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The user simply provides a high-level goal like:\n\n> *\"Analyze global AI startup funding trends from 2020–2025 and generate a report with charts.\"*\n\nThe system **automatically**:\n\n1. **Plans** — Decomposes the goal into executable sub-tasks\n2. **Researches** — Searches the web, collects data, and retrieves documents\n3. **Analyzes** — Writes and executes Python code for statistical analysis\n4. **Visualizes** — Generates charts and graphs (Plotly + Matplotlib)\n5. **Reports** — Produces a structured report with insights and predictions\n6. **Learns** — Stores results in memory and builds a knowledge graph\n\n---\n\n## 🏗️ System Architecture\n\n```\nUser Goal\n   │\n   ▼\n┌──────────────────────┐\n│  CrewAI Orchestrator  │  ◄── Plans & coordinates all agents\n│  (Sequential Process) │\n└──────────┬───────────┘\n           │\n   ┌───────┼───────────────────┐\n   │       │                   │\n   ▼       ▼                   ▼\n┌──────────┐ ┌──────────┐ ┌──────────┐\n│ Research │ │  Data    │ │  Report  │  ◄── CrewAI Agents\n│ Analyst  these │  Scientist│ │  Writer  │      with tools\n└────┬─────┘ └────┬─────┘ └────┬─────┘\n     │            │             │\n     ▼            ▼             ▼\n┌──────────────────────────────────────────┐\n│        CrewAI Tool Layer                 │\n│  Web Search │ Code Executor │ Dataset    │\n│  Scraper    │ (Sandboxed)   │ Fetcher    │\n│  Memory Store │ Doc Retriever (RAG)       │\n└──────────────────┬───────────────────────┘\n                   │\n        ┌──────────┼──────────┐\n        ▼          ▼          ▼\n   ┌────────┐ ┌────────┐ ┌────────────┐\n   │Postgres│ │ChromaDB│ │ Knowledge  │\n   │  (DB)  │ │(Vector)│ │   Graph    │\n   └────────┘ └────────┘ └────────────┘\n```\n\n---\n\n## ✨ Key Features\n\n### 🤖 CrewAI Multi-Agent System\n| CrewAI Agent | Role | Tools |\n|-------------|------|-------|\n| **Senior Research Analyst** | Web search, page scraping, data collection | Web Search, Scrape Webpage, Fetch Dataset, Memory |\n| **Data Scientist** | Statistical analysis, trend computation, code execution | Run Python Analysis, Fetch Dataset, Memory |\n| **Research Report Writer** | Compiles findings into structured reports | Search Memory |\n| **Visualization Agent** | Creates charts (bar, line, pie, scatter, area) with Plotly | *(post-processing)* |\n\n### 🔧 Autonomous Tool Usage\n- **Web Search** — SerpAPI with DuckDuckGo fallback\n- **Dataset Fetcher** — CSV/JSON download + synthetic data generation\n- **Code Executor** — Sandboxed Python execution (Pandas, NumPy)\n- **Document Retriever** — RAG pipeline with ChromaDB embeddings\n\n### 🧠 Memory & Knowledge\n- **Persistent Memory** — Stores research results across sessions (DB + vector store)\n- **Knowledge Graph** — Connects concepts and relationships discovered during research\n- **RAG Pipeline** — Retrieves relevant past research for informed analysis\n\n### 📊 Output\n- **Structured Reports** — HTML + JSON with executive summary, trends, predictions\n- **Interactive Charts** — Plotly (interactive HTML) + PNG exports\n- **Real-time Progress** — SSE streaming of task execution status\n\n### 🖥️ Interactive Dashboard\n- Research input with example queries\n- Live execution pipeline visualization\n- Report viewer with table of contents\n- Knowledge graph canvas visualization\n- Project history browser\n\n---\n\n## 🛠️ Tech Stack\n\n| Layer | Technologies |\n|-------|-------------|\n| **Backend** | Python 3.12, FastAPI, SQLAlchemy, Pydantic |\n| **Multi-Agent** | CrewAI (sequential process, tool delegation) |\n| **AI/LLM** | Ollama + DeepSeek-R1 (100% free & local), OpenAI-compatible |\n| **Embeddings** | Ollama nomic-embed-text (local, free) |\n| **Vector DB** | Lightweight NumPy vector store (cosine similarity) |\n| **Database** | SQLite (dev) / PostgreSQL 16 (production) |\n| **Data** | Pandas, NumPy, statistical analysis |\n| **Visualization** | Plotly, Matplotlib, Kaleido |\n| **Frontend** | Next.js 15, React 19, TypeScript, Tailwind CSS |\n| **Infra** | Docker Compose, Redis |\n\n---\n\n## 🚀 Getting Started\n\n### Prerequisites\n\n- **Python 3.12+**\n- **Node.js 20+**\n- **Ollama** (free, local LLM server) — [Download here](https://ollama.com)\n\n### Step 0: Install Ollama & Models (One-Time Setup)\n\n```bash\n# 1. Install Ollama from https://ollama.com (one-click installer)\n\n# 2. Pull the DeepSeek-R1 model (1.1 GB download)\nollama pull deepseek-r1:1.5b\n\n# 3. Pull the embedding model (274 MB download)\nollama pull nomic-embed-text\n\n# Ollama will automatically run a local API server on http://localhost:11434\n```\n\n### Option 1: Docker Compose (Recommended)\n\n```bash\n# 1. Clone the repository\ngit clone <repo-url>\ncd \"Autonomous AI Research and Data Analysis Agent\"\n\n# 2. Create .env from template\ncp .env.example .env\n# Default settings use Ollama – no API key needed!\n\n# 3. Start all services\ndocker compose up --build\n\n# Frontend: http://localhost:3000\n# Backend API: http://localhost:8000\n# API Docs: http://localhost:8000/docs\n```\n\n### Option 2: Manual Setup\n\n#### Backend\n\n```bash\n# 1. Create virtual environment\ncd backend\npython -m venv .venv\n.venv\\Scripts\\activate  # Windows\n# source .venv/bin/activate  # macOS/Linux\n\n# 2. Install dependencies\npip install -r requirements.txt\n\n# 3. Set up environment variables\ncp ../.env.example ../.env\n# Default settings use Ollama – no changes needed!\n\n# 4. Make sure Ollama is running\n# (it starts automatically after install, or run: ollama serve)\n\n# 5. Run the server\npython main.py\n# API available at http://localhost:8000\n# Docs at http://localhost:8000/docs\n```\n\n#### Frontend\n\n```bash\n# 1. Install dependencies\ncd frontend\nnpm install\n\n# 2. Start dev server\nnpm run dev\n# Dashboard at http://localhost:3000\n```\n\n---\n\n## 📡 API Endpoints\n\n### Agent\n\n| Method | Endpoint | Description |\n|--------|----------|-------------|\n| `POST` | `/api/agent/research` | Create a research plan from a goal |\n| `POST` | `/api/agent/research/{id}/execute` | Execute plan (SSE streaming) |\n| `GET` | `/api/agent/projects` | List all research projects |\n| `GET` | `/api/agent/projects/{id}` | Get project details |\n\n### Reports\n\n| Method | Endpoint | Description |\n|--------|----------|-------------|\n| `GET` | `/api/reports/` | List all reports |\n| `GET` | `/api/reports/{id}` | Get report content |\n| `GET` | `/api/reports/{id}/html` | Get HTML report |\n| `GET` | `/api/reports/{id}/download` | Download report JSON |\n\n### Memory\n\n| Method | Endpoint | Description |\n|--------|----------|-------------|\n| `GET` | `/api/memory/` | List memories |\n| `GET` | `/api/memory/search?q=...` | Semantic memory search |\n| `GET` | `/api/memory/knowledge-graph` | Get knowledge graph |\n\n### Health\n\n| Method | Endpoint | Description |\n|--------|----------|-------------|\n| `GET` | `/api/health` | Health check |\n\n---\n\n## 📖 Example Workflow\n\n### Input\n```\n\"Analyze global AI startup funding trends from 2020-2025.\"\n```\n\n### Agent Execution\n1. **Planner** creates 7 tasks across 4 agents\n2. **Research Agent** searches the web for AI funding data\n3. **Research Agent** collects/generates a dataset\n4. **Analysis Agent** writes Python code to compute trends, growth rates, correlations\n5. **Visualization Agent** creates bar charts, line charts, pie charts\n6. **Report Agent** compiles everything into a structured report\n7. **Knowledge Graph** extracts entities and relationships\n\n### Output\nA comprehensive report containing:\n- **Executive Summary** — Market overview and key takeaways\n- **Key Trends** — Year-over-year funding growth, deal sizes\n- **Statistical Insights** — Mean, median, correlations, CAGR\n- **Visualizations** — 3–4 interactive charts\n- **Predictions** — Forecasted trends based on analysis\n- **Recommendations** — Strategic insights\n\n---\n\n## 📁 Project Structure\n\n```\n├── backend/\n│   ├── main.py                  # FastAPI application entry point\n│   ├── config.py                # Environment & path configuration\n│   ├── requirements.txt         # Python dependencies\n│   ├── Dockerfile\n│   ├── agents/\n│   │   ├── orchestrator.py      # CrewAI Crew – coordinates all agents\n│   │   ├── crewai_tools.py      # CrewAI tool wrappers for search, code, memory\n│   │   ├── visualization_agent.py # Chart generation (Plotly + Matplotlib)\n│   │   └── report_agent.py      # Report compilation (HTML + JSON)\n│   ├── tools/\n│   │   ├── web_search.py        # SerpAPI / DuckDuckGo search\n│   │   ├── dataset_fetcher.py   # Dataset download & generation\n│   │   ├── code_executor.py     # Sandboxed Python executor\n│   │   └── document_retriever.py # RAG document retrieval\n│   ├── memory/\n│   │   ├── memory_store.py      # Persistent memory (DB + vector)\n│   │   └── knowledge_graph.py   # Entity-relationship graph\n│   ├── services/\n│   │   ├── llm_service.py       # OpenAI API wrapper\n│   │   └── embedding_service.py # ChromaDB vector store\n│   ├── database/\n│   │   ├── connection.py        # SQLAlchemy async engine\n│   │   └── models.py            # ORM models\n│   ├── models/\n│   │   └── schemas.py           # Pydantic request/response models\n│   └── routers/\n│       ├── agent_router.py      # /api/agent endpoints\n│       ├── reports_router.py    # /api/reports endpoints\n│       └── memory_router.py     # /api/memory endpoints\n├── frontend/\n│   ├── src/\n│   │   ├── app/\n│   │   │   ├── layout.tsx       # Root layout\n│   │   │   ├── page.tsx         # Main dashboard page\n│   │   │   └── globals.css      # Global styles + Tailwind\n│   │   └── components/\n│   │       ├── Header.tsx       # Navigation header\n│   │       ├── ResearchInput.tsx # Goal input with examples\n│   │       ├── ExecutionPipeline.tsx # Live task progress\n│   │       ├── ReportViewer.tsx  # Report display\n│   │       ├── ProjectHistory.tsx # Past projects list\n│   │       └── KnowledgeGraphViewer.tsx # Interactive graph canvas\n│   ├── package.json\n│   ├── tailwind.config.js\n│   ├── next.config.js\n│   └── Dockerfile\n├── docker-compose.yml\n├── .env.example\n├── .gitignore\n└── README.md\n```\n\n---\n\n## 🔒 Security\n\n- **Sandboxed Code Execution** — Python analysis runs in a restricted namespace with no file I/O or dangerous imports\n- **Input Validation** — Pydantic models validate all API inputs with length limits\n- **CORS** — Configurable origin whitelist\n- **No Credential Exposure** — API keys loaded from environment variables only\n\n---\n\n## 🧩 Advanced Features\n\n| Feature | Description |\n|---------|-------------|\n| **CrewAI Multi-Agent** | CrewAI Crew with sequential process — agents autonomously use tools and pass context |\n| **Autonomous Code Execution** | Analysis agent writes and runs Python scripts for statistical analysis |\n| **Knowledge Graph** | Extracts entities/relationships from research and stores them in a graph DB |\n| **RAG Pipeline** | Retrieves previously stored documents and research for context-aware reasoning |\n| **Real-time Streaming** | Server-Sent Events (SSE) stream live progress updates to the dashboard |\n| **Interactive Dashboard** | React/Next.js frontend with live pipeline, report viewer, and graph explorer |\n| **Synthetic Data Generation** | Generates realistic sample datasets when external data isn't available |\n| **Memory Persistence** | Results stored in PostgreSQL + ChromaDB for future queries |\n\n---\n\n## 📜 License\n\nMIT License — see [LICENSE](LICENSE) for details.\n","readmeExcerpt":"🧠 AutoRes - Autonomous AI Research & Data Analyst Agent A multi-agent autonomous AI system that plans, researches, analyzes data, generates visualizations, and produces comprehensive reports — all from a single natural language goal. --- 📌 Overview This project implements a fully autonomous research agent powered by **CrewAI's multi-agent framework**. 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