AI Financial Research Agent

An autonomous stock research agent built with LangGraph, LangChain, and the ReAct pattern. It researches any ticker — fundamentals, news sentiment, technical indicators, and analyst consensus — then produces a structured buy/sell investment brief.

Includes a Streamlit web UI, MCP server for tool exposure, and session tracking for the full ReAct loop.

Based on Building a Financial Research Agent with ReAct, LangGraph, and LangChain.

Features

ReAct Agent Loop — Agent decides tool order dynamically (Reason → Act → Observe)
4 Research Tools — Fundamentals, news sentiment, technicals, analyst ratings
Streamlit UI — Live ReAct step tracking + investment brief display
Session Tracking — Persisted JSON logs of every research session
MCP Server — Expose tools to Cursor, Claude Desktop, or any MCP client
Ticker Normalization — Handles mixed case input (reliance.ns → RELIANCE.NS)

Project Structure

hljs language-graphql

financial-research-agent/
├── app/
│   └── streamlit_app.py      # Streamlit web interface
├── financial_mcp/
│   ├── server.py             # MCP server (stdio transport)
│   └── config.json           # Sample MCP client config
├── src/financial_agent/
│   ├── state.py              # LangGraph agent state
│   ├── tools.py              # Research tools (yfinance, Tavily, pandas-ta)
│   ├── agent.py              # LLM + system prompt
│   ├── graph.py              # LangGraph ReAct graph
│   ├── runner.py             # Streaming runner with tracking
│   ├── utils.py              # Ticker normalization
│   └── tracking/
│       └── session_tracker.py  # ReAct step & session persistence
├── data/sessions/            # Tracked research sessions (JSON)
├── main.py                   # CLI entry point
├── requirements.txt
├── .env.example
└── README.md

Setup

1. Clone and install

hljs language-bash

cd financial-research-agent
python -m venv .venv
.venv\Scripts\activate        # Windows
pip install -r requirements.txt

2. Configure API keys

Copy .env.example to .env and add your keys:

hljs language-env

OPENAI_API_KEY=your_openai_key
TAVILY_API_KEY=your_tavily_key
OPENAI_MODEL=gpt-4o

Get a free Tavily key at tavily.com.

3. Run

Streamlit UI (recommended):

hljs language-bash

streamlit run app/streamlit_app.py

CLI:

hljs language-bash

python main.py RELIANCE.NS
python main.py AAPL

MCP Server:

hljs language-bash

python -m financial_mcp.server

Add to your MCP client config (see financial_mcp/config.json):

hljs language-json

{
  "mcpServers": {
    "financial-research-agent": {
      "command": "python",
      "args": ["-m", "financial_mcp.server"],
      "cwd": "/path/to/financial-research-agent",
      "env": { "PYTHONPATH": "/path/to/financial-research-agent/src" }
    }
  }
}

Architecture

hljs language-sql

User: "Research RELIANCE.NS"
        │
        ▼
  ┌─────────────┐     tool_call      ┌─────────────┐
  │  Agent Node │ ─────────────────► │  Tools Node │
  │   (LLM)     │ ◄───────────────── │  (ToolNode) │
  └─────────────┘    observation     └─────────────┘
        │
        ▼ (no more tool calls)
  Final Investment Brief

Session Tracking

Every research run is tracked step-by-step:

Step Type	Description
`tool_call`	Agent decides to call a tool
`tool_result`	Tool observation returned
`final_brief`	Structured investment brief

Sessions are saved to data/sessions/{session_id}_{ticker}.json.

Supported Tickers

Market	Format	Example
US	`SYMBOL`	`AAPL`, `MSFT`
NSE	`SYMBOL.NS`	`RELIANCE.NS`
BSE	`SYMBOL.BO`	`RELIANCE.BO`

License

MIT

AI Financial Research Agent

Includes a Streamlit web UI, MCP server for tool exposure, and session tracking for the full ReAct loop.

Based on Building a Financial Research Agent with ReAct, LangGraph, and LangChain.

Features

ReAct Agent Loop — Agent decides tool order dynamically (Reason → Act → Observe)
4 Research Tools — Fundamentals, news sentiment, technicals, analyst ratings
Streamlit UI — Live ReAct step tracking + investment brief display
Session Tracking — Persisted JSON logs of every research session
MCP Server — Expose tools to Cursor, Claude Desktop, or any MCP client
Ticker Normalization — Handles mixed case input (reliance.ns → RELIANCE.NS)

Project Structure

hljs language-graphql

financial-research-agent/
├── app/
│   └── streamlit_app.py      # Streamlit web interface
├── financial_mcp/
│   ├── server.py             # MCP server (stdio transport)
│   └── config.json           # Sample MCP client config
├── src/financial_agent/
│   ├── state.py              # LangGraph agent state
│   ├── tools.py              # Research tools (yfinance, Tavily, pandas-ta)
│   ├── agent.py              # LLM + system prompt
│   ├── graph.py              # LangGraph ReAct graph
│   ├── runner.py             # Streaming runner with tracking
│   ├── utils.py              # Ticker normalization
│   └── tracking/
│       └── session_tracker.py  # ReAct step & session persistence
├── data/sessions/            # Tracked research sessions (JSON)
├── main.py                   # CLI entry point
├── requirements.txt
├── .env.example
└── README.md

Setup

1. Clone and install

hljs language-bash

cd financial-research-agent
python -m venv .venv
.venv\Scripts\activate        # Windows
pip install -r requirements.txt

2. Configure API keys

Copy .env.example to .env and add your keys:

hljs language-env

OPENAI_API_KEY=your_openai_key
TAVILY_API_KEY=your_tavily_key
OPENAI_MODEL=gpt-4o

Get a free Tavily key at tavily.com.

3. Run

Streamlit UI (recommended):

hljs language-bash

streamlit run app/streamlit_app.py

CLI:

hljs language-bash

python main.py RELIANCE.NS
python main.py AAPL

MCP Server:

hljs language-bash

python -m financial_mcp.server

Add to your MCP client config (see financial_mcp/config.json):

hljs language-json

{
  "mcpServers": {
    "financial-research-agent": {
      "command": "python",
      "args": ["-m", "financial_mcp.server"],
      "cwd": "/path/to/financial-research-agent",
      "env": { "PYTHONPATH": "/path/to/financial-research-agent/src" }
    }
  }
}

Architecture

hljs language-sql

User: "Research RELIANCE.NS"
        │
        ▼
  ┌─────────────┐     tool_call      ┌─────────────┐
  │  Agent Node │ ─────────────────► │  Tools Node │
  │   (LLM)     │ ◄───────────────── │  (ToolNode) │
  └─────────────┘    observation     └─────────────┘
        │
        ▼ (no more tool calls)
  Final Investment Brief

Session Tracking

Every research run is tracked step-by-step:

Step Type	Description
`tool_call`	Agent decides to call a tool
`tool_result`	Tool observation returned
`final_brief`	Structured investment brief

Sessions are saved to data/sessions/{session_id}_{ticker}.json.

Supported Tickers

Market	Format	Example
US	`SYMBOL`	`AAPL`, `MSFT`
NSE	`SYMBOL.NS`	`RELIANCE.NS`
BSE	`SYMBOL.BO`	`RELIANCE.BO`

License

MIT

ai-financial-research-agent

AI Financial Research Agent

Features

Project Structure

Setup

1. Clone and install

2. Configure API keys

3. Run

Architecture

Session Tracking

Supported Tickers

License

Similar Packages

ai-financial-research-agent

AI Financial Research Agent

Features

Project Structure

Setup

1. Clone and install

2. Configure API keys

3. Run

Architecture

Session Tracking

Supported Tickers

License

Similar Packages