Research Agent with LangGraph

This project implements a research agent using LangGraph and LangChain. The agent is capable of researching any domain using three powerful tools and can be deployed with a Chainlit frontend.

Features

Domain-specific research assistant
Three specialized research tools:
1. Web Search - For general queries and recent information
2. Research Paper Search - For academic papers and scientific information
3. Data Analysis - For analyzing data provided by the user
Interactive Chainlit web interface
Configurable research domain

Architecture

This project uses:

LangGraph: For creating the agent's workflow graph with cyclic behavior
LangChain: For tool integration and language model interactions
Chainlit: For the web-based frontend
OpenAI GPT-4o: As the underlying language model

Installation

Clone this repository
Install dependencies using UV (recommended) or pip:

# Using UV
uv pip install -r requirements.txt

Configuration

Create a .env file in the project root with the following content:

OPENAI_API_KEY=your_openai_api_key
TAVILY_API_KEY=your_tavily_api_key

Usage

To run the application:

chainlit run app.py -w

This will start the Chainlit server and open a web browser with the interface.

File Structure

agent.py: Contains the LangGraph implementation of the research agent
tools.py: Defines the three research tools
app.py: Chainlit web interface
chainlit.md: Welcome page content for Chainlit
requirements.txt: Dependencies for the project

Customization

You can customize the agent by:

Modifying the DOMAIN variable in app.py
Adding new tools in tools.py
Changing the system prompt in agent.py

Example Queries

"What are the latest developments in quantum computing?"
"Find research papers about climate change impacts on agriculture"
"Analyze this data: [paste JSON or CSV]"

License

MIT