{"id":31767699,"url":"https://github.com/hq969/multi-agent-financial-research-assistant","last_synced_at":"2026-04-09T17:35:54.844Z","repository":{"id":316562199,"uuid":"1063898807","full_name":"hq969/Multi-Agent-Financial-Research-Assistant","owner":"hq969","description":"The Multi-Agent Financial Research Assistant is an AI-powered system that helps investors and analysts make smarter financial decisions. 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Built with **LangGraph**, **LangChain**, **Streamlit**, **AWS Lambda**, and **DynamoDB**.\n\n---\n\n## 🚀 Features\n- **Agent A**: Fetches live market news \u0026 stock data via APIs.\n- **Agent B**: Summarizes text and performs sentiment analysis.\n- **Agent C**: Generates investor-friendly insights and reports using GPT-4.\n- **LangGraph Orchestration**: Manages agent workflows.\n- **AWS Lambda + DynamoDB**: Enables serverless execution \u0026 persistence.\n- **Streamlit Dashboard**: User-friendly interface for exploring insights.\n\n---\n\n## 📂 Project Structure\n```\n├── agents\n│   ├── agent_a_fetcher.py      # Fetch live financial data \u0026 news\n│   ├── agent_b_analyzer.py     # Summarization \u0026 sentiment analysis\n│   └── agent_c_reporter.py     # Report generation\n├── orchestration\n│   └── langgraph_orchestrator.py   # Orchestrates agent workflows\n├── lambda\n│   └── lambda_handler.py       # AWS Lambda handler\n├── utils\n│   └── dynamo.py               # DynamoDB helper functions\n├── streamlit_app.py            # Streamlit dashboard\n├── run_local.py                # Local runner for development\n├── requirements.txt            # Python dependencies\n├── .env.example                # Example environment variables\n└── README.md                   # Project documentation\n```\n\n---\n\n## ⚙️ Setup\n### 1. Clone Repository\n```bash\ngit clone https://github.com/hq969/multi-agent-financial-assistant.git\ncd multi-agent-financial-assistant\n```\n\n### 2. Install Dependencies\n```bash\npip install -r requirements.txt\n```\n\n### 3. Configure Environment Variables\nCopy `.env.example` to `.env` and update with your credentials:\n```bash\nOPENAI_API_KEY=your-openai-api-key\nNEWS_API_KEY=your-newsapi-key\nMARKET_API_KEY=your-alpha-vantage-key\nDYNAMO_TABLE=FinancialReports\n```\n\n---\n\n## ▶️ Usage\n### Run Locally\n```bash\npython run_local.py\n```\n\n### Streamlit Dashboard\n```bash\nstreamlit run streamlit_app.py\n```\nThen open [http://localhost:8501](http://localhost:8501) in your browser.\n\n### Deploy to AWS Lambda\n- Package with dependencies.\n- Set environment variables in Lambda.\n- Ensure DynamoDB table exists (`FinancialReports`).\n- Deploy handler: `lambda/lambda_handler.lambda_handler`.\n\n---\n\n## 📊 Example Workflow\n1. Agent A fetches **stock news + financial data**.\n2. Agent B generates a **summary + sentiment analysis**.\n3. Agent C compiles **reports for investors**.\n4. Orchestrator pipelines results to **DynamoDB \u0026 UI**.\n\n---\n\n## 🛠️ Tech Stack\n- **Frontend**: Streamlit\n- **Backend**: AWS Lambda (Python)\n- **Data Storage**: DynamoDB\n- **AI/LLM**: OpenAI GPT-4 via LangChain\n- **Workflow**: LangGraph\n- **APIs**: NewsAPI, Alpha Vantage\n\n---\n\n## ✅ Next Steps\n- Add **unit tests** in a `tests/` folder.\n- Create **CI/CD pipeline** for Lambda + Streamlit.\n- Add **Mermaid architecture diagram** to README.\n\n---\n\n## 👨‍💻 Author\nBuilt by Harsh Sonkar ⚡\n\n---\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhq969%2Fmulti-agent-financial-research-assistant","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhq969%2Fmulti-agent-financial-research-assistant","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhq969%2Fmulti-agent-financial-research-assistant/lists"}