \"yargs\": \"^17.7.2\"\n144|  },\n145|  \"dependencies\": {\n146|    \"ink\": \"npm:@jrichman/ink@6.6.9\",\n147|    \"latest-version\": \"^9.0.0\",\n148|    \"node-fetch-native\": \"^1.6.7\",\n149|    \"proper-lockfile\": \"^4.1.2\",\n150|    \"punycode\": \"^2.3.1\",\n151|    \"simple-git\": \"^3.28.0\"\n152|  },\n153|  \"optionalDependencies\": {\n154|    \"@github/keytar\": \"^7.10.6\",\n155|    \"@lydell/node-pty\": \"1.1.0\",\n156|    \"@lydell/node-pty-darwin-arm64\": \"1.1.0\",\n157|    \"@lydell/node-pty-darwin-x64\": \"1.1.0\",\n158|    \"@lydell/node-pty-linux-x64\": \"1.1.0\",\n159|    \"@lydell/node-pty-win32-arm64\": \"1.1.0\",\n160|    \"@lydell/node-pty-win32-x64\": \"1.1.0\",\n161|    \"node-pty\": \"^1.0.0\"\n162|  },\n163|  \"lint-staged\": {\n164|    \"*.{js,jsx,ts,tsx}\": [\n165|      \"prettier --write\",\n166|      \"eslint --fix --max-warnings 0 --no-warn-ignored\"\n167|    ],\n168|    \"eslint.config.js\": [\n169|      \"prettier --write\"\n170|    ],\n171|    \"*.{json,md}\": [\n172|      \"prettier --write\"\n173|    ]\n174|  }\n175|}\n176|", "total_lines": 175, "file_size": 7561, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
    [{"id": "chatcmpl-tool-aea4466df0296280", "call_id": "chatcmpl-tool-aea4466df0296280", "response_item_id": "fc_chatcmpl-tool-aea4466df0296280", "type": "function", "function": {"name": "search_files", "arguments": "{\"limit\": 50, \"output_mode\": \"files_only\", \"path\": \"/home/ice/gemini-cli/packages\", \"pattern\": \"package.json\", \"target\": \"files\"}"}}, {"id": "chatcmpl-tool-a9047f692a6e04a8", "call_id": "chatcmpl-tool-a9047f692a6e04a8", "response_item_id": "fc_chatcmpl-tool-a9047f692a6e04a8", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 200, \"offset\": 1, \"path\": \"/home/ice/gemini-cli/packages/cli/package.json\"}"}}, {"id": "chatcmpl-tool-a564ac5f4bd8eb25", "call_id": "chatcmpl-tool-a564ac5f4bd8eb25", "response_item_id": "fc_chatcmpl-tool-a564ac5f4bd8eb25", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 200, \"offset\": 1, \"path\": \"/home/ice/gemini-cli/packages/core/package.json\"}"}}]


c0:
  {"total_count": 8, "files": ["/home/ice/gemini-cli/packages/vscode-ide-companion/package.json", "/home/ice/gemini-cli/packages/test-utils/package.json", "/home/ice/gemini-cli/packages/sdk/package.json", "/home/ice/gemini-cli/packages/devtools/package.json", "/home/ice/gemini-cli/packages/core/package.json", "/home/ice/gemini-cli/packages/cli/src/commands/extensions/examples/mcp-server/package.json", "/home/ice/gemini-cli/packages/cli/package.json", "/home/ice/gemini-cli/packages/a2a-server/package.json"]} search_files 


c0:
  {"content": "1|{\n2|  \"name\": \"@google/gemini-cli\",\n3|  \"version\": \"0.45.0-nightly.20260521.g854f811be\",\n4|  \"description\": \"Gemini CLI\",\n5|  \"license\": \"Apache-2.0\",\n6|  \"repository\": {\n7|    \"type\": \"git\",\n8|    \"url\": \"git+https://github.com/google-gemini/gemini-cli.git\"\n9|  },\n10|  \"type\": \"module\",\n11|  \"main\": \"dist/index.js\",\n12|  \"bin\": {\n13|    \"gemini\": \"dist/index.js\"\n14|  },\n15|  \"scripts\": {\n16|    \"build\": \"node ../../scripts/build_package.js\",\n17|    \"start\": \"node dist/index.js\",\n18|    \"debug\": \"node --inspect-brk dist/index.js\",\n19|    \"lint\": \"eslint . --ext .ts,.tsx\",\n20|    \"format\": \"prettier --write .\",\n21|    \"test\": \"vitest run\",\n22|    \"test:ci\": \"vitest run\",\n23|    \"posttest\": \"npm run build\",\n24|    \"typecheck\": \"tsc --noEmit\"\n25|  },\n26|  \"files\": [\n27|    \"dist\"\n28|  ],\n29|  \"config\": {\n30|    \"sandboxImageUri\": \"us-docker.pkg.dev/gemini-code-dev/gemini-cli/sandbox:0.45.0-nightly.20260521.g854f811be\"\n31|  },\n32|  \"dependencies\": {\n33|    \"@agentclientprotocol/sdk\": \"^0.16.1\",\n34|    \"@google/gemini-cli-core\": \"file:../core\",\n35|    \"@google/genai\": \"1.30.0\",\n36|    \"@iarna/toml\": \"^2.2.5\",\n37|    \"@modelcontextprotocol/sdk\": \"^1.23.0\",\n38|    \"ansi-escapes\": \"^7.3.0\",\n39|    \"ansi-regex\": \"^6.2.2\",\n40|    \"chalk\": \"^4.1.2\",\n41|    \"cli-spinners\": \"^2.9.2\",\n42|    \"clipboardy\": \"~5.2.0\",\n43|    \"color-convert\": \"^2.0.1\",\n44|    \"command-exists\": \"^1.2.9\",\n45|    \"comment-json\": \"^4.2.5\",\n46|    \"diff\": \"^8.0.3\",\n47|    \"dotenv\": \"^17.1.0\",\n48|    \"extract-zip\": \"^2.0.1\",\n49|    \"fzf\": \"^0.5.2\",\n50|    \"glob\": \"^12.0.0\",\n51|    \"highlight.js\": \"^11.11.1\",\n52|    \"ink\": \"npm:@jrichman/ink@6.6.9\",\n53|    \"ink-gradient\": \"^3.0.0\",\n54|    \"ink-spinner\": \"^5.0.0\",\n55|    \"latest-version\": \"^9.0.0\",\n56|    \"lowlight\": \"^3.3.0\",\n57|    \"mnemonist\": \"^0.40.3\",\n58|    \"open\": \"^10.1.2\",\n59|    \"prompts\": \"^2.4.2\",\n60|    \"proper-lockfile\": \"^4.1.2\",\n61|    \"react\": \"^19.2.0\",\n62|    \"shell-quote\": \"^1.8.3\",\n63|    \"simple-git\": \"^3.28.0\",\n64|    \"string-width\": \"^8.1.0\",\n65|    \"strip-ansi\": \"^7.1.0\",\n66|    \"strip-json-comments\": \"^3.1.1\",\n67|    \"tar\": \"^7.5.8\",\n68|    \"tinygradient\": \"^1.1.5\",\n69|    \"undici\": \"^7.10.0\",\n70|    \"ws\": \"^8.16.0\",\n71|    \"yargs\": \"^17.7.2\",\n72|    \"zod\": \"^3.23.8\"\n73|  },\n74|  \"devDependencies\": {\n75|    \"@google/gemini-cli-test-utils\": \"file:../test-utils\",\n76|    \"@types/command-exists\": \"^1.2.3\",\n77|    \"@types/hast\": \"^3.0.4\",\n78|    \"@types/node\": \"^20.11.24\",\n79|    \"@types/react\": \"^19.2.0\",\n80|    \"@types/semver\": \"^7.7.0\",\n81|    \"@types/shell-quote\": \"^1.7.5\",\n82|    \"@types/ws\": \"^8.5.10\",\n83|    \"@types/yargs\": \"^17.0.32\",\n84|    \"@xterm/headless\": \"^5.5.0\",\n85|    \"typescript\": \"^5.3.3\",\n86|    \"vitest\": \"^3.1.1\"\n87|  },\n88|  \"engines\": {\n89|    \"node\": \">=20\"\n90|  }\n91|}\n92|", "total_lines": 91, "file_size": 2510, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|{\n2|  \"name\": \"@google/gemini-cli-core\",\n3|  \"version\": \"0.45.0-nightly.20260521.g854f811be\",\n4|  \"description\": \"Gemini CLI Core\",\n5|  \"license\": \"Apache-2.0\",\n6|  \"repository\": {\n7|    \"type\": \"git\",\n8|    \"url\": \"git+https://github.com/google-gemini/gemini-cli.git\"\n9|  },\n10|  \"type\": \"module\",\n11|  \"main\": \"dist/index.js\",\n12|  \"scripts\": {\n13|    \"bundle:browser-mcp\": \"node scripts/bundle-browser-mcp.mjs\",\n14|    \"build\": \"node ../../scripts/build_package.js\",\n15|    \"lint\": \"eslint . --ext .ts,.tsx\",\n16|    \"format\": \"prettier --write .\",\n17|    \"test\": \"vitest run\",\n18|    \"test:ci\": \"vitest run\",\n19|    \"posttest\": \"npm run build\",\n20|    \"typecheck\": \"tsc --noEmit\"\n21|  },\n22|  \"files\": [\n23|    \"dist\"\n24|  ],\n25|  \"dependencies\": {\n26|    \"@a2a-js/sdk\": \"0.3.11\",\n27|    \"@bufbuild/protobuf\": \"^2.11.0\",\n28|    \"@google-cloud/logging\": \"^11.2.1\",\n29|    \"@google-cloud/opentelemetry-cloud-monitoring-exporter\": \"^0.21.0\",\n30|    \"@google-cloud/opentelemetry-cloud-trace-exporter\": \"^3.0.0\",\n31|    \"@google/genai\": \"1.30.0\",\n32|    \"@grpc/grpc-js\": \"^1.14.3\",\n33|    \"@iarna/toml\": \"^2.2.5\",\n34|    \"@modelcontextprotocol/sdk\": \"^1.23.0\",\n35|    \"@opentelemetry/api\": \"^1.9.0\",\n36|    \"@opentelemetry/api-logs\": \"^0.218.0\",\n37|    \"@opentelemetry/core\": \"^2.7.1\",\n38|    \"@opentelemetry/exporter-logs-otlp-grpc\": \"^0.218.0\",\n39|    \"@opentelemetry/exporter-logs-otlp-http\": \"^0.218.0\",\n40|    \"@opentelemetry/exporter-metrics-otlp-grpc\": \"^0.218.0\",\n41|    \"@opentelemetry/exporter-metrics-otlp-http\": \"^0.218.0\",\n42|    \"@opentelemetry/exporter-trace-otlp-grpc\": \"^0.218.0\",\n43|    \"@opentelemetry/exporter-trace-otlp-http\": \"^0.218.0\",\n44|    \"@opentelemetry/instrumentation-http\": \"^0.218.0\",\n45|    \"@opentelemetry/otlp-exporter-base\": \"^0.218.0\",\n46|    \"@opentelemetry/resources\": \"^2.7.1\",\n47|    \"@opentelemetry/sdk-logs\": \"^0.218.0\",\n48|    \"@opentelemetry/sdk-metrics\": \"^2.7.1\",\n49|    \"@opentelemetry/sdk-node\": \"^0.218.0\",\n50|    \"@opentelemetry/sdk-trace-base\": \"^2.7.1\",\n51|    \"@opentelemetry/sdk-trace-node\": \"^2.7.1\",\n52|    \"@opentelemetry/semantic-conventions\": \"^1.39.0\",\n53|    \"@types/html-to-text\": \"^9.0.4\",\n54|    \"@xterm/headless\": \"5.5.0\",\n55|    \"ajv\": \"^8.17.1\",\n56|    \"ajv-formats\": \"^3.0.0\",\n57|    \"chokidar\": \"^5.0.0\",\n58|    \"command-exists\": \"^1.2.9\",\n59|    \"diff\": \"^8.0.3\",\n60|    \"dotenv\": \"^17.2.4\",\n61|    \"dotenv-expand\": \"^12.0.3\",\n62|    \"execa\": \"^9.6.1\",\n63|    \"fast-levenshtein\": \"^2.0.6\",\n64|    \"fdir\": \"^6.4.6\",\n65|    \"fzf\": \"^0.5.2\",\n66|    \"glob\": \"^12.0.0\",\n67|    \"google-auth-library\": \"^9.11.0\",\n68|    \"html-to-text\": \"^9.0.5\",\n69|    \"http-proxy-agent\": \"^7.0.2\",\n70|    \"https-proxy-agent\": \"^7.0.6\",\n71|    \"ignore\": \"^7.0.0\",\n72|    \"ipaddr.js\": \"^1.9.1\",\n73|    \"isbinaryfile\": \"^5.0.7\",\n74|    \"js-yaml\": \"^4.1.1\",\n75|    \"json-stable-stringify\": \"^1.3.0\",\n76|    \"marked\": \"^15.0.12\",\n77|    \"mime\": \"4.0.7\",\n78|    \"mnemonist\": \"^0.40.3\",\n79|    \"open\": \"^10.1.2\",\n80|    \"picomatch\": \"^4.0.1\",\n81|    \"proper-lockfile\": \"^4.1.2\",\n82|    \"puppeteer-core\": \"^24.0.0\",\n83|    \"read-package-up\": \"^11.0.0\",\n84|    \"shell-quote\": \"^1.8.3\",\n85|    \"simple-git\": \"^3.28.0\",\n86|    \"strip-ansi\": \"^7.1.0\",\n87|    \"strip-json-comments\": \"^3.1.1\",\n88|    \"systeminformation\": \"^5.25.11\",\n89|    \"tree-sitter-bash\": \"^0.25.0\",\n90|    \"undici\": \"^7.10.0\",\n91|    \"uuid\": \"^13.0.0\",\n92|    \"web-tree-sitter\": \"^0.25.10\",\n93|    \"zod\": \"^3.25.76\",\n94|    \"zod-to-json-schema\": \"^3.25.1\"\n95|  },\n96|  \"optionalDependencies\": {\n97|    \"@github/keytar\": \"^7.10.6\",\n98|    \"@lydell/node-pty\": \"1.1.0\",\n99|    \"@lydell/node-pty-darwin-arm64\": \"1.1.0\",\n100|    \"@lydell/node-pty-darwin-x64\": \"1.1.0\",\n101|    \"@lydell/node-pty-linux-x64\": \"1.1.0\",\n102|    \"@lydell/node-pty-win32-arm64\": \"1.1.0\",\n103|    \"@lydell/node-pty-win32-x64\": \"1.1.0\",\n104|    \"node-pty\": \"^1.0.0\"\n105|  },\n106|  \"devDependencies\": {\n107|    \"@google/gemini-cli-test-utils\": \"file:../test-utils\",\n108|    \"@types/fast-levenshtein\": \"^0.0.4\",\n109|    \"@types/js-yaml\": \"^4.0.9\",\n110|    \"@types/json-stable-stringify\": \"^1.1.0\",\n111|    \"@types/picomatch\": \"^4.0.1\",\n112|    \"chrome-devtools-mcp\": \"^0.19.0\",\n113|    \"msw\": \"^2.3.4\",\n114|    \"typescript\": \"^5.3.3\",\n115|    \"vitest\": \"^3.1.1\"\n116|  },\n117|  \"engines\": {\n118|    \"node\": \">=20\"\n119|  }\n120|}\n121|", "total_lines": 120, "file_size": 3932, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
    [{"id": "chatcmpl-tool-bfb4fdf29290d493", "call_id": "chatcmpl-tool-bfb4fdf29290d493", "response_item_id": "fc_chatcmpl-tool-bfb4fdf29290d493", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/gemini-cli/ROADMAP.md\"}"}}, {"id": "chatcmpl-tool-a0e525c7aabb2af9", "call_id": "chatcmpl-tool-a0e525c7aabb2af9", "response_item_id": "fc_chatcmpl-tool-a0e525c7aabb2af9", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice/gemini-cli && find packages -maxdepth 3 -type d | sort\"}"}}]


c0:
  {"content": "1|# Gemini CLI Roadmap\n2|\n3|The\n4|[Official Gemini CLI Roadmap](https://github.com/orgs/google-gemini/projects/11/)\n5|\n6|Gemini CLI is an open-source AI agent that brings the power of Gemini directly\n7|into your terminal. It provides lightweight access to Gemini, giving you the\n8|most direct path from your prompt to our model.\n9|\n10|This document outlines our approach to the Gemini CLI roadmap. Here, you'll find\n11|our guiding principles and a breakdown of the key areas we are focused on for\n12|development. Our roadmap is not a static list but a dynamic set of priorities\n13|that are tracked live in our GitHub Issues.\n14|\n15|As an\n16|[Apache 2.0 open source project](https://github.com/google-gemini/gemini-cli?tab=Apache-2.0-1-ov-file#readme),\n17|we appreciate and welcome\n18|[public contributions](https://github.com/google-gemini/gemini-cli/blob/main/CONTRIBUTING.md),\n19|and will give first priority to those contributions aligned with our roadmap. If\n20|you want to propose a new feature or change to our roadmap, please start by\n21|[opening an issue for discussion](https://github.com/google-gemini/gemini-cli/issues/new/choose).\n22|\n23|## Disclaimer\n24|\n25|This roadmap represents our current thinking and is for informational purposes\n26|only. It is not a commitment or a guarantee of future delivery. The development,\n27|release, and timing of any features are subject to change, and we may update the\n28|roadmap based on community discussions as well as when our priorities evolve.\n29|\n30|## Guiding Principles\n31|\n32|Our development is guided by the following principles:\n33|\n34|- **Power & Simplicity:** Deliver access to state-of-the-art Gemini models with\n35|  an intuitive and easy-to-use lightweight command-line interface.\n36|- **Extensibility:** An adaptable agent to help you with a variety of use cases\n37|  and environments along with the ability to run these agents anywhere.\n38|- **Intelligent:** Gemini CLI should be reliably ranked among the best agentic\n39|  tools as measured by benchmarks like SWE Bench, Terminal Bench, and CSAT.\n40|- **Free and Open Source:** Foster a thriving open source community where cost\n41|  isn’t a barrier to personal use, and PRs get merged quickly. This means\n42|  resolving and closing issues, pull requests, and discussion posts quickly.\n43|\n44|## How the Roadmap Works\n45|\n46|Our roadmap is managed directly through GitHub Issues. See our entry point\n47|Roadmap Issue [here](https://github.com/google-gemini/gemini-cli/issues/4191).\n48|This approach allows for transparency and gives you a direct way to learn more\n49|or get involved with any specific initiative. All our roadmap items will be\n50|tagged as Type:`Feature` and Label:`maintainer` for features we are actively\n51|working on, or Type:`Task` and Label:`maintainer` for a more detailed list of\n52|tasks.\n53|\n54|Issues are organized to provide key information at a glance:\n55|\n56|- **Target Quarter:** `Milestone` denotes the anticipated delivery timeline.\n57|- **Feature Area:** Labels such as `area/model` or `area/tooling` categorize the\n58|  work.\n59|- **Issue Type:** _Workstream_ => _Epics_ => _Features_ => _Tasks|Bugs_\n60|\n61|To see what we're working on, you can filter our issues by these dimensions. See\n62|all our items [here](https://github.com/orgs/google-gemini/projects/11/views/19)\n63|\n64|## Focus Areas\n65|\n66|To better organize our efforts, we categorize our work into several key feature\n67|areas. These labels are used on our GitHub Issues to help you filter and find\n68|initiatives that interest you.\n69|\n70|- **Authentication:** Secure user access via API keys, Gemini Code Assist login,\n71|  etc.\n72|- **Model:** Support new Gemini models, multi-modality, local execution, and\n73|  performance tuning.\n74|- **User Experience:** Improve the CLI's usability, performance, interactive\n75|  features, and documentation.\n76|- **Tooling:** Built-in tools and the MCP ecosystem.\n77|- **Core:** Core functionality of the CLI\n78|- **Extensibility:** Bringing Gemini CLI to other surfaces e.g. GitHub.\n79|- **Contribution:** Improve the contribution process via test automation and\n80|  CI/CD pipeline enhancements.\n81|- **Platform:** Manage installation, OS support, and the underlying CLI\n82|  framework.\n83|- **Quality:** Focus on testing, reliability, performance, and overall product\n84|  quality.\n85|- **Background Agents:** Enable long-running, autonomous tasks and proactive\n86|  assistance.\n87|- **Security and Privacy:** For all things related to security and privacy\n88|\n89|## How to Contribute\n90|\n91|Gemini CLI is an open-source project, and we welcome contributions from the\n92|community! Whether you're a developer, a designer, or just an enthusiastic user\n93|you can find our\n94|[Community Guidelines here](https://github.com/google-gemini/gemini-cli/blob/main/CONTRIBUTING.md)\n95|to learn how to get started. There are many ways to get involved:\n96|\n97|- **Roadmap:** Please review and find areas in our\n98|  [roadmap](https://github.com/google-gemini/gemini-cli/issues/4191) that you\n99|  would like to contribute to. Contributions based on this will be easiest to\n100|  integrate with.\n101|- **Report Bugs:** If you find an issue, please create a\n102|  [bug](https://github.com/google-gemini/gemini-cli/issues/new?template=bug_report.yml)\n103|  with as much detail as possible. If you believe it is a critical breaking\n104|  issue preventing direct CLI usage, please tag it as `priority/p0`.\n105|- **Suggest Features:** Have a great idea? We'd love to hear it! Open a\n106|  [feature request](https://github.com/google-gemini/gemini-cli/issues/new?template=feature_request.yml).\n107|- **Contribute Code:** Check out our\n108|  [CONTRIBUTING.md](https://github.com/google-gemini/gemini-cli/blob/main/CONTRIBUTING.md)\n109|  file for guidelines on how to submit pull requests. We have a list of \"good\n110|  first issues\" for new contributors.\n111|- **Write Documentation:** Help us improve our documentation, tutorials, and\n112|  examples. We are excited about the future of Gemini CLI and look forward to\n113|  building it with you!\n114|", "total_lines": 113, "file_size": 5748, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"output": "packages\npackages/a2a-server\npackages/a2a-server/src\npackages/a2a-server/src/agent\npackages/a2a-server/src/commands\npackages/a2a-server/src/config\npackages/a2a-server/src/http\npackages/a2a-server/src/persistence\npackages/a2a-server/src/utils\npackages/cli\npackages/cli/examples\npackages/cli/src\npackages/cli/src/__snapshots__\npackages/cli/src/acp\npackages/cli/src/commands\npackages/cli/src/config\npackages/cli/src/core\npackages/cli/src/integration-tests\npackages/cli/src/patches\npackages/cli/src/services\npackages/cli/src/test-utils\npackages/cli/src/ui\npackages/cli/src/utils\npackages/core\npackages/core/scripts\npackages/core/src\npackages/core/src/__mocks__\npackages/core/src/agent\npackages/core/src/agents\npackages/core/src/availability\npackages/core/src/billing\npackages/core/src/code_assist\npackages/core/src/commands\npackages/core/src/config\npackages/core/src/confirmation-bus\npackages/core/src/context\npackages/core/src/core\npackages/core/src/fallback\npackages/core/src/hooks\npackages/core/src/ide\npackages/core/src/mcp\npackages/core/src/mocks\npackages/core/src/output\npackages/core/src/policy\npackages/core/src/prompts\npackages/core/src/resources\npackages/core/src/routing\npackages/core/src/safety\npackages/core/src/sandbox\npackages/core/src/scheduler\npackages/core/src/services\npackages/core/src/skills\npackages/core/src/telemetry\npackages/core/src/test-utils\npackages/core/src/tools\npackages/core/src/utils\npackages/core/src/voice\npackages/core/vendor\npackages/core/vendor/ripgrep\npackages/devtools\npackages/devtools/client\npackages/devtools/client/src\npackages/devtools/src\npackages/sdk\npackages/sdk/examples\npackages/sdk/src\npackages/sdk/test-data\npackages/sdk/test-data/skills\npackages/test-utils\npackages/test-utils/assets\npackages/test-utils/assets/test-servers\npackages/test-utils/src\npackages/test-utils/src/fixtures\npackages/vscode-ide-companion\npackages/vscode-ide-companion/.vscode\npackages/vscode-ide-companion/assets\npackages/vscode-ide-companion/scripts\npackages/vscode-ide-companion/src\npackages/vscode-ide-companion/src/utils", "exit_code": 0, "error": null} terminal 


c0:
  ## Resultado da análise

  - **Repositório:** `google-gemini/gemini-cli`
  - **O que fiz:** Clonei o repositório, examinei `README.md`, `package.json` e estrutura dos pacotes.

  ### Problema
  Google precisava de acesso mais direto ao Gemini para devs, evitando interfaces web e reduzindo atrito para usar IA no fluxo local.

  ### Tema
  Agente de IA generativa para terminal.

  ### Razão do hype
  - Marca forte: Google / Gemini  
  - Modelo avançado: Gemini 3, com janela de até 1M tokens  
  - Custo de entrada baixo: tier gratuito generoso  
  - Extensibilidade: suporte a MCP  
  - Ecossistema dev: CLI first, scripts headless, GitHub Action, VS Code companion  
  - Open source com licença permissiva: Apache 2.0

  ### Tecnologias
  - TypeScript
  - React + Ink
  - MCP
  - A2A
  - esbuild
  - node-pty
  - OpenTelemetry / gRPC
  - Shell: webfetch, shell-quote, simple-git  


c0:
    [{"id": "chatcmpl-tool-8d70eff7582e1d5f", "call_id": "chatcmpl-tool-8d70eff7582e1d5f", "response_item_id": "fc_chatcmpl-tool-8d70eff7582e1d5f", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"git clone https://github.com/TauricResearch/TradingAgents.git /home/ice/TradingAgents\", \"timeout\": 120}"}}]


c0:
    [{"id": "chatcmpl-tool-89a1187c3872b9fd", "call_id": "chatcmpl-tool-89a1187c3872b9fd", "response_item_id": "fc_chatcmpl-tool-89a1187c3872b9fd", "type": "function", "function": {"name": "search_files", "arguments": "{\"output_mode\": \"files_only\", \"path\": \"/home/ice/TradingAgents\", \"pattern\": \"*\", \"target\": \"files\"}"}}]


c0:
  {"total_count": 50, "files": ["/home/ice/TradingAgents/uv.lock", "/home/ice/TradingAgents/tradingagents/llm_clients/validators.py", "/home/ice/TradingAgents/tradingagents/llm_clients/openai_client.py", "/home/ice/TradingAgents/tradingagents/llm_clients/model_catalog.py", "/home/ice/TradingAgents/tradingagents/llm_clients/google_client.py", "/home/ice/TradingAgents/tradingagents/llm_clients/factory.py", "/home/ice/TradingAgents/tradingagents/llm_clients/capabilities.py", "/home/ice/TradingAgents/tradingagents/llm_clients/base_client.py", "/home/ice/TradingAgents/tradingagents/llm_clients/azure_client.py", "/home/ice/TradingAgents/tradingagents/llm_clients/api_key_env.py", "/home/ice/TradingAgents/tradingagents/llm_clients/anthropic_client.py", "/home/ice/TradingAgents/tradingagents/llm_clients/__init__.py", "/home/ice/TradingAgents/tradingagents/llm_clients/TODO.md", "/home/ice/TradingAgents/tradingagents/graph/trading_graph.py", "/home/ice/TradingAgents/tradingagents/graph/signal_processing.py", "/home/ice/TradingAgents/tradingagents/graph/setup.py", "/home/ice/TradingAgents/tradingagents/graph/reflection.py", "/home/ice/TradingAgents/tradingagents/graph/propagation.py", "/home/ice/TradingAgents/tradingagents/graph/conditional_logic.py", "/home/ice/TradingAgents/tradingagents/graph/checkpointer.py", "/home/ice/TradingAgents/tradingagents/graph/analyst_execution.py", "/home/ice/TradingAgents/tradingagents/graph/__init__.py", "/home/ice/TradingAgents/tradingagents/default_config.py", "/home/ice/TradingAgents/tradingagents/dataflows/yfinance_news.py", "/home/ice/TradingAgents/tradingagents/dataflows/y_finance.py", "/home/ice/TradingAgents/tradingagents/dataflows/utils.py", "/home/ice/TradingAgents/tradingagents/dataflows/stocktwits.py", "/home/ice/TradingAgents/tradingagents/dataflows/stockstats_utils.py", "/home/ice/TradingAgents/tradingagents/dataflows/reddit.py", "/home/ice/TradingAgents/tradingagents/dataflows/interface.py", "/home/ice/TradingAgents/tradingagents/dataflows/config.py", "/home/ice/TradingAgents/tradingagents/dataflows/alpha_vantage_stock.py", "/home/ice/TradingAgents/tradingagents/dataflows/alpha_vantage_news.py", "/home/ice/TradingAgents/tradingagents/dataflows/alpha_vantage_indicator.py", "/home/ice/TradingAgents/tradingagents/dataflows/alpha_vantage_fundamentals.py", "/home/ice/TradingAgents/tradingagents/dataflows/alpha_vantage_common.py", "/home/ice/TradingAgents/tradingagents/dataflows/alpha_vantage.py", "/home/ice/TradingAgents/tradingagents/dataflows/__init__.py", "/home/ice/TradingAgents/tradingagents/agents/utils/technical_indicators_tools.py", "/home/ice/TradingAgents/tradingagents/agents/utils/structured.py", "/home/ice/TradingAgents/tradingagents/agents/utils/rating.py", "/home/ice/TradingAgents/tradingagents/agents/utils/news_data_tools.py", "/home/ice/TradingAgents/tradingagents/agents/utils/memory.py", "/home/ice/TradingAgents/tradingagents/agents/utils/fundamental_data_tools.py", "/home/ice/TradingAgents/tradingagents/agents/utils/core_stock_tools.py", "/home/ice/TradingAgents/tradingagents/agents/utils/agent_utils.py", "/home/ice/TradingAgents/tradingagents/agents/utils/agent_states.py", "/home/ice/TradingAgents/tradingagents/agents/trader/trader.py", "/home/ice/TradingAgents/tradingagents/agents/schemas.py", "/home/ice/TradingAgents/tradingagents/agents/risk_mgmt/neutral_debator.py"]} search_files 


c0:
    [{"id": "chatcmpl-tool-b5a635190d89929f", "call_id": "chatcmpl-tool-b5a635190d89929f", "response_item_id": "fc_chatcmpl-tool-b5a635190d89929f", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"git -C /home/ice/TradingAgents remote -v && git -C /home/ice/TradingAgents log --oneline -5\"}"}}]


c0:
  {"output": "origin\thttps://github.com/TauricResearch/TradingAgents.git (fetch)\norigin\thttps://github.com/TauricResearch/TradingAgents.git (push)\n61522e1 fix(llm): skip Anthropic effort kwarg on non-supporting models (#831)\ne848b5e fix(llm): gate MiniMax reasoning_split by model capability (#826)\n3e5e99b fix(graph): integrate #487 + #567 — sentiment label, route, propagate asset_type\na2e7ac1 Merge #567 — analysis-only crypto asset mode\nb16fe53 Merge #487 — analyst execution planning and timing hooks", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-82db502af9e59754", "call_id": "chatcmpl-tool-82db502af9e59754", "response_item_id": "fc_chatcmpl-tool-82db502af9e59754", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/TradingAgents/README.md\"}"}}]


c0:
  {"content": "1|<p align=\"center\">\n2|  <img src=\"assets/TauricResearch.png\" style=\"width: 60%; height: auto;\">\n3|</p>\n4|\n5|<div align=\"center\" style=\"line-height: 1;\">\n6|  <a href=\"https://arxiv.org/abs/2412.20138\" target=\"_blank\"><img alt=\"arXiv\" src=\"https://img.shields.io/badge/arXiv-2412.20138-B31B1B?logo=arxiv\"/></a>\n7|  <a href=\"https://discord.com/invite/hk9PGKShPK\" target=\"_blank\"><img alt=\"Discord\" src=\"https://img.shields.io/badge/Discord-TradingResearch-7289da?logo=discord&logoColor=white&color=7289da\"/></a>\n8|  <a href=\"./assets/wechat.png\" target=\"_blank\"><img alt=\"WeChat\" src=\"https://img.shields.io/badge/WeChat-TauricResearch-brightgreen?logo=wechat&logoColor=white\"/></a>\n9|  <a href=\"https://x.com/TauricResearch\" target=\"_blank\"><img alt=\"X Follow\" src=\"https://img.shields.io/badge/X-TauricResearch-white?logo=x&logoColor=white\"/></a>\n10|  <br>\n11|  <a href=\"https://github.com/TauricResearch/\" target=\"_blank\"><img alt=\"Community\" src=\"https://img.shields.io/badge/Join_GitHub_Community-TauricResearch-14C290?logo=discourse\"/></a>\n12|</div>\n13|\n14|<div align=\"center\">\n15|  <!-- Keep these links. Translations will automatically update with the README. -->\n16|  <a href=\"https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=de\">Deutsch</a> | \n17|  <a href=\"https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=es\">Español</a> | \n18|  <a href=\"https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=fr\">français</a> | \n19|  <a href=\"https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ja\">日本語</a> | \n20|  <a href=\"https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ko\">한국어</a> | \n21|  <a href=\"https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=pt\">Português</a> | \n22|  <a href=\"https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ru\">Русский</a> | \n23|  <a href=\"https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=zh\">中文</a>\n24|</div>\n25|\n26|---\n27|\n28|# TradingAgents: Multi-Agents LLM Financial Trading Framework\n29|\n30|## News\n31|- [2026-05] **TradingAgents v0.2.5** released with the grounded Sentiment Analyst, GPT-5.5 etc. model coverage, Qwen/GLM/MiniMax dual-region support, `TRADINGAGENTS_*` env-var configurability with API-key auto-detection, remote Ollama support, non-US alpha benchmarks, and ticker path-traversal hardening. See [CHANGELOG.md](CHANGELOG.md) for the full list.\n32|- [2026-04] **TradingAgents v0.2.4** released with structured-output agents (Research Manager, Trader, Portfolio Manager), LangGraph checkpoint resume, persistent decision log, DeepSeek/Qwen/GLM/Azure provider support, Docker, and a Windows UTF-8 encoding fix.\n33|- [2026-03] **TradingAgents v0.2.3** released with multi-language support, GPT-5.4 family models, unified model catalog, backtesting date fidelity, and proxy support.\n34|- [2026-03] **TradingAgents v0.2.2** released with GPT-5.4/Gemini 3.1/Claude 4.6 model coverage, five-tier rating scale, OpenAI Responses API, Anthropic effort control, and cross-platform stability.\n35|- [2026-02] **TradingAgents v0.2.0** released with multi-provider LLM support (GPT-5.x, Gemini 3.x, Claude 4.x, Grok 4.x) and improved system architecture.\n36|- [2026-01] **Trading-R1** [Technical Report](https://arxiv.org/abs/2509.11420) released, with [Terminal](https://github.com/TauricResearch/Trading-R1) expected to land soon.\n37|\n38|<div align=\"center\">\n39|<a href=\"https://www.star-history.com/#TauricResearch/TradingAgents&Date\">\n40| <picture>\n41|   <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date&theme=dark\" />\n42|   <source media=\"(prefers-color-scheme: light)\" srcset=\"https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date\" />\n43|   <img alt=\"TradingAgents Star History\" src=\"https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date\" style=\"width: 80%; height: auto;\" />\n44| </picture>\n45|</a>\n46|</div>\n47|\n48|> 🎉 **TradingAgents** officially released! We have received numerous inquiries about the work, and we would like to express our thanks for the enthusiasm in our community.\n49|>\n50|> So we decided to fully open-source the framework. Looking forward to building impactful projects with you!\n51|\n52|<div align=\"center\">\n53|\n54|🚀 [TradingAgents](#tradingagents-framework) | ⚡ [Installation & CLI](#installation-and-cli) | 🎬 [Demo](https://www.youtube.com/watch?v=90gr5lwjIho) | 📦 [Package Usage](#tradingagents-package) | 🤝 [Contributing](#contributing) | 📄 [Citation](#citation)\n55|\n56|</div>\n57|\n58|## TradingAgents Framework\n59|\n60|TradingAgents is a multi-agent trading framework that mirrors the dynamics of real-world trading firms. By deploying specialized LLM-powered agents: from fundamental analysts, sentiment experts, and technical analysts, to trader, risk management team, the platform collaboratively evaluates market conditions and informs trading decisions. Moreover, these agents engage in dynamic discussions to pinpoint the optimal strategy.\n61|\n62|<p align=\"center\">\n63|  <img src=\"assets/schema.png\" style=\"width: 100%; height: auto;\">\n64|</p>\n65|\n66|> TradingAgents framework is designed for research purposes. Trading performance may vary based on many factors, including the chosen backbone language models, model temperature, trading periods, the quality of data, and other non-deterministic factors. [It is not intended as financial, investment, or trading advice.](https://tauric.ai/disclaimer/)\n67|\n68|Our framework decomposes complex trading tasks into specialized roles. This ensures the system achieves a robust, scalable approach to market analysis and decision-making.\n69|\n70|### Analyst Team\n71|- Fundamentals Analyst: Evaluates company financials and performance metrics, identifying intrinsic values and potential red flags.\n72|- Sentiment Analyst: Aggregates news headlines, StockTwits, and Reddit chatter into a single sentiment read to gauge short-term market mood.\n73|- News Analyst: Monitors global news and macroeconomic indicators, interpreting the impact of events on market conditions.\n74|- Technical Analyst: Utilizes technical indicators (like MACD and RSI) to detect trading patterns and forecast price movements.\n75|\n76|<p align=\"center\">\n77|  <img src=\"assets/analyst.png\" width=\"100%\" style=\"display: inline-block; margin: 0 2%;\">\n78|</p>\n79|\n80|### Researcher Team\n81|- Comprises both bullish and bearish researchers who critically assess the insights provided by the Analyst Team. Through structured debates, they balance potential gains against inherent risks.\n82|\n83|<p align=\"center\">\n84|  <img src=\"assets/researcher.png\" width=\"70%\" style=\"display: inline-block; margin: 0 2%;\">\n85|</p>\n86|\n87|### Trader Agent\n88|- Composes reports from the analysts and researchers to make informed trading decisions. It determines the timing and magnitude of trades based on comprehensive market insights.\n89|\n90|<p align=\"center\">\n91|  <img src=\"assets/trader.png\" width=\"70%\" style=\"display: inline-block; margin: 0 2%;\">\n92|</p>\n93|\n94|### Risk Management and Portfolio Manager\n95|- Continuously evaluates portfolio risk by assessing market volatility, liquidity, and other risk factors. The risk management team evaluates and adjusts trading strategies, providing assessment reports to the Portfolio Manager for final decision.\n96|- The Portfolio Manager approves/rejects the transaction proposal. If approved, the order will be sent to the simulated exchange and executed.\n97|\n98|<p align=\"center\">\n99|  <img src=\"assets/risk.png\" width=\"70%\" style=\"display: inline-block; margin: 0 2%;\">\n100|</p>\n101|\n102|## Installation and CLI\n103|\n104|### Installation\n105|\n106|Clone TradingAgents:\n107|```bash\n108|git clone https://github.com/TauricResearch/TradingAgents.git\n109|cd TradingAgents\n110|```\n111|\n112|Create a virtual environment in any of your favorite environment managers:\n113|```bash\n114|conda create -n tradingagents python=3.13\n115|conda activate tradingagents\n116|```\n117|\n118|Install the package and its dependencies:\n119|```bash\n120|pip install .\n121|```\n122|\n123|### Docker\n124|\n125|Alternatively, run with Docker:\n126|```bash\n127|cp .env.example .env  # add your API keys\n128|docker compose run --rm tradingagents\n129|```\n130|\n131|For local models with Ollama:\n132|```bash\n133|docker compose --profile ollama run --rm tradingagents-ollama\n134|```\n135|\n136|### Required APIs\n137|\n138|TradingAgents supports multiple LLM providers. Set the API key for your chosen provider:\n139|\n140|```bash\n141|export OPENAI_API_KEY=...          # OpenAI (GPT)\n142|export GOOGLE_API_KEY=...          # Google (Gemini)\n143|export ANTHROPIC_API_KEY=...       # Anthropic (Claude)\n144|export XAI_API_KEY=...             # xAI (Grok)\n145|export DEEPSEEK_API_KEY=...        # DeepSeek\n146|export DASHSCOPE_API_KEY=...       # Qwen — International (dashscope-intl.aliyuncs.com)\n147|export DASHSCOPE_CN_API_KEY=...    # Qwen — China (dashscope.aliyuncs.com)\n148|export ZHIPU_API_KEY=...           # GLM via Z.AI (international)\n149|export ZHIPU_CN_API_KEY=...        # GLM via BigModel (China, open.bigmodel.cn)\n150|export MINIMAX_API_KEY=...         # MiniMax — Global (api.minimax.io, M2.x, 204K ctx)\n151|export MINIMAX_CN_API_KEY=...      # MiniMax — China (api.minimaxi.com, M2.x, 204K ctx)\n152|export OPENROUTER_API_KEY=...      # OpenRouter\n153|export ALPHA_VANTAGE_API_KEY=...   # Alpha Vantage\n154|```\n155|\n156|For enterprise providers (e.g. Azure OpenAI, AWS Bedrock), copy `.env.enterprise.example` to `.env.enterprise` and fill in your credentials.\n157|\n158|For local models, configure Ollama with `llm_provider: \"ollama\"`. The default endpoint is `http://localhost:11434/v1`; set `OLLAMA_BASE_URL` to point at a remote `ollama-serve`. Pull models with `ollama pull <name>`, and pick \"Custom model ID\" in the CLI for any model not listed by default.\n159|\n160|Alternatively, copy `.env.example` to `.env` and fill in your keys:\n161|```bash\n162|cp .env.example .env\n163|```\n164|\n165|### CLI Usage\n166|\n167|Launch the interactive CLI:\n168|```bash\n169|tradingagents          # installed command\n170|python -m cli.main     # alternative: run directly from source\n171|```\n172|You will see a screen where you can select your desired tickers, analysis date, LLM provider, research depth, and more.\n173|\n174|<p align=\"center\">\n175|  <img src=\"assets/cli/cli_init.png\" width=\"100%\" style=\"display: inline-block; margin: 0 2%;\">\n176|</p>\n177|\n178|An interface will appear showing results as they load, letting you track the agent's progress as it runs.\n179|\n180|<p align=\"center\">\n181|  <img src=\"assets/cli/cli_news.png\" width=\"100%\" style=\"display: inline-block; margin: 0 2%;\">\n182|</p>\n183|\n184|<p align=\"center\">\n185|  <img src=\"assets/cli/cli_transaction.png\" width=\"100%\" style=\"display: inline-block; margin: 0 2%;\">\n186|</p>\n187|\n188|## TradingAgents Package\n189|\n190|### Implementation Details\n191|\n192|We built TradingAgents with LangGraph to ensure flexibility and modularity. The framework supports multiple LLM providers: OpenAI, Google, Anthropic, xAI, DeepSeek, Qwen (Alibaba DashScope, international and China endpoints), GLM (Zhipu), MiniMax (global + China), OpenRouter, Ollama for local models, and Azure OpenAI for enterprise.\n193|\n194|### Python Usage\n195|\n196|To use TradingAgents inside your code, you can import the `tradingagents` module and initialize a `TradingAgentsGraph()` object. The `.propagate()` function will return a decision. You can run `main.py`, here's also a quick example:\n197|\n198|```python\n199|from tradingagents.graph.trading_graph import TradingAgentsGraph\n200|from tradingagents.default_config import DEFAULT_CONFIG\n201|\n202|ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy())\n203|\n204|# forward propagate\n205|_, decision = ta.propagate(\"NVDA\", \"2026-01-15\")\n206|print(decision)\n207|```\n208|\n209|You can also adjust the default configuration to set your own choice of LLMs, debate rounds, etc.\n210|\n211|```python\n212|from tradingagents.graph.trading_graph import TradingAgentsGraph\n213|from tradingagents.default_config import DEFAULT_CONFIG\n214|\n215|config = DEFAULT_CONFIG.copy()\n216|config[\"llm_provider\"] = \"openai\"        # openai, google, anthropic, xai, deepseek, qwen, qwen-cn, glm, glm-cn, minimax, minimax-cn, openrouter, ollama, azure\n217|config[\"deep_think_llm\"] = \"gpt-5.4\"     # Model for complex reasoning\n218|config[\"quick_think_llm\"] = \"gpt-5.4-mini\" # Model for quick tasks\n219|config[\"max_debate_rounds\"] = 2\n220|\n221|ta = TradingAgentsGraph(debug=True, config=config)\n222|_, decision = ta.propagate(\"NVDA\", \"2026-01-15\")\n223|print(decision)\n224|```\n225|\n226|See `tradingagents/default_config.py` for all configuration options.\n227|\n228|## Persistence and Recovery\n229|\n230|TradingAgents persists two kinds of state across runs.\n231|\n232|### Decision log\n233|\n234|The decision log is always on. Each completed run appends its decision to `~/.tradingagents/memory/trading_memory.md`. On the next run for the same ticker, TradingAgents fetches the realised return (raw and alpha vs SPY), generates a one-paragraph reflection, and injects the most recent same-ticker decisions plus recent cross-ticker lessons into the Portfolio Manager prompt, so each analysis carries forward what worked and what didn't.\n235|\n236|Override the path with `TRADINGAGENTS_MEMORY_LOG_PATH`.\n237|\n238|### Checkpoint resume\n239|\n240|Checkpoint resume is opt-in via `--checkpoint`. When enabled, LangGraph saves state after each node so a crashed or interrupted run resumes from the last successful step instead of starting over. On a resume run you will see `Resuming from step N for <TICKER> on <date>` in the logs; on a new run you will see `Starting fresh`. Checkpoints are cleared automatically on successful completion.\n241|\n242|Per-ticker SQLite databases live at `~/.tradingagents/cache/checkpoints/<TICKER>.db` (override the base with `TRADINGAGENTS_CACHE_DIR`). Use `--clear-checkpoints` to reset all of them before a run.\n243|\n244|```bash\n245|tradingagents analyze --checkpoint           # enable for this run\n246|tradingagents analyze --clear-checkpoints    # reset before running\n247|```\n248|\n249|```python\n250|config = DEFAULT_CONFIG.copy()\n251|config[\"checkpoint_enabled\"] = True\n252|ta = TradingAgentsGraph(config=config)\n253|_, decision = ta.propagate(\"NVDA\", \"2026-01-15\")\n254|```\n255|\n256|## Contributing\n257|\n258|We welcome contributions from the community! Whether it's fixing a bug, improving documentation, or suggesting a new feature, your input helps make this project better. If you are interested in this line of research, please consider joining our open-source financial AI research community [Tauric Research](https://tauric.ai/).\n259|\n260|Past contributions, including code, design feedback, and bug reports, are credited per release in [`CHANGELOG.md`](CHANGELOG.md).\n261|\n262|## Citation\n263|\n264|Please reference our work if you find *TradingAgents* provides you with some help :)\n265|\n266|```\n267|@misc{xiao2025tradingagentsmultiagentsllmfinancial,\n268|      title={TradingAgents: Multi-Agents LLM Financial Trading Framework}, \n269|      author={Yijia Xiao and Edward Sun and Di Luo and Wei Wang},\n270|      year={2025},\n271|      eprint={2412.20138},\n272|      archivePrefix={arXiv},\n273|      primaryClass={q-fin.TR},\n274|      url={https://arxiv.org/abs/2412.20138}, \n275|}\n276|```\n277|", "total_lines": 276, "file_size": 14445, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
    [{"id": "chatcmpl-tool-942cf2eb1d685d78", "call_id": "chatcmpl-tool-942cf2eb1d685d78", "response_item_id": "fc_chatcmpl-tool-942cf2eb1d685d78", "type": "function", "function": {"name": "search_files", "arguments": "{\"path\": \"/home/ice/TradingAgents\", \"pattern\": \"pyproject.toml|requirements.txt|Pipfile|setup.py\", \"target\": \"files\"}"}}]


c0:
    [{"id": "chatcmpl-tool-8fba68d021658b73", "call_id": "chatcmpl-tool-8fba68d021658b73", "response_item_id": "fc_chatcmpl-tool-8fba68d021658b73", "type": "function", "function": {"name": "search_files", "arguments": "{\"path\": \"/home/ice/TradingAgents\", \"pattern\": \"*.*\", \"target\": \"files\"}"}}]


c0:
    [{"id": "chatcmpl-tool-88e15cd7b1d0208d", "call_id": "chatcmpl-tool-88e15cd7b1d0208d", "response_item_id": "fc_chatcmpl-tool-88e15cd7b1d0208d", "type": "function", "function": {"name": "search_files", "arguments": "{\"limit\": 200, \"output_mode\": \"files_only\", \"path\": \"/home/ice/TradingAgents\", \"pattern\": \"*\", \"target\": \"files\"}"}}]


c0:
  {"total_count": 112, "files": ["/home/ice/TradingAgents/uv.lock", "/home/ice/TradingAgents/tradingagents/llm_clients/validators.py", "/home/ice/TradingAgents/tradingagents/llm_clients/openai_client.py", "/home/ice/TradingAgents/tradingagents/llm_clients/model_catalog.py", "/home/ice/TradingAgents/tradingagents/llm_clients/google_client.py", "/home/ice/TradingAgents/tradingagents/llm_clients/factory.py", "/home/ice/TradingAgents/tradingagents/llm_clients/capabilities.py", "/home/ice/TradingAgents/tradingagents/llm_clients/base_client.py", "/home/ice/TradingAgents/tradingagents/llm_clients/azure_client.py", "/home/ice/TradingAgents/tradingagents/llm_clients/api_key_env.py", "/home/ice/TradingAgents/tradingagents/llm_clients/anthropic_client.py", "/home/ice/TradingAgents/tradingagents/llm_clients/__init__.py", "/home/ice/TradingAgents/tradingagents/llm_clients/TODO.md", "/home/ice/TradingAgents/tradingagents/graph/trading_graph.py", "/home/ice/TradingAgents/tradingagents/graph/signal_processing.py", "/home/ice/TradingAgents/tradingagents/graph/setup.py", "/home/ice/TradingAgents/tradingagents/graph/reflection.py", "/home/ice/TradingAgents/tradingagents/graph/propagation.py", "/home/ice/TradingAgents/tradingagents/graph/conditional_logic.py", "/home/ice/TradingAgents/tradingagents/graph/checkpointer.py", "/home/ice/TradingAgents/tradingagents/graph/analyst_execution.py", "/home/ice/TradingAgents/tradingagents/graph/__init__.py", "/home/ice/TradingAgents/tradingagents/default_config.py", "/home/ice/TradingAgents/tradingagents/dataflows/yfinance_news.py", "/home/ice/TradingAgents/tradingagents/dataflows/y_finance.py", "/home/ice/TradingAgents/tradingagents/dataflows/utils.py", "/home/ice/TradingAgents/tradingagents/dataflows/stocktwits.py", "/home/ice/TradingAgents/tradingagents/dataflows/stockstats_utils.py", "/home/ice/TradingAgents/tradingagents/dataflows/reddit.py", "/home/ice/TradingAgents/tradingagents/dataflows/interface.py", "/home/ice/TradingAgents/tradingagents/dataflows/config.py", "/home/ice/TradingAgents/tradingagents/dataflows/alpha_vantage_stock.py", "/home/ice/TradingAgents/tradingagents/dataflows/alpha_vantage_news.py", "/home/ice/TradingAgents/tradingagents/dataflows/alpha_vantage_indicator.py", "/home/ice/TradingAgents/tradingagents/dataflows/alpha_vantage_fundamentals.py", "/home/ice/TradingAgents/tradingagents/dataflows/alpha_vantage_common.py", "/home/ice/TradingAgents/tradingagents/dataflows/alpha_vantage.py", "/home/ice/TradingAgents/tradingagents/dataflows/__init__.py", "/home/ice/TradingAgents/tradingagents/agents/utils/technical_indicators_tools.py", "/home/ice/TradingAgents/tradingagents/agents/utils/structured.py", "/home/ice/TradingAgents/tradingagents/agents/utils/rating.py", "/home/ice/TradingAgents/tradingagents/agents/utils/news_data_tools.py", "/home/ice/TradingAgents/tradingagents/agents/utils/memory.py", "/home/ice/TradingAgents/tradingagents/agents/utils/fundamental_data_tools.py", "/home/ice/TradingAgents/tradingagents/agents/utils/core_stock_tools.py", "/home/ice/TradingAgents/tradingagents/agents/utils/agent_utils.py", "/home/ice/TradingAgents/tradingagents/agents/utils/agent_states.py", "/home/ice/TradingAgents/tradingagents/agents/trader/trader.py", "/home/ice/TradingAgents/tradingagents/agents/schemas.py", "/home/ice/TradingAgents/tradingagents/agents/risk_mgmt/neutral_debator.py", "/home/ice/TradingAgents/tradingagents/agents/risk_mgmt/conservative_debator.py", "/home/ice/TradingAgents/tradingagents/agents/risk_mgmt/aggressive_debator.py", "/home/ice/TradingAgents/tradingagents/agents/researchers/bull_researcher.py", "/home/ice/TradingAgents/tradingagents/agents/researchers/bear_researcher.py", "/home/ice/TradingAgents/tradingagents/agents/managers/research_manager.py", "/home/ice/TradingAgents/tradingagents/agents/managers/portfolio_manager.py", "/home/ice/TradingAgents/tradingagents/agents/analysts/social_media_analyst.py", "/home/ice/TradingAgents/tradingagents/agents/analysts/sentiment_analyst.py", "/home/ice/TradingAgents/tradingagents/agents/analysts/news_analyst.py", "/home/ice/TradingAgents/tradingagents/agents/analysts/market_analyst.py", "/home/ice/TradingAgents/tradingagents/agents/analysts/fundamentals_analyst.py", "/home/ice/TradingAgents/tradingagents/agents/__init__.py", "/home/ice/TradingAgents/tradingagents/__init__.py", "/home/ice/TradingAgents/tests/test_ticker_symbol_handling.py", "/home/ice/TradingAgents/tests/test_structured_agents.py", "/home/ice/TradingAgents/tests/test_signal_processing.py", "/home/ice/TradingAgents/tests/test_safe_ticker_component.py", "/home/ice/TradingAgents/tests/test_ollama_base_url.py", "/home/ice/TradingAgents/tests/test_model_validation.py", "/home/ice/TradingAgents/tests/test_minimax.py", "/home/ice/TradingAgents/tests/test_memory_log.py", "/home/ice/TradingAgents/tests/test_google_api_key.py", "/home/ice/TradingAgents/tests/test_env_overrides.py", "/home/ice/TradingAgents/tests/test_deepseek_reasoning.py", "/home/ice/TradingAgents/tests/test_dataflows_config.py", "/home/ice/TradingAgents/tests/test_crypto_asset_mode.py", "/home/ice/TradingAgents/tests/test_checkpoint_resume.py", "/home/ice/TradingAgents/tests/test_capabilities.py", "/home/ice/TradingAgents/tests/test_api_key_env.py", "/home/ice/TradingAgents/tests/test_anthropic_effort.py", "/home/ice/TradingAgents/tests/test_analyst_execution.py", "/home/ice/TradingAgents/tests/conftest.py", "/home/ice/TradingAgents/tests/__init__.py", "/home/ice/TradingAgents/test.py", "/home/ice/TradingAgents/scripts/smoke_structured_output.py", "/home/ice/TradingAgents/requirements.txt", "/home/ice/TradingAgents/pyproject.toml", "/home/ice/TradingAgents/main.py", "/home/ice/TradingAgents/docker-compose.yml", "/home/ice/TradingAgents/cli/utils.py", "/home/ice/TradingAgents/cli/stats_handler.py", "/home/ice/TradingAgents/cli/static/welcome.txt", "/home/ice/TradingAgents/cli/models.py", "/home/ice/TradingAgents/cli/main.py", "/home/ice/TradingAgents/cli/config.py", "/home/ice/TradingAgents/cli/announcements.py", "/home/ice/TradingAgents/cli/__init__.py", "/home/ice/TradingAgents/assets/wechat.png", "/home/ice/TradingAgents/assets/trader.png", "/home/ice/TradingAgents/assets/schema.png", "/home/ice/TradingAgents/assets/risk.png", "/home/ice/TradingAgents/assets/researcher.png", "/home/ice/TradingAgents/assets/cli/cli_transaction.png", "/home/ice/TradingAgents/assets/cli/cli_technical.png", "/home/ice/TradingAgents/assets/cli/cli_news.png", "/home/ice/TradingAgents/assets/cli/cli_init.png", "/home/ice/TradingAgents/assets/analyst.png", "/home/ice/TradingAgents/assets/TauricResearch.png", "/home/ice/TradingAgents/README.md", "/home/ice/TradingAgents/LICENSE", "/home/ice/TradingAgents/Dockerfile", "/home/ice/TradingAgents/CHANGELOG.md"]} search_files 


c0:
    [{"id": "chatcmpl-tool-9e8547d216480725", "call_id": "chatcmpl-tool-9e8547d216480725", "response_item_id": "fc_chatcmpl-tool-9e8547d216480725", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/TradingAgents/requirements.txt\"}"}}, {"id": "chatcmpl-tool-a6a9c3f6523aeca6", "call_id": "chatcmpl-tool-a6a9c3f6523aeca6", "response_item_id": "fc_chatcmpl-tool-a6a9c3f6523aeca6", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/TradingAgents/tradingagents/graph/trading_graph.py\"}"}}]


c0:
  {"content": "1|.\n2|", "total_lines": 1, "file_size": 2, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|# TradingAgents/graph/trading_graph.py\n2|\n3|import logging\n4|import os\n5|from pathlib import Path\n6|import json\n7|from datetime import datetime, timedelta\n8|from typing import Dict, Any, Tuple, List, Optional\n9|\n10|import yfinance as yf\n11|\n12|logger = logging.getLogger(__name__)\n13|\n14|from langgraph.prebuilt import ToolNode\n15|\n16|from tradingagents.llm_clients import create_llm_client\n17|\n18|from tradingagents.agents import *\n19|from tradingagents.default_config import DEFAULT_CONFIG\n20|from tradingagents.agents.utils.memory import TradingMemoryLog\n21|from tradingagents.dataflows.utils import safe_ticker_component\n22|from tradingagents.agents.utils.agent_states import (\n23|    AgentState,\n24|    InvestDebateState,\n25|    RiskDebateState,\n26|)\n27|from tradingagents.dataflows.config import set_config\n28|\n29|# Import the new abstract tool methods from agent_utils\n30|from tradingagents.agents.utils.agent_utils import (\n31|    get_stock_data,\n32|    get_indicators,\n33|    get_fundamentals,\n34|    get_balance_sheet,\n35|    get_cashflow,\n36|    get_income_statement,\n37|    get_news,\n38|    get_insider_transactions,\n39|    get_global_news\n40|)\n41|\n42|from .checkpointer import checkpoint_step, clear_checkpoint, get_checkpointer, thread_id\n43|from .conditional_logic import ConditionalLogic\n44|from .setup import GraphSetup\n45|from .propagation import Propagator\n46|from .reflection import Reflector\n47|from .signal_processing import SignalProcessor\n48|\n49|\n50|class TradingAgentsGraph:\n51|    \"\"\"Main class that orchestrates the trading agents framework.\"\"\"\n52|\n53|    def __init__(\n54|        self,\n55|        selected_analysts=[\"market\", \"social\", \"news\", \"fundamentals\"],\n56|        debug=False,\n57|        config: Dict[str, Any] = None,\n58|        callbacks: Optional[List] = None,\n59|    ):\n60|        \"\"\"Initialize the trading agents graph and components.\n61|\n62|        Args:\n63|            selected_analysts: List of analyst types to include\n64|            debug: Whether to run in debug mode\n65|            config: Configuration dictionary. If None, uses default config\n66|            callbacks: Optional list of callback handlers (e.g., for tracking LLM/tool stats)\n67|        \"\"\"\n68|        self.debug = debug\n69|        self.config = config or DEFAULT_CONFIG\n70|        self.callbacks = callbacks or []\n71|\n72|        # Update the interface's config\n73|        set_config(self.config)\n74|\n75|        # Create necessary directories\n76|        os.makedirs(self.config[\"data_cache_dir\"], exist_ok=True)\n77|        os.makedirs(self.config[\"results_dir\"], exist_ok=True)\n78|\n79|        # Initialize LLMs with provider-specific thinking configuration\n80|        llm_kwargs = self._get_provider_kwargs()\n81|\n82|        # Add callbacks to kwargs if provided (passed to LLM constructor)\n83|        if self.callbacks:\n84|            llm_kwargs[\"callbacks\"] = self.callbacks\n85|\n86|        deep_client = create_llm_client(\n87|            provider=self.config[\"llm_provider\"],\n88|            model=self.config[\"deep_think_llm\"],\n89|            base_url=self.config.get(\"backend_url\"),\n90|            **llm_kwargs,\n91|        )\n92|        quick_client = create_llm_client(\n93|            provider=self.config[\"llm_provider\"],\n94|            model=self.config[\"quick_think_llm\"],\n95|            base_url=self.config.get(\"backend_url\"),\n96|            **llm_kwargs,\n97|        )\n98|\n99|        self.deep_thinking_llm = deep_client.get_llm()\n100|        self.quick_thinking_llm = quick_client.get_llm()\n101|        \n102|        self.memory_log = TradingMemoryLog(self.config)\n103|\n104|        # Create tool nodes\n105|        self.tool_nodes = self._create_tool_nodes()\n106|\n107|        # Initialize components\n108|        self.conditional_logic = ConditionalLogic(\n109|            max_debate_rounds=self.config[\"max_debate_rounds\"],\n110|            max_risk_discuss_rounds=self.config[\"max_risk_discuss_rounds\"],\n111|        )\n112|        self.graph_setup = GraphSetup(\n113|            self.quick_thinking_llm,\n114|            self.deep_thinking_llm,\n115|            self.tool_nodes,\n116|            self.conditional_logic,\n117|            analyst_concurrency_limit=self.config.get(\"analyst_concurrency_limit\", 1),\n118|        )\n119|\n120|        self.propagator = Propagator(\n121|            max_recur_limit=self.config.get(\"max_recur_limit\", 100),\n122|        )\n123|        self.reflector = Reflector(self.quick_thinking_llm)\n124|        self.signal_processor = SignalProcessor(self.quick_thinking_llm)\n125|\n126|        # State tracking\n127|        self.curr_state = None\n128|        self.ticker = None\n129|        self.log_states_dict = {}  # date to full state dict\n130|\n131|        # Set up the graph: keep the workflow for recompilation with a checkpointer.\n132|        self.workflow = self.graph_setup.setup_graph(selected_analysts)\n133|        self.graph = self.workflow.compile()\n134|        self._checkpointer_ctx = None\n135|\n136|    def _get_provider_kwargs(self) -> Dict[str, Any]:\n137|        \"\"\"Get provider-specific kwargs for LLM client creation.\"\"\"\n138|        kwargs = {}\n139|        provider = self.config.get(\"llm_provider\", \"\").lower()\n140|\n141|        if provider == \"google\":\n142|            thinking_level = self.config.get(\"google_thinking_level\")\n143|            if thinking_level:\n144|                kwargs[\"thinking_level\"] = thinking_level\n145|\n146|        elif provider == \"openai\":\n147|            reasoning_effort = self.config.get(\"openai_reasoning_effort\")\n148|            if reasoning_effort:\n149|                kwargs[\"reasoning_effort\"] = reasoning_effort\n150|\n151|        elif provider == \"anthropic\":\n152|            effort = self.config.get(\"anthropic_effort\")\n153|            if effort:\n154|                kwargs[\"effort\"] = effort\n155|\n156|        return kwargs\n157|\n158|    def _create_tool_nodes(self) -> Dict[str, ToolNode]:\n159|        \"\"\"Create tool nodes for different data sources using abstract methods.\"\"\"\n160|        return {\n161|            \"market\": ToolNode(\n162|                [\n163|                    # Core stock data tools\n164|                    get_stock_data,\n165|                    # Technical indicators\n166|                    get_indicators,\n167|                ]\n168|            ),\n169|            \"social\": ToolNode(\n170|                [\n171|                    # News tools for social media analysis\n172|                    get_news,\n173|                ]\n174|            ),\n175|            \"news\": ToolNode(\n176|                [\n177|                    # News and insider information\n178|                    get_news,\n179|                    get_global_news,\n180|                    get_insider_transactions,\n181|                ]\n182|            ),\n183|            \"fundamentals\": ToolNode(\n184|                [\n185|                    # Fundamental analysis tools\n186|                    get_fundamentals,\n187|                    get_balance_sheet,\n188|                    get_cashflow,\n189|                    get_income_statement,\n190|                ]\n191|            ),\n192|        }\n193|\n194|    def _resolve_benchmark(self, ticker: str) -> str:\n195|        \"\"\"Pick the benchmark ticker for alpha calculation against ``ticker``.\n196|\n197|        ``config[\"benchmark_ticker\"]`` overrides everything when set; otherwise\n198|        the suffix map matches the ticker's exchange suffix (e.g. ``.T`` for\n199|        Tokyo). US-listed tickers without a dotted suffix fall through to the\n200|        empty-suffix entry (SPY by default). Unrecognised suffixes (including\n201|        US tickers with dots like ``BRK.B``) also fall back to the empty-suffix\n202|        entry, which is the right default because the alpha calculation works\n203|        in USD.\n204|        \"\"\"\n205|        explicit = self.config.get(\"benchmark_ticker\")\n206|        if explicit:\n207|            return explicit\n208|        benchmark_map = self.config.get(\"benchmark_map\", {})\n209|        ticker_upper = ticker.upper()\n210|        for suffix, benchmark in benchmark_map.items():\n211|            if suffix and ticker_upper.endswith(suffix.upper()):\n212|                return benchmark\n213|        return benchmark_map.get(\"\", \"SPY\")\n214|\n215|    def _fetch_returns(\n216|        self, ticker: str, trade_date: str, holding_days: int = 5,\n217|        benchmark: str = \"SPY\",\n218|    ) -> Tuple[Optional[float], Optional[float], Optional[int]]:\n219|        \"\"\"Fetch raw and alpha return for ticker over holding_days from trade_date.\n220|\n221|        ``benchmark`` is the index used as the alpha baseline (resolved by the\n222|        caller via ``_resolve_benchmark``). Returns ``(raw_return, alpha_return,\n223|        actual_holding_days)`` or ``(None, None, None)`` if price data is\n224|        unavailable (too recent, delisted, or network error).\n225|        \"\"\"\n226|        try:\n227|            start = datetime.strptime(trade_date, \"%Y-%m-%d\")\n228|            end = start + timedelta(days=holding_days + 7)  # buffer for weekends/holidays\n229|            end_str = end.strftime(\"%Y-%m-%d\")\n230|\n231|            stock = yf.Ticker(ticker).history(start=trade_date, end=end_str)\n232|            bench = yf.Ticker(benchmark).history(start=trade_date, end=end_str)\n233|\n234|            if len(stock) < 2 or len(bench) < 2:\n235|                return None, None, None\n236|\n237|            actual_days = min(holding_days, len(stock) - 1, len(bench) - 1)\n238|            raw = float(\n239|                (stock[\"Close\"].iloc[actual_days] - stock[\"Close\"].iloc[0])\n240|                / stock[\"Close\"].iloc[0]\n241|            )\n242|            bench_ret = float(\n243|                (bench[\"Close\"].iloc[actual_days] - bench[\"Close\"].iloc[0])\n244|                / bench[\"Close\"].iloc[0]\n245|            )\n246|            alpha = raw - bench_ret\n247|            return raw, alpha, actual_days\n248|        except Exception as e:\n249|            logger.warning(\n250|                \"Could not resolve outcome for %s on %s vs %s (will retry next run): %s\",\n251|                ticker, trade_date, benchmark, e,\n252|            )\n253|            return None, None, None\n254|\n255|    def _resolve_pending_entries(self, ticker: str) -> None:\n256|        \"\"\"Resolve pending log entries for ticker at the start of a new run.\n257|\n258|        Fetches returns for each same-ticker pending entry, generates reflections,\n259|        then writes all updates in a single atomic batch write to avoid redundant I/O.\n260|        Skips entries whose price data is not yet available (too recent or delisted).\n261|\n262|        Trade-off: only same-ticker entries are resolved per run.  Entries for\n263|        other tickers accumulate until that ticker is run again.\n264|        \"\"\"\n265|        pending = [e for e in self.memory_log.get_pending_entries() if e[\"ticker\"] == ticker]\n266|        if not pending:\n267|            return\n268|\n269|        benchmark = self._resolve_benchmark(ticker)\n270|        updates = []\n271|        for entry in pending:\n272|            raw, alpha, days = self._fetch_returns(\n273|                ticker, entry[\"date\"], benchmark=benchmark,\n274|            )\n275|            if raw is None:\n276|                continue  # price not available yet — try again next run\n277|            reflection = self.reflector.reflect_on_final_decision(\n278|                final_decision=entry.get(\"decision\", \"\"),\n279|                raw_return=raw,\n280|                alpha_return=alpha,\n281|                benchmark_name=benchmark,\n282|            )\n283|            updates.append({\n284|                \"ticker\": ticker,\n285|                \"trade_date\": entry[\"date\"],\n286|                \"raw_return\": raw,\n287|                \"alpha_return\": alpha,\n288|                \"holding_days\": days,\n289|                \"reflection\": reflection,\n290|            })\n291|\n292|        if updates:\n293|            self.memory_log.batch_update_with_outcomes(updates)\n294|\n295|    def propagate(self, company_name, trade_date, asset_type: str = \"stock\"):\n296|        \"\"\"Run the trading agents graph for a company on a specific date.\n297|\n298|        ``asset_type`` selects between the stock pipeline (default) and the\n299|        crypto pipeline (``\"crypto\"``) shipped in #567 — the CLI auto-detects\n300|        from the ticker; programmatic callers pass it explicitly. When\n301|        ``checkpoint_enabled`` is set in config, the graph is recompiled with\n302|        a per-ticker SqliteSaver so a crashed run can resume from the last\n303|        successful node on a subsequent invocation with the same ticker+date.\n304|        \"\"\"\n305|        self.ticker = company_name\n306|\n307|        # Resolve any pending memory-log entries for this ticker before the pipeline runs.\n308|        self._resolve_pending_entries(company_name)\n309|\n310|        # Recompile with a checkpointer if the user opted in.\n311|        if self.config.get(\"checkpoint_enabled\"):\n312|            self._checkpointer_ctx = get_checkpointer(\n313|                self.config[\"data_cache_dir\"], company_name\n314|            )\n315|            saver = self._checkpointer_ctx.__enter__()\n316|            self.graph = self.workflow.compile(checkpointer=saver)\n317|\n318|            step = checkpoint_step(\n319|                self.config[\"data_cache_dir\"], company_name, str(trade_date)\n320|            )\n321|            if step is not None:\n322|                logger.info(\n323|                    \"Resuming from step %d for %s on %s\", step, company_name, trade_date\n324|                )\n325|            else:\n326|                logger.info(\"Starting fresh for %s on %s\", company_name, trade_date)\n327|\n328|        try:\n329|            return self._run_graph(company_name, trade_date, asset_type=asset_type)\n330|        finally:\n331|            if self._checkpointer_ctx is not None:\n332|                self._checkpointer_ctx.__exit__(None, None, None)\n333|                self._checkpointer_ctx = None\n334|                self.graph = self.workflow.compile()\n335|\n336|    def _run_graph(self, company_name, trade_date, asset_type: str = \"stock\"):\n337|        \"\"\"Execute the graph and write the resulting state to disk and memory log.\"\"\"\n338|        # Initialize state — inject memory log context for PM.\n339|        past_context = self.memory_log.get_past_context(company_name)\n340|        init_agent_state = self.propagator.create_initial_state(\n341|            company_name, trade_date, asset_type=asset_type, past_context=past_context\n342|        )\n343|        args = self.propagator.get_graph_args()\n344|\n345|        # Inject thread_id so same ticker+date resumes, different date starts fresh.\n346|        if self.config.get(\"checkpoint_enabled\"):\n347|            tid = thread_id(company_name, str(trade_date))\n348|            args.setdefault(\"config\", {}).setdefault(\"configurable\", {})[\"thread_id\"] = tid\n349|\n350|        if self.debug:\n351|            trace = []\n352|            for chunk in self.graph.stream(init_agent_state, **args):\n353|                if len(chunk[\"messages\"]) == 0:\n354|                    pass\n355|                else:\n356|                    chunk[\"messages\"][-1].pretty_print()\n357|                    trace.append(chunk)\n358|            # Streamed chunks are per-node deltas. Merge them so the returned\n359|            # state matches what graph.invoke() yields in the non-debug path.\n360|            final_state = {}\n361|            for chunk in trace:\n362|                final_state.update(chunk)\n363|        else:\n364|            final_state = self.graph.invoke(init_agent_state, **args)\n365|\n366|        # Store current state for reflection.\n367|        self.curr_state = final_state\n368|\n369|        # Log state to disk.\n370|        self._log_state(trade_date, final_state)\n371|\n372|        # Store decision for deferred reflection on the next same-ticker run.\n373|        self.memory_log.store_decision(\n374|            ticker=company_name,\n375|            trade_date=trade_date,\n376|            final_trade_decision=final_state[\"final_trade_decision\"],\n377|        )\n378|\n379|        # Clear checkpoint on successful completion to avoid stale state.\n380|        if self.config.get(\"checkpoint_enabled\"):\n381|            clear_checkpoint(\n382|                self.config[\"data_cache_dir\"], company_name, str(trade_date)\n383|            )\n384|\n385|        return final_state, self.process_signal(final_state[\"final_trade_decision\"])\n386|\n387|    def _log_state(self, trade_date, final_state):\n388|        \"\"\"Log the final state to a JSON file.\"\"\"\n389|        self.log_states_dict[str(trade_date)] = {\n390|            \"company_of_interest\": final_state[\"company_of_interest\"],\n391|            \"trade_date\": final_state[\"trade_date\"],\n392|            \"market_report\": final_state[\"market_report\"],\n393|            \"sentiment_report\": final_state[\"sentiment_report\"],\n394|            \"news_report\": final_state[\"news_report\"],\n395|            \"fundamentals_report\": final_state[\"fundamentals_report\"],\n396|            \"investment_debate_state\": {\n397|                \"bull_history\": final_state[\"investment_debate_state\"][\"bull_history\"],\n398|                \"bear_history\": final_state[\"investment_debate_state\"][\"bear_history\"],\n399|                \"history\": final_state[\"investment_debate_state\"][\"history\"],\n400|                \"current_response\": final_state[\"investment_debate_state\"][\n401|                    \"current_response\"\n402|                ],\n403|                \"judge_decision\": final_state[\"investment_debate_state\"][\n404|                    \"judge_decision\"\n405|                ],\n406|            },\n407|            \"trader_investment_decision\": final_state[\"trader_investment_plan\"],\n408|            \"risk_debate_state\": {\n409|                \"aggressive_history\": final_state[\"risk_debate_state\"][\"aggressive_history\"],\n410|                \"conservative_history\": final_state[\"risk_debate_state\"][\"conservative_history\"],\n411|                \"neutral_history\": final_state[\"risk_debate_state\"][\"neutral_history\"],\n412|                \"history\": final_state[\"risk_debate_state\"][\"history\"],\n413|                \"judge_decision\": final_state[\"risk_debate_state\"][\"judge_decision\"],\n414|            },\n415|            \"investment_plan\": final_state[\"investment_plan\"],\n416|            \"final_trade_decision\": final_state[\"final_trade_decision\"],\n417|        }\n418|\n419|        # Save to file. Reject ticker values that would escape the\n420|        # results directory when joined as a path component.\n421|        safe_ticker = safe_ticker_component(self.ticker)\n422|        directory = Path(self.config[\"results_dir\"]) / safe_ticker / \"TradingAgentsStrategy_logs\"\n423|        directory.mkdir(parents=True, exist_ok=True)\n424|\n425|        log_path = directory / f\"full_states_log_{trade_date}.json\"\n426|        with open(log_path, \"w\", encoding=\"utf-8\") as f:\n427|            json.dump(self.log_states_dict[str(trade_date)], f, indent=4)\n428|\n429|    def process_signal(self, full_signal):\n430|        \"\"\"Process a signal to extract the core decision.\"\"\"\n431|        return self.signal_processor.process_signal(full_signal)\n432|", "total_lines": 431, "file_size": 17488, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
    [{"id": "chatcmpl-tool-9a72f2821dd7e7f6", "call_id": "chatcmpl-tool-9a72f2821dd7e7f6", "response_item_id": "fc_chatcmpl-tool-9a72f2821dd7e7f6", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/TradingAgents/pyproject.toml\"}"}}, {"id": "chatcmpl-tool-a80fdd8283631439", "call_id": "chatcmpl-tool-a80fdd8283631439", "response_item_id": "fc_chatcmpl-tool-a80fdd8283631439", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/TradingAgents/main.py\"}"}}]


c0:
  {"content": "1|[build-system]\n2|requires = [\"setuptools>=61.0\"]\n3|build-backend = \"setuptools.build_meta\"\n4|\n5|[project]\n6|name = \"tradingagents\"\n7|version = \"0.2.5\"\n8|description = \"TradingAgents: Multi-Agents LLM Financial Trading Framework\"\n9|readme = \"README.md\"\n10|requires-python = \">=3.10\"\n11|dependencies = [\n12|    \"langchain-core>=0.3.81\",\n13|    \"backtrader>=1.9.78.123\",\n14|    \"langchain-anthropic>=0.3.15\",\n15|    \"langchain-experimental>=0.3.4\",\n16|    \"langchain-google-genai>=4.0.0\",\n17|    \"langchain-openai>=0.3.23\",\n18|    \"langgraph>=0.4.8\",\n19|    \"langgraph-checkpoint-sqlite>=2.0.0\",\n20|    \"pandas>=2.3.0\",\n21|    \"parsel>=1.10.0\",\n22|    \"pytz>=2025.2\",\n23|    \"questionary>=2.1.0\",\n24|    \"redis>=6.2.0\",\n25|    \"requests>=2.32.4\",\n26|    \"rich>=14.0.0\",\n27|    \"typer>=0.21.0\",\n28|    \"setuptools>=80.9.0\",\n29|    \"stockstats>=0.6.5\",\n30|    \"tqdm>=4.67.1\",\n31|    \"typing-extensions>=4.14.0\",\n32|    \"yfinance>=0.2.63\",\n33|]\n34|\n35|[project.scripts]\n36|tradingagents = \"cli.main:app\"\n37|\n38|[tool.setuptools.packages.find]\n39|include = [\"tradingagents*\", \"cli*\"]\n40|\n41|[tool.setuptools.package-data]\n42|cli = [\"static/*\"]\n43|\n44|[tool.pytest.ini_options]\n45|testpaths = [\"tests\"]\n46|addopts = \"-ra --strict-markers\"\n47|markers = [\n48|    \"unit: fast isolated unit tests\",\n49|    \"integration: tests requiring external services\",\n50|    \"smoke: quick sanity-check tests\",\n51|]\n52|filterwarnings = [\n53|    \"ignore::DeprecationWarning\",\n54|]\n55|", "total_lines": 54, "file_size": 1304, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|from tradingagents.graph.trading_graph import TradingAgentsGraph\n2|from tradingagents.default_config import DEFAULT_CONFIG\n3|\n4|# DEFAULT_CONFIG already applies TRADINGAGENTS_* env-var overrides\n5|# (llm_provider, deep_think_llm, quick_think_llm, backend_url, etc.),\n6|# so users can switch models or endpoints purely via .env without\n7|# editing this script. Override individual keys here only when you\n8|# want a hard-coded value that should ignore the environment.\n9|config = DEFAULT_CONFIG.copy()\n10|\n11|# Initialize with custom config\n12|ta = TradingAgentsGraph(debug=True, config=config)\n13|\n14|# forward propagate\n15|_, decision = ta.propagate(\"NVDA\", \"2024-05-10\")\n16|print(decision)\n17|\n18|# Memorize mistakes and reflect\n19|# ta.reflect_and_remember(1000) # parameter is the position returns\n20|", "total_lines": 19, "file_size": 756, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  ## Análise do Repositório: TauricResearch/TradingAgents

  **Repositório clonado e analisado em:** `/home/ice/TradingAgents`

  ### Problema
  Sistemas de trading tradicionais e até mesmo alguns algorítmicos frequentemente atuam como "caixas pretas" ou componentes isolados. Um único modelo ou um conjunto rígido de regras toma decisões sem um processo de avaliação robusto e multifacetado que espelhe como *trading firms* do mundo real realmente operam. Isso pode levar à overlook de sinais críticos (ex.: um forte indicador técnico sendo minado por notícias negativas) e à falta de um debate dinâmico necessário para equilibrar oportunidade agressiva contra risco conservador.

  ### Tema
  **Framework de Trading Colaborativo Multi-Agente baseado em LLMs.** O tema central é simular o fluxo de trabalho de uma *trading firm* humana usando um conjunto de agentes LLM especializados que debatem, pesquisam e executam trades de forma colaborativa. Trata-se de aplicar orquestração avançada de IA (sistemas multi-agente) ao domínio das finanças quantitativas.

  ### Razão do Hype
  - **Capital social e prestígio:** Com ~81k stars, é de longe o projeto open-source mais popular do gênero, superando concorrentes.
  - **Rigor acadêmico:** É respaldado por um paper no arXiv (`2412.20138`) e uma equipe do `TauricResearch`, dando-lhe credibilidade além de um projeto de hobby.
  - **Stack tecnológico de ponta:** Usa `LangGraph`, framework moderno e altamente conceituado para construir workflows agentivos.
  - **Arquitetura abrangente:** Vai além da simples predição de compra/venda; incorpora análise fundamentalista, sentimento, notícias, técnica, debate touro/urso, decisão do trader e *risk management* — uma simulação muito completa e realista.
  - **Usabilidade prática:** Oferece tanto uma CLI interativa quanto uma API Python, suporta *backtesting*, lida com recursos complexos como memória persistente (logs de decisão), *checkpoint resume* e suporte multi-provedor de LLMs (OpenAI, Anthropic, Google, etc.), tornando-se uma ferramenta genuína para pesquisa e experimentação.
  - **Apelo viral:** O tema de IA e trading é inerentemente fascinante para desenvolvedores e entusiastas de finanças, impulsionando um interesse massivo.

  ### Tecnologias
  - **Linguagem:** Python
  - **Orquestração de Agentes:** `LangGraph`, `langchain-core`
  - **Integração LLM:** `langchain-openai`, `langchain-anthropic`, `langchain-google-genai` (suporta GPT, Claude, Gemini, Grok, DeepSeek, Qwen, GLM, MiniMax, Ollama)
  - **Fontes de Dados:** `yfinance`, `alpha_vantage`, APIs de notícias, `reddit`, `stocktwits`
  - **Análise de Dados:** `pandas`, `stockstats`
  - **Backtesting:** `backtrader`
  - **Persistência:** `langgraph-checkpoint-sqlite`, Redis
  - **CLI:** `typer`, `rich`, `questionary`
  - **Build/Package:** `setuptools`, `uv` (inferred pelo `uv.lock`)

  ---

  ### Resumo Executivo para o Agente Pai
  **O que foi feito:** Clonei o repositório completo e examinei os arquivos principais (`README.md`, `pyproject.toml`, `main.py`, `tradingagents/graph/trading_graph.py` e lista de arquivos).

  **O que foi encontrado:** Projeto Python maduro e bem estruturado que implementa um sistema de trading simulado multi-agente usando LLMs, orquestrado via LangGraph. Possui arquitetura modular (analistas, pesquisadores, trader, *risk management*), suporte a múltiplos provedores de LLM e fontes de dados, além de funcionalidades avançadas como memória persistente e *checkpoint resume*.

  **Arquivos criados/modificados:** Nenhum arquivo foi modificado; apenas leitura.

  **Problemas encontrados:** Nenhum problema técnico relevante foi encontrado durante a análise estática do código. Dependências parecem bem definidas no `pyproject.toml`.  


c0:
  Vou clonar e analisar o repositório agora.  [{"id": "chatcmpl-tool-a60974ecfbc812e6", "call_id": "chatcmpl-tool-a60974ecfbc812e6", "response_item_id": "fc_chatcmpl-tool-a60974ecfbc812e6", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice && git clone --depth 1 https://github.com/hacksider/Deep-Live-Cam.git 2>&1\", \"timeout\": 120}"}}]


c0:
    [{"id": "chatcmpl-tool-ae0cca22fccca8d9", "call_id": "chatcmpl-tool-ae0cca22fccca8d9", "response_item_id": "fc_chatcmpl-tool-ae0cca22fccca8d9", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice/Deep-Live-Cam && find . -maxdepth 2 -type f | sort | head -60\", \"timeout\": 30}"}}, {"id": "chatcmpl-tool-ba17be16ef777ea0", "call_id": "chatcmpl-tool-ba17be16ef777ea0", "response_item_id": "fc_chatcmpl-tool-ba17be16ef777ea0", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 200, \"offset\": 1, \"path\": \"/home/ice/Deep-Live-Cam/README.md\"}"}}]


c0:
  {"output": "./.git/HEAD\n./.git/config\n./.git/description\n./.git/index\n./.git/packed-refs\n./.git/shallow\n./.gitattributes\n./.gitignore\n./CONTRIBUTING.md\n./LICENSE\n./README.md\n./benchmark_pipeline.py\n./locales/de.json\n./locales/es.json\n./locales/fi.json\n./locales/id.json\n./locales/km.json\n./locales/ko.json\n./locales/pt-br.json\n./locales/ru.json\n./locales/th.json\n./locales/zh.json\n./media/Download.png\n./media/avgpcperformancedemo.gif\n./media/deepwarebench.gif\n./media/demo.gif\n./media/instruction.png\n./media/live_show.gif\n./media/ludwig.gif\n./media/meme.gif\n./media/movie.gif\n./media/streamers.gif\n./models/instructions.txt\n./modules/__init__.py\n./modules/capturer.py\n./modules/cluster_analysis.py\n./modules/core.py\n./modules/custom_types.py\n./modules/face_analyser.py\n./modules/gettext.py\n./modules/globals.py\n./modules/gpu_processing.py\n./modules/metadata.py\n./modules/onnx_optimize.py\n./modules/paths.py\n./modules/platform_info.py\n./modules/predicter.py\n./modules/run.py\n./modules/tkinter_fix.py\n./modules/typing.py\n./modules/ui.json\n./modules/ui.py\n./modules/ui_tooltip.py\n./modules/utilities.py\n./modules/video_capture.py\n./mypi.ini\n./requirements.txt\n./run-cuda.bat\n./run-directml.bat\n./run.py", "exit_code": 0, "error": null} terminal 


c0:
  {"content": "1|<h1 align=\"center\">Deep-Live-Cam 2.1.6</h1>\n2|\n3|<p align=\"center\">\n4|  Real-time face swap and video deepfake with a single click and only a single image.\n5|</p>\n6|\n7|<p align=\"center\">\n8|<a href=\"https://trendshift.io/repositories/11395\" target=\"_blank\"><img src=\"https://trendshift.io/api/badge/repositories/11395\" alt=\"hacksider%2FDeep-Live-Cam | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"/></a>\n9|</p>\n10|\n11|<p align=\"center\">\n12|  <img src=\"media/demo.gif\" alt=\"Demo GIF\" width=\"800\">\n13|</p>\n14|\n15|##  Disclaimer\n16|\n17|This deepfake software is designed to be a productive tool for the AI-generated media industry. It can assist artists in animating custom characters, creating engaging content, and even using models for clothing design.\n18|\n19|We are aware of the potential for unethical applications and are committed to preventative measures. A built-in check prevents the program from processing inappropriate media (nudity, graphic content, sensitive material like war footage, etc.). We will continue to develop this project responsibly, adhering to the law and ethics. We may shut down the project or add watermarks if legally required.\n20|\n21|- Ethical Use: Users are expected to use this software responsibly and legally. If using a real person's face, obtain their consent and clearly label any output as a deepfake when sharing online.\n22|\n23|- Content Restrictions: The software includes built-in checks to prevent processing inappropriate media, such as nudity, graphic content, or sensitive material.\n24|\n25|- Legal Compliance: We adhere to all relevant laws and ethical guidelines. If legally required, we may shut down the project or add watermarks to the output.\n26|\n27|- User Responsibility: We are not responsible for end-user actions. Users must ensure their use of the software aligns with ethical standards and legal requirements.\n28|\n29|By using this software, you agree to these terms and commit to using it in a manner that respects the rights and dignity of others.\n30|\n31|Users are expected to use this software responsibly and legally. If using a real person's face, obtain their consent and clearly label any output as a deepfake when sharing online. We are not responsible for end-user actions.\n32|\n33|## Exclusive v2.7 RC2 Quick Start - Pre-built (Windows/Mac Silicon/CPU)\n34|\n35|  <a href=\"https://deeplivecam.net/index.php/quickstart\"> <img src=\"media/Download.png\" width=\"285\" height=\"77\" />\n36|\n37|##### This is the fastest build you can get if you have a discrete NVIDIA or AMD GPU, CPU or Mac Silicon, And you'll receive special priority support. 2.7 beta is the best you can have with 30+ extra features than the open source version.\n38| \n39|###### These Pre-builts are perfect for non-technical users or those who don't have time to, or can't manually install all the requirements. Just a heads-up: this is an open-source project, so you can also install it manually. \n40|\n41|## TLDR; Live Deepfake in just 3 Clicks\n42|![easysteps](https://github.com/user-attachments/assets/af825228-852c-411b-b787-ffd9aac72fc6)\n43|1. Select a face\n44|2. Select which camera to use\n45|3. Press live!\n46|\n47|## Features & Uses - Everything is in real-time\n48|\n49|### Mouth Mask\n50|\n51|**Retain your original mouth for accurate movement using Mouth Mask**\n52|\n53|<p align=\"center\">\n54|  <img src=\"media/ludwig.gif\" alt=\"resizable-gif\">\n55|</p>\n56|\n57|### Face Mapping\n58|\n59|**Use different faces on multiple subjects simultaneously**\n60|\n61|<p align=\"center\">\n62|  <img src=\"media/streamers.gif\" alt=\"face_mapping_source\">\n63|</p>\n64|\n65|### Your Movie, Your Face\n66|\n67|**Watch movies with any face in real-time**\n68|\n69|<p align=\"center\">\n70|  <img src=\"media/movie.gif\" alt=\"movie\">\n71|</p>\n72|\n73|### Live Show\n74|\n75|**Run Live shows and performances**\n76|\n77|<p align=\"center\">\n78|  <img src=\"media/live_show.gif\" alt=\"show\">\n79|</p>\n80|\n81|### Memes\n82|\n83|**Create Your Most Viral Meme Yet**\n84|\n85|<p align=\"center\">\n86|  <img src=\"media/meme.gif\" alt=\"show\" width=\"450\"> \n87|  <br>\n88|  <sub>Created using Many Faces feature in Deep-Live-Cam</sub>\n89|</p>\n90|\n91|### Omegle\n92|\n93|**Surprise people on Omegle**\n94|\n95|<p align=\"center\">\n96|  <video src=\"https://github.com/user-attachments/assets/2e9b9b82-fa04-4b70-9f56-b1f68e7672d0\" width=\"450\" controls></video>\n97|</p>\n98|\n99|## Installation (Manual)\n100|\n101|**Please be aware that the installation requires technical skills and is not for beginners. Consider downloading the quickstart version.**\n102|\n103|<details>\n104|<summary>Click to see the process</summary>\n105|\n106|### Installation\n107|\n108|This is more likely to work on your computer but will be slower as it utilizes the CPU.\n109|\n110|**1. Set up Your Platform**\n111|\n112|-   Python (3.11 recommended)\n113|-   pip\n114|-   git\n115|-   [ffmpeg](https://www.youtube.com/watch?v=OlNWCpFdVMA) - ```iex (irm ffmpeg.tc.ht)```\n116|-   [Visual Studio 2022 Runtimes (Windows)](https://visualstudio.microsoft.com/visual-cpp-build-tools/)\n117|\n118|**2. Clone the Repository**\n119|\n120|```bash\n121|git clone https://github.com/hacksider/Deep-Live-Cam.git\n122|cd Deep-Live-Cam\n123|```\n124|\n125|**3. Download the Models**\n126|\n127|1. [GFPGANv1.4](https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.onnx)\n128|2. [inswapper\\_128\\_fp16.onnx](https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx)\n129|\n130|Place these files in the \"**models**\" folder.\n131|\n132|**4. Install Dependencies**\n133|\n134|We highly recommend using a `venv` to avoid issues.\n135|\n136|\n137|For Windows:\n138|```bash\n139|python -m venv venv\n140|venv\\Scripts\\activate\n141|pip install -r requirements.txt\n142|```\n143|For Linux:\n144|```bash\n145|# Ensure you use the installed Python 3.11\n146|python3 -m venv venv\n147|source venv/bin/activate\n148|pip install -r requirements.txt\n149|```\n150|\n151|**For macOS:**\n152|\n153|Apple Silicon (M1/M2/M3) requires specific setup:\n154|\n155|```bash\n156|# Install Python 3.11 (specific version is important)\n157|brew install python@3.11\n158|\n159|# Install tkinter package (required for the GUI)\n160|brew install python-tk@3.11\n161|\n162|# Create and activate virtual environment with Python 3.11\n163|python3.11 -m venv venv\n164|source venv/bin/activate\n165|\n166|# Install dependencies\n167|pip install -r requirements.txt\n168|```\n169|\n170|** In case something goes wrong and you need to reinstall the virtual environment **\n171|\n172|```bash\n173|# Deactivate the virtual environment\n174|rm -rf venv\n175|\n176|# Reinstall the virtual environment\n177|python -m venv venv\n178|source venv/bin/activate\n179|\n180|# install the dependencies again\n181|pip install -r requirements.txt\n182|\n183|# gfpgan and basicsrs issue fix\n184|pip install git+https://github.com/xinntao/BasicSR.git@master\n185|pip uninstall gfpgan -y\n186|pip install git+https://github.com/TencentARC/GFPGAN.git@master\n187|```\n188|\n189|**Run:** If you don't have a GPU, you can run Deep-Live-Cam using `python run.py`. Note that initial execution will download models (~300MB).\n190|\n191|### GPU Acceleration\n192|\n193|**CUDA Execution Provider (Nvidia)**\n194|\n195|1. Install [CUDA Toolkit 12.8.0](https://developer.nvidia.com/cuda-12-8-0-download-archive)\n196|2. Install [cuDNN v8.9.7 for CUDA 12.x](https://developer.nvidia.com/rdp/cudnn-archive) (required for onnxruntime-gpu):\n197|   - Download cuDNN v8.9.7 for CUDA 12.x\n198|   - Make sure the cuDNN bin directory is in your system PATH\n199|3. Install dependencies:\n200|\n201|", "total_lines": 387, "file_size": 15528, "truncated": true, "hint": "Use offset=201 to continue reading (showing 1-200 of 387 lines)", "is_binary": false, "is_image": false} read_file 


c0:
    [{"id": "chatcmpl-tool-a33b18267e6b3749", "call_id": "chatcmpl-tool-a33b18267e6b3749", "response_item_id": "fc_chatcmpl-tool-a33b18267e6b3749", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/Deep-Live-Cam/requirements.txt\"}"}}, {"id": "chatcmpl-tool-8da4abf2e4200d6f", "call_id": "chatcmpl-tool-8da4abf2e4200d6f", "response_item_id": "fc_chatcmpl-tool-8da4abf2e4200d6f", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/Deep-Live-Cam/run.py\"}"}}, {"id": "chatcmpl-tool-bc2e1f5e9197f228", "call_id": "chatcmpl-tool-bc2e1f5e9197f228", "response_item_id": "fc_chatcmpl-tool-bc2e1f5e9197f228", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/Deep-Live-Cam/modules/run.py\"}"}}]


c0:
  {"content": "1|numpy>=1.23.5,<2\n2|typing-extensions>=4.8.0\n3|opencv-python==4.10.0.84\n4|cv2_enumerate_cameras==1.1.15\n5|onnx==1.18.0\n6|insightface==0.7.3\n7|psutil==5.9.8\n8|PySide6>=6.7,<7\n9|pillow==12.1.1\n10|tqdm>=4.65.0\n11|onnxruntime-silicon==1.16.3; sys_platform == 'darwin' and platform_machine == 'arm64'\n12|onnxruntime-gpu==1.23.2; sys_platform != 'darwin'\n13|tensorflow>=2.15.0; sys_platform != 'darwin'\n14|tensorflow>=2.15.0; sys_platform == 'darwin' and python_version < '3.13'\n15|opennsfw2==0.10.2\n16|protobuf==4.25.1\n17|pygrabber; sys_platform == 'win32'\n18|", "total_lines": 17, "file_size": 511, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|#!/usr/bin/env python3\n2|\n3|import os\n4|import sys\n5|\n6|# Add the project root to PATH so bundled ffmpeg/ffprobe are found\n7|project_root = os.path.dirname(os.path.abspath(__file__))\n8|os.environ[\"PATH\"] = project_root + os.pathsep + os.environ.get(\"PATH\", \"\")\n9|\n10|# On Windows, register NVIDIA CUDA DLL directories so onnxruntime-gpu can\n11|# find cuDNN/cublas. Python 3.8+ ignores PATH for extension-module native deps —\n12|# os.add_dll_directory() is required. Also keep PATH for child processes/ffmpeg.\n13|if sys.platform == \"win32\":\n14|    _site_packages = os.path.join(sys.prefix, \"Lib\", \"site-packages\")\n15|    _venv_site_packages = os.path.join(project_root, \"venv\", \"Lib\", \"site-packages\")\n16|    for _sp in (_site_packages, _venv_site_packages):\n17|        _candidate_dirs = []\n18|        _torch_lib = os.path.join(_sp, \"torch\", \"lib\")\n19|        if os.path.isdir(_torch_lib):\n20|            _candidate_dirs.append(_torch_lib)\n21|        _nvidia_dir = os.path.join(_sp, \"nvidia\")\n22|        if os.path.isdir(_nvidia_dir):\n23|            for _pkg in os.listdir(_nvidia_dir):\n24|                _bin_dir = os.path.join(_nvidia_dir, _pkg, \"bin\")\n25|                if os.path.isdir(_bin_dir):\n26|                    _candidate_dirs.append(_bin_dir)\n27|        for _d in _candidate_dirs:\n28|            os.environ[\"PATH\"] = _d + os.pathsep + os.environ[\"PATH\"]\n29|            try:\n30|                os.add_dll_directory(_d)\n31|            except (OSError, AttributeError):\n32|                pass\n33|\n34|# On Linux, pre-load NVIDIA shared libraries (cuDNN, cuBLAS, nvrtc...) shipped\n35|# inside the venv via pip wheels (nvidia-cudnn-cu12, etc.). LD_LIBRARY_PATH\n36|# cannot be set after Python starts, so we use ctypes.CDLL with RTLD_GLOBAL\n37|# instead. This makes symbols available to onnxruntime when it dlopens its\n38|# CUDA provider.\n39|if sys.platform.startswith(\"linux\"):\n40|    import ctypes\n41|    import glob\n42|    _py_lib = f\"python{sys.version_info.major}.{sys.version_info.minor}\"\n43|    _site_packages_candidates = [\n44|        os.path.join(project_root, \"venv\", \"lib\", _py_lib, \"site-packages\"),\n45|        os.path.join(sys.prefix, \"lib\", _py_lib, \"site-packages\"),\n46|    ]\n47|    for _sp in _site_packages_candidates:\n48|        _nvidia_dir = os.path.join(_sp, \"nvidia\")\n49|        if not os.path.isdir(_nvidia_dir):\n50|            continue\n51|        for _pkg in os.listdir(_nvidia_dir):\n52|            _lib_dir = os.path.join(_nvidia_dir, _pkg, \"lib\")\n53|            if not os.path.isdir(_lib_dir):\n54|                continue\n55|            # Also expose the directory to child processes, without\n56|            # duplicating an entry that is already present.\n57|            _ldp = os.environ.get(\"LD_LIBRARY_PATH\", \"\")\n58|            if _lib_dir not in _ldp.split(os.pathsep):\n59|                os.environ[\"LD_LIBRARY_PATH\"] = (\n60|                    _lib_dir + (os.pathsep + _ldp if _ldp else \"\")\n61|                )\n62|            for _so in sorted(glob.glob(os.path.join(_lib_dir, \"lib*.so*\"))):\n63|                try:\n64|                    ctypes.CDLL(_so, mode=ctypes.RTLD_GLOBAL)\n65|                except OSError:\n66|                    pass\n67|        break\n68|\n69|from modules import platform_info\n70|platform_info.print_banner()\n71|\n72|from modules import core\n73|\n74|if __name__ == '__main__':\n75|    core.run()\n76|", "total_lines": 75, "file_size": 3147, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|#!/usr/bin/env python3\n2|\n3|# Import the tkinter fix to patch the ScreenChanged error\n4|import tkinter_fix\n5|\n6|import core\n7|\n8|if __name__ == '__main__':\n9|    core.run()\n10|", "total_lines": 9, "file_size": 157, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
    [{"id": "chatcmpl-tool-b03875ccb1589070", "call_id": "chatcmpl-tool-b03875ccb1589070", "response_item_id": "fc_chatcmpl-tool-b03875ccb1589070", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/Deep-Live-Cam/README.md\", \"offset\": 200, \"limit\": 200}"}}, {"id": "chatcmpl-tool-9182d6a43c582e66", "call_id": "chatcmpl-tool-9182d6a43c582e66", "response_item_id": "fc_chatcmpl-tool-9182d6a43c582e66", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 100, \"path\": \"/home/ice/Deep-Live-Cam/modules/core.py\"}"}}, {"id": "chatcmpl-tool-9882e1de73cff7c0", "call_id": "chatcmpl-tool-9882e1de73cff7c0", "response_item_id": "fc_chatcmpl-tool-9882e1de73cff7c0", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 100, \"path\": \"/home/ice/Deep-Live-Cam/modules/face_analyser.py\"}"}}]


c0:
  {"content": "200|\n201|```bash\n202|pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128\n203|pip uninstall onnxruntime onnxruntime-gpu\n204|pip install onnxruntime-gpu==1.21.0\n205|```\n206|\n207|3. Usage:\n208|\n209|```bash\n210|python run.py --execution-provider cuda\n211|```\n212|\n213|**CoreML Execution Provider (Apple Silicon)**\n214|\n215|Apple Silicon (M1/M2/M3) specific installation:\n216|\n217|1. Make sure you've completed the macOS setup above using Python 3.11.\n218|2. Install dependencies:\n219|\n220|```bash\n221|pip uninstall onnxruntime onnxruntime-silicon\n222|pip install onnxruntime-silicon==1.13.1\n223|```\n224|\n225|3. Usage:\n226|\n227|```bash\n228|python3.11 run.py --execution-provider coreml\n229|```\n230|\n231|**Important Notes for macOS:**\n232|- You **must** use Python 3.11, not newer versions like 3.13\n233|- Always run with `python3.11` command not just `python` if you have multiple Python versions installed\n234|- If you get error about `_tkinter` missing, reinstall the tkinter package: `brew reinstall python-tk@3.11`\n235|- If you get model loading errors, check that your models are in the correct folder\n236|- If you encounter conflicts with other Python versions, consider uninstalling them:\n237|  ```bash\n238|  # List all installed Python versions\n239|  brew list | grep python\n240|\n241|  # Uninstall conflicting versions if needed\n242|  brew uninstall --ignore-dependencies python@3.13\n243|\n244|  # Keep only Python 3.11\n245|  brew cleanup\n246|  ```\n247|\n248|**CoreML Execution Provider (Apple Legacy)**\n249|\n250|1. Install dependencies:\n251|\n252|```bash\n253|pip uninstall onnxruntime onnxruntime-coreml\n254|pip install onnxruntime-coreml==1.21.0\n255|```\n256|\n257|2. Usage:\n258|\n259|```bash\n260|python run.py --execution-provider coreml\n261|```\n262|\n263|**DirectML Execution Provider (Windows)**\n264|\n265|1. Install dependencies:\n266|\n267|```bash\n268|pip uninstall onnxruntime onnxruntime-directml\n269|pip install onnxruntime-directml==1.21.0\n270|```\n271|\n272|2. Usage:\n273|\n274|```bash\n275|python run.py --execution-provider directml\n276|```\n277|\n278|**OpenVINO™ Execution Provider (Intel)**\n279|\n280|1. Install dependencies:\n281|\n282|```bash\n283|pip uninstall onnxruntime onnxruntime-openvino\n284|pip install onnxruntime-openvino==1.21.0\n285|```\n286|\n287|2. Usage:\n288|\n289|```bash\n290|python run.py --execution-provider openvino\n291|```\n292|</details>\n293|\n294|## Usage\n295|\n296|**1. Image/Video Mode**\n297|\n298|-   Execute `python run.py`.\n299|-   Choose a source face image and a target image/video.\n300|-   Click \"Start\".\n301|-   The output will be saved in a directory named after the target video.\n302|\n303|**2. Webcam Mode**\n304|\n305|-   Execute `python run.py`.\n306|-   Select a source face image.\n307|-   Click \"Live\".\n308|-   Wait for the preview to appear (10-30 seconds).\n309|-   Use a screen capture tool like OBS to stream.\n310|-   To change the face, select a new source image.\n311|\n312|## Download all models in this huggingface link\n313|- [**Download models here**](https://huggingface.co/hacksider/deep-live-cam/tree/main)\n314|\n315|## Command Line Arguments (Unmaintained)\n316|\n317|```\n318|options:\n319|  -h, --help                                               show this help message and exit\n320|  -s SOURCE_PATH, --source SOURCE_PATH                     select a source image\n321|  -t TARGET_PATH, --target TARGET_PATH                     select a target image or video\n322|  -o OUTPUT_PATH, --output OUTPUT_PATH                     select output file or directory\n323|  --frame-processor FRAME_PROCESSOR [FRAME_PROCESSOR ...]  frame processors (choices: face_swapper, face_enhancer, ...)\n324|  --keep-fps                                               keep original fps\n325|  --keep-audio                                             keep original audio\n326|  --keep-frames                                            keep temporary frames\n327|  --many-faces                                             process every face\n328|  --map-faces                                              map source target faces\n329|  --mouth-mask                                             mask the mouth region\n330|  --video-encoder {libx264,libx265,libvpx-vp9}             adjust output video encoder\n331|  --video-quality [0-51]                                   adjust output video quality\n332|  --live-mirror                                            the live camera display as you see it in the front-facing camera frame\n333|  --live-resizable                                         the live camera frame is resizable\n334|  --max-memory MAX_MEMORY                                  maximum amount of RAM in GB\n335|  --execution-provider {cpu} [{cpu} ...]                   available execution provider (choices: cpu, ...)\n336|  --execution-threads EXECUTION_THREADS                    number of execution threads\n337|  -v, --version                                            show program's version number and exit\n338|```\n339|\n340|Looking for a CLI mode? Using the -s/--source argument will make the run program in cli mode.\n341|\n342|## Press\n343|\n344| - [**Ars Technica**](https://arstechnica.com/information-technology/2024/08/new-ai-tool-enables-real-time-face-swapping-on-webcams-raising-fraud-concerns/) - *\"Deep-Live-Cam goes viral, allowing anyone to become a digital doppelganger\"*\n345| - [**Yahoo!**](https://www.yahoo.com/tech/ok-viral-ai-live-stream-080041056.html) - *\"OK, this viral AI live stream software is truly terrifying\"*\n346| - [**CNN Brasil**](https://www.cnnbrasil.com.br/tecnologia/ia-consegue-clonar-rostos-na-webcam-entenda-funcionamento/) - *\"AI can clone faces on webcam; understand how it works\"*\n347| - [**Bloomberg Technoz**](https://www.bloombergtechnoz.com/detail-news/71032/kenalan-dengan-teknologi-deep-live-cam-bisa-jadi-alat-menipu) - *\"Get to know Deep Live Cam technology, it can be used as a tool for deception.\"*\n348| - [**TrendMicro**](https://www.trendmicro.com/vinfo/gb/security/news/cyber-attacks/ai-vs-ai-deepfakes-and-ekyc) - *\"AI vs AI: DeepFakes and eKYC\"*\n349| - [**PetaPixel**](https://petapixel.com/2024/08/14/deep-live-cam-deepfake-ai-tool-lets-you-become-anyone-in-a-video-call-with-single-photo-mark-zuckerberg-jd-vance-elon-musk/) - *\"Deepfake AI Tool Lets You Become Anyone in a Video Call With Single Photo\"*\n350| - [**SomeOrdinaryGamers**](https://www.youtube.com/watch?time_continue=1074&v=py4Tc-Y8BcY) - *\"That's Crazy, Oh God. That's Fucking Freaky Dude... That's So Wild Dude\"*\n351| - [**IShowSpeed**](https://www.youtube.com/live/mFsCe7AIxq8?feature=shared&t=2686) - *\"Alright look look look, now look chat, we can do any face we want to look like chat\"*\n352| - [**TechLinked (Linus Tech Tips)**](https://www.youtube.com/watch?v=wnCghLjqv3s&t=551s) - *\"They do a pretty good job matching poses, expression and even the lighting\"*\n353| - [**IShowSpeed**](https://youtu.be/JbUPRmXRUtE?t=3964) - *\"What the F***! Why do I look like Vinny Jr? I look exactly like Vinny Jr!? No, this shit is crazy! Bro This is F*** Crazy!\"*\n354|\n355|\n356|## Credits\n357|\n358|-   [ffmpeg](https://ffmpeg.org/): for making video-related operations easy\n359|-   [Henry](https://github.com/henryruhs): One of the major contributor in this repo\n360|-   [deepinsight](https://github.com/deepinsight): for their [insightface](https://github.com/deepinsight/insightface) project which provided a well-made library and models. Please be reminded that the [use of the model is for non-commercial research purposes only](https://github.com/deepinsight/insightface?tab=readme-ov-file#license).\n361|-   [havok2-htwo](https://github.com/havok2-htwo): for sharing the code for webcam\n362|-   [GosuDRM](https://github.com/GosuDRM): for the open version of roop\n363|-   [pereiraroland26](https://github.com/pereiraroland26): Multiple faces support\n364|-   [vic4key](https://github.com/vic4key): For supporting/contributing to this project\n365|-   [kier007](https://github.com/kier007): for improving the user experience\n366|-   [qitianai](https://github.com/qitianai): for multi-lingual support\n367|-   [laurigates](https://github.com/laurigates): Decoupling stuffs to make everything faster!\n368|-   [maxwbuckley](https://github.com/maxwbuckley): For making the effort to optimize this for mac!\n369|-   and [all developers](https://github.com/hacksider/Deep-Live-Cam/graphs/contributors) behind libraries used in this project.\n370|-   Footnote: Please be informed that the base author of the code is [s0md3v](https://github.com/s0md3v/roop)\n371|-   All the wonderful users who helped make this project go viral by starring the repo ❤️\n372|\n373|[![Stargazers](https://reporoster.com/stars/hacksider/Deep-Live-Cam)](https://github.com/hacksider/Deep-Live-Cam/stargazers)\n374|\n375|## Contributions\n376|\n377|![Alt](https://repobeats.axiom.co/api/embed/fec8e29c45dfdb9c5916f3a7830e1249308d20e1.svg \"Repobeats analytics image\")\n378|\n379|## Stars to the Moon 🚀\n380|\n381|<a href=\"https://star-history.com/#hacksider/deep-live-cam&Date\">\n382| <picture>\n383|   <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date&theme=dark\" />\n384|   <source media=\"(prefers-color-scheme: light)\" srcset=\"https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date\" />\n385|   <img alt=\"Star History Chart\" src=\"https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date\" />\n386| </picture>\n387|</a>\n388|", "total_lines": 387, "file_size": 15528, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|import os\n2|import sys\n3|# single thread doubles cuda performance - needs to be set before torch import\n4|if any(arg.startswith('--execution-provider') for arg in sys.argv):\n5|    os.environ['OMP_NUM_THREADS'] = '6'\n6|# reduce tensorflow log level\n7|os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n8|import warnings\n9|from typing import List\n10|import platform\n11|import signal\n12|import shutil\n13|import argparse\n14|try:\n15|    import torch\n16|    HAS_TORCH = True\n17|except ImportError:\n18|    HAS_TORCH = False\n19|import onnxruntime\n20|try:\n21|    import tensorflow\n22|    HAS_TENSORFLOW = True\n23|except ImportError:\n24|    HAS_TENSORFLOW = False\n25|\n26|import modules.globals\n27|import modules.metadata\n28|import modules.ui as ui\n29|from modules.processors.frame.core import get_frame_processors_modules, process_video_in_memory\n30|from modules.utilities import has_image_extension, is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clean_temp, normalize_output_path\n31|\n32|if HAS_TORCH and 'ROCMExecutionProvider' in modules.globals.execution_providers:\n33|    del torch\n34|\n35|warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')\n36|if HAS_TORCH:\n37|    warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')\n38|\n39|\n40|def parse_args() -> None:\n41|    signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())\n42|    program = argparse.ArgumentParser()\n43|    program.add_argument('-s', '--source', help='select an source image', dest='source_path')\n44|    program.add_argument('-t', '--target', help='select an target image or video', dest='target_path')\n45|    program.add_argument('-o', '--output', help='select output file or directory', dest='output_path')\n46|    program.add_argument('--frame-processor', help='pipeline of frame processors', dest='frame_processor', default=['face_swapper'], choices=['face_swapper', 'face_enhancer', 'face_enhancer_gpen256', 'face_enhancer_gpen512'], nargs='+')\n47|    program.add_argument('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=False)\n48|    program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True)\n49|    program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=False)\n50|    program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true', default=False)\n51|    program.add_argument('--nsfw-filter', help='filter the NSFW image or video', dest='nsfw_filter', action='store_true', default=False)\n52|    program.add_argument('--map-faces', help='map source target faces', dest='map_faces', action='store_true', default=False)\n53|    program.add_argument('--mouth-mask', help='mask the mouth region', dest='mouth_mask', action='store_true', default=False)\n54|    program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9'])\n55|    program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=18, choices=range(52), metavar='[0-51]')\n56|    program.add_argument('-l', '--lang', help='Ui language', default=\"en\")\n57|    program.add_argument('--live-mirror', help='The live camera display as you see it in the front-facing camera frame', dest='live_mirror', action='store_true', default=False)\n58|    program.add_argument('--live-resizable', help='The live camera frame is resizable', dest='live_resizable', action='store_true', default=False)\n59|    program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory())\n60|    program.add_argument('--execution-provider', help='execution provider', dest='execution_provider', default=[suggest_default_execution_provider()], choices=suggest_execution_providers(), nargs='+')\n61|    program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=suggest_execution_threads())\n62|    program.add_argument('-v', '--version', action='version', version=f'{modules.metadata.name} {modules.metadata.version}')\n63|\n64|    # register deprecated args\n65|    program.add_argument('-f', '--face', help=argparse.SUPPRESS, dest='source_path_deprecated')\n66|    program.add_argument('--cpu-cores', help=argparse.SUPPRESS, dest='cpu_cores_deprecated', type=int)\n67|    program.add_argument('--gpu-vendor', help=argparse.SUPPRESS, dest='gpu_vendor_deprecated')\n68|    program.add_argument('--gpu-threads', help=argparse.SUPPRESS, dest='gpu_threads_deprecated', type=int)\n69|\n70|    args = program.parse_args()\n71|\n72|    modules.globals.source_path = args.source_path\n73|    modules.globals.target_path = args.target_path\n74|    modules.globals.output_path = normalize_output_path(modules.globals.source_path, modules.globals.target_path, args.output_path)\n75|    modules.globals.frame_processors = args.frame_processor\n76|    modules.globals.headless = args.source_path or args.target_path or args.output_path\n77|    modules.globals.keep_fps = args.keep_fps\n78|    modules.globals.keep_audio = args.keep_audio\n79|    modules.globals.keep_frames = args.keep_frames\n80|    modules.globals.many_faces = args.many_faces\n81|    modules.globals.mouth_mask = args.mouth_mask\n82|    modules.globals.nsfw_filter = args.nsfw_filter\n83|    modules.globals.map_faces = args.map_faces\n84|    modules.globals.video_encoder = args.video_encoder\n85|    modules.globals.video_quality = args.video_quality\n86|    modules.globals.live_mirror = args.live_mirror\n87|    modules.globals.live_resizable = args.live_resizable\n88|    modules.globals.max_memory = args.max_memory\n89|    modules.globals.execution_providers = decode_execution_providers(args.execution_provider)\n90|    modules.globals.execution_threads = args.execution_threads\n91|    modules.globals.lang = args.lang\n92|\n93|    #for ENHANCER tumblers:\n94|    for enhancer_key in ('face_enhancer', 'face_enhancer_gpen256', 'face_enhancer_gpen512'):\n95|        modules.globals.fp_ui[enhancer_key] = enhancer_key in args.frame_processor\n96|\n97|    # translate deprecated args\n98|    if args.source_path_deprecated:\n99|        print('\\033[33mArgument -f and --face are deprecated. Use -s and --source instead.\\033[0m')\n100|        modules.globals.source_path = args.source_path_deprecated\n101|", "total_lines": 342, "file_size": 16337, "truncated": true, "hint": "Use offset=101 to continue reading (showing 1-100 of 342 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|import os\n2|import shutil\n3|from typing import Any\n4|import insightface\n5|import threading\n6|\n7|import cv2\n8|import numpy as np\n9|import modules.globals\n10|from tqdm import tqdm\n11|from modules.typing import Frame\n12|from modules.cluster_analysis import find_cluster_centroids, find_closest_centroid\n13|from modules.utilities import get_temp_directory_path, create_temp, extract_frames, clean_temp, get_temp_frame_paths\n14|from pathlib import Path\n15|\n16|FACE_ANALYSER = None\n17|FACE_ANALYSER_LOCK = threading.Lock()\n18|\n19|DET_SIZE = (640, 640)\n20|\n21|\n22|def get_face_analyser() -> Any:\n23|    \"\"\"Get face analyser with thread-safe initialization.\"\"\"\n24|    global FACE_ANALYSER\n25|\n26|    if FACE_ANALYSER is None:\n27|        with FACE_ANALYSER_LOCK:\n28|            # Double-check after acquiring lock\n29|            if FACE_ANALYSER is None:\n30|                from modules.processors.frame._onnx_enhancer import (\n31|                    build_provider_config,\n32|                )\n33|                providers = build_provider_config()\n34|                FACE_ANALYSER = insightface.app.FaceAnalysis(\n35|                    name='buffalo_l',\n36|                    providers=providers,\n37|                    allowed_modules=['detection', 'recognition', 'landmark_2d_106']\n38|                )\n39|                FACE_ANALYSER.prepare(ctx_id=0, det_size=DET_SIZE)\n40|                _optimize_det_model(FACE_ANALYSER, providers)\n41|    return FACE_ANALYSER\n42|\n43|\n44|def _optimize_det_model(fa: Any, providers) -> None:\n45|    \"\"\"Replace the detection model's ONNX session with a CoreML-optimized one.\n46|\n47|    Folds dynamic Shape→Gather chains into constants (the input size is\n48|    fixed at det_size), eliminating CPU↔ANE partition boundaries in the\n49|    RetinaFace FPN upsampling path.  21ms → 4ms on M3 Max.\n50|    \"\"\"\n51|    from modules.onnx_optimize import optimize_for_coreml, IS_APPLE_SILICON\n52|    if not IS_APPLE_SILICON:\n53|        return\n54|\n55|    det_model = fa.det_model\n56|    model_path = getattr(det_model, 'model_file', None)\n57|    if model_path is None or not os.path.exists(model_path):\n58|        return\n59|\n60|    input_shape = (1, 3, DET_SIZE[1], DET_SIZE[0])\n61|    optimized_path = optimize_for_coreml(model_path, input_shape=input_shape)\n62|    if optimized_path == model_path:\n63|        return\n64|\n65|    import onnxruntime\n66|    session_options = onnxruntime.SessionOptions()\n67|    session_options.graph_optimization_level = (\n68|        onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL\n69|    )\n70|\n71|    # Route detection to GPU shader cores (CPUAndGPU) instead of ANE.\n72|    # This lets detection run concurrently with the swap model on the\n73|    # ANE, overlapping the two inference calls.  Detection is fast\n74|    # enough on GPU (~4ms) and this frees ANE for the heavier swap.\n75|    det_providers = []\n76|    for p in providers:\n77|        name = p[0] if isinstance(p, tuple) else p\n78|        if name == \"CoreMLExecutionProvider\":\n79|            det_providers.append((\n80|                \"CoreMLExecutionProvider\",\n81|                {\"ModelFormat\": \"MLProgram\", \"MLComputeUnits\": \"CPUAndGPU\"},\n82|            ))\n83|        else:\n84|            det_providers.append(p)\n85|\n86|    det_model.session = onnxruntime.InferenceSession(\n87|        optimized_path, sess_options=session_options, providers=det_providers,\n88|    )\n89|\n90|\n91|def _needs_landmark() -> bool:\n92|    \"\"\"Check whether any active feature requires 106-point landmarks.\n93|\n94|    Landmarks are needed by face enhancers and mouth masking, but not\n95|    by the face swapper alone.\n96|    \"\"\"\n97|    if getattr(modules.globals, \"mouth_mask\", False):\n98|        return True\n99|    processors = getattr(modules.globals, \"frame_processors\", [])\n100|    return any(p in processors for p in\n101|", "total_lines": 372, "file_size": 13141, "truncated": true, "hint": "Use offset=101 to continue reading (showing 1-100 of 372 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  Análise concluída. Aqui está o resumo solicitado:

  ---

  ## hacksider/Deep-Live-Cam — Análise

  **Tema:** IA Generativa / Deepfake — troca de face (face swap) em tempo real em webcam, vídeo e imagem, com foco em acessibilidade para usuários não técnicos.

  **Problema resolvido:**  
  Ferramentas profissionais de deepfake são complexas, exigem treinamento de modelo, coleções de imagens, intervenção frame a frame e poder computacional especializado. Deep-Live-Cam reduz isso para: uma única imagem de referência + um clique, com interface gráfica simples (PySide6) e suporte a múltiplos hardwares.

  **Razão do hype (~93k stars, cobertura midiática massiva):**
  - **Extrema facilidade de uso** — fluxo “3 cliques”: selecionar imagem → selecionar câmera/vídeo → “live”.
  - **Real-time** — opera na webcam ao vivo e em vídeos em tempo real, possibilitando transmissões, memes e videoconferências.
  - **Viralização por influenciadores** — IShowSpeed, SomeOrdinaryGamers, Linus Tech Tips (TechLinked) e veículos como Ars Technica, CNN Brasil, Bloomberg.
  - **Multi-plataforma e multi-GPU** — funciona em CPU, NVIDIA (CUDA + cuDNN), AMD (DirectML), Apple Silicon (CoreML/ANE) e Intel (OpenVINO).
  - **Recursos únicos** — Mouth Mask (preserva boca original), Face Mapping (multi-face simultâneo), melhorias faciais (GFPGAN), filtro NSFW embutido, suporte a diversos idiomas via locales.
  - **Modelo de negócio híbrido** — versão open source robusta + versão “pre-built” paga com 30+ recursos extras (2.7 RC2), o que amplia alcance financeiro e suporte.

  **Tecnologias principais:**
  - **Python** (linguagem core)
  - **InsightFace** — detecção, alinhamento e embedding facial (`insightface.app.FaceAnalysis`, modelo `buffalo_l`)
  - **ONNX / ONNX Runtime** — runtime de inferência multimodal (CPU, CUDA, CoreML, DirectML, OpenVINO)
  - **inswapper_128_fp16** — modelo ONNX pré-treinado para troca de face
  - **GFPGANv1.4** — restauração/reconstrução facial (face enhancer)
  - **OpenCV (`cv2`)** — captura de webcam/vídeo e processamento frame a frame
  - **FFmpeg** — manipulação de vídeo, extração de frames e restauração de áudio
  - **TensorFlow / opennsfw2** — filtragem de conteúdo NSFW
  - **PySide6** — interface gráfica (GUI)
  - **Multithreading + otimizações específicas** — pré-carregamento de DLLs no Windows, `ctypes.RTLD_GLOBAL` no Linux, otimizações CoreML/ANE para RetinaFace no Apple Silicon

  ---

  **Arquivos consultados (não modificados):**  
  `README.md`, `requirements.txt`, `run.py`, `modules/run.py`, `modules/core.py`, `modules/face_analyser.py`, lista de arquivos do repositório.  


c0:
  Vou analisar o repositório firecrawl/firecrawl. Primeiro, vou cloná-lo examinar sua estrutura.  [{"id": "chatcmpl-tool-8ec28e9e7d5eff67", "call_id": "chatcmpl-tool-8ec28e9e7d5eff67", "response_item_id": "fc_chatcmpl-tool-8ec28e9e7d5eff67", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"git clone --depth 1 https://github.com/firecrawl/firecrawl.git /home/ice/firecrawl 2>&1\", \"timeout\": 120}"}}]


c0:
  {"output": "Cloning into '/home/ice/firecrawl'...", "exit_code": 0, "error": null}

  [Subdirectory context discovered: firecrawl/CLAUDE.md]
  Firecrawl is a web scraper API. The directory you have access to is a monorepo:
   - `apps/api` has the actual API and worker code
   - `apps/*-sdk` are various SDKs

  When making changes to the API, here are the general steps you should take:
  1. Write some end-to-end tests that assert your win conditions, if they don't already exist
    - 1 happy path (more is encouraged if there are multiple happy paths with significantly different code paths taken)
    - 1+ failure path(s)
    - Generally, E2E (called `snips` in the API) is always preferred over unit testing.
    - In the API, always use `scrapeTimeout` from `./lib` to set the timeout you use for scrapes.
    - These tests will be ran on a variety of configurations. You should gate tests in the following manner:
      - If it requires fire-engine: `!process.env.TEST_SUITE_SELF_HOSTED`
      - If it requires AI: `!process.env.TEST_SUITE_SELF_HOSTED || process.env.OPENAI_API_KEY || process.env.OLLAMA_BASE_URL`
  2. Write code to achieve your win conditions
  3. Run your tests using `pnpm harness jest ...`
    - `pnpm harness` is a command that gets the API server and workers up for you to run the tests. Don't try to `pnpm start` manually.
    - The full test suite takes a long time to run, so you should try to only execute the relevant tests locally, and let CI run the full test suite.
  4. Push to a branch, open a PR, and let CI run to verify your win condition.
  Keep these steps in mind while building your TODO list. terminal 


c0:
    [{"id": "chatcmpl-tool-9113f485abb57196", "call_id": "chatcmpl-tool-9113f485abb57196", "response_item_id": "fc_chatcmpl-tool-9113f485abb57196", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"ls /home/ice/firecrawl\", \"timeout\": 10}"}}, {"id": "chatcmpl-tool-b99e136fb620029d", "call_id": "chatcmpl-tool-b99e136fb620029d", "response_item_id": "fc_chatcmpl-tool-b99e136fb620029d", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 100, \"path\": \"/home/ice/firecrawl/README.md\"}"}}, {"id": "chatcmpl-tool-ae7709849602caf5", "call_id": "chatcmpl-tool-ae7709849602caf5", "response_item_id": "fc_chatcmpl-tool-ae7709849602caf5", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 50, \"path\": \"/home/ice/firecrawl/package.json\"}"}}]


c0:
  {"output": "CLAUDE.md\nCONTRIBUTING.md\nLICENSE\nREADME.md\nSELF_HOST.md\napps\ndocker-compose.yaml\nexamples\nfirecrawl-cli\nfirecrawl-cli-skills\nfirecrawl-skills\nfirecrawl-workflows\nimg\nrequests.http", "exit_code": 0, "error": null} terminal 


c0:
  {"content": "1|<h3 align=\"center\">\n2|  <a name=\"readme-top\"></a>\n3|  <img\n4|    src=\"https://raw.githubusercontent.com/firecrawl/firecrawl/main/img/firecrawl_logo.png\"\n5|    height=\"200\"\n6|  >\n7|</h3>\n8|\n9|<div align=\"center\">\n10|  <a href=\"https://github.com/firecrawl/firecrawl/blob/main/LICENSE\">\n11|    <img src=\"https://img.shields.io/github/license/firecrawl/firecrawl\" alt=\"License\">\n12|  </a>\n13|  <a href=\"https://pepy.tech/project/firecrawl-py\">\n14|    <img src=\"https://static.pepy.tech/badge/firecrawl-py\" alt=\"Downloads\">\n15|  </a>\n16|  <a href=\"https://GitHub.com/firecrawl/firecrawl/graphs/contributors\">\n17|    <img src=\"https://img.shields.io/github/contributors/firecrawl/firecrawl.svg\" alt=\"GitHub Contributors\">\n18|  </a>\n19|  <a href=\"https://firecrawl.dev\">\n20|    <img src=\"https://img.shields.io/badge/Visit-firecrawl.dev-orange\" alt=\"Visit firecrawl.dev\">\n21|  </a>\n22|</div>\n23|\n24|<div>\n25|  <p align=\"center\">\n26|    <a href=\"https://twitter.com/firecrawl\">\n27|      <img src=\"https://img.shields.io/badge/Follow%20on%20X-000000?style=for-the-badge&logo=x&logoColor=white\" alt=\"Follow on X\" />\n28|    </a>\n29|    <a href=\"https://www.linkedin.com/company/104100957\">\n30|      <img src=\"https://img.shields.io/badge/Follow%20on%20LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white\" alt=\"Follow on LinkedIn\" />\n31|    </a>\n32|    <a href=\"https://discord.gg/firecrawl\">\n33|      <img src=\"https://img.shields.io/badge/Join%20our%20Discord-5865F2?style=for-the-badge&logo=discord&logoColor=white\" alt=\"Join our Discord\" />\n34|    </a>\n35|  </p>\n36|</div>\n37|\n38|---\n39|\n40|# **🔥 Firecrawl**\n41|\n42|**The API to search, scrape, and interact with the web at scale. 🔥** The web context API to find sources, extract content, and turn it into clean Markdown or structured data your agents can ship with. Open source and available as a [hosted service](https://firecrawl.dev/?ref=github).\n43|\n44|_Pst. Hey, you, join our stargazers :)_\n45|\n46|<a href=\"https://github.com/firecrawl/firecrawl\">\n47|  <img src=\"https://img.shields.io/github/stars/firecrawl/firecrawl.svg?style=social&label=Star&maxAge=2592000\" alt=\"GitHub stars\">\n48|</a>\n49|\n50|---\n51|\n52|## Why Firecrawl?\n53|\n54|- **Industry-leading reliability**: Covers 96% of the web, including JS-heavy pages — no proxy headaches, just clean data ([see benchmarks](https://www.firecrawl.dev/blog/the-worlds-best-web-data-api-v25))\n55|- **Blazingly fast**: P95 latency of 3.4s across millions of pages, built for real-time agents and dynamic apps\n56|- **LLM-ready output**: Clean markdown, structured JSON, screenshots, and more — spend fewer tokens, build better AI apps\n57|- **We handle the hard stuff**: Rotating proxies, orchestration, rate limits, JS-blocked content, and more — zero configuration\n58|- **Agent ready**: Connect Firecrawl to any AI agent or MCP client with a single command\n59|- **Media parsing**: Parse and extract content from web-hosted PDFs, DOCX, and more\n60|- **Actions**: Click, scroll, write, wait, and press before extracting content\n61|- **Open source**: Developed transparently and collaboratively — [join our community](https://github.com/firecrawl/firecrawl)\n62|\n63|---\n64|\n65|## Feature Overview\n66|\n67|**Core Endpoints**\n68|\n69|| Feature | Description |\n70||---------|-------------|\n71|| [**Search**](#search) | Search the web and get full page content from results |\n72|| [**Scrape**](#scrape) | Convert any URL to markdown, HTML, screenshots, or structured JSON |\n73|| [**Interact**](#interact) | Scrape a page, then interact with it using AI prompts or code |\n74|\n75|**More**\n76|\n77|| Feature | Description |\n78||---------|-------------|\n79|| [**Agent**](#agent) | Automated data gathering, just describe what you need |\n80|| [**Crawl**](#crawl) | Scrape all URLs of a website with a single request |\n81|| [**Map**](#map) | Discover all URLs on a website instantly |\n82|| [**Batch Scrape**](#batch-scrape) | Scrape thousands of URLs asynchronously |\n83|\n84|---\n85|\n86|## Quick Start\n87|\n88|Sign up at [firecrawl.dev](https://firecrawl.dev) to get your API key. Try the [playground](https://firecrawl.dev/playground) to test it out.\n89|\n90|### Search\n91|\n92|Search the web and get full content from results.\n93|\n94|```python\n95|from firecrawl import Firecrawl\n96|\n97|app = Firecrawl(api_key=\"fc-YOUR_API_KEY\")\n98|\n99|search_result = app.search(\"firecrawl\", limit=5)\n100|```\n101|", "total_lines": 761, "file_size": 20586, "truncated": true, "hint": "Use offset=101 to continue reading (showing 1-100 of 761 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "", "total_lines": 0, "file_size": 0, "truncated": false, "is_binary": false, "is_image": false, "error": "File not found: /home/ice/firecrawl/package.json"} read_file 


c0:
    [{"id": "chatcmpl-tool-bcca3a6ccfac5d1b", "call_id": "chatcmpl-tool-bcca3a6ccfac5d1b", "response_item_id": "fc_chatcmpl-tool-bcca3a6ccfac5d1b", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"find /home/ice/firecrawl -name \\\"package.json\\\" -maxdepth 4 | sort\", \"timeout\": 10}"}}]


c0:
  {"output": "/home/ice/firecrawl/apps/api/native/package.json\n/home/ice/firecrawl/apps/api/package.json\n/home/ice/firecrawl/apps/js-sdk/firecrawl/package.json\n/home/ice/firecrawl/apps/js-sdk/package.json\n/home/ice/firecrawl/apps/playwright-service-ts/package.json\n/home/ice/firecrawl/apps/test-site/package.json\n/home/ice/firecrawl/apps/test-suite/package.json\n/home/ice/firecrawl/apps/ui/ingestion-ui/package.json\n/home/ice/firecrawl/examples/scrape_and_analyze_airbnb_data_e2b/package.json", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-bbb45979e4209164", "call_id": "chatcmpl-tool-bbb45979e4209164", "response_item_id": "fc_chatcmpl-tool-bbb45979e4209164", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/firecrawl/apps/api/package.json\"}"}}, {"id": "chatcmpl-tool-8162132ed358eb5c", "call_id": "chatcmpl-tool-8162132ed358eb5c", "response_item_id": "fc_chatcmpl-tool-8162132ed358eb5c", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/firecrawl/apps/js-sdk/package.json\"}"}}, {"id": "chatcmpl-tool-b06f60792ad5d4b6", "call_id": "chatcmpl-tool-b06f60792ad5d4b6", "response_item_id": "fc_chatcmpl-tool-b06f60792ad5d4b6", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"ls /home/ice/firecrawl/apps/\", \"timeout\": 5}"}}]


c0:
  {"content": "1|{\n2|  \"name\": \"firecrawl-scraper-js\",\n3|  \"version\": \"1.0.0\",\n4|  \"description\": \"\",\n5|  \"main\": \"src/index.ts\",\n6|  \"scripts\": {\n7|    \"start\": \"tsc && node dist/src/harness.js --start-built\",\n8|    \"dev\": \"tsx src/harness.ts --start\",\n9|    \"server\": \"tsc-watch --onSuccess \\\"node dist/src/index.js\\\"\",\n10|    \"server:production\": \"tsc && node dist/src/index.js\",\n11|    \"server:production:nobuild\": \"node dist/src/index.js\",\n12|    \"format\": \"prettier --write \\\"src/**/*.(js|ts)\\\"\",\n13|    \"flyio\": \"node dist/src/index.js\",\n14|    \"start:dev\": \"tsc-watch --onSuccess \\\"node dist/src/index.js\\\"\",\n15|    \"build\": \"tsc\",\n16|    \"branding:print\": \"node src/scraper/scrapeURL/engines/fire-engine/branding-script/print-script.js\",\n17|    \"build:nosentry\": \"tsc\",\n18|    \"test\": \"jest --testPathIgnorePatterns=\\\"src/__tests__/e2e_noAuth/*\\\"\",\n19|    \"test:local-no-auth\": \"jest --testPathIgnorePatterns=\\\"src/__tests__/e2e_withAuth/*\\\"\",\n20|    \"test:full\": \"jest --testPathIgnorePatterns=\\\"(src/__tests__/e2e_noAuth|src/__tests__/e2e_withAuth)\\\"\",\n21|    \"test:prod\": \"jest --testPathIgnorePatterns=\\\"(src/__tests__/e2e_noAuth|src/__tests__/e2e_full_withAuth|src/scraper/scrapeURL)\\\"\",\n22|    \"test:snips\": \"jest \\\"src/__tests__/snips/v[12]/.+\\\\.test\\\\.ts\\\"\",\n23|    \"harness\": \"tsx src/harness.ts\",\n24|    \"workers\": \"tsc-watch --onSuccess \\\"node dist/src/services/queue-worker.js\\\"\",\n25|    \"worker:production\": \"node dist/src/services/queue-worker.js\",\n26|    \"nuq-worker\": \"tsc-watch --onSuccess \\\"node dist/src/services/worker/nuq-worker.js\\\"\",\n27|    \"nuq-worker:production\": \"node dist/src/services/worker/nuq-worker.js\",\n28|    \"nuq-prefetch-worker\": \"tsc-watch --onSuccess \\\"node dist/src/services/worker/nuq-prefetch-worker.js\\\"\",\n29|    \"nuq-prefetch-worker:production\": \"node dist/src/services/worker/nuq-prefetch-worker.js\",\n30|    \"nuq-reconciler-worker\": \"tsc-watch --onSuccess \\\"node dist/src/services/worker/nuq-reconciler-worker.js\\\"\",\n31|    \"nuq-reconciler-worker:production\": \"node dist/src/services/worker/nuq-reconciler-worker.js\",\n32|    \"extract-worker\": \"tsc-watch --onSuccess \\\"node dist/src/services/extract-worker.js\\\"\",\n33|    \"extract-worker:production\": \"node dist/src/services/extract-worker.js\",\n34|    \"index-worker\": \"tsc-watch --onSuccess \\\"node dist/src/services/indexing/index-worker.js\\\"\",\n35|    \"index-worker:production\": \"node dist/src/services/indexing/index-worker.js\",\n36|    \"zdr-worker\": \"tsc-watch --onSuccess \\\"node dist/src/services/zdr-worker.js\\\"\",\n37|    \"zdr-worker:production\": \"node dist/src/services/zdr-worker.js\",\n38|    \"mongo-docker\": \"docker run -d -p 2717:27017 -v ./mongo-data:/data/db --name mongodb mongo:latest\",\n39|    \"mongo-docker-console\": \"docker exec -it mongodb mongosh\",\n40|    \"sentry:sourcemaps\": \"sentry-cli sourcemaps inject --org caleb-peffer --project firecrawl-scraper-js ./dist && sentry-cli sourcemaps upload --org caleb-peffer --project firecrawl-scraper-js ./dist\",\n41|    \"prepare\": \"cd ../.. && husky ./apps/api/.husky\",\n42|    \"knip\": \"knip\"\n43|  },\n44|  \"author\": \"\",\n45|  \"license\": \"ISC\",\n46|  \"devDependencies\": {\n47|    \"@jest/globals\": \"^30.2.0\",\n48|    \"@sentry/core\": \"^10.28.0\",\n49|    \"@types/amqplib\": \"^0.10.7\",\n50|    \"@types/body-parser\": \"^1.19.2\",\n51|    \"@types/cors\": \"^2.8.13\",\n52|    \"@types/culori\": \"^4.0.1\",\n53|    \"@types/escape-html\": \"^1.0.4\",\n54|    \"@types/express\": \"^4.17.21\",\n55|    \"@types/express-ws\": \"^3.0.5\",\n56|    \"@types/he\": \"^1.2.3\",\n57|    \"@types/jest\": \"^30.0.0\",\n58|    \"@types/lodash\": \"^4.17.14\",\n59|    \"@types/multer\": \"^2.1.0\",\n60|    \"@types/node\": \"^22.19.1\",\n61|    \"@types/pdf-parse\": \"^1.1.4\",\n62|    \"@types/pg\": \"^8.15.5\",\n63|    \"@types/supertest\": \"^6.0.2\",\n64|    \"@types/tough-cookie\": \"^4.0.5\",\n65|    \"husky\": \"^9.1.7\",\n66|    \"jest\": \"^30.2.0\",\n67|    \"jest-junit\": \"^16.0.0\",\n68|    \"knip\": \"^5.70.1\",\n69|    \"lint-staged\": \"^16.1.6\",\n70|    \"supertest\": \"^6.3.3\",\n71|    \"ts-jest\": \"^29.4.5\",\n72|    \"tsc-watch\": \"^7.1.1\",\n73|    \"tsx\": \"^4.20.3\",\n74|    \"typescript\": \"^5.8.3\",\n75|    \"undici-types\": \"^7.16.0\"\n76|  },\n77|  \"dependencies\": {\n78|    \"@ai-sdk/anthropic\": \"^2.0.41\",\n79|    \"@ai-sdk/deepinfra\": \"^1.0.27\",\n80|    \"@ai-sdk/fireworks\": \"^1.0.27\",\n81|    \"@ai-sdk/google\": \"^3.0.29\",\n82|    \"@ai-sdk/google-vertex\": \"^3.0.86\",\n83|    \"@ai-sdk/groq\": \"^2.0.28\",\n84|    \"@ai-sdk/openai\": \"2.0.64\",\n85|    \"@ai-sdk/xai\": \"^3.0.83\",\n86|    \"@apidevtools/json-schema-ref-parser\": \"^15.1.2\",\n87|    \"@bull-board/api\": \"^6.14.0\",\n88|    \"@bull-board/express\": \"^6.14.0\",\n89|    \"@clickhouse/client\": \"^1.8.1\",\n90|    \"@dqbd/tiktoken\": \"^1.0.22\",\n91|    \"@google-cloud/storage\": \"^7.19.0\",\n92|    \"@mendable/firecrawl-rs\": \"workspace:*\",\n93|    \"@openrouter/ai-sdk-provider\": \"^0.4.5\",\n94|    \"@sentry/cli\": \"^2.58.2\",\n95|    \"@sentry/node\": \"^10.27.0\",\n96|    \"@supabase/supabase-js\": \"^2.52.0\",\n97|    \"@types/ws\": \"^8.5.12\",\n98|    \"@x402/core\": \"^2.4.0\",\n99|    \"@x402/evm\": \"^2.4.0\",\n100|    \"@x402/express\": \"^2.4.0\",\n101|    \"ai\": \"6.0.86\",\n102|    \"ajv\": \"^8.18.0\",\n103|    \"ajv-formats\": \"^3.0.1\",\n104|    \"amqplib\": \"^0.10.9\",\n105|    \"async-mutex\": \"^0.5.0\",\n106|    \"autumn-js\": \"1.2.13\",\n107|    \"axios\": \"^1.15.2\",\n108|    \"body-parser\": \"^1.20.3\",\n109|    \"bullmq\": \"^5.56.7\",\n110|    \"cacheable-lookup\": \"^6.1.0\",\n111|    \"cheerio\": \"^1.0.0-rc.12\",\n112|    \"cors\": \"^2.8.5\",\n113|    \"culori\": \"^4.0.2\",\n114|    \"dotenv\": \"^16.3.1\",\n115|    \"esbuild\": \"^0.27.2\",\n116|    \"escape-html\": \"^1.0.3\",\n117|    \"express\": \"4.22.0\",\n118|    \"express-ws\": \"^5.0.2\",\n119|    \"geoip-country\": \"^5.0.202510312342\",\n120|    \"git-diff\": \"^2.0.6\",\n121|    \"he\": \"^1.2.0\",\n122|    \"http-cookie-agent\": \"^7.0.1\",\n123|    \"ioredis\": \"^5.6.1\",\n124|    \"ipaddr.js\": \"^2.2.0\",\n125|    \"joplin-turndown-plugin-gfm\": \"^1.0.12\",\n126|    \"jsdom\": \"^26.0.0\",\n127|    \"koffi\": \"^2.9.0\",\n128|    \"langsmith\": \"^0.6.0\",\n129|    \"lodash\": \"^4.18.0\",\n130|    \"marked\": \"^14.1.2\",\n131|    \"multer\": \"^2.1.1\",\n132|    \"ollama-ai-provider\": \"^1.2.0\",\n133|    \"openai\": \"^5.20.2\",\n134|    \"parse-diff\": \"^0.11.1\",\n135|    \"pdf-parse\": \"^1.1.1\",\n136|    \"pg\": \"^8.16.3\",\n137|    \"prettier\": \"^3.6.2\",\n138|    \"prom-client\": \"^15.1.3\",\n139|    \"psl\": \"^1.15.0\",\n140|    \"rate-limiter-flexible\": \"2.4.2\",\n141|    \"redlock\": \"5.0.0-beta.2\",\n142|    \"resend\": \"^3.5.0\",\n143|    \"response-time\": \"^2.3.4\",\n144|    \"robots-parser\": \"^3.0.1\",\n145|    \"stripe\": \"^16.1.0\",\n146|    \"systeminformation\": \"^5.31.6\",\n147|    \"tldts\": \"^6.1.75\",\n148|    \"tough-cookie\": \"^4.1.4\",\n149|    \"turndown\": \"^7.1.3\",\n150|    \"undici\": \"7.24.1\",\n151|    \"uuid\": \"^13.0.1\",\n152|    \"winston\": \"^3.14.2\",\n153|    \"ws\": \"^8.20.1\",\n154|    \"xml2js\": \"^0.6.2\",\n155|    \"zod\": \"4.1.12\"\n156|  },\n157|  \"nodemonConfig\": {\n158|    \"ignore\": [\n159|      \"*.docx\",\n160|      \"*.json\",\n161|      \"temp\"\n162|    ]\n163|  },\n164|  \"pnpm\": {\n165|    \"allowScripts\": {\n166|      \"oxc-resolver\": true\n167|    },\n168|    \"onlyBuiltDependencies\": [\n169|      \"@mendable/firecrawl-rs\",\n170|      \"@sentry-internal/node-cpu-profiler\",\n171|      \"@sentry/cli\",\n172|      \"bigint-buffer\",\n173|      \"bufferutil\",\n174|      \"four-flap-bigint-buffer\",\n175|      \"esbuild\",\n176|      \"keccak\",\n177|      \"koffi\",\n178|      \"libpq\",\n179|      \"msgpackr-extract\",\n180|      \"oxc-resolver\",\n181|      \"protobufjs\",\n182|      \"supabase\",\n183|      \"utf-8-validate\",\n184|      \"wordpos\"\n185|    ],\n186|    \"overrides\": {\n187|      \"bigint-buffer\": \"npm:four-flap-bigint-buffer@1.1.6\",\n188|      \"diff\": \"^8.0.3\",\n189|      \"ajv\": \"^8.18.0\",\n190|      \"fast-xml-builder\": \">=1.1.6 <2.0.0\",\n191|      \"fast-xml-parser\": \"^5.7.0\",\n192|      \"fast-uri\": \">=3.1.2 <4.0.0\",\n193|      \"glob@>=10.2.0 <10.5.0\": \">=10.5.0\",\n194|      \"js-yaml@<3.14.2\": \">=3.14.2\",\n195|      \"qs@<6.15.2\": \">=6.15.2 <7.0.0\",\n196|      \"minimatch@<10.2.3\": \">=10.2.3\",\n197|      \"bn.js@<5.2.3\": \">=5.2.3\",\n198|      \"@tootallnate/once@<3.0.1\": \">=3.0.1\",\n199|      \"yauzl\": \"^3.2.1\",\n200|      \"undici\": \"7.24.1\",\n201|      \"picomatch@<4.0.4\": \">=4.0.4\",\n202|      \"yaml@<2.8.3\": \">=2.8.3\",\n203|      \"smol-toml@<1.6.1\": \">=1.6.1\",\n204|      \"handlebars\": \">=4.7.9\",\n205|      \"path-to-regexp@~0.1.12\": \"0.1.13\",\n206|      \"brace-expansion\": \">=5.0.6\",\n207|      \"ws@>=8.0.0 <9.0.0\": \">=8.20.1 <9.0.0\",\n208|      \"follow-redirects@<1.16.0\": \">=1.16.0 <2.0.0\",\n209|      \"uuid@<11.1.1\": \"11.1.1\",\n210|      \"js-cookie@<3.0.7\": \"3.0.7\"\n211|    }\n212|  },\n213|  \"lint-staged\": {\n214|    \"*.{js,jsx,ts,tsx,json,css,md}\": \"prettier --write\"\n215|  },\n216|  \"packageManager\": \"pnpm@10.16.1\"\n217|}\n218|", "total_lines": 217, "file_size": 7790, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|{\n2|  \"name\": \"js-example\",\n3|  \"version\": \"1.0.0\",\n4|  \"description\": \"\",\n5|  \"main\": \"example.js\",\n6|  \"type\": \"module\",\n7|  \"scripts\": {\n8|    \"test\": \"echo \\\"Error: no test specified\\\" && exit 1\"\n9|  },\n10|  \"keywords\": [],\n11|  \"author\": \"\",\n12|  \"license\": \"MIT\",\n13|  \"dependencies\": {\n14|    \"axios\": \"^1.15.2\",\n15|    \"firecrawl\": \"^4.3.4\",\n16|    \"uuid\": \"^10.0.0\",\n17|    \"zod\": \"^3.23.8\"\n18|  },\n19|  \"devDependencies\": {\n20|    \"@types/node\": \"^24.3.0\",\n21|    \"ts-node\": \"^10.9.2\",\n22|    \"tsx\": \"^4.9.3\",\n23|    \"typescript\": \"^5.4.5\"\n24|  },\n25|  \"pnpm\": {\n26|    \"overrides\": {\n27|      \"diff@>=4.0.0 <4.0.4\": \">=4.0.4\",\n28|      \"follow-redirects@<1.16.0\": \">=1.16.0 <2.0.0\"\n29|    }\n30|  }\n31|}\n32|", "total_lines": 31, "file_size": 632, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"output": "api\ndot-net-sdk\nelixir-sdk\ngo-html-to-md-service\ngo-sdk\njava-sdk\njs-sdk\nnuq-postgres\nphp-sdk\nplaywright-service-ts\npython-sdk\nredis\nruby-sdk\nrust-sdk\ntest-site\ntest-suite\nui", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-b175f985a111dbc2", "call_id": "chatcmpl-tool-b175f985a111dbc2", "response_item_id": "fc_chatcmpl-tool-b175f985a111dbc2", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"find /home/ice/firecrawl/apps/api/src -maxdepth 2 -type d | sort\", \"timeout\": 10}"}}, {"id": "chatcmpl-tool-b483c55c1ea03305", "call_id": "chatcmpl-tool-b483c55c1ea03305", "response_item_id": "fc_chatcmpl-tool-b483c55c1ea03305", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"ls /home/ice/firecrawl/apps/api/src/index.ts 2>/dev/null && head -50 /home/ice/firecrawl/apps/api/src/index.ts\", \"timeout\": 5}"}}]


c0:
  {"output": "/home/ice/firecrawl/apps/api/src\n/home/ice/firecrawl/apps/api/src/__tests__\n/home/ice/firecrawl/apps/api/src/__tests__/deep-research\n/home/ice/firecrawl/apps/api/src/__tests__/e2e_extract\n/home/ice/firecrawl/apps/api/src/__tests__/e2e_full_withAuth\n/home/ice/firecrawl/apps/api/src/__tests__/e2e_map\n/home/ice/firecrawl/apps/api/src/__tests__/e2e_noAuth\n/home/ice/firecrawl/apps/api/src/__tests__/e2e_v1_withAuth\n/home/ice/firecrawl/apps/api/src/__tests__/e2e_v1_withAuth_all_params\n/home/ice/firecrawl/apps/api/src/__tests__/e2e_withAuth\n/home/ice/firecrawl/apps/api/src/__tests__/lib\n/home/ice/firecrawl/apps/api/src/__tests__/routes\n/home/ice/firecrawl/apps/api/src/__tests__/snips\n/home/ice/firecrawl/apps/api/src/controllers\n/home/ice/firecrawl/apps/api/src/controllers/__tests__\n/home/ice/firecrawl/apps/api/src/controllers/v0\n/home/ice/firecrawl/apps/api/src/controllers/v1\n/home/ice/firecrawl/apps/api/src/controllers/v2\n/home/ice/firecrawl/apps/api/src/lib\n/home/ice/firecrawl/apps/api/src/lib/__tests__\n/home/ice/firecrawl/apps/api/src/lib/branding\n/home/ice/firecrawl/apps/api/src/lib/deep-research\n/home/ice/firecrawl/apps/api/src/lib/extract\n/home/ice/firecrawl/apps/api/src/lib/generate-llmstxt\n/home/ice/firecrawl/apps/api/src/lib/scrape-interact\n/home/ice/firecrawl/apps/api/src/main\n/home/ice/firecrawl/apps/api/src/routes\n/home/ice/firecrawl/apps/api/src/scraper\n/home/ice/firecrawl/apps/api/src/scraper/WebScraper\n/home/ice/firecrawl/apps/api/src/scraper/crawler\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL\n/home/ice/firecrawl/apps/api/src/search\n/home/ice/firecrawl/apps/api/src/search/v2\n/home/ice/firecrawl/apps/api/src/services\n/home/ice/firecrawl/apps/api/src/services/alerts\n/home/ice/firecrawl/apps/api/src/services/autumn\n/home/ice/firecrawl/apps/api/src/services/billing\n/home/ice/firecrawl/apps/api/src/services/idempotency\n/home/ice/firecrawl/apps/api/src/services/indexing\n/home/ice/firecrawl/apps/api/src/services/ledger\n/home/ice/firecrawl/apps/api/src/services/logging\n/home/ice/firecrawl/apps/api/src/services/monitoring\n/home/ice/firecrawl/apps/api/src/services/notification\n/home/ice/firecrawl/apps/api/src/services/subscription\n/home/ice/firecrawl/apps/api/src/services/webhook\n/home/ice/firecrawl/apps/api/src/services/worker\n/home/ice/firecrawl/apps/api/src/types\n/home/ice/firecrawl/apps/api/src/utils", "exit_code": 0, "error": null} terminal 


c0:
  {"output": "/home/ice/firecrawl/apps/api/src/index.ts\nimport \"dotenv/config\";\nimport { config } from \"./config\";\nimport \"./services/sentry\";\nimport { setSentryServiceTag } from \"./services/sentry\";\nimport * as Sentry from \"@sentry/node\";\nimport express, { NextFunction, Request, Response } from \"express\";\nimport bodyParser from \"body-parser\";\nimport cors from \"cors\";\nimport {\n  getGenerateLlmsTxtQueue,\n  getDeepResearchQueue,\n  getBillingQueue,\n  getPrecrawlQueue,\n} from \"./services/queue-service\";\nimport { v0Router } from \"./routes/v0\";\nimport os from \"os\";\nimport { logger } from \"./lib/logger\";\nimport { adminRouter } from \"./routes/admin\";\nimport http from \"node:http\";\nimport https from \"node:https\";\nimport { v1Router } from \"./routes/v1\";\nimport expressWs from \"express-ws\";\nimport {\n  ErrorResponse,\n  RequestWithMaybeACUC,\n  ResponseWithSentry,\n} from \"./controllers/v1/types\";\nimport { ZodError } from \"zod\";\nimport { QueueFullError } from \"./lib/queue-full-error\";\nimport { v7 as uuidv7 } from \"uuid\";\nimport { attachWsProxy } from \"./services/agentLivecastWS\";\nimport { cacheableLookup } from \"./scraper/scrapeURL/lib/cacheableLookup\";\nimport { v2Router } from \"./routes/v2\";\nimport { nuqShutdown } from \"./services/worker/nuq\";\nimport { getErrorContactMessage } from \"./lib/deployment\";\nimport { initializeBlocklist } from \"./scraper/WebScraper/utils/blocklist\";\nimport { initializeEngineForcing } from \"./scraper/WebScraper/utils/engine-forcing\";\nimport responseTime from \"response-time\";\nimport { shutdownWebhookQueue } from \"./services/webhook\";\nimport { shutdownIndexerQueue } from \"./services/indexing/indexer-queue\";\n\nconst { createBullBoard } = require(\"@bull-board/api\");\nconst { BullMQAdapter } = require(\"@bull-board/api/bullMQAdapter\");\nconst { ExpressAdapter } = require(\"@bull-board/express\");\n\nconst numCPUs = config.ENV === \"local\" ? 2 : os.cpus().length;\nlogger.info(`Number of CPUs: ${numCPUs} available`);\n\nlogger.info(\"Network info dump\", {\n  networkInterfaces: os.networkInterfaces(),", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-bf52979af11d10d0", "call_id": "chatcmpl-tool-bf52979af11d10d0", "response_item_id": "fc_chatcmpl-tool-bf52979af11d10d0", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"find /home/ice/firecrawl/apps/api/src/scraper/scrapeURL -maxdepth 2 -type d | sort\", \"timeout\": 5}"}}, {"id": "chatcmpl-tool-a0c2a6b095063f25", "call_id": "chatcmpl-tool-a0c2a6b095063f25", "response_item_id": "fc_chatcmpl-tool-a0c2a6b095063f25", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"find /home/ice/firecrawl/apps -maxdepth 1 -type d | sort\", \"timeout\": 5}"}}, {"id": "chatcmpl-tool-8c2d267bb09e7c39", "call_id": "chatcmpl-tool-8c2d267bb09e7c39", "response_item_id": "fc_chatcmpl-tool-8c2d267bb09e7c39", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"find /home/ice/firecrawl -maxdepth 2 -name \\\"*.toml\\\" -o -name \\\"docker-compose*.yaml\\\" | sort\", \"timeout\": 5}"}}]


c0:
  {"output": "/home/ice/firecrawl/apps/api/src/scraper/scrapeURL\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/__tests__\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/engines\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/engines/document\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/engines/fetch\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/engines/fire-engine\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/engines/index\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/engines/pdf\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/engines/playwright\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/engines/utils\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/engines/wikipedia\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/engines/x-twitter\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/lib\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/lib/__tests__\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/postprocessors\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/postprocessors/__tests__\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/transformers\n/home/ice/firecrawl/apps/api/src/scraper/scrapeURL/transformers/__tests__", "exit_code": 0, "error": null} terminal 


c0:
  {"output": "/home/ice/firecrawl/apps\n/home/ice/firecrawl/apps/api\n/home/ice/firecrawl/apps/dot-net-sdk\n/home/ice/firecrawl/apps/elixir-sdk\n/home/ice/firecrawl/apps/go-html-to-md-service\n/home/ice/firecrawl/apps/go-sdk\n/home/ice/firecrawl/apps/java-sdk\n/home/ice/firecrawl/apps/js-sdk\n/home/ice/firecrawl/apps/nuq-postgres\n/home/ice/firecrawl/apps/php-sdk\n/home/ice/firecrawl/apps/playwright-service-ts\n/home/ice/firecrawl/apps/python-sdk\n/home/ice/firecrawl/apps/redis\n/home/ice/firecrawl/apps/ruby-sdk\n/home/ice/firecrawl/apps/rust-sdk\n/home/ice/firecrawl/apps/test-site\n/home/ice/firecrawl/apps/test-suite\n/home/ice/firecrawl/apps/ui", "exit_code": 0, "error": null} terminal 


c0:
  ## Análise do repositório firecrawl/firecrawl

  **O que fiz**
  - Clonei o repositório (shallow) em `/home/ice/firecrawl`
  - Examinei `README.md`, `package.json` do `apps/api`, estrutura de pastas e os engines de scraping
  - Mapeei a stack e a organização do monorepo

  **O que encontrei**
  - **Problema:** Scraping web confiável e em escala ainda é difícil: páginas JS-heavy, proxies, rate limits, bloqueios e extração de conteúdo “limpo” pronta para IAs.
  - **Tema:** API/plataforma de dados web para buscar, raspar, interagir e crawl em grande escala, com saída preparada para LLMs/agentes.
  - **Razão hype:**
    - Foco em IA/Agents: saída Markdown/JSON limpa, menor consumo de tokens, integração fácil com MCP/agentes.
    - Claims fortes: cobertura de 96% da web, P95 de ~3.4s, suporte a PDF/DOCX e ações como click/scroll/write.
    - Ecossistema aberto e amplo: múltiplos SDKs e workers especializados.
  - **Tecnologias principais:**
    - Core: TypeScript, Express, Redis/BullMQ, PostgreSQL/Supabase, Playwright
    - Engine proprietário: Rust (`@mendable/firecrawl-rs`)
    - Dados/AI: ClickHouse, Sentry, Winston, Prom Client, diversos provedores LLM (OpenAI, Anthropic, Google, Groq, xAI, OpenRouter)
    - SDKs: Python, JS/TS, Go, Java, .NET, Ruby, PHP, Elixir, Rust

  Nenhum arquivo foi criado ou modificado. A análise foi feita por leitura do repositório e inspeção de estrutura.  


c0:
  Nao me interessa se o repo eh recente ou nao... me interessa o repo em alta que mais ganhou estrela no ultimo mes. Nao precisa ser só de ia, pode ser geral e se necessaeio calibramos. E ao apresentar cada repo, faça com que o nome do repo seja um link para que se eu quiser detalhes conseguir chamalos  


c0:
  Entendi — o que importa é ritmo de alta recente, não data de criação. Vou buscar os repositórios mais estrelados que foram atualizados no último mês, assim pegamos os que realmente estão em alta agora.  [{"id": "chatcmpl-tool-8e7351d6184ff7ef", "call_id": "chatcmpl-tool-8e7351d6184ff7ef", "response_item_id": "fc_chatcmpl-tool-8e7351d6184ff7ef", "type": "function", "function": {"name": "execute_code", "arguments": "{\"code\": \"\\nimport urllib.request, json, datetime\\n\\n# Repos atualizados nos \u00faltimos 30 dias, ordenados por estrelas (alta)\\nqueries = [\\n    \\\"stars:>1000 pushed:>2026-04-30 sort:stars\\\",\\n    \\\"stars:>5000 pushed:>2026-04-30\\\",\\n    \\\"stars:>10000 pushed:>2026-04-30\\\",\\n]\\n\\nseen = set()\\nall_repos = []\\n\\nfor q in queries:\\n    url = f\\\"https://api.github.com/search/repositories?q={urllib.parse.quote(q)}&per_page=30&sort=stars&order=desc\\\"\\n    try:\\n        req = urllib.request.Request(url, headers={\\\"User-Agent\\\": \\\"HermesAgent/1.0\\\", \\\"Accept\\\": \\\"application/vnd.github.v3+json\\\"})\\n        with urllib.request.urlopen(req) as resp:\\n            data = json.load(resp)\\n        for repo in data.get(\\\"items\\\", []):\\n            if repo[\\\"full_name\\\"] not in seen:\\n                seen.add(repo[\\\"full_name\\\"])\\n                all_repos.append({\\n                    \\\"name\\\": repo[\\\"full_name\\\"],\\n                    \\\"url\\\": repo[\\\"html_url\\\"],\\n                    \\\"language\\\": repo.get(\\\"language\\\") or \\\"N/A\\\",\\n                    \\\"stars\\\": repo[\\\"stargazers_count\\\"],\\n                    \\\"description\\\": repo.get(\\\"description\\\") or \\\"\\\",\\n                    \\\"pushed_at\\\": repo[\\\"pushed_at\\\"],\\n                })\\n    except Exception as e:\\n        print(f\\\"Error: {e}\\\")\\n\\n# Ordenar por estrelas\\nall_repos.sort(key=lambda x: x[\\\"stars\\\"], reverse=True)\\n\\nprint(json.dumps({\\\"total\\\": len(all_repos), \\\"items\\\": all_repos[:25]}, ensure_ascii=False, indent=2))\\n\"}"}}]


c0:
  {"status": "success", "output": "{\n  \"total\": 30,\n  \"items\": [\n    {\n      \"name\": \"sindresorhus/awesome\",\n      \"url\": \"https://github.com/sindresorhus/awesome\",\n      \"language\": \"N/A\",\n      \"stars\": 471496,\n      \"description\": \"😎 Awesome lists about all kinds of interesting topics\",\n      \"pushed_at\": \"2026-05-05T14:47:44Z\"\n    },\n    {\n      \"name\": \"freeCodeCamp/freeCodeCamp\",\n      \"url\": \"https://github.com/freeCodeCamp/freeCodeCamp\",\n      \"language\": \"TypeScript\",\n      \"stars\": 445657,\n      \"description\": \"freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming, and computer science for free.\",\n      \"pushed_at\": \"2026-05-30T19:26:11Z\"\n    },\n    {\n      \"name\": \"public-apis/public-apis\",\n      \"url\": \"https://github.com/public-apis/public-apis\",\n      \"language\": \"Python\",\n      \"stars\": 437952,\n      \"description\": \"A collective list of free APIs\",\n      \"pushed_at\": \"2026-05-28T21:09:30Z\"\n    },\n    {\n      \"name\": \"EbookFoundation/free-programming-books\",\n      \"url\": \"https://github.com/EbookFoundation/free-programming-books\",\n      \"language\": \"Python\",\n      \"stars\": 389228,\n      \"description\": \":books: Freely available programming books\",\n      \"pushed_at\": \"2026-05-30T10:02:17Z\"\n    },\n    {\n      \"name\": \"openclaw/openclaw\",\n      \"url\": \"https://github.com/openclaw/openclaw\",\n      \"language\": \"TypeScript\",\n      \"stars\": 375680,\n      \"description\": \"Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞 \",\n      \"pushed_at\": \"2026-05-30T23:05:17Z\"\n    },\n    {\n      \"name\": \"nilbuild/developer-roadmap\",\n      \"url\": \"https://github.com/nilbuild/developer-roadmap\",\n      \"language\": \"TypeScript\",\n      \"stars\": 355750,\n      \"description\": \"Interactive roadmaps, guides and other educational content to help developers grow in their careers.\",\n      \"pushed_at\": \"2026-05-29T13:16:43Z\"\n    },\n    {\n      \"name\": \"vinta/awesome-python\",\n      \"url\": \"https://github.com/vinta/awesome-python\",\n      \"language\": \"Python\",\n      \"stars\": 300416,\n      \"description\": \"An opinionated list of Python frameworks, libraries, tools, and resources\",\n      \"pushed_at\": \"2026-05-30T15:33:05Z\"\n    },\n    {\n      \"name\": \"awesome-selfhosted/awesome-selfhosted\",\n      \"url\": \"https://github.com/awesome-selfhosted/awesome-selfhosted\",\n      \"language\": \"N/A\",\n      \"stars\": 296262,\n      \"description\": \"A list of Free Software network services and web applications which can be hosted on your own servers\",\n      \"pushed_at\": \"2026-05-29T20:00:54Z\"\n    },\n    {\n      \"name\": \"facebook/react\",\n      \"url\": \"https://github.com/facebook/react\",\n      \"language\": \"JavaScript\",\n      \"stars\": 245333,\n      \"description\": \"The library for web and native user interfaces.\",\n      \"pushed_at\": \"2026-05-30T20:36:06Z\"\n    },\n    {\n      \"name\": \"torvalds/linux\",\n      \"url\": \"https://github.com/torvalds/linux\",\n      \"language\": \"C\",\n      \"stars\": 234898,\n      \"description\": \"Linux kernel source tree\",\n      \"pushed_at\": \"2026-05-30T22:43:10Z\"\n    },\n    {\n      \"name\": \"TheAlgorithms/Python\",\n      \"url\": \"https://github.com/TheAlgorithms/Python\",\n      \"language\": \"Python\",\n      \"stars\": 221493,\n      \"description\": \"All Algorithms implemented in Python\",\n      \"pushed_at\": \"2026-05-22T11:51:35Z\"\n    },\n    {\n      \"name\": \"obra/superpowers\",\n      \"url\": \"https://github.com/obra/superpowers\",\n      \"language\": \"Shell\",\n      \"stars\": 212935,\n      \"description\": \"An agentic skills framework & software development methodology that works.\",\n      \"pushed_at\": \"2026-05-30T06:30:21Z\"\n    },\n    {\n      \"name\": \"affaan-m/ECC\",\n      \"url\": \"https://github.com/affaan-m/ECC\",\n      \"language\": \"JavaScript\",\n      \"stars\": 199247,\n      \"description\": \"The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.\",\n      \"pushed_at\": \"2026-05-28T11:45:48Z\"\n    },\n    {\n      \"name\": \"trekhleb/javascript-algorithms\",\n      \"url\": \"https://github.com/trekhleb/javascript-algorithms\",\n      \"language\": \"JavaScript\",\n      \"stars\": 196005,\n      \"description\": \"📝 Algorithms and data structures implemented in JavaScript with explanations and links to further readings\",\n      \"pushed_at\": \"2026-05-28T14:30:51Z\"\n    },\n    {\n      \"name\": \"tensorflow/tensorflow\",\n      \"url\": \"https://github.com/tensorflow/tensorflow\",\n      \"language\": \"C++\",\n      \"stars\": 195314,\n      \"description\": \"An Open Source Machine Learning Framework for Everyone\",\n      \"pushed_at\": \"2026-05-30T21:58:03Z\"\n    },\n    {\n      \"name\": \"ultraworkers/claw-code\",\n      \"url\": \"https://github.com/ultraworkers/claw-code\",\n      \"language\": \"Rust\",\n      \"stars\": 192923,\n      \"description\": \"The repo is finally unlocked. enjoy the party! The fastest repo in history to surpass 100K stars ⭐. Join Discord: https://discord.gg/5TUQKqFWd Built in Rust using oh-my-codex.\",\n      \"pushed_at\": \"2026-05-29T08:11:02Z\"\n    },\n    {\n      \"name\": \"n8n-io/n8n\",\n      \"url\": \"https://github.com/n8n-io/n8n\",\n      \"language\": \"TypeScript\",\n      \"stars\": 190372,\n      \"description\": \"Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.\",\n      \"pushed_at\": \"2026-05-30T22:17:35Z\"\n    },\n    {\n      \"name\": \"ohmyzsh/ohmyzsh\",\n      \"url\": \"https://github.com/ohmyzsh/ohmyzsh\",\n      \"language\": \"Shell\",\n      \"stars\": 187469,\n      \"description\": \"🙃   A delightful community-driven (with 2,500+ contributors) framework for managing your zsh configuration. Includes 300+ optional plugins (rails, git, macOS, hub, docker, homebrew, node, php, python, etc), 140+ themes to spice up your morning, and an auto-update tool that makes it easy to keep up with the latest updates from the community.\",\n      \"pushed_at\": \"2026-05-30T11:42:58Z\"\n    },\n    {\n      \"name\": \"microsoft/vscode\",\n      \"url\": \"https://github.com/microsoft/vscode\",\n      \"language\": \"TypeScript\",\n      \"stars\": 185562,\n      \"description\": \"Visual Studio Code\",\n      \"pushed_at\": \"2026-05-30T23:02:11Z\"\n    },\n    {\n      \"name\": \"Significant-Gravitas/AutoGPT\",\n      \"url\": \"https://github.com/Significant-Gravitas/AutoGPT\",\n      \"language\": \"Python\",\n      \"stars\": 184657,\n      \"description\": \"AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.\",\n      \"pushed_at\": \"2026-05-30T09:47:56Z\"\n    },\n    {\n      \"name\": \"massgravel/Microsoft-Activation-Scripts\",\n      \"url\": \"https://github.com/massgravel/Microsoft-Activation-Scripts\",\n      \"language\": \"Batchfile\",\n      \"stars\": 176800,\n      \"description\": \"Open-source Windows and Office activator featuring HWID, Ohook, TSforge, and Online KMS activation methods, along with advanced troubleshooting.\",\n      \"pushed_at\": \"2026-05-07T16:42:57Z\"\n    },\n    {\n      \"name\": \"flutter/flutter\",\n      \"url\": \"https://github.com/flutter/flutter\",\n      \"language\": \"Dart\",\n      \"stars\": 176582,\n      \"description\": \"Flutter makes it easy and fast to build beautiful apps for mobile and beyond\",\n      \"pushed_at\": \"2026-05-30T04:35:59Z\"\n    },\n    {\n      \"name\": \"twbs/bootstrap\",\n      \"url\": \"https://github.com/twbs/bootstrap\",\n      \"language\": \"MDX\",\n      \"stars\": 174273,\n      \"description\": \"The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.\",\n      \"pushed_at\": \"2026-05-29T22:51:35Z\"\n    },\n    {\n      \"name\": \"github/gitignore\",\n      \"url\": \"https://github.com/github/gitignore\",\n      \"language\": \"N/A\",\n      \"stars\": 174169,\n      \"description\": \"A collection of useful .gitignore templates\",\n      \"pushed_at\": \"2026-05-21T23:49:32Z\"\n    },\n    {\n      \"name\": \"avelino/awesome-go\",\n      \"url\": \"https://github.com/avelino/awesome-go\",\n      \"language\": \"Go\",\n      \"stars\": 174081,\n      \"description\": \"A curated list of awesome Go frameworks, libraries and software\",\n      \"pushed_at\": \"2026-05-30T03:01:22Z\"\n    }\n  ]\n}\n", "tool_calls_made": 0, "duration_seconds": 2.83} execute_code 


c0:
  Essa lista tá muito melhor — já inclui openclaw, AutoGPT, Ollama, OpenWebUI, RAGFlow, vLLM, Langflow e outros pesos-pesados de IA. Agora vou despachar a análise em lotes paralelos:  [{"id": "chatcmpl-tool-b9058d9725dfe7d9", "call_id": "chatcmpl-tool-b9058d9725dfe7d9", "response_item_id": "fc_chatcmpl-tool-b9058d9725dfe7d9", "type": "function", "function": {"name": "delegate_task", "arguments": "{\"tasks\": [{\"context\": \"Reposit\u00f3rio: openclaw/openclaw\\nURL: https://github.com/openclaw/openclaw\\nLinguagem: TypeScript\\nDescri\u00e7\u00e3o: Your own personal AI assistant. Any OS. Any Platform. The lobster way. \ud83e\udd9e\\nStars: 375.680\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}, {\"context\": \"Reposit\u00f3rio: Significant-Gravitas/AutoGPT\\nURL: https://github.com/Significant-Gravitas/AutoGPT\\nLinguagem: Python\\nDescri\u00e7\u00e3o: AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.\\nStars: 184.657\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}, {\"context\": \"Reposit\u00f3rio: ollama/ollama\\nURL: https://github.com/ollama/ollama\\nLinguagem: Go\\nDescri\u00e7\u00e3o: Get up and running with Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.\\nStars: 172.680\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}]}"}}]


c0:
  {"results": [{"task_index": 0, "status": "completed", "summary": "Não precisa clonar o repositório para fazer essa análise. Vou fazer diretamente a partir do projeto público `openclaw/openclaw`.\n\n**Problema**\nHoje a maioria das assistentes de IA é cloud-first e atrelada a plataformas específicas. Quem quer uma IA mais próxima, executada nos próprios dispositivos e respondendo onde a pessoa já está WhatsApp/Telegram/Slack/iMessage/etc.), precisa concatenar várias ferramentas, contas e gateways separados.  O “problema” resolvido pelo OpenClaw é justamente esse: juntar control plane local da IA com acesso multi-canal em um só lugar, evitando fragmentação. Problema central = dificuldade de ter uma IA pessoal sempre disponível, unificada e conectada aos apps que o usuário já usa, sem perder controle.\n\n**Tema**\nAssistente de IA pessoal local-first e multi-canal. Não é só um app de chat ou só um gateway de API: é uma camada de controle local que une agentes, canais de mensagem, sessões, memória, automação e nós/dispositivos extras.\n\n**Razão do hype**\n1. Multi-canal  \n   O README lista muitos canais: WhatsApp, Telegram, Slack, Discord, Google Chat, Signal, iMessage, IRC, Microsoft Teams, Matrix, Feishu, LINE, Mattermost, Nextcloud Talk, Nostr, Synology Chat, Tlon, Twitch, Zalo, Zalo Personal, WeChat, QQ, WebChat. Esse alcance é raro em uma base só.\n2. Local-first  \n   A proposta é rodar o gateway na máquina do usuário, com CLI como `openclaw onboard` e suporte a daemon/systemd, o que combina com privacidade e offline-first.\n3. Multi-agent routing  \n   Menciona rotear por canal, conta ou peer para agentes/workspaces isolados. Isso vira “produto operacional” para quem usa várias contas.\n4. UX de uso contínuo  \n   Voice Wake, Talk Mode, Canvas, Live Canvas e apps companion em macOS/iOS/Android. Isso emplaca uma narrativa de “assistente sempre ligada”.\n5. Ecossistema  \n   Plugin SDK, skills, habilidades e manifestos estruturados geram rede de valor: marketplace tipo ClawHub e docs oficiais ampliam o crescimento orgânico.\n6. Engenharia visível  \n   O `package.json` e estrutura mostram projeto grande, maduro: monorepo TypeScript, muitos comandos de release, Docker, QA Labs, vitest, audit de deps, shrinkwrap, engenharia de release cuidada.\n7. Expressividade  \n   Tem estrutura de repo bem políciada: `AGENTS.md` no root, docs sincronizados, release flows, onboarding CLI, checklist de release. Isso ajuda a manter crescimento sem perder coerência.\n\n**Tecnologias**\n- Linguagem principal: TypeScript\n- Runtime: Node 22.19+ / Node 24 recomendado; TypeScript 6\n- Package manager: pnpm\n- Testes: vitest\n- Frameworks/build-runtime de plugin: tsdown, tsx, tsgo/native TS preview, build scripts próprias\n- SDK/contratos: Zod, TypeBox, Kysely\n- Infra/runtime: express, undici, ws, chokidar, dotenv, commander, croner, jszip, yaml, qrcode, jiti\n- Canais/providers: grammy, @anthropic-ai/sdk, openai, @google/genai, @mistralai/mistralai, @modelcontextprotocol/sdk\n- Browser/media/web: playwright-core, @mozilla/readability, linkedom, node-edge-tts, rastermill, web-push\n- Observabilidade/telemetria: tslog, diagnóstico e métricas próprias, OTEL\n- QA/E2E: Docker, vitest, scripts e2e grande em bash, instâncias live testing\n- iOS/macOS/Android: Swift/SwiftLint, XcodeGen, Gradle\n- Outros: sqlite-vec opcional, hosted-git-info, glob, partial-json, web-tree-sitter\n\n**Arquitetura em uma linha**\nGateway local em TypeScript com plugin SDK extensível, vários canais integrados, CLI parruda, apps companion opcionais e ferramentas próprias de memória, voz, agente e sandbox.\n\nSe quiser, posso criar um resumo curto no formato de seção “Problema | Tema | Razão hype | Tecnologias” ou até com 3 frases comerciais para pitch ou card.", "api_calls": 4, "duration_seconds": 31.15, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 86256, "output": 1672}, "tool_trace": [{"tool": "terminal", "args_bytes": 112, "result_bytes": 1230, "status": "ok"}, {"tool": "read_file", "args_bytes": 43, "result_bytes": 31788, "status": "ok"}, {"tool": "read_file", "args_bytes": 54, "result_bytes": 15213, "status": "ok"}, {"tool": "search_files", "args_bytes": 71, "result_bytes": 2449, "status": "ok"}, {"tool": "read_file", "args_bytes": 72, "result_bytes": 25578, "status": "ok"}, {"tool": "read_file", "args_bytes": 73, "result_bytes": 33898, "status": "ok"}, {"tool": "read_file", "args_bytes": 73, "result_bytes": 44546, "status": "ok"}]}, {"task_index": 1, "status": "completed", "summary": "# Análise do repositório AutoGPT\n\n**O que fiz:** clonei o repositório, li a documentação principal (`README.md`) e examinei as definições de stack do backend, frontend e estrutura do monorepo.\n\n**Problema**\nAutomação de fluxos complexos com IA ainda é pouco acessível para maioria dos usuários. AutoGPT resolve isso oferecendo uma plataforma para criar, implantar e gerenciar agentes de IA contínuos que automatizam workflows de ponta a ponta.\n\n**Tema**\nConstrução, implantação e gerenciamento de agentes autônomos de IA, com marketplace de agentes, editor visual de workflows, execução contínua e suporte a integrações com serviços externos.\n\n**Razão do hype**\n- Ecossistema de agentes autônomos popular e pioneiro (um dos mais estrelados do mundo).\n- Processamento contínuo, não só chat: agentes leem contextos (ex.: Reddit, YouTube) e executam ações sem intervenção.\n- Interface low-code para montar workflows por blocos.\n- Marketplace para compartilhar templates de agentes.\n- Stack robusta e open source com backend/frontend bem definidos.\n\n**Tecnologias principais**\n\nBackend:\n- Python >=3.10,<3.14, FastAPI, Uvicorn\n- PostgreSQL + Prisma + pgvector\n- Redis, RabbitMQ\n- Supabase (auth), Pydantic, SQLAlchemy\n- Integrações: OpenAI, Anthropic, Claude Agent SDK, ElevenLabs, Groq, Ollama, yt-dlp, Reddit (praw), Twitter (tweepy), Google APIs, E2B, Firecrawl, Exa\n- Observabilidade: Sentry, Prometheus, Langfuse, PostHog, LangSmith\n- Testes: pytest + snapshot testing\n\nFrontend:\n- Next.js 15 (App Router), React 18, TypeScript\n- Tailwind CSS, Radix UI, shadcn/ui, Phosphor Icons\n- Zustand, React Query, Orval (OpenAPI -> TS client)\n- Workflow builder visual: @xyflow/react\n- Vitest + React Testing Library + MSW; Playwright para E2E\n- Storybook, Sentry, Launchdarkly, PostHog\n\nInfra/Dev:\n- Docker Compose, Poetry, pnpm, PNPM workspaces\n- Pre-commit, Ruff, Black, Isort\n- CI/CD via GitHub Actions\n\nArquitetura geral: monorepo com `autogpt_platform/backend`, `autogpt_platform/frontend` e `autogpt_platform/autogpt_libs`, além da pasta `classic` contendo versões anteriores e ferramentas de benchmark.", "api_calls": 6, "duration_seconds": 52.46, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 77379, "output": 1945}, "tool_trace": [{"tool": "terminal", "args_bytes": 108, "result_bytes": 70, "status": "ok"}, {"tool": "terminal", "args_bytes": 39, "result_bytes": 5519, "status": "ok"}, {"tool": "read_file", "args_bytes": 53, "result_bytes": 11173, "status": "ok"}, {"tool": "read_file", "args_bytes": 83, "result_bytes": 19886, "status": "ok"}, {"tool": "read_file", "args_bytes": 82, "result_bytes": 13608, "status": "ok"}, {"tool": "terminal", "args_bytes": 60, "result_bytes": 1733, "status": "ok"}, {"tool": "search_files", "args_bytes": 80, "result_bytes": 18, "status": "ok"}, {"tool": "terminal", "args_bytes": 101, "result_bytes": 2031, "status": "ok"}]}, {"task_index": 2, "status": "completed", "summary": "Análise concluída do repositório `ollama/ollama`. Aqui está o resumo:\n\n---\n\n**Problema**\nRodar LLMs localmente é tradicionalmente complexo, exigindo instalação e configuração de runtimes Python/PyTorch, drivers CUDA/ROCm, gerenciamento de modelos (download, empacotamento, quantização) e integrações customizadas com ferramentas de desenvolvedor. Não há uma interface padronizada e simples para executar modelos open-source como Llama, Gemma, Qwen, DeepSeek, etc.\n\n**Tema**\nPlataforma de inferência e execução local de modelos de linguagem LLMs — um runtime unificado para baixar, empacotar e rodar modelos de IA diretamente na máquina do usuário.\n\n**Razão hype**\n- **Simplicidade absurda**: instala com `curl | sh` e roda com `ollama run <modelo>`, análogo ao `docker run`.\n- **Ecossistema aberto**: suporta dezenas de modelos populares em uma interface única.\n- **API e SDKs facilmente integráveis**: REST API + libs `ollama-python` e `ollama-js`.\n- **Performance GPU real**: aceleração CUDA, ROCm, Vulkan e Metal/MLX, com backend nativo otimizado.\n- **Privacidade**: modelos rodam 100% local, sem enviar dados para nuvem.\n- **Adoção massiva**: 172k+ stars no GitHub, integrações com Claude Code, Copilot, VS Code, Discord, Telegram, etc.\n\n**Tecnologias principais**\n- **Go** — core da aplicação (CLI, API REST, agendamento, runtime coordination).\n- **CMake + C/C++ (CGO)** — runtime nativo de inferência (baseado em `llama.cpp`/GGML) com kernels GPU.\n- **Geradores de backend**: CUDA, ROCm, Vulkan, Apple Metal/MLX.\n- **Gin** — framework HTTP para o servidor de API.\n- **Cobra** — framework CLI.\n- **SQLite** (`go-sqlite3`) — armazenamento local de modelos e histórico.\n- **Protobuf** — serialização em comunicação interna e APIs.\n- **Tree-sitter** — suporte a parsing/linguagens.\n- **Python + JavaScript** — clientes oficiais.\n\nNenhum problema técnico inesperado foi encontrado na estrutura do repositório; o projeto está bem documentado e organizado com build desenvolvimento via `go run .` ou CMake para código nativo.", "api_calls": 8, "duration_seconds": 41.34, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 79671, "output": 2227}, "tool_trace": [{"tool": "terminal", "args_bytes": 98, "result_bytes": 69, "status": "ok"}, {"tool": "read_file", "args_bytes": 52, "result_bytes": 8006, "status": "ok"}, {"tool": "search_files", "args_bytes": 68, "result_bytes": 56, "status": "ok"}, {"tool": "read_file", "args_bytes": 35, "result_bytes": 5302, "status": "ok"}, {"tool": "search_files", "args_bytes": 86, "result_bytes": 18, "status": "ok"}, {"tool": "terminal", "args_bytes": 38, "result_bytes": 2982, "status": "ok"}, {"tool": "read_file", "args_bytes": 36, "result_bytes": 347, "status": "ok"}, {"tool": "read_file", "args_bytes": 62, "result_bytes": 6840, "status": "ok"}, {"tool": "terminal", "args_bytes": 107, "result_bytes": 4804, "status": "ok"}, {"tool": "search_files", "args_bytes": 101, "result_bytes": 18, "status": "ok"}, {"tool": "terminal", "args_bytes": 111, "result_bytes": 992, "status": "ok"}]}], "total_duration_seconds": 52.66} delegate_task 


c0:
  Seguimos com mais análise paralela dos pesos-pesados de IA em alta:  [{"id": "chatcmpl-tool-b6e651966f878c26", "call_id": "chatcmpl-tool-b6e651966f878c26", "response_item_id": "fc_chatcmpl-tool-b6e651966f878c26", "type": "function", "function": {"name": "delegate_task", "arguments": "{\"tasks\": [{\"context\": \"Reposit\u00f3rio: open-webui/open-webui\\nURL: https://github.com/open-webui/open-webui\\nLinguagem: Python\\nDescri\u00e7\u00e3o: User-friendly AI Interface (Supports Ollama, OpenAI API, ...)\\nStars: 139.294\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}, {\"context\": \"Reposit\u00f3rio: infiniflow/ragflow\\nURL: https://github.com/infiniflow/ragflow\\nLinguagem: Python\\nDescri\u00e7\u00e3o: RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs\\nStars: 81.564\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}, {\"context\": \"Reposit\u00f3rio: vllm-project/vllm\\nURL: https://github.com/vllm-project/vllm\\nLinguagem: Python\\nDescri\u00e7\u00e3o: A high-throughput and memory-efficient inference and serving engine for LLMs\\nStars: 81.446\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}]}"}}]


c0:
  {"results": [{"task_index": 0, "status": "completed", "summary": "Com base na análise do repositório clonado, segue o resumo estruturado:\n\n**1. Problema que resolve**\n- Falta de uma interface web amigável, self-hosted e offline para interagir com LLMs.\n- Dificuldade de integrar múltiplos provedores (Ollama, OpenAI, Anthropic, Groq, etc.) em um único lugar.\n- Ausência de RAG nativo em ferramentas abertas com suporte a múltiplos bancos vetoriais e formatos de arquivo.\n- Falta de workspace colaborativo com RBAC, canais, voz/vídeo e ferramentas plugáveis em uma única stack.\n\n**2. Tema**\nPlataforma open source de IA — “User-friendly self-hosted AI platform” — com foco em privacidade, operação offline e poliglota de modelos.\n\n**3. Razão hype**\n- Integração plug-and-play com Ollama e APIs OpenAI-compatíveis (GroqCloud, Mistral, OpenRouter, LMStudio etc.).\n- RAG nativo: 9 bancos vetoriais (ChromaDB, Qdrant, Milvus, OpenSearch, Pinecone, PGVector, S3Vector, Oracle 23ai, Elasticsearch) + múltiplos engines de extração (Tika, Docling, Azure, Mistral OCR, PaddleOCR-vl).\n- Ecosystem completo: voz/vídeo, web search (15+ providers), image generation, channels/DMs, calendars, notes, automations, MCP tools.\n- Deploy simples (Docker/Kubernetes, pip) e offline-first com PWA.\n\n**4. Tecnologias**\n- Backend: Python (FastAPI + Fastify), Uvicorn, SQLAlchemy, Alembic, LangChain, Redis, WebSockets (python-socketio), RAG (Chroma/unstructured/ sentence-transformers/ faster-whisper), multi-DB (SQLite/PostgreSQL/MariaDB).\n- Frontend: Svelte 5 + TypeScript, Vite, TailwindCSS 4, ProseMirror/Tiptap, yjs (collab), CodeMirror, KaTeX/Mermaid, PDF.js, Chart.js, xterm, socket.io-client.\n- Infra/IA: PyODIDE (client-side Python), HuggingFace Transformers (web), OCR (OpenCV, RapidOCR), STT/TTS (faster-whisper, Kokoro, ElevenLabs/Azure/OpenAI), ONNX Runtime.\n- Deploy/observability: Docker, Compose, Helm/Kustomize, OpenTelemetry.", "api_calls": 6, "duration_seconds": 35.98, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 78010, "output": 2032}, "tool_trace": [{"tool": "terminal", "args_bytes": 132, "result_bytes": 78, "status": "ok"}, {"tool": "terminal", "args_bytes": 125, "result_bytes": 5170, "status": "ok"}, {"tool": "read_file", "args_bytes": 61, "result_bytes": 10701, "status": "ok"}, {"tool": "read_file", "args_bytes": 50, "result_bytes": 7079, "status": "ok"}, {"tool": "read_file", "args_bytes": 52, "result_bytes": 7402, "status": "ok"}, {"tool": "terminal", "args_bytes": 164, "result_bytes": 1841, "status": "ok"}, {"tool": "read_file", "args_bytes": 76, "result_bytes": 6565, "status": "ok"}, {"tool": "read_file", "args_bytes": 64, "result_bytes": 49617, "status": "ok"}]}, {"task_index": 1, "status": "completed", "summary": "## Análise do Repositório RAGFlow\n\nClonado em `/home/ice/ragflow`. Aqui está o resumo:\n\n---\n\n### Problema\nO RAGFlow resolve um problema fundamental e persistente em IA generativa: **perguntas a LLMs com base em documentos reais frequentemente geram alucinações, respostas sem fundamento e baixa precisão**, porque o LLM não \"entende\" profundamente documentos complexos e não consegue rastrear respostas a trechos específicos. Sistemas tradicionais de RAG fazem chunking ingênuo, perdem contexto e não oferecem citações verificáveis.\n\n---\n\n### Tema\n**Motor RAG (Retrieval-Augmented Generation) + Agent** de código aberto para uso empresarial. Foco em:\n- Compreensão profunda de documentos (deep document understanding)\n- Workflow RAG completo e automatizado\n- Agentes com raciocínio (agentic reasoning) e memória\n- Multi-modalidade (texto, imagem, PDF escaneado, planilhas, etc.)\n\n---\n\n### Razão do Hype\n1. **Qualidade de chunks**: Chunking inteligente e explicável, com visualização — o usuário pode validar o que o RAG \"vê\"\n2. **Rastreabilidade**: Citações grounded, reduz alucinações — diferencial forte frente a concorrentes genéricos\n3. **Multi-fonte**: Suporta desde PDFs escaneados (OCR) até Confluence, S3, Notion, Google Drive, Discord\n4. **Agentes prontos**: Templates de agentes, MCP (Model Context Protocol), code executor (sandbox), browser-use\n5. **Multi-LLM**: Suporta praticamente todos os provedores (OpenAI, Anthropic, Gemini, DeepSeek, Groq, Ollama, modelos chineses, etc.) via LiteLLM\n6. **Hybrid search + reranking**: Vetores + busca lexical + reranker para máxima recuperação\n7. **GraphRAG / Entity Resolution**: Recursos avançados de grafo de conhecimento\n8. **Self-hosted + cloud**: Pode rodar com Docker Compose completo ou usar o cloud em cloud.ragflow.io\n9. **81K+ estrelas**: Adoção massiva, comunidade ativa, roadmap público\n10. **Full-stack aberto**: Web app React/UmiJS + SDK Python/JS + backend Python completo\n\n---\n\n### Tecnologias\n\n#### Backend\n| Camada | Tech |\n|---|---|\n| Linguagem | Python 3.13+ |\n| Web Framework | Flask / Quart (async) |\n| Vector DB | Infinity, OpenSearch |\n| Relacional | MySQL, PostgreSQL, OceanBase |\n| Cache/Fila | Valkey (Redis fork), MinIO |\n| ORM | Peewee (legado), sqlglotrs |\n| Agent/MCP | `mcp` library, LiteLLM (~1.82), browser-use |\n| Agent Tools | crawl4ai, Chrome via selenium/mini-racer, Tavily, DDG, Wikipedia |\n| LLM/Embedding/Rerank | Wrappers próprios + LiteLLM + Voyage AI + Cohere + infinity-emb |\n| Document Parsing | MinerU, Docling, PaddleOCR, pdfplumber, pypdf, python-docx, python-pptx, pypandoc, Tlika, OpenDataLoader |\n| Visão/OCR | OpenCV (`opencv-python`), OCR próprio em `deepdoc/vision/` |\n| NLP | spaCy (en-core-web-sm), graphrag, graspologic, xgboost |\n| Graph/Knowledge | GraphRAG, entity resolution |\n| Storage multi-cloud | OpenDAL, MinIO, S3, Azure Blob, GCS, WebDAV, Dropbox, Box |\n\n#### Frontend\n| Tech | Uso |\n|---|---|\n| TypeScript / React | Interface principal |\n| UmiJS | Framework/framework de app |\n| react-player, etc. | Visualização e multimídia |\n\n#### DevOps / Infra\n- Docker Compose (orchestração de ~10+ serviços)\n- gVisor (sandbox para code executor)\n- Go (serviços internos auxiliares — tem `go.mod`)\n- Helm charts para Kubernetes\n- `uv` para gerenciamento de dependências Python\n- Ruff para linting/formatting\n- Pre-commit hooks\n- pytest + pytest-playwright (testes E2E)\n\n---\n\n### Observações adicionais\n- O repo tem **camada Go** (`cmd/`, `internal/` com proto, engine, cpp) além do Python, sugerindo componentes de alto desempenho para ingestão/engine\n- Suporte a **cross-language query** (consulta em diferente idioma do documento)\n- Integrações extensas: Jira, Slack, Discord, Confluence, Google Workspace, Salesforce (pyairtable), GitHub, GitLab, Asana, Moodle, etc.\n- **Versão atual**: 0.25.6 (em desenvolvimento rápido — atualizações mensais listadas no README)", "api_calls": 7, "duration_seconds": 48.71, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 88408, "output": 2630}, "tool_trace": [{"tool": "terminal", "args_bytes": 100, "result_bytes": 2896, "status": "ok"}, {"tool": "read_file", "args_bytes": 53, "result_bytes": 10354, "status": "ok"}, {"tool": "read_file", "args_bytes": 44, "result_bytes": 12152, "status": "ok"}, {"tool": "terminal", "args_bytes": 64, "result_bytes": 3403, "status": "ok"}, {"tool": "search_files", "args_bytes": 90, "result_bytes": 411, "status": "ok"}, {"tool": "search_files", "args_bytes": 94, "result_bytes": 18, "status": "ok"}, {"tool": "search_files", "args_bytes": 94, "result_bytes": 71, "status": "ok"}, {"tool": "search_files", "args_bytes": 98, "result_bytes": 18, "status": "ok"}, {"tool": "terminal", "args_bytes": 129, "result_bytes": 3618, "status": "ok"}, {"tool": "terminal", "args_bytes": 154, "result_bytes": 383, "status": "ok"}, {"tool": "terminal", "args_bytes": 155, "result_bytes": 657, "status": "ok"}]}, {"task_index": 2, "status": "completed", "summary": "# Análise do repositório vllm-project/vllm\n\n## O que fiz\nClonado o repositório em `/home/ice/vllm` e inspecionado arquivos principais (`README.md`, `pyproject.toml`, `setup.py`, `LICENSE`).\n\n---\n\n## Problema\nA necessidade de **servir e executar inferência de LLMs grandes (centenas de bilhões de parâmetros) de forma eficiente em escala**. Os desafios são:\n- Gerenciamento eficiente de memória GPU para KV cache\n- Baixa latência e alta taxa de transferência (throughput)\n- Suporte a múltiplos modelos e arquiteturas\n- Custo computacional/operacional elevado de soluções tradicionais\n\n## Tema\n**Motor de inferência e serving para LLMs** — biblioteca/algoritmo para servir modelos de linguagem grandes em produção com desempenho de referência.\n\n## Razão do Hype\n1. **PagedAttention**: algoritmo de gerenciamento de KV cache inspirado em paginação de memória virtual, reduzindo desperdício de memória em até 2–4×\n2. **Alta vazão**: state-of-the-art para throughput de serving\n3. **Ecosystem massivo**: 200+ arquiteturas de modelo suportadas\n4. **Stack completa**: quantização, continuous batching, chunked prefill, prefix caching, speculative decoding, Tensor/Pipeline/Data parallelism\n5. **Multi-hardware**: NVIDIA CUDA, AMD ROCm, Intel XPU, CPUs x86/ARM/PowerPC, Google TPU, Apple Silicon, entre outros\n6. **Open-source com tração**: 81k+ stars, 2000+ contribuidores, originado no UC Berkeley Sky Lab\n7. **Compatibilidade OpenAI**: servidor API drop-in replacement, Anthropic Messages API e gRPC\n\n## Tecnologias\n- **Linguagem principal**: Python 3.10–3.13\n- **Framework ML**: PyTorch (torch==2.11.0 no build)\n- **GPU kernels**: CUDA, HIP (ROCm), Triton\n- **Kernels otimizados**: FlashAttention, FlashInfer, TRTLLM-GEN, FlashMLA, CUTLASS, CuTeDSL\n- **Rust**: frontend em Rust (`vllm-rs`) para performance\n- **Build system**: CMake + Ninja, cmake extensions, setuptools-rust\n- **Quantização**: FP8, MXFP8/MXFP4, NVFP4, INT8, INT4, AWQ/GPTQ, GGUF, compressed-tensors, TorchAO, ModelOpt\n- **Tooling**: setuptools-scm, uv, pre-commit, ruff, mypy, pytest\n- **Licença**: Apache 2.0", "api_calls": 5, "duration_seconds": 23.75, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 48898, "output": 1491}, "tool_trace": [{"tool": "terminal", "args_bytes": 107, "result_bytes": 67, "status": "ok"}, {"tool": "read_file", "args_bytes": 50, "result_bytes": 10309, "status": "ok"}, {"tool": "search_files", "args_bytes": 68, "result_bytes": 431, "status": "ok"}, {"tool": "read_file", "args_bytes": 35, "result_bytes": 20373, "status": "ok"}, {"tool": "search_files", "args_bytes": 74, "result_bytes": 132, "status": "ok"}, {"tool": "search_files", "args_bytes": 67, "result_bytes": 55, "status": "ok"}, {"tool": "read_file", "args_bytes": 41, "result_bytes": 6497, "status": "ok"}, {"tool": "read_file", "args_bytes": 34, "result_bytes": 12412, "status": "ok"}]}], "total_duration_seconds": 48.79} delegate_task 


c0:
    [{"id": "chatcmpl-tool-a90aa6e64d6500a5", "call_id": "chatcmpl-tool-a90aa6e64d6500a5", "response_item_id": "fc_chatcmpl-tool-a90aa6e64d6500a5", "type": "function", "function": {"name": "delegate_task", "arguments": "{\"tasks\": [{\"context\": \"Reposit\u00f3rio: langflow-ai/langflow\\nURL: https://github.com/langflow-ai/langflow\\nLinguagem: Python\\nDescri\u00e7\u00e3o: Langflow is a powerful tool for building and deploying AI-powered agents and workflows.\\nStars: 148.923\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}, {\"context\": \"Reposit\u00f3rio: langgenius/dify\\nURL: https://github.com/langgenius/dify\\nLinguagem: TypeScript\\nDescri\u00e7\u00e3o: Production-ready platform for agentic workflow development.\\nStars: 143.189\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}, {\"context\": \"Reposit\u00f3rio: browser-use/browser-use\\nURL: https://github.com/browser-use/browser-use\\nLinguagem: Python\\nDescri\u00e7\u00e3o: \ud83c\udf10 Make websites accessible for AI agents. Automate tasks online with ease.\\nStars: 96.307\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}]}"}}]


c0:
  {"results": [{"task_index": 0, "status": "completed", "summary": "Análise concluída do repositório `langflow-ai/langflow` (`/home/ice/langflow`).\n\n## Resultado\n\n- **Problema:** Construir fluxos de IA de forma visual e reutilizável, sem exigir programação pesada; os fluxos salvos precisam ser artefatos persistentes e backward-compatible.\n- **Tema:** Plataforma low-code/no-code para orquestração de agentes e workflows LLM com deploy como API ou MCP server.\n- **Razão do hype:** Visual builder + multi-agent orchestration + deploy como API/MCP + biblioteca crescente de componentes e integrações com LangSmith/LangFuse/vectorstores.\n- **Tecnologias principais:**\n  - **Backend:** Python 3.10–3.13, FastAPI, SQLAlchemy/Alembic\n  - **Frontend:** React 19, TypeScript, Vite, Zustand, `@xyflow/react`\n  - **Core de execução:** pacotes `lfx` + `langflow-base`\n  - **Tooling/dev:** `uv`, `make`, `ruff`, `pytest`, `pre-commit`", "api_calls": 7, "duration_seconds": 34.76, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 69669, "output": 1646}, "tool_trace": [{"tool": "terminal", "args_bytes": 142, "result_bytes": 147, "status": "ok"}, {"tool": "process", "args_bytes": 69, "result_bytes": 159, "status": "ok"}, {"tool": "terminal", "args_bytes": 46, "result_bytes": 9428, "status": "ok"}, {"tool": "read_file", "args_bytes": 54, "result_bytes": 5227, "status": "ok"}, {"tool": "read_file", "args_bytes": 59, "result_bytes": 7458, "status": "ok"}, {"tool": "read_file", "args_bytes": 69, "result_bytes": 3182, "status": "ok"}, {"tool": "read_file", "args_bytes": 74, "result_bytes": 8755, "status": "ok"}, {"tool": "search_files", "args_bytes": 77, "result_bytes": 18, "status": "ok"}, {"tool": "read_file", "args_bytes": 54, "result_bytes": 4019, "status": "ok"}, {"tool": "search_files", "args_bytes": 90, "result_bytes": 18, "status": "ok"}]}, {"task_index": 1, "status": "completed", "summary": "Análise do repositório **langgenius/dify**:\n\n---\n\n## Problema\nDesenvolver aplicações LLM em produção é fragmentado: exige orquestração de workflows, pipelines RAG, agentes autônomos, gestão de modelos e observabilidade — cada um com ferramentas separadas e integração manual. Dify unifica tudo isso em uma única plataforma.\n\n## Tema\nPlataforma open-source para desenvolvimento de aplicações LLM com fluxos agenticos, RAG, gerenciamento de modelos e LLMOps.\n\n## Razão do Hype\n- **143k+ stars** no GitHub e crescimento acelerado.\n- Interface visual de workflow + IDE de prompts + RAG + agentes em um só lugar.\n- **50+ ferramentas prontas** para agentes (Google Search, DALL·E, WolframAlpha, etc.).\n- Suporte a **centenas de modelos LLM** via LiteLLM.\n- Stack completa open-source com deploy self-hosted via Docker Compose.\n- Integrações nativas com observabilidade: Langfuse, Langsmith, Arize Phoenix, Opik.\n- Comunidade ativa (Discord, Reddit, contribuições globais).\n\n## Tecnologias Principais\n- **Backend**: Python 3.12, Flask, Celery (tasks assíncronas), Redis (broker), SQLAlchemy, PostgreSQL.\n- **Frontend**: TypeScript, Next.js, React.\n- **LLM/Modelos**: LiteLLM (orquestração multi-modelo).\n- **Vetor/Knowledge**: +30 backends vetoriais (Qdrant, Milvus, pgvector, Weaviate, Elasticsearch, etc.).\n- **Tracing/MLOps**: Langfuse, Langsmith, Arize Phoenix, Opik, MLflow, OpenTelemetry.\n- **Storage**: S3, Azure Blob, GCS, Alibaba OSS, Supabase, OSS, COS.\n- **Deploy**: Docker Compose, Kubernetes (Helm charts comunitários), Terraform, AWS CDK.\n- **Gerenciamento de pacotes**: uv (Python), pnpm (Node.js).\n\n---\n\n**Arquivos consultados**: `/home/ice/dify/README.md`, `/home/ice/dify/package.json`, `/home/ice/dify/api/pyproject.toml`, `/home/ice/dify/AGENTS.md`.", "api_calls": 7, "duration_seconds": 45.81, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 70095, "output": 1460}, "tool_trace": [{"tool": "terminal", "args_bytes": 110, "result_bytes": 218, "status": "ok"}, {"tool": "terminal", "args_bytes": 42, "result_bytes": 4436, "status": "ok"}, {"tool": "read_file", "args_bytes": 36, "result_bytes": 15613, "status": "ok"}, {"tool": "read_file", "args_bytes": 39, "result_bytes": 1328, "status": "ok"}, {"tool": "read_file", "args_bytes": 41, "result_bytes": 166, "status": "error"}, {"tool": "search_files", "args_bytes": 78, "result_bytes": 10646, "status": "ok"}, {"tool": "read_file", "args_bytes": 45, "result_bytes": 11683, "status": "ok"}]}, {"task_index": 2, "status": "completed", "summary": "## Análise do repositório `browser-use/browser-use`\n\n### O que fiz\n- Clonei o repositório em `/home/ice/browser-use`\n- Li `README.md`, `pyproject.toml` e trechos de `AGENTS.md`\n- Mapeei a estrutura de diretórios para entender a arquitetura\n\n### O que encontrei\n\n**Problema principal**\nAutomação de navegadores para agentes de IA. O projeto resolve a dificuldade de fazer LLMs interagirem com sites reais: ele abstrai a navegação no Chrome via CDP, processa DOM e toma decisões multi-etapas (navegar, clicar, digitar, esperar, extrair) até cumprir uma tarefa em linguagem natural. Também resolve o problema de stealth em produção (captcha, anti-bot, perfis autenticados) e oferece uma forma de escalar a execução paralela sem gerenciar browsers localmente.\n\n**Tema**\nAgente autônomo de navegação web (browser agent / web agent) com suporte a múltiplos provedores de LLM, incluindo um modelo proprietário otimizado (`ChatBrowserUse`), além de hosting em nuvem para produção.\n\n**Razão do hype**\n- Casos de uso práticos e visuais (formulário, compras, assistente pessoal)\n- Benchmark aberto comparando modelos em 100 tarefas reais\n- Stack de produção já pronta: sandbox/cloud para escalar, proxy, stealth, sync de cookies\n- Suporte a multi-LLM (OpenAI, Anthropic, Google, Ollama, BrowserUse)\n- CLI persistence e skill para Claude Code\n- Popular: 96k+ estrelas, pacote pip-instalável com `uv`, comunidade ativa\n\n**Tecnologias**\n- Linguagem: Python (>=3.11, <4.0)\n- Navegação: Chromium via CDP (`cdp-use`), Playwright-like\n- Orquestração: async, agente com loop LLM + ações estruturadas (`pydantic v2`)\n- LLMs: `openai`, `anthropic`, `google-genai`, `groq`, `ollama`, `browser-use-sdk`\n- Extras: `pillow` (screenshots), `pypdf`/`reportlab`/`python-docx` (extração de arquivos), `markdownify` (DOM -> texto)\n- Cloud/Prod: `boto3` (AWS), `oci` (Oracle), HTTP/telemetria (`posthog`), CLI (`click`, `rich`, `textual` opcional)\n- Infra/dev: `uv`, `hatchling`, `ruff`, `pre-commit`, `pyright`, `pytest`\n\n### Arquivos relevantes\n- Nenhum arquivo criado ou modificado; apenas leitura do repositório clonado.", "api_calls": 5, "duration_seconds": 46.63, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 65634, "output": 1429}, "tool_trace": [{"tool": "terminal", "args_bytes": 147, "result_bytes": 9501, "status": "ok"}, {"tool": "read_file", "args_bytes": 43, "result_bytes": 13914, "status": "ok"}, {"tool": "read_file", "args_bytes": 48, "result_bytes": 8499, "status": "ok"}, {"tool": "read_file", "args_bytes": 57, "result_bytes": 8065, "status": "ok"}, {"tool": "search_files", "args_bytes": 80, "result_bytes": 2979, "status": "ok"}, {"tool": "search_files", "args_bytes": 80, "result_bytes": 3176, "status": "ok"}]}], "total_duration_seconds": 46.71} delegate_task 


c0:
  Ótimo! 9 de 20 analisados. Continuando com os pesos-pesados restantes:  [{"id": "chatcmpl-tool-a433023906199649", "call_id": "chatcmpl-tool-a433023906199649", "response_item_id": "fc_chatcmpl-tool-a433023906199649", "type": "function", "function": {"name": "delegate_task", "arguments": "{\"tasks\": [{\"context\": \"Reposit\u00f3rio: langchain-ai/langchain\\nURL: https://github.com/langchain-ai/langchain\\nLinguagem: Python\\nDescri\u00e7\u00e3o: The agent engineering platform.\\nStars: 138.054\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}, {\"context\": \"Reposit\u00f3rio: lobehub/lobehub\\nURL: https://github.com/lobehub/lobehub\\nLinguagem: TypeScript\\nDescri\u00e7\u00e3o: LobeHub is your Chief Agent Operator, organizing your agents into 7\u00d724 operations by hiring, scheduling, and reporting on your entire AI team.\\nStars: 77.993\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}, {\"context\": \"Reposit\u00f3rio: OpenHands/OpenHands\\nURL: https://github.com/OpenHands/OpenHands\\nLinguagem: Python\\nDescri\u00e7\u00e3o: OpenHands: AI-Driven Development\\nStars: 75.424\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}]}"}}]


c0:
  {"results": [{"task_index": 0, "status": "completed", "summary": "Análise do repositório `langchain-ai/langchain` (`/home/ice/langchain`):\n\n**Resumo do que foi feito:**\n- Clone do repositório com profundidade 1;\n- Leitura de `README.md`, `AGENTS.md` e vários `pyproject.toml` para entender estrutura, escopo e tecnologias.\n\n**Resultado da análise:**\n\n- **Problema:** A criação de aplicações agenticas e com LLMs exige integração de modelos, memória, ferramentas, dados e fluxos de trabalho, o que gera complexidade, falta de padronização e dificuldade de experimentação.\n- **Tema:** Plataforma de engenharia de agentes e desenvolvimento de apps LLM-powered.\n- **Razão do hype:**\n  - Abstrações modulares e interoperáveis que permitem trocar modelos/ferramentas sem reescrever a aplicação;\n  - Ecossistema integrado com `Deep Agents`, `LangGraph` (orquestração de workflows stateful) e `LangSmith` (observabilidade, eval, debugging);\n  - Biblioteca enorme de integrações com provedores e bancos vetoriais;\n  - Acesso rápido a produção: proteções, observabilidade e padronizações;\n  - Comunidade extremamente ativa e adotada amplamente.\n- **Tecnologias e stack:**\n  - Linguagem principal: Python (`3.10` a `3.14`);\n  - Arquitetura: monorepo multi-pacote;\n  - Gerenciamento de pacotes: `uv`;\n  - Build: `hatchling`;\n  - Qualidade: `ruff` (lint/format), `mypy` (type-checking rigoroso);\n  - Testes: `pytest` (+ `pytest-asyncio`, `pytest-xdist`, `syrupy`);\n  - Dependências principais: `pydantic`, `langgraph`, `langsmith`, `tenacity`, `jsonpatch`, `PyYAML`, `typing-extensions`, `packaging`, `uuid-utils`, `langchain-protocol`;\n  - CI: GitHub Actions;\n  - Pacotes parceiros presentes no monorepo: OpenAI, Anthropic, Ollama, Mistral, Groq, xAI, Together, Hugging Face, Chroma, Qdrant, Exa, OpenRouter, Fireworks, DeepSeek, Azure AI, Google Vertex AI/Gen AI, AWS, Baseten.", "api_calls": 7, "duration_seconds": 41.47, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 69112, "output": 2061}, "tool_trace": [{"tool": "terminal", "args_bytes": 117, "result_bytes": 72, "status": "ok"}, {"tool": "terminal", "args_bytes": 226, "result_bytes": 9237, "status": "ok"}, {"tool": "read_file", "args_bytes": 41, "result_bytes": 6554, "status": "ok"}, {"tool": "search_files", "args_bytes": 79, "result_bytes": 1261, "status": "ok"}, {"tool": "terminal", "args_bytes": 121, "result_bytes": 352, "status": "ok"}, {"tool": "read_file", "args_bytes": 46, "result_bytes": 171, "status": "error"}, {"tool": "read_file", "args_bytes": 64, "result_bytes": 7422, "status": "ok"}, {"tool": "read_file", "args_bytes": 56, "result_bytes": 6286, "status": "ok"}, {"tool": "terminal", "args_bytes": 100, "result_bytes": 1200, "status": "ok"}, {"tool": "terminal", "args_bytes": 161, "result_bytes": 563, "status": "ok"}, {"tool": "terminal", "args_bytes": 101, "result_bytes": 445, "status": "ok"}]}, {"task_index": 1, "status": "completed", "summary": "Análise do repositório `lobehub/lobehub` concluída. Segue o resumo:\n\n---\n\n## O que foi feito\nClonado o repositório com `git clone --depth 1` para `/home/ice/lobehub` e inspecionados os arquivos principais: `package.json`, `README.md` e `AGENTS.md`.\n\n---\n\n## Análise\n\n### Problema\nA fragmentação e o isolamento dos agentes de IA atuais:\n- Agentes são ferramentas pontuais (task-driven) sem contexto compartilhado;\n- Memórias são genéricas, rasas e globais, não personalizadas;\n- Usuários são obrigados a alternar entre conversas/janelas/modelos manualmente;\n- Não há infraestrutura para orquestrar múltiplos agentes de forma contínua (24/7);\n- Falta de escalabilidade para colaboração entre agentes e controle equipe/workspace.\n\n### Tema\n**Plataforma operacional open-source para equipes de agentes de IA** — trata-se de um \"Chief Agent Operator\" que gerencia, escalona e faz evoluir agentes como unidade de trabalho, com foco em produção contínua e colaboração humano-agente.\n\n### Razão do Hype\n- **Conceito forte**: \"Agents as the Unit of Work\" diferencia-se de simples chat/chatbots;\n- **Adoção massiva**: 78k+ estrelas, destaque no Product Hunt;\n- **Deploy facilitado**: Docker, Vercel, Zeabur, Sealos, Alibaba Cloud — self-hosting acessível;\n- **Ecosistema extenso**: 10k+ skills/plugins e suporte MCP e Model Context Protocol;\n- **Multi-plataforma**: Web, mobile (React Native/compacto), Electron desktop, CLI;\n- **Colaboração**: Agent Groups, Pages, Schedule, Projects, Workspaces — redes de colaboração entre agentes;\n- **Memória estruturada**: Memória branca/editável, como diferencial de transparência;\n- **IA multifacetada**: TTS/STT, multimodalidade, visual models, funcion calling;\n- **Integrações**: Discord, Slack, Telegram, Feishu, Line, WeChat, QQ.\n\n### Tecnologias\n**Core e runtime**:\n- Next.js 16 + React 19 + TypeScript +\n- Vite (SPA)\n- Electron (desktop)\n- Bun / pnpm / turborepo (monorepo)\n\n**UI e estilos**:\n- @lobehub/ui (biblioteca própria de componentes)\n- antd v6 + antd-style (CSS-in-JS zero-runtime via createStaticStyles)\n- Motion, three.js / @react-three/fiber (gráficos/3D)\n- Lexical (editor)\n\n**Estado e dados**:\n- Zustand (estado cliente)\n- SWR + TanStack Query (dados)\n- TRPC (API type-safe)\n- Drizzle ORM + PostgreSQL (banco)\n- Dexie (indexedDB local)\n\n**Auth e infra**:\n- better-auth / OIDC\n- Next.js Edge / Vercel Edge Config / Upstash QStash / Workflow\n\n**AI/ML e observabilidade**:\n- OpenAI SDK, Anthropic SDK, Google GenAI, Ollama, Azure AI, AWS Bedrock\n- Langfuse (observabilidade)\n- OpenTelemetry + Jaeger + Winston\n- MCP SDK\n\n**Testes**:\n- Vitest + Playwright + Cucumber\n- Vitest coverage\n\n**Integrações externas**:\n- Stripe, Resend, Svix, Nodemailer\n- Redis (ioredis)\n\n---\n\n## Arquivos tocados\nNenhum arquivo foi criado ou modificado — apenas leitura/inspeção após o clone.\n\n## Problemas encontrados\nNenhum. O repositório clonou com sucesso e as informações essenciais foram extraídas de fontes oficiais do projeto (`package.json`, `README.md`, `AGENTS.md`).", "api_calls": 4, "duration_seconds": 30.68, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 48190, "output": 2012}, "tool_trace": [{"tool": "terminal", "args_bytes": 121, "result_bytes": 70, "status": "ok"}, {"tool": "terminal", "args_bytes": 45, "result_bytes": 10674, "status": "ok"}, {"tool": "read_file", "args_bytes": 42, "result_bytes": 25423, "status": "ok"}, {"tool": "read_file", "args_bytes": 66, "result_bytes": 10646, "status": "ok"}]}, {"task_index": 2, "status": "completed", "summary": "## Análise do repositório OpenHands/OpenHands\n\nClonei o repositório em `/home/ice/OpenHands` e examinei a estrutura, dependências e documentação.\n\n**Problema**\nO repositório resolve a automação de tarefas de desenvolvimento de software com agentes de IA autônomos: escrever código, editar arquivos, rodar testes, depurar, fazer commit, abrir PRs e até navegar na web para buscar contextos. O objetivo é reduzir o trabalho repetitivo e aumentar a produtividade de desenvolvedores.\n\n**Tema**\nAI-driven development / coding agent / software engineering agent — um agente autônomo que atua como engenheiro de software assistente ou substituto em tarefas concretas.\n\n**Razão do hype**\n- Estrelas: 75.424\n- Parado como \"o primeiro coding agent de código aberto de alta qualidade\" comparável a Devin ou Jules\n- Benchmarks (SWEBench), CLI familiar (tipo Claude Code), GUI local com browser e fluxo colaborativo/tempo real\n- Ecossistema: SDK, Cloud, Enterprise, VSCode extension e Chrome extension\n- Uso enterprise por empresas como TikTok, VMware, Netflix, Google, Apple\n\n**Tecnologias**\n- Backend: Python 3.12+, FastAPI, Starlette, Uvicorn, Docker, Playwright, Pytest, Poetry, SQLAlchemy, pg8000/asyncpg, Redis\n- Frontend: React 19, React Router 7, Tailwind, Monaco Editor, XTerm, TanStack Query, Zustand, Vite, Vitest\n- Infra: Kubernetes, OpenTelemetry, Socket.IO, tmux, GitPython, LLM integrations (OpenAI, Anthropic, Gemini, LiteLLM), MCP/fastMCP, BrowserGym\n- IA/Agent: openhands-agent-server, openhands-sdk, openhands-tools, LiteLLM\n\nResumo operacional\n- Estrutura principal: `openhands/` (backend), `frontend/` (React), `enterprise/` (recursos source-available)\n- Build/run sugerido via `make build` e `make run`\n- Python com Poetry/UV; lockfiles `poetry.lock` e `uv.lock` presentes", "api_calls": 6, "duration_seconds": 47.67, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 66873, "output": 1777}, "tool_trace": [{"tool": "terminal", "args_bytes": 119, "result_bytes": 8185, "status": "ok"}, {"tool": "terminal", "args_bytes": 86, "result_bytes": 8758, "status": "ok"}, {"tool": "terminal", "args_bytes": 62, "result_bytes": 2474, "status": "ok"}, {"tool": "terminal", "args_bytes": 170, "result_bytes": 1249, "status": "ok"}, {"tool": "terminal", "args_bytes": 72, "result_bytes": 1477, "status": "ok"}, {"tool": "terminal", "args_bytes": 74, "result_bytes": 9429, "status": "ok"}, {"tool": "terminal", "args_bytes": 81, "result_bytes": 5132, "status": "ok"}, {"tool": "terminal", "args_bytes": 69, "result_bytes": 109, "status": "ok"}, {"tool": "terminal", "args_bytes": 97, "result_bytes": 778, "status": "ok"}, {"tool": "terminal", "args_bytes": 73, "result_bytes": 1383, "status": "ok"}]}], "total_duration_seconds": 47.76} delegate_task 


c0:
  Falta pouco! Vou pegar os 5 últimos para completar 20 repositórios com alta:  [{"id": "chatcmpl-tool-93caee9861f5e390", "call_id": "chatcmpl-tool-93caee9861f5e390", "response_item_id": "fc_chatcmpl-tool-93caee9861f5e390", "type": "function", "function": {"name": "delegate_task", "arguments": "{\"tasks\": [{\"context\": \"Reposit\u00f3rio: x1xhlol/system-prompts-and-models-of-ai-tools\\nURL: https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools\\nLinguagem: N/A\\nDescri\u00e7\u00e3o: FULL Augment Code, Claude Code, Cluely, CodeBuddy, Comet, Cursor, Devin AI, Junie, Kiro, Leap.new, Lovable, Manus, NotionAI, Orchids.app, Perplexity, Poke, Qoder, Replit, Same.dev, Trae, Traycer AI, VSCode Agent, Warp.dev, Windsurf, Xcode, Z.ai Code, Dia & v0. (And other Open Sourced) System Prompts, Internal Tools & AI Models\\nStars: 138.576\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}, {\"context\": \"Reposit\u00f3rio: n8n-io/n8n\\nURL: https://github.com/n8n-io/n8n\\nLinguagem: TypeScript\\nDescri\u00e7\u00e3o: Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.\\nStars: 190.372\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}, {\"context\": \"Reposit\u00f3rio: obra/superpowers\\nURL: https://github.com/obra/superpowers\\nLinguagem: Shell\\nDescri\u00e7\u00e3o: An agentic skills framework & software development methodology that works.\\nStars: 212.935\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}]}"}}]


c0:
  {"results": [{"task_index": 0, "status": "completed", "summary": "Análise completa do repositório **x1xhlol/system-prompts-and-models-of-ai-tools**:\n\n---\n\n### Problema\nFalta de acesso centralizado e transparente aos **system prompts** e definições de ferramentas internas dos principais agentes/IDEs de IA. Esses artefatos são proprietários e raramente documentados/publicados oficialmente, dificultando auditoria de segurança, pesquisa em prompt engineering, e construção de ferramentas compatíveis.\n\n### Tema\nArquivo/documentação coletiva de **prompts de sistema e esquemas JSON/YAML de ferramentas** usados por agentes de código e assistentes de IA (Cursor, Claude Code, Devin, Manus, v0, Windsurf, etc.). Também inclui prompts abertos de projetos open source.\n\n### Razão do Hype\n- **Curiosidade massiva** sobre o “cérebro” de ferramentas populares (138k+ stars).\n- **Valor prático** para engenharia de prompt e desenvolvimento de agentes.\n- **Alerta de segurança** (explicitado no README): prompts expostos viram vetor de ataques, impulsionando discussões sobre proteção de segredos de IA.\n- **Facilidade de uso**: repositório organizado por ferramenta, com arquivos legíveis.\n- **Natureza de “vazamento/coleta”** que gera engajamento viral na comunidade dev.\n\n### Tecnologias Envolvidas\n- **Formatos de manifesto**: `.txt` (prompts), `.json` (tools/schemas), `.yaml` (configs).\n- **Alvos tecnológicos**: LLMs como Claude, GPT-5, Gemini, e seus SDKs/APIs.\n- **Ecossistemas das ferramentas coletadas**: majoritariamente TypeScript/JavaScript, Python, Shell, e ambientes como Electron/VS Code.\n- **Sem código executável próprio** — repositório de referência/documentação, sem linguagem de programação específica.\n\nNenhum arquivo foi criado ou modificado; a análise foi feita diretamente no clone local em `/home/ice/repo-analyze`.", "api_calls": 5, "duration_seconds": 26.72, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 41530, "output": 1768}, "tool_trace": [{"tool": "terminal", "args_bytes": 171, "result_bytes": 2109, "status": "ok"}, {"tool": "read_file", "args_bytes": 44, "result_bytes": 3721, "status": "ok"}, {"tool": "terminal", "args_bytes": 105, "result_bytes": 2731, "status": "ok"}, {"tool": "read_file", "args_bytes": 91, "result_bytes": 5492, "status": "ok"}, {"tool": "read_file", "args_bytes": 97, "result_bytes": 6165, "status": "ok"}, {"tool": "read_file", "args_bytes": 99, "result_bytes": 5259, "status": "ok"}, {"tool": "terminal", "args_bytes": 264, "result_bytes": 56, "status": "ok"}]}, {"task_index": 1, "status": "completed", "summary": "# Análise do Repositório: n8n-io/n8n\n\n## Problema\nO n8n resolve o problema de **automação de fluxos de trabalho (workflow automation)** para equipes técnicas. Ele preenche a lacuna entre no-code e código, permitindo que desenvolvedores criem automações complexas sem perder a flexibilidade de programar quando necessário. A dor principal atendida é a fragmentação de ferramentas de integração — em vez de usar múltiplos serviços SaaS fechados, o usuário tem uma plataforma auto-hospedável ou cloud com 400+ integrações nativas.\n\n## Tema\nPlataforma de automação de workflows — combina:\n- Interface visual \"node-based\" (arrastar e conectar)\n- Capacidade de código customizado (JavaScript/Python)\n- IA nativa (agentes LangChain, modelos próprios)\n- Foco em equipes técnicas e enterprise (permissões avançadas, SSO, air-gapped)\n\n## Razão do Hype\n1. **Fair-code + Self-hosted**: Licença Sustainable Use permite usar livremente, ao contrário de concorrentes fechados (Zapier, Make)\n2. **AI-native**: Integração direta com LangChain, agente de IA na UI — agradando o hype atual de AI Agents\n3. **Ctrl total sobre dados**: Muito relevante para empresas com compliance (healthcare, finance)\n4. **400+ integrações + 900+ templates**: Ecossistema crescente rapidamente\n5. **Open source com 190k stars**: Validação massiva da comunidade\n6. **Extensibilidade**: Criar nodes customizados, usar npm packages, código próprio nos fluxos\n7. **Versatilidade de deploy**: npx, Docker, Kubernetes, cloud próprio\n\n## Tecnologias\n\n### Backend\n- **Node.js + TypeScript** (>=22.22)\n- **Express** (servidor REST API)\n- **TypeORM** (ORM para banco de dados)\n- **Jest** (testes unitários)\n- **pnpm workspaces + Turborepo** (monorepo)\n- **Bull** (fila de jobs — com patch customizado)\n\n### Frontend\n- **Vue 3 + TypeScript**\n- **Vite**\n- **Pinia** (state management)\n- **Storybook** (UI library)\n- **Vitest** (testes frontend)\n- **@n8n/design-system** (design system próprio)\n- **Playwright** (E2E)\n\n### Infra/DevOps\n- **Docker** (distribuição principal)\n- **PostgreSQL / SQLite** (banco)\n- **PostHog** (feature flags)\n- **Linear** (ticket tracking)\n- **Biome** (formatting) + **ESLint** + **lefthook** (git hooks)\n- **OpenTelemetry** (observabilidade)\n\n### Padrões Arquiteturais\n- **Dependency Injection** via `@n8n/di`\n- **Controller-Service-Repository** no backend\n- **Event-driven** (event bus interno)\n- **Monorepo** com pacotes isolados: `cli`, `core`, `workflow`, `nodes-base`, `editor-ui`, `api-types`, `i18n`\n\n---\n\n**Resumo executivo**: n8n é um sucesso porque combinou modelagem visual de workflows com código real e IA — tudo sob licença open-source/fair-code que permite hospedagem própria. A stack técnica (TypeScript fullstack, Vue 3, Node) é moderna e acessível, enquanto a arquitetura de monorepo bem estruturada permite evolução rápida. O hype é justificado pela convergência de três megatrends: low-code, AI agents e data sovereignty.", "api_calls": 4, "duration_seconds": 28.92, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 36790, "output": 1472}, "tool_trace": [{"tool": "terminal", "args_bytes": 103, "result_bytes": 9198, "status": "ok"}, {"tool": "search_files", "args_bytes": 73, "result_bytes": 3660, "status": "ok"}, {"tool": "read_file", "args_bytes": 43, "result_bytes": 10303, "status": "ok"}, {"tool": "read_file", "args_bytes": 54, "result_bytes": 4073, "status": "ok"}]}, {"task_index": 2, "status": "completed", "summary": "## Análise do repositório `obra/superpowers`\n\n**Repositório analisado:** `/home/ice/superpowers` (clone de https://github.com/obra/superpowers)\n\n---\n\n### Problema\nAgentes de codificação (Claude Code, Codex CLI, Gemini CLI, Cursor, Copilot, etc.) tendem a pular direto para escrever código sem planejamento, resultando em implementações ruins, falta de testes e fluxos de trabalho ineficientes. Não há uma metodologia de desenvolvimento de software padronizada que oriente esses agentes através de um processo estruturado de análise, design, implementação e revisão.\n\n---\n\n### Tema\nFramework de skills para agentes de coding — uma metodologia completa de desenvolvimento de software composta por skills acionáveis que orientam o agente a: brainstorm antes de codificar, criar planos, executar via subagentes, aplicar TDD estrito, revisar código sistematicamente e fechar branches corretamente.\n\n---\n\n### Razão do Hype (212k+ stars)\n- **Não é mais um modelo de IA** — é uma camada que ensina agentes existentes a serem melhores engenheiros de software, algo que ainda falta no mercado.\n- **Framework-agnóstico** — funciona com Claude Code, Codex CLI, Codex App, Factory Droid, Gemini CLI, OpenCode, Cursor e GitHub Copilot CLI.\n- **Zero-dependência** — não requer ferramentas externas; adiciona valor puro ao fluxo do agente.\n- **Skills acionáveis automaticamente** — o agente carrega um bootstrap que dispara as skills certas nos momentos certos do processo.\n- **Metodologia já validada** — criada por Jesse Vincent (@obra) e a Prime Radiant, com release announcement e filosofia documentada.\n- O projeto resolve um problema real e universal: a diferença entre um agente \"escrever código\" e um agente \"fazer engenharia de software\".\n\n---\n\n### Tecnologias\n| Item | Detalhe |\n|------|---------|\n| **Linguagem principal** | Markdown (skills instrucionais) + Shell |\n| **Runtime/package** | JavaScript/Node.js (`package.json` v5.1.0) |\n| **Integração OpenCode** | `.opencode/plugins/superpowers.js` |\n| **Hooks** | `hooks/` (disparo automático de skills) |\n| **Skills** | 14 arquivos SKILL.md em `skills/` |\n| **Docs** | `docs/` com guias por harness |\n| **Extensões** | `gemini-extension.json` |\n| **Testes** | `tests/` |\n\n---\n\n### Skills incluídas\n1. `brainstorming`\n2. `writing-plans`\n3. `subagent-driven-development`\n4. `executing-plans`\n5. `test-driven-development`\n6. `requesting-code-review`\n7. `receiving-code-review`\n8. `finishing-a-development-branch`\n9. `using-git-worktrees`\n10. `systematic-debugging`\n11. `dispatching-parallel-agents`\n12. `verification-before-completion`\n13. `writing-skills`\n14. `using-superpowers`", "api_calls": 4, "duration_seconds": 22.1, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 23258, "output": 1331}, "tool_trace": [{"tool": "terminal", "args_bytes": 105, "result_bytes": 74, "status": "ok"}, {"tool": "terminal", "args_bytes": 54, "result_bytes": 8320, "status": "ok"}, {"tool": "read_file", "args_bytes": 70, "result_bytes": 9532, "status": "ok"}, {"tool": "read_file", "args_bytes": 46, "result_bytes": 262, "status": "ok"}, {"tool": "terminal", "args_bytes": 61, "result_bytes": 362, "status": "ok"}]}], "total_duration_seconds": 29.0} delegate_task 


c0:
  Falta pouco, só 5 mais para chegar em 20. Vou pegar os pesos-pesados de IA que ainda faltam dessa lista de alta recente:  [{"id": "chatcmpl-tool-b04a1e859c759a11", "call_id": "chatcmpl-tool-b04a1e859c759a11", "response_item_id": "fc_chatcmpl-tool-b04a1e859c759a11", "type": "function", "function": {"name": "delegate_task", "arguments": "{\"tasks\": [{\"context\": \"Reposit\u00f3rio: google-gemini/gemini-cli\\nURL: https://github.com/google-gemini/gemini-cli\\nLinguagem: TypeScript\\nDescri\u00e7\u00e3o: An open-source AI agent that brings the power of Gemini directly into your terminal.\\nStars: 104.753\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}, {\"context\": \"Reposit\u00f3rio: TauricResearch/TradingAgents\\nURL: https://github.com/TauricResearch/TradingAgents\\nLinguagem: Python\\nDescri\u00e7\u00e3o: TradingAgents: Multi-Agents LLM Financial Trading Framework\\nStars: 80.978\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}, {\"context\": \"Reposit\u00f3rio: Shubhamsaboo/awesome-llm-apps\\nURL: https://github.com/Shubhamsaboo/awesome-llm-apps\\nLinguagem: Python\\nDescri\u00e7\u00e3o: 100+ AI Agent & RAG apps you can actually run \u2014 clone, customize, ship.\\nStars: 112.264\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}]}"}}]


c0:
  {"results": [{"task_index": 0, "status": "completed", "summary": "## Resultado da análise\n\n- **Repositório:** `google-gemini/gemini-cli`\n- **O que fiz:** Clonei o repositório, examinei `README.md`, `package.json` e estrutura dos pacotes.\n\n### Problema\nGoogle precisava de acesso mais direto ao Gemini para devs, evitando interfaces web e reduzindo atrito para usar IA no fluxo local.\n\n### Tema\nAgente de IA generativa para terminal.\n\n### Razão do hype\n- Marca forte: Google / Gemini  \n- Modelo avançado: Gemini 3, com janela de até 1M tokens  \n- Custo de entrada baixo: tier gratuito generoso  \n- Extensibilidade: suporte a MCP  \n- Ecossistema dev: CLI first, scripts headless, GitHub Action, VS Code companion  \n- Open source com licença permissiva: Apache 2.0\n\n### Tecnologias\n- TypeScript\n- React + Ink\n- MCP\n- A2A\n- esbuild\n- node-pty\n- OpenTelemetry / gRPC\n- Shell: webfetch, shell-quote, simple-git", "api_calls": 6, "duration_seconds": 30.76, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 65817, "output": 1242}, "tool_trace": [{"tool": "terminal", "args_bytes": 114, "result_bytes": 73, "status": "ok"}, {"tool": "terminal", "args_bytes": 48, "result_bytes": 2411, "status": "ok"}, {"tool": "search_files", "args_bytes": 78, "result_bytes": 609, "status": "ok"}, {"tool": "read_file", "args_bytes": 42, "result_bytes": 15532, "status": "ok"}, {"tool": "read_file", "args_bytes": 45, "result_bytes": 9037, "status": "ok"}, {"tool": "search_files", "args_bytes": 129, "result_bytes": 510, "status": "ok"}, {"tool": "read_file", "args_bytes": 85, "result_bytes": 3286, "status": "ok"}, {"tool": "read_file", "args_bytes": 86, "result_bytes": 4969, "status": "ok"}, {"tool": "read_file", "args_bytes": 43, "result_bytes": 6322, "status": "ok"}, {"tool": "terminal", "args_bytes": 82, "result_bytes": 2170, "status": "ok"}]}, {"task_index": 1, "status": "completed", "summary": "## Análise do Repositório: TauricResearch/TradingAgents\n\n**Repositório clonado e analisado em:** `/home/ice/TradingAgents`\n\n### Problema\nSistemas de trading tradicionais e até mesmo alguns algorítmicos frequentemente atuam como \"caixas pretas\" ou componentes isolados. Um único modelo ou um conjunto rígido de regras toma decisões sem um processo de avaliação robusto e multifacetado que espelhe como *trading firms* do mundo real realmente operam. Isso pode levar à overlook de sinais críticos (ex.: um forte indicador técnico sendo minado por notícias negativas) e à falta de um debate dinâmico necessário para equilibrar oportunidade agressiva contra risco conservador.\n\n### Tema\n**Framework de Trading Colaborativo Multi-Agente baseado em LLMs.** O tema central é simular o fluxo de trabalho de uma *trading firm* humana usando um conjunto de agentes LLM especializados que debatem, pesquisam e executam trades de forma colaborativa. Trata-se de aplicar orquestração avançada de IA (sistemas multi-agente) ao domínio das finanças quantitativas.\n\n### Razão do Hype\n- **Capital social e prestígio:** Com ~81k stars, é de longe o projeto open-source mais popular do gênero, superando concorrentes.\n- **Rigor acadêmico:** É respaldado por um paper no arXiv (`2412.20138`) e uma equipe do `TauricResearch`, dando-lhe credibilidade além de um projeto de hobby.\n- **Stack tecnológico de ponta:** Usa `LangGraph`, framework moderno e altamente conceituado para construir workflows agentivos.\n- **Arquitetura abrangente:** Vai além da simples predição de compra/venda; incorpora análise fundamentalista, sentimento, notícias, técnica, debate touro/urso, decisão do trader e *risk management* — uma simulação muito completa e realista.\n- **Usabilidade prática:** Oferece tanto uma CLI interativa quanto uma API Python, suporta *backtesting*, lida com recursos complexos como memória persistente (logs de decisão), *checkpoint resume* e suporte multi-provedor de LLMs (OpenAI, Anthropic, Google, etc.), tornando-se uma ferramenta genuína para pesquisa e experimentação.\n- **Apelo viral:** O tema de IA e trading é inerentemente fascinante para desenvolvedores e entusiastas de finanças, impulsionando um interesse massivo.\n\n### Tecnologias\n- **Linguagem:** Python\n- **Orquestração de Agentes:** `LangGraph`, `langchain-core`\n- **Integração LLM:** `langchain-openai`, `langchain-anthropic`, `langchain-google-genai` (suporta GPT, Claude, Gemini, Grok, DeepSeek, Qwen, GLM, MiniMax, Ollama)\n- **Fontes de Dados:** `yfinance`, `alpha_vantage`, APIs de notícias, `reddit`, `stocktwits`\n- **Análise de Dados:** `pandas`, `stockstats`\n- **Backtesting:** `backtrader`\n- **Persistência:** `langgraph-checkpoint-sqlite`, Redis\n- **CLI:** `typer`, `rich`, `questionary`\n- **Build/Package:** `setuptools`, `uv` (inferred pelo `uv.lock`)\n\n---\n\n### Resumo Executivo para o Agente Pai\n**O que foi feito:** Clonei o repositório completo e examinei os arquivos principais (`README.md`, `pyproject.toml`, `main.py`, `tradingagents/graph/trading_graph.py` e lista de arquivos).\n\n**O que foi encontrado:** Projeto Python maduro e bem estruturado que implementa um sistema de trading simulado multi-agente usando LLMs, orquestrado via LangGraph. Possui arquitetura modular (analistas, pesquisadores, trader, *risk management*), suporte a múltiplos provedores de LLM e fontes de dados, além de funcionalidades avançadas como memória persistente e *checkpoint resume*.\n\n**Arquivos criados/modificados:** Nenhum arquivo foi modificado; apenas leitura.\n\n**Problemas encontrados:** Nenhum problema técnico relevante foi encontrado durante a análise estática do código. Dependências parecem bem definidas no `pyproject.toml`.", "api_calls": 11, "duration_seconds": 49.01, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 109824, "output": 3149}, "tool_trace": [{"tool": "terminal", "args_bytes": 116, "result_bytes": 86, "status": "ok"}, {"tool": "search_files", "args_bytes": 99, "result_bytes": 3380, "status": "ok"}, {"tool": "terminal", "args_bytes": 106, "result_bytes": 544, "status": "ok"}, {"tool": "read_file", "args_bytes": 45, "result_bytes": 15963, "status": "ok"}, {"tool": "search_files", "args_bytes": 117, "result_bytes": 18, "status": "ok"}, {"tool": "search_files", "args_bytes": 72, "result_bytes": 3380, "status": "ok"}, {"tool": "search_files", "args_bytes": 113, "result_bytes": 6785, "status": "ok"}, {"tool": "read_file", "args_bytes": 52, "result_bytes": 115, "status": "ok"}, {"tool": "read_file", "args_bytes": 72, "result_bytes": 19975, "status": "ok"}, {"tool": "read_file", "args_bytes": 50, "result_bytes": 1702, "status": "ok"}, {"tool": "read_file", "args_bytes": 43, "result_bytes": 941, "status": "ok"}]}, {"task_index": 2, "status": "completed", "summary": "Resumo da análise:\n\n- O que fiz: clonei o repositório em `/home/ice/awesome-llm-apps`, explorei `README.md` e listei os diretórios/arquivos para entender o escopo.\n- Problema: desenvolvedores repetidamente recriam pipelines de RAG, loops de agentes e integrações MCP do zero, sem referências testadas e prontas para adaptar.\n- Tema: repositório-receituário/templates de aplicações LLM executáveis, focado em agentes, RAG, MCP, voz, skills, otimização e fine-tuning.\n- Razão do hype: volume grande de templates funcionais, frequente atualização com tendências atuais do mercado, curadoria própria (não só links), múltiplos providers/modelos e foco em executar rapidamente.\n- Tecnologias: Python; interfaces como Streamlit; RAG com embeddings e bancos vetoriais; frameworks de agentes/MM Labs como ADK, CrewAI e AG2; MCP; voz via APIs de tempo real (OpenAI RealTime, Gemini Live); multimodal; fine-tuning com Gemma e Llama; e integrações com GitHub, Notion, Gmail, PDF, YouTube, Substack, ArXiv.", "api_calls": 4, "duration_seconds": 29.8, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 32609, "output": 936}, "tool_trace": [{"tool": "terminal", "args_bytes": 109, "result_bytes": 79, "status": "ok"}, {"tool": "search_files", "args_bytes": 73, "result_bytes": 4422, "status": "ok"}, {"tool": "read_file", "args_bytes": 75, "result_bytes": 13605, "status": "ok"}, {"tool": "read_file", "args_bytes": 77, "result_bytes": 8683, "status": "ok"}]}], "total_duration_seconds": 49.09} delegate_task 


c0:
    [{"id": "chatcmpl-tool-97ff4a57a7757cdf", "call_id": "chatcmpl-tool-97ff4a57a7757cdf", "response_item_id": "fc_chatcmpl-tool-97ff4a57a7757cdf", "type": "function", "function": {"name": "delegate_task", "arguments": "{\"tasks\": [{\"context\": \"Reposit\u00f3rio: firecrawl/firecrawl\\nURL: https://github.com/firecrawl/firecrawl\\nLinguagem: TypeScript\\nDescri\u00e7\u00e3o: The API to search, scrape, and interact with the web at scale. \ud83d\udd25\\nStars: 126.493\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}, {\"context\": \"Reposit\u00f3rio: hacksider/Deep-Live-Cam\\nURL: https://github.com/hacksider/Deep-Live-Cam\\nLinguagem: Python\\nDescri\u00e7\u00e3o: real time face swap and one-click video deepfake with only a single image\\nStars: 93.440\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}]}"}}]


c0:
  {"results": [{"task_index": 0, "status": "completed", "summary": "## Análise do repositório firecrawl/firecrawl\n\n**O que fiz**\n- Clonei o repositório (shallow) em `/home/ice/firecrawl`\n- Examinei `README.md`, `package.json` do `apps/api`, estrutura de pastas e os engines de scraping\n- Mapeei a stack e a organização do monorepo\n\n**O que encontrei**\n- **Problema:** Scraping web confiável e em escala ainda é difícil: páginas JS-heavy, proxies, rate limits, bloqueios e extração de conteúdo “limpo” pronta para IAs.\n- **Tema:** API/plataforma de dados web para buscar, raspar, interagir e crawl em grande escala, com saída preparada para LLMs/agentes.\n- **Razão hype:**\n  - Foco em IA/Agents: saída Markdown/JSON limpa, menor consumo de tokens, integração fácil com MCP/agentes.\n  - Claims fortes: cobertura de 96% da web, P95 de ~3.4s, suporte a PDF/DOCX e ações como click/scroll/write.\n  - Ecossistema aberto e amplo: múltiplos SDKs e workers especializados.\n- **Tecnologias principais:**\n  - Core: TypeScript, Express, Redis/BullMQ, PostgreSQL/Supabase, Playwright\n  - Engine proprietário: Rust (`@mendable/firecrawl-rs`)\n  - Dados/AI: ClickHouse, Sentry, Winston, Prom Client, diversos provedores LLM (OpenAI, Anthropic, Google, Groq, xAI, OpenRouter)\n  - SDKs: Python, JS/TS, Go, Java, .NET, Ruby, PHP, Elixir, Rust\n\nNenhum arquivo foi criado ou modificado. A análise foi feita por leitura do repositório e inspeção de estrutura.", "api_calls": 7, "duration_seconds": 35.94, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 61103, "output": 1950}, "tool_trace": [{"tool": "terminal", "args_bytes": 118, "result_bytes": 1603, "status": "ok"}, {"tool": "terminal", "args_bytes": 52, "result_bytes": 238, "status": "ok"}, {"tool": "read_file", "args_bytes": 55, "result_bytes": 4742, "status": "ok"}, {"tool": "read_file", "args_bytes": 57, "result_bytes": 169, "status": "error"}, {"tool": "terminal", "args_bytes": 96, "result_bytes": 531, "status": "ok"}, {"tool": "read_file", "args_bytes": 53, "result_bytes": 9708, "status": "ok"}, {"tool": "read_file", "args_bytes": 56, "result_bytes": 949, "status": "ok"}, {"tool": "terminal", "args_bytes": 57, "result_bytes": 234, "status": "ok"}, {"tool": "terminal", "args_bytes": 94, "result_bytes": 2441, "status": "ok"}, {"tool": "terminal", "args_bytes": 139, "result_bytes": 2177, "status": "ok"}, {"tool": "terminal", "args_bytes": 111, "result_bytes": 1237, "status": "ok"}, {"tool": "terminal", "args_bytes": 85, "result_bytes": 685, "status": "ok"}, {"tool": "terminal", "args_bytes": 123, "result_bytes": 84, "status": "ok"}]}, {"task_index": 1, "status": "completed", "summary": "Análise concluída. Aqui está o resumo solicitado:\n\n---\n\n## hacksider/Deep-Live-Cam — Análise\n\n**Tema:** IA Generativa / Deepfake — troca de face (face swap) em tempo real em webcam, vídeo e imagem, com foco em acessibilidade para usuários não técnicos.\n\n**Problema resolvido:**  \nFerramentas profissionais de deepfake são complexas, exigem treinamento de modelo, coleções de imagens, intervenção frame a frame e poder computacional especializado. Deep-Live-Cam reduz isso para: uma única imagem de referência + um clique, com interface gráfica simples (PySide6) e suporte a múltiplos hardwares.\n\n**Razão do hype (~93k stars, cobertura midiática massiva):**\n- **Extrema facilidade de uso** — fluxo “3 cliques”: selecionar imagem → selecionar câmera/vídeo → “live”.\n- **Real-time** — opera na webcam ao vivo e em vídeos em tempo real, possibilitando transmissões, memes e videoconferências.\n- **Viralização por influenciadores** — IShowSpeed, SomeOrdinaryGamers, Linus Tech Tips (TechLinked) e veículos como Ars Technica, CNN Brasil, Bloomberg.\n- **Multi-plataforma e multi-GPU** — funciona em CPU, NVIDIA (CUDA + cuDNN), AMD (DirectML), Apple Silicon (CoreML/ANE) e Intel (OpenVINO).\n- **Recursos únicos** — Mouth Mask (preserva boca original), Face Mapping (multi-face simultâneo), melhorias faciais (GFPGAN), filtro NSFW embutido, suporte a diversos idiomas via locales.\n- **Modelo de negócio híbrido** — versão open source robusta + versão “pre-built” paga com 30+ recursos extras (2.7 RC2), o que amplia alcance financeiro e suporte.\n\n**Tecnologias principais:**\n- **Python** (linguagem core)\n- **InsightFace** — detecção, alinhamento e embedding facial (`insightface.app.FaceAnalysis`, modelo `buffalo_l`)\n- **ONNX / ONNX Runtime** — runtime de inferência multimodal (CPU, CUDA, CoreML, DirectML, OpenVINO)\n- **inswapper_128_fp16** — modelo ONNX pré-treinado para troca de face\n- **GFPGANv1.4** — restauração/reconstrução facial (face enhancer)\n- **OpenCV (`cv2`)** — captura de webcam/vídeo e processamento frame a frame\n- **FFmpeg** — manipulação de vídeo, extração de frames e restauração de áudio\n- **TensorFlow / opennsfw2** — filtragem de conteúdo NSFW\n- **PySide6** — interface gráfica (GUI)\n- **Multithreading + otimizações específicas** — pré-carregamento de DLLs no Windows, `ctypes.RTLD_GLOBAL` no Linux, otimizações CoreML/ANE para RetinaFace no Apple Silicon\n\n---\n\n**Arquivos consultados (não modificados):**  \n`README.md`, `requirements.txt`, `run.py`, `modules/run.py`, `modules/core.py`, `modules/face_analyser.py`, lista de arquivos do repositório.", "api_calls": 5, "duration_seconds": 35.23, "model": "stepfun/step-3.7-flash:free", "exit_reason": "completed", "tokens": {"input": 39902, "output": 2044}, "tool_trace": [{"tool": "terminal", "args_bytes": 118, "result_bytes": 76, "status": "ok"}, {"tool": "terminal", "args_bytes": 104, "result_bytes": 1293, "status": "ok"}, {"tool": "read_file", "args_bytes": 72, "result_bytes": 7980, "status": "ok"}, {"tool": "read_file", "args_bytes": 52, "result_bytes": 684, "status": "ok"}, {"tool": "read_file", "args_bytes": 42, "result_bytes": 3609, "status": "ok"}, {"tool": "read_file", "args_bytes": 50, "result_bytes": 297, "status": "ok"}, {"tool": "read_file", "args_bytes": 74, "result_bytes": 9802, "status": "ok"}, {"tool": "read_file", "args_bytes": 65, "result_bytes": 6816, "status": "ok"}, {"tool": "read_file", "args_bytes": 74, "result_bytes": 4139, "status": "ok"}]}], "total_duration_seconds": 35.98} delegate_task 


c0:
  Pronto! Os 20 repositórios mais estrelados e ativos do último mês foram analisados por agentes paralelos. Aqui está o resultado estruturado com links clicáveis:

  ---

  ## 🏆 TOP 20 REPOSITÓRIOS EM ALTA — ÚLTIMO MÊS
  *Repos atualizados recentemente com maior volume de estrelas*

  ---

  ### 🤖 Agentes de IA & Orquestração

  | # | Repo | Stars | Linguagem | Problema | Por que trending |
  |---|------|-------|-----------|----------|------------------|
  | 1 | [openclaw/openclaw](https://github.com/openclaw/openclaw) | 375K | TypeScript | Assistente pessoal local-first multi-canal | 20+ canais (WhatsApp, Telegram, Discord, iMessage), local-first, multi-agent routing |
  | 2 | [obra/superpowers](https://github.com/obra/superpowers) | 213K | Shell | Framework de skills para agentes de coding | Metodologia completa (brainstorm→TDD→code review→git), funciona com Claude Code, Codex, Gemini |
  | 3 | [Significant-Gravitas/AutoGPT](https://github.com/Significant-Gravitas/AutoGPT) | 185K | Python | Plataforma de agentes autônomos acessível | Pioneiro em agentes contínuos, marketplace, editor visual low-code |
  | 4 | [lobehub/lobehub](https://github.com/lobehub/lobehub) | 78K | TypeScript | "Chief Agent Operator" para equipes de IA | Orquestração 24/7 de agentes, memória estruturada, 10k+ plugins |

  ---

  ### 🧠 Plataformas LLM & RAG

  | # | Repo | Stars | Linguagem | Problema | Por que trending |
  |---|------|-------|-----------|----------|------------------|
  | 5 | [langchain-ai/langchain](https://github.com/langchain-ai/langchain) | 138K | Python | Orquestração de agentes e workflows LLM | Abstrações modulares, LangGraph + LangSmith, ecossistema massivo |
  | 6 | [langgenius/dify](https://github.com/langgenius/dify) | 143K | TypeScript | Plataforma production-ready para apps LLM | Visual builder + RAG + agentes, 50+ ferramentas, deploy self-hosted |
  | 7 | [langflow-ai/langflow](https://github.com/langflow-ai/langflow) | 149K | Python | Builder visual de fluxos de IA low-code | Deploy como API/MCP, biblioteca crescente de componentes |
  | 8 | [infiniflow/ragflow](https://github.com/infiniflow/ragflow) | 82K | Python | Motor RAG + Agent para redução de alucinações | Chunking inteligente, citações verificáveis, GraphRAG, multi-fonte |

  ---

  ### ⚡ Inferência & Serving de LLMs

  | # | Repo | Stars | Linguagem | Problema | Por que trending |
  |---|------|-------|-----------|----------|------------------|
  | 9 | [ollama/ollama](https://github.com/ollama/ollama) | 173K | Go | Rodar LLMs localmente sem complexidade | `ollama run <modelo>`, suporte multi-modelo, API REST simples |
  | 10 | [vllm-project/vllm](https://github.com/vllm-project/vllm) | 81K | Python | Servir LLMs grandes com alta performance | PagedAttention (2-4x menos memória), 200+ arquiteturas, GPU/CPU/TPU |

  ---

  ### 🖥️ Interfaces & UIs para IA

  | # | Repo | Stars | Linguagem | Problema | Por que trending |
  |---|------|-------|-----------|----------|------------------|
  | 11 | [open-webui/open-webui](https://github.com/open-webui/open-webui) | 139K | Python | Interface web amigável para LLMs self-hosted | RAG nativo, 9 bancos vetoriais, voz/vídeo, PWA offline-first |
  | 12 | [google-gemini/gemini-cli](https://github.com/google-gemini/gemini-cli) | 105K | TypeScript | Gemini direto no terminal | Google + Gemini 3 (1M tokens), tier gratuito, MCP, Apache 2.0 |

  ---

  ### 🌐 Web Scraping & Automação

  | # | Repo | Stars | Linguagem | Problema | Por que trending |
  |---|------|-------|-----------|----------|------------------|
  | 13 | [firecrawl/firecrawl](https://github.com/firecrawl/firecrawl) | 126K | TypeScript | API de scraping pronta para IA/agentes | P95 3.4s, 9 SDKs, saída Markdown/JSON limpa, engine Rust proprietário |
  | 14 | [browser-use/browser-use](https://github.com/browser-use/browser-use) | 96K | Python | Automate websites com agentes de IA | Browser agent com CDP, stealth nativo, benchmark aberto, multi-LLM |

  ---

  ### 💰 Finanças & Trading

  | # | Repo | Stars | Linguagem | Problema | Por que trending |
  |---|------|-------|-----------|----------|------------------|
  | 15 | [TauricResearch/TradingAgents](https://github.com/TauricResearch/TradingAgents) | 81K | Python | Trading com múltiplos agentes LLM | Simula trading firm com debate touro/urso, backtesting, paper arXiv |

  ---

  ### 🛠️ Dev Tools & Workflow

  | # | Repo | Stars | Linguagem | Problema | Por que trending |
  |---|------|-------|-----------|----------|------------------|
  | 16 | [n8n-io/n8n](https://github.com/n8n-io/n8n) | 190K | TypeScript | Automação de workflows fair-code | Visual builder + código + IA nativa, 400+ integrações, self-hosted |
  | 17 | [OpenHands/OpenHands](https://github.com/OpenHands/OpenHands) | 75K | Python | Coding agent autônomo open-source | Comparável a Devin, benchmarks SWEBench, CLI + GUI, enterprise-ready |

  ---

  ### 📚 Recursos & Referências

  | # | Repo | Stars | Linguagem | Problema | Por que trending |
  |---|------|-------|-----------|----------|------------------|
  | 18 | [x1xhlol/system-prompts-and-models-of-ai-tools](https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools) | 139K | N/A | Centralizar prompts de sistema de ferramentas de IA | Curadoria de 30+ ferramentas (Cursor, Claude Code, Devin, v0), valor para prompt engineering |
  | 19 | [Shubhamsaboo/awesome-llm-apps](https://github.com/Shubhamsaboo/awesome-llm-apps) | 112K | Python | Templates executáveis de apps LLM | 100+ apps prontas (RAG, agentes, MCP, voz), clone e rode |

  ---

  ### 🎭 Entretenimento & Multimídia

  | # | Repo | Stars | Linguagem | Problema | Por que trending |
  |---|------|-------|-----------|----------|------------------|
  | 20 | [hacksider/Deep-Live-Cam](https://github.com/hacksider/Deep-Live-Cam) | 93K | Python | Face swap em tempo real com 1 clique | Viral por influenciadores, multi-plataforma (CPU/CUDA/CoreML), interface simples |

  ---

  ## 📊 Resumo por Tema

  - **Agentes de IA**: 4 repos — openclaw, AutoGPT, superpowers, lobehub
  - **Plataformas LLM/RAG**: 4 repos — Langchain, Dify, Langflow, RAGFlow
  - **Inferência**: 2 repos — Ollama, vLLM
  - **Interfaces**: 2 repos — OpenWebUI, Gemini CLI
  - **Web/Automação**: 2 repos — Firecrawl, browser-use
  - **Finanças**: 1 repo — TradingAgents
  - **Dev Tools**: 2 repos — n8n, OpenHands
  - **Recursos**: 2 repos — System Prompts, Awesome LLM Apps
  - **Multimídia**: 1 repo — Deep-Live-Cam

  Quer que eu detalhe algum repositório específico ou calibre a seleção de alguma forma?  


c0:
  [CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Respond ONLY to the latest user message that appears AFTER this summary — that message is the single source of truth for what to do right now. If the latest user message is consistent with the '## Active Task' section, you may use the summary as background. If the latest user message contradicts, supersedes, changes topic from, or in any way diverges from '## Active Task' / '## In Progress' / '## Pending User Asks' / '## Remaining Work', the latest message WINS — discard those stale items entirely and do not 'wrap up the old task first'. Reverse signals in the latest message (e.g. 'stop', 'undo', 'roll back', 'just verify', 'don't do that anymore', 'never mind', a new topic) must immediately end any in-flight work described in the summary; do not re-surface it in later turns. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:
  ## Active Task
  None (user's request for 20 trending repos with analysis and thematic grouping was fulfilled; awaiting potential follow-up on trendshift.io access or recurring reporting setup)

  ## Goal
  The user aims to track GitHub trending repository performance via trendshift.io (an analytics platform with weekly star gain rankings and project evolution graphs), receive structured analysis of top repositories (weekly/monthly) including problem statement, use case, and trending rationale, and automate recurring reporting if desired. Additionally, explored multi-agent orchestration capabilities and repository trustworthiness assessment.

  ## Constraints & Preferences
  - User explicitly prefers data sourced from trendshift.io over generic GitHub Trending, though accepted temporary fallback to GitHub Trending when trendshift.io was inaccessible.
  - Requests structured reports with consistent fields: repo name, programming language, total stars, link, problem solved, and reason for trending.
  - Prefers reports delivered directly in the chat interface.
  - Open to automating recurring reporting via cronjob.
  - For repository comparisons, requires explicit trustworthiness assessment.
  - For trending reports, wants repositories grouped by thematic category (IA, dev, etc.) with clickable links to GitHub repositories.

  ## Completed Actions
  1. ATTEMPTED browser_navigate to https://github.com/trending — failed due to missing local system browser dependencies [tool: browser_navigate]
  2. RAN web_search for "GitHub trending repositories this week top most stars" — retrieved general search results for context [tool: web_search]
  3. EXECUTED curl on https://github.com/trendding — retrieved raw HTML, extracted top 10 weekly trending repositories [tool: terminal]
  4. RAN execute_code (Python: requests, re, textwrap) to parse GitHub Trending weekly HTML, extract repo metadata, fetch and parse READMEs for all 10 weekly repos — generated full weekly analysis report [tool: execute_code]
  5. QUERIED candidate analytics sites (gitstar-ranking.com, star-history.com, ossinsight.io) via curl — confirmed OSS Insight matches description but data is client-side JS-rendered and inaccessible [tool: terminal]
  6. RAN bulk domain lookup for gitstar/gitrank variant domains — found gitstar.xyz returns HTTP 200 but is parked/for sale [tool: terminal, execute_code]
  7. CONFIRMED trendshift.io as user-specified analytics site after user provided URL https://trendshift.io/ [tool: user input]
  8. ATTEMPTED to fetch trendshift.io/repositories/weekly and multiple API endpoints — all returned Next.js JS shell, 404, or 405 errors [tool: terminal]
  9. ATTEMPTED to extract API endpoints from trendshift.io JS bundles — failed to locate usable public data source [tool: execute_code]
  10. GENERATED fallback weekly report using GitHub Trending data, explicitly noted source is fallback [tool: execute_code]
  11. EXECUTED curl on https://github.com/trending?since=monthly — retrieved raw HTML for monthly trending page [tool: terminal]
  12. RAN execute_code (Python: requests, re) to parse GitHub Trending monthly HTML, extract top 10 repositories [tool: execute_code]
  13. RAN execute_code (Python: requests, concurrent.futures) to fetch metadata and README summaries for 10 monthly trending repos — generated full monthly analysis report [tool: execute_code]
  14. PRESENTED monthly fallback report to user, with follow-up options — initial send_message failed due to chat not found, re-sent successfully [tool: send_message, execute_code]
  15. INSTALLED Playwright and Chromium via npx in /home/ice/trendshift-project [tool: terminal]
  16. ATTEMPTED to install and configure Cloak Browser as alternative per user suggestion [tool: terminal, write_file]
  17. IDENTIFIED missing system dependency libglib2.0.so.0 preventing Chromium execution [tool: terminal]
  18. ATTEMPTED browser automation on https://share.google/eQH9zakjhcCu1QPML — redirected to hermes-workspace.com [tool: browser_navigate, terminal]
  19. FETCHED GitHub API metadata for both outsourc-e/hermes-workspace and NousResearch/hermes-agent [tool: terminal, execute_code]
  20. READ README.md of outsourc-e/hermes-workspace to assess trustworthiness [tool: terminal]
  21. PROVIDED comparative trustworthiness analysis: hermes-workspace is legitimate complementary UI project, not official but not malicious [tool: execute_code]
  22. EXPLAINED multi-agent delegation capabilities (delegate_task) to user [tool: skill_view]
  23. DEMONSTRATED multi-agent orchestration: spawned 7 batches of parallel agents (3 per batch) to analyze 20 GitHub repositories [tool: delegate_task]
  24. FETCHED list of 30 most-starred repositories updated in last 30 days via GitHub Search API [tool: execute_code]
  25. ANALYZED repositories: openclaw/openclaw, Significant-Gravitas/AutoGPT, ollama/ollama, open-webui/open-webui, infiniflow/ragflow, vllm-project/vllm, langflow-ai/langflow, langgenius/dify, browser-use/browser-use, langchain-ai/langchain, lobehub/lobehub, OpenHands/OpenHands, x1xhlol/system-prompts-and-models-of-ai-tools, n8n-io/n8n, obra/superpowers, google-gemini/gemini-cli, TauricResearch/TradingAgents, Shubhamsaboo/awesome-llm-apps [tool: delegate_task]
  26. GROUPED results into thematic categories: IA/ML, Dev Tools, Security, Data/Finance, OSINT, Gamification/UX, Creative Automation [tool: delegate_task]

  ## Active State
  - Working directory: /home/ice/trendshift-project (created for Playwright/Cloak Browser attempt)
  - Created files: /home/ice/trendshift-project/fetch-trendshift.mjs (Playwright/Cloak script, non-functional)
  - Installed packages: playwright-core@latest, cloakbrowser@latest in /home/ice/trendshift-project
  - System dependency libglib2.0.so.0 missing, blocking headless Chromium execution
  - 20 repository analyses completed via multi-agent delegation; results presented in chat
  - No persistent local files from analysis (agents operated in isolated environments)

  ## In Progress
  No active in-progress tasks; last completed action was delivering the thematic grouping of 20 trending repositories.

  ## Blocked
  - System dependency libglib2.0.so.0 (and other Chromium libraries) not installed; prevents Playwright/Cloak Browser execution. User provided install command: `sudo apt-get install libglib2.0-0 libnss3 libatk1.0-0 libatk-bridge2.0-0 libcups2 libdrm2 libgbm1 libpango-1.0-0 libasound2`
  - Direct access to trendshift.io remains blocked: site is fully client-side JS-rendered, no public API endpoints located. All tested endpoints return 404/405 or empty JS shells.

  ## Key Decisions
  1. Fallback to GitHub Trending as data source after exhausting all options to access trendshift.io directly.
  2. Used Python requests + regex parsing to extract repo metadata and README summaries from GitHub Trending HTML due to unavailable browser automation.
  3. Switched search strategy from "repositories created in last 30 days" to "repositories updated in last 30 days with high star counts" to capture trending projects regardless of age.
  4. Employed multi-agent delegation (delegate_task) to parallelize analysis of 20 repositories, completing in 7 batches of 3.
  5. Adopted thematic clustering of results by domain (IA, Dev Tools, Security, etc.) rather than numerical ranking.
  6. Switched from Playwright to Cloak Browser per user suggestion for more permissive browser automation, but both blocked by missing system libraries.

  ## Resolved Questions
  1. User asked for top 10 weekly trending GitHub repos with individual README analysis and structured report: Delivered full report including repo name, language, total stars, link, problem solved, and trending rationale for all 10 repos.
  2. User asked to identify the analytics site with weekly star gain rankings and evolution graphs: Confirmed trendshift.io as correct site after user provided URL, ruled out OSS Insight and gitstar.xyz.
  3. User asked for top 10 monthly trending repos with same analysis: Delivered full report using GitHub Trending monthly fallback data.
  4. User asked to analyze repository at https://share.google/eQH9zakjhcCu1QPML and compare with official Hermes Agent: Determined it is outsourc-e/hermes-workspace, a legitimate complementary UI project, not official NousResearch/hermes-agent but not malicious.
  5. User asked about multi-agent functionality: Demonstrated via delegate_task, spawning parallel agents for repository analysis.
  6. User requested 20 most-starred repositories from last month with thematic grouping: Delivered analysis of 20 repositories grouped by theme (IA/ML, Dev Tools, Security, Data/Finance, OSINT, Gamification/UX, Creative Automation) with clickable links.

  ## Pending User Asks
  None (all requests fulfilled; awaiting potential follow-up on trendshift.io access or recurring reporting setup)

  ## Relevant Files
  - https://github.com/trending (weekly): Source for weekly trending repository list
  - https://github.com/trending?since=monthly: Source for monthly trending repository list
  - https://trendshift.io/repositories/weekly: User-specified target analytics site, inaccessible
  - https://share.google/eQH9zakjhcCu1QPML: Redirects to hermes-workspace.com
  - /home/ice/trendshift-project/fetch-trendshift.mjs: Created Playwright/Cloak Browser script (non-functional)
  - /home/ice/trendshift-project/package.json: npm project initialized
  - /home/ice/trendshift-project/node_modules/: playwright-core and cloakbrowser installed

  ## Remaining Work
  1. If user confirms fallback is acceptable, execute selected next step (generate .md file, fetch daily top 10, configure cronjob).
  2. If user requests continued access to trendshift.io, install missing system libraries and retry Playwright/Cloak Browser.
  3. If user requests trendshift.io-specific data (star gain rankings, evolution graphs), prioritize implementing access to that platform's data sources.

  ## Critical Context
  - Trendshift.io is fully client-side rendered Next.js application; all standard HTTP requests return empty JS hydration shells with no embedded data, and no public API endpoints are documented or discoverable.
  - GitHub Trending weekly/monthly lists successfully parsed via curl + Python regex, with README summaries fetched via GitHub public API.
  - User explicitly prefers trendshift.io as data source due to star gain ranking and evolution graph features; GitHub Trending fallback is temporary workaround.
  - All trendshift.io access attempts returned consistent errors: HTTP 200 with empty JS shell for page routes, HTTP 404/405 for all tested API endpoints.
  - Multi-agent delegation capability demonstrated successfully with 7 parallel batches analyzing 20 repositories.
  - User provided specific system dependencies needed for Chromium: libglib2.0-0, libnss3, libatk1.0-0, libatk-bridge2.0-0, libcups2, libdrm2, libgbm1, libpango-1.0-0, libasound2.
  - Cloak Browser (https://cloakbrowser.dev/) investigated as alternative to Playwright but blocked by same system dependencies.

  --- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---

    [{"id": "chatcmpl-tool-97ff4a57a7757cdf", "call_id": "chatcmpl-tool-97ff4a57a7757cdf", "response_item_id": "fc_chatcmpl-tool-97ff4a57a7757cdf", "type": "function", "function": {"name": "delegate_task", "arguments": "{\"tasks\": [{\"context\": \"Reposit\u00f3rio: firecrawl/firecrawl\\nURL: https://github.com/firecrawl/firecrawl\\nLinguagem: TypeScript\\nDescri\u00e7\u00e3o: The API to search, scrape, and interact with the web at scale. \ud83d\udd25\\nStars: 126.493\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}, {\"context\": \"Reposit\u00f3rio: hacksider/Deep-Live-Cam\\nURL: https://github.com/hacksider/Deep-Live-Cam\\nLinguagem: Python\\nDescri\u00e7\u00e3o: real time face swap and one-click video deepfake with only a single image\\nStars: 93.440\\n\\nAnalise e retorne: Problema, Tema, Raz\u00e3o hype, Tecnologias\", \"goal\": \"Analisar reposit\u00f3rio\", \"toolsets\": [\"web\", \"terminal\", \"file\"]}]}"}}]


