Chat LangChain

A simple documentation assistant built with LangGraph.

Overview

This is a documentation assistant agent that helps answer questions about LangChain, LangGraph, and LangSmith. It demonstrates how to build a production-ready agent using:

• LangGraph - For agent orchestration and state management
• LangChain Agents - For agent creation with middleware support
• Guardrails - To keep conversations on-topic

The repo also includes a Next.js frontend in frontend/ for the public chat UI.

Features

• Documentation Search - Searches official LangChain docs
• Support KB - Searches the Pylon knowledge base for known issues
• Link Validation - Verifies URLs before including in responses
• Guardrails - Filters off-topic queries

Quick Start

Prerequisites

• Python 3.11+
• uv (recommended) or pip

Installation

# Clone the repository
git clone https://github.com/langchain-ai/chat-langchain.git
cd chat-langchain

# Install dependencies with uv
uv sync

# Or with pip
pip install -e . "langgraph-cli[inmem]"

Configuration

# Copy environment template
cp .env.example .env

# Edit .env with your API keys

Required Environment Variables

Running Locally

Backend

# Start LangGraph development server
uv run langgraph dev

# Or with pip
langgraph dev

Open LangGraph Studio: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024

Frontend

cd frontend
npm ci
npm run dev:local

The frontend expects the LangGraph server at http://127.0.0.1:2024 by default. If you want trace sharing from the UI, set LANGSMITH_API_KEY in frontend/.env.local.

Project Structure

├── src/
│   ├── agent/
│   │   ├── docs_graph.py      # Main docs agent
│   │   └── config.py          # Model configuration
│   ├── tools/
│   │   ├── docs_tools.py      # Documentation search
│   │   ├── pylon_tools.py     # Support KB tools
│   │   └── link_check_tools.py # URL validation
│   ├── prompts/
│   │   └── docs_agent_prompt.py
│   └── middleware/
│       ├── guardrails_middleware.py
│       └── retry_middleware.py
├── frontend/                  # Next.js public chat UI
├── langgraph.json             # LangGraph configuration
└── pyproject.toml             # Python project config

How It Works

The agent uses a docs-first research strategy:

1. Guardrails Check - Validates the query is LangChain-related
2. Documentation Search - Searches official docs via Mintlify
3. Knowledge Base - Searches Pylon for known issues/solutions
4. Link Validation - Verifies any URLs before including them
5. Response Generation - Synthesizes a helpful answer

Deployment

LangGraph Cloud

1. Push to GitHub
2. Connect repository in LangSmith
3. Configure environment variables
4. Deploy

Resources

• LangChain Documentation
• LangGraph Documentation
• LangSmith Documentation

License

MIT