langchain-fmp-data

A LangChain integration for Financial Modeling Prep (FMP) API, providing easy access to financial data through LangChain tools and agents.

Features

• FMPDataToolkit: Query-based toolkit for retrieving specific financial data tools
• FMPDataTool: AI-powered agent for natural language financial data queries
• Comprehensive Financial Data: Access to stock prices, financial statements, economic indicators, and more
• LangGraph Integration: Built on LangGraph for reliable agent workflows
• Vector Search: Intelligent tool selection using embeddings and similarity search

Installation

pip install -U langchain-fmp-data

Quick Start

Prerequisites

You'll need API keys for:

• Financial Modeling Prep (FMP) - Get your API key
• OpenAI - Get your API key

Set them as environment variables:

export FMP_API_KEY="your-fmp-api-key"
export OPENAI_API_KEY="your-openai-api-key"

Using FMPDataToolkit

The toolkit allows you to retrieve specific financial data tools based on your query:

import os
from langchain_fmp_data import FMPDataToolkit

os.environ["FMP_API_KEY"] = "your-fmp-api-key"
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"

# Get tools for specific financial data needs
query = "Stock market prices, fundamental and technical data"
fmp_toolkit = FMPDataToolkit(query=query, num_results=10)

tools = fmp_toolkit.get_tools()
for tool in tools:
    print(f"- {tool.name}: {tool.description}")

Using FMPDataTool

The FMPDataTool provides an AI agent that can answer complex financial questions:

import os
from langchain_fmp_data import FMPDataTool

os.environ["FMP_API_KEY"] = "your-fmp-api-key"
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"

# Initialize the tool
tool = FMPDataTool()

# Ask financial questions in natural language
response = tool.invoke({"query": "What is the latest price of Bitcoin?"})
print(response)

# Get structured data
response = tool.invoke({
    "query": "Show me Apple's revenue for the last 4 quarters",
    "response_format": "data_structure"
})
print(response)

Response Formats

The FMPDataTool supports three response formats:

• natural_language: Human-readable text response (default)
• data_structure: Structured JSON data
• both: Both natural language and structured data

from langchain_fmp_data import FMPDataTool, ResponseFormat

tool = FMPDataTool()

# Natural language response
response = tool.invoke({
    "query": "What is Tesla's P/E ratio?",
    "response_format": ResponseFormat.NATURAL_LANGUAGE
})

# Structured data response
response = tool.invoke({
    "query": "Get AAPL stock data",
    "response_format": ResponseFormat.DATA_STRUCTURE
})

# Both formats
response = tool.invoke({
    "query": "Show me Microsoft's financial metrics",
    "response_format": ResponseFormat.BOTH
})

Development

Setup

1. Clone the repository:

git clone https://github.com/MehdiZare/langchain-fmp-data.git
cd langchain-fmp-data

2. Install uv (if not already installed):

curl -LsSf https://astral.sh/uv/install.sh | sh

3. Install dependencies:

uv sync --extra dev --extra test

Running Tests

# Run all tests
uv run pytest

# Run with coverage
uv run pytest --cov=src/langchain_fmp_data --cov-report=term-missing

# Run specific test file
uv run pytest tests/unit_tests/test_tools.py

Code Quality

This project uses several tools to maintain code quality:

• Ruff: Fast Python linter and formatter
• Mypy: Static type checking

# Linting
uv run ruff check src/langchain_fmp_data/

# Formatting
uv run ruff format src/langchain_fmp_data/

# Type checking
uv run mypy src/langchain_fmp_data/

CI/CD

GitHub Actions Workflows

• CI: Runs on all PRs

  • Linting with Ruff
  • Type checking with Mypy
  • Security scanning with Bandit
  • Tests on Python 3.10, 3.11, 3.12, 3.13, 3.14
  • Code coverage reporting to Codecov

• Release: Automated version management and publishing

  • Tag-based versioning from PR labels
  • Publishing to PyPI with trusted publishing
  • GitHub release creation with notes

• Dev Release: Development releases to TestPyPI

  • Automatic dev version calculation
  • Published on every push to dev branch

PR Labels for Versioning

• release:major: Bumps major version (1.0.0 → 2.0.0)
• release:minor: Bumps minor version (1.0.0 → 1.1.0)
• release:patch: Bumps patch version (1.0.0 → 1.0.1)

Contributing

Contributions are welcome! Please follow these guidelines:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes and ensure tests pass
4. Commit your changes with a descriptive message
5. Push to your fork and open a Pull Request

Commit Message Format

Follow conventional commits format:

• feat: New features
• fix: Bug fixes
• docs: Documentation changes
• test: Test additions or modifications
• refactor: Code refactoring
• chore: Maintenance tasks

Project Structure

langchain-fmp-data/
├── src/
│   └── langchain_fmp_data/ # Main package code
│       ├── __init__.py     # Package exports
│       ├── agent.py        # LangGraph agent implementation
│       ├── tools.py        # FMPDataTool implementation
│       └── toolkits.py     # FMPDataToolkit implementation
├── tests/                  # Test suite
│   ├── unit_tests/         # Unit tests
│   └── integration_tests/  # Integration tests
├── .github/                # GitHub Actions workflows
├── pyproject.toml          # Project configuration
├── README.md               # This file
└── CLAUDE.md               # AI assistant documentation

Dependencies

• Python: 3.10 - 3.14
• LangChain: ^1.0.0
• LangChain Core: ^1.0.0
• LangChain OpenAI: ^1.0.0
• LangGraph: ^1.0.0
• FMP-Data: ^2.1.5 with LangChain extras

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

• Issue Tracker
• Discussions

Acknowledgments

• Financial Modeling Prep for providing comprehensive financial data APIs
• LangChain for the excellent framework
• OpenAI for embedding and language models

Changelog

See CHANGELOG.md for a detailed list of changes.

Made with ❤️ by Mehdi Zare