https://github.com/hq969/multi-agent-financial-research-assistant
The Multi-Agent Financial Research Assistant is an AI-powered system that helps investors and analysts make smarter financial decisions. It uses multiple specialized agents to fetch, analyze, and summarize real-time financial data and news.
https://github.com/hq969/multi-agent-financial-research-assistant
aws dynamodb lamdba langchain langgraph openai streamlit
Last synced: 4 months ago
JSON representation
The Multi-Agent Financial Research Assistant is an AI-powered system that helps investors and analysts make smarter financial decisions. It uses multiple specialized agents to fetch, analyze, and summarize real-time financial data and news.
- Host: GitHub
- URL: https://github.com/hq969/multi-agent-financial-research-assistant
- Owner: hq969
- License: mit
- Created: 2025-09-25T09:09:12.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-09-25T09:33:15.000Z (10 months ago)
- Last Synced: 2025-09-25T11:23:08.359Z (10 months ago)
- Topics: aws, dynamodb, lamdba, langchain, langgraph, openai, streamlit
- Language: Python
- Homepage:
- Size: 32.2 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 📊 Multi-Agent Financial Research Assistant
A serverless, AI-powered assistant for financial research that fetches live market news and data, performs sentiment analysis, and generates investor-friendly reports. Built with **LangGraph**, **LangChain**, **Streamlit**, **AWS Lambda**, and **DynamoDB**.
---
## 🚀 Features
- **Agent A**: Fetches live market news & stock data via APIs.
- **Agent B**: Summarizes text and performs sentiment analysis.
- **Agent C**: Generates investor-friendly insights and reports using GPT-4.
- **LangGraph Orchestration**: Manages agent workflows.
- **AWS Lambda + DynamoDB**: Enables serverless execution & persistence.
- **Streamlit Dashboard**: User-friendly interface for exploring insights.
---
## 📂 Project Structure
```
├── agents
│ ├── agent_a_fetcher.py # Fetch live financial data & news
│ ├── agent_b_analyzer.py # Summarization & sentiment analysis
│ └── agent_c_reporter.py # Report generation
├── orchestration
│ └── langgraph_orchestrator.py # Orchestrates agent workflows
├── lambda
│ └── lambda_handler.py # AWS Lambda handler
├── utils
│ └── dynamo.py # DynamoDB helper functions
├── streamlit_app.py # Streamlit dashboard
├── run_local.py # Local runner for development
├── requirements.txt # Python dependencies
├── .env.example # Example environment variables
└── README.md # Project documentation
```
---
## ⚙️ Setup
### 1. Clone Repository
```bash
git clone https://github.com/hq969/multi-agent-financial-assistant.git
cd multi-agent-financial-assistant
```
### 2. Install Dependencies
```bash
pip install -r requirements.txt
```
### 3. Configure Environment Variables
Copy `.env.example` to `.env` and update with your credentials:
```bash
OPENAI_API_KEY=your-openai-api-key
NEWS_API_KEY=your-newsapi-key
MARKET_API_KEY=your-alpha-vantage-key
DYNAMO_TABLE=FinancialReports
```
---
## ▶️ Usage
### Run Locally
```bash
python run_local.py
```
### Streamlit Dashboard
```bash
streamlit run streamlit_app.py
```
Then open [http://localhost:8501](http://localhost:8501) in your browser.
### Deploy to AWS Lambda
- Package with dependencies.
- Set environment variables in Lambda.
- Ensure DynamoDB table exists (`FinancialReports`).
- Deploy handler: `lambda/lambda_handler.lambda_handler`.
---
## 📊 Example Workflow
1. Agent A fetches **stock news + financial data**.
2. Agent B generates a **summary + sentiment analysis**.
3. Agent C compiles **reports for investors**.
4. Orchestrator pipelines results to **DynamoDB & UI**.
---
## 🛠️ Tech Stack
- **Frontend**: Streamlit
- **Backend**: AWS Lambda (Python)
- **Data Storage**: DynamoDB
- **AI/LLM**: OpenAI GPT-4 via LangChain
- **Workflow**: LangGraph
- **APIs**: NewsAPI, Alpha Vantage
---
## ✅ Next Steps
- Add **unit tests** in a `tests/` folder.
- Create **CI/CD pipeline** for Lambda + Streamlit.
- Add **Mermaid architecture diagram** to README.
---
## 👨💻 Author
Built by Harsh Sonkar ⚡
---