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Web search is handled by the **Tavily API**.\n\n## Output format\n\nEvery report includes:\n- **Executive Summary** — top-level overview\n- **Per-subtopic sections** — detailed findings with inline source attribution\n- **Key Takeaways** — distilled conclusions\n- **Sources** — numbered markdown links to every URL found during research\n\n## Project structure\n\n```\nAutoResearch-Agent/\n├── backend/\n│   ├── agents/\n│   │   ├── planner.py      # breaks query into subtopics\n│   │   ├── researcher.py   # web search + fact collection\n│   │   ├── analyst.py      # deduplication + credibility ranking\n│   │   └── writer.py       # markdown report generation\n│   ├── tools/\n│   │   └── tavily_tool.py  # CrewAI tool wrapping Tavily search\n│   ├── config/\n│   │   └── settings.py     # env var management via pydantic-settings\n│   ├── crew.py             # pipeline orchestration\n│   ├── main.py             # FastAPI app\n│   └── schemas.py          # request/response models\n├── frontend/\n│   └── app.py              # Streamlit UI\n├── .env.example\n└── requirements.txt\n```\n\n## Prerequisites\n\n- Python 3.12+\n- An [Anthropic API key](https://console.anthropic.com/)\n- A [Tavily API key](https://tavily.com/)\n\n## Setup\n\n```bash\n# 1. Clone the repo\ngit clone https://github.com/mukuldatta/AutoResearch-Agent.git\ncd AutoResearch-Agent\n\n# 2. Create and activate a virtual environment\npython -m venv .venv\nsource .venv/bin/activate      # Windows: .venv\\Scripts\\activate\n\n# 3. Install dependencies\npip install -r requirements.txt\n\n# 4. Configure environment variables\ncp .env.example .env\n# Edit .env and fill in your API keys\n```\n\n## Running\n\nOpen two terminals from the project root.\n\n**Terminal 1 — backend:**\n```bash\ncd backend\nuvicorn main:app --reload\n```\n\n**Terminal 2 — frontend:**\n```bash\ncd frontend\nstreamlit run app.py\n```\n\nThen open [http://localhost:8501](http://localhost:8501) in your browser.\n\n## Tech stack\n\n| Layer | Technology |\n|---|---|\n| LLM | Claude Haiku 4.5 (Anthropic) |\n| Multi-agent framework | CrewAI |\n| Web search | Tavily |\n| Backend API | FastAPI + Uvicorn |\n| Frontend | Streamlit |\n| Config | pydantic-settings |\n\n## Environment variables\n\n| Variable | Description |\n|---|---|\n| `ANTHROPIC_API_KEY` | Anthropic API key for Claude |\n| `TAVILY_API_KEY` | Tavily API key for web search |\n\nCopy `.env.example` to `.env` and fill in your keys. **Never commit `.env`** — it is listed in `.gitignore`.\n","readmeExcerpt":"AutoResearch Agent An AI-powered research automation system that orchestrates four specialized agents — planner, researcher, analyst, and writer — to produce a structured, fully-sourced markdown report on any topic. 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Configure environment variables\ncp .env.example .env\n# Edit .env and fill in your API keys"},{"language":"bash","snippet":"cd backend\nuvicorn main:app --reload"},{"language":"bash","snippet":"cd frontend\nstreamlit run app.py"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[],"languages":["python"],"docsSourceLabel":"GITHUB REPOS","editorialOverview":"Multi-agent research pipeline using CrewAI + Groq LLaMA 3.3 70B — autonomously breaks down complex topics and generates structured, cited research reports AutoResearch Agent An AI-powered research automation system that orchestrates four specialized agents — planner, researcher, analyst, and writer — to produce a structured, fully-sourced markdown report on any topic. How it works Each agent is powered by **Claude Haiku 4.5**. Web search is handled by the **Tavily API**. 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