LangChain + Neo4j Integration

Overview

LangChain is a Python toolkit for building applications powered by large language models. It provides composable chains and agents, a vast integration ecosystem, memory and retrieval systems, and production essentials like callbacks, tracing, and evaluation tools.

Installation:

pip install langchain langchain-neo4j langchain-mcp-adapters

Key Features:

  • Composable chains and ReAct-style agents

  • Native Neo4j integrations via langchain-neo4j package

  • MCP server support through langchain-mcp-adapters

  • Custom tool creation with the @tool decorator

  • Support for virtually every major LLM provider (OpenAI, Anthropic, Google, Cohere, Mistral, AWS Bedrock, Azure, and more)

Examples

Notebook Description

langchain.ipynb

Walkthrough of LangChain with Neo4j integration, including MCP server setup, custom tool creation, vector search, and query execution

Extension Points

1. MCP Integration

LangChain supports MCP servers via the langchain-mcp-adapters package. Use MultiServerMCPClient to connect to MCP servers and retrieve tools.

  • Neo4j MCP Server: Leverage mcp-neo4j-cypher for schema reading and Cypher query execution

2. Direct Neo4j Integrations

The langchain-neo4j package provides native integrations for more control:

  • Neo4jGraph: Direct connection to Neo4j for executing Cypher queries within custom tools

  • Neo4jVector: Vector store integration for semantic search over graph data with support for hybrid search and custom retrieval queries

3. Custom Tools/Functions

Define custom Neo4j tools using the @tool decorator:

  • Specify tool name and description via docstrings

  • Implement functions that execute Cypher queries via Neo4jGraph or Neo4jVector

  • Return results as structured JSON

  • Combine MCP tools with custom tools in a single agent

4. Neo4j Checkpoint Savers

The langchain-neo4j package provides checkpoint savers for persisting agent state to Neo4j:

  • Neo4jSaver: Synchronous checkpointer for storing conversation history and agent state

  • AsyncNeo4jSaver: Async variant for non-blocking checkpoint operations

from langgraph.checkpoint.neo4j import AsyncNeo4jSaver

async with await AsyncNeo4jSaver.from_conn_string(
    uri=NEO4J_URI,
    user=NEO4J_USERNAME,
    password=NEO4J_PASSWORD,
    database=NEO4J_DATABASE
) as checkpointer:
    await checkpointer.setup()

    agent = create_agent(
        model,
        tools,
        system_prompt=prompt,
        checkpointer=checkpointer,
    )

MCP Authentication

Supported Mechanisms:

✅ Environment Variables (STDIO transport) - For local MCP servers like mcp-neo4j-cypher, credentials are passed via the env parameter at spawn time.

✅ HTTP Headers (HTTP/SSE transport) - For remote MCP servers, pass API keys or bearer tokens via the headers parameter (e.g., Authorization: Bearer ${API_TOKEN}).

❌ OAuth 2.0 (in-client) - OAuth2 MCP authentication in-client is not currently supported by the Python SDK.

Configuration Example (STDIO transport):

cypher_mcp_config = {
    "neo4j-database": {
        "transport": "stdio",
        "command": "uvx",
        "args": ["mcp-neo4j-cypher"],
        "env": {
            "NEO4J_URI": os.environ.get("NEO4J_URI"),
            "NEO4J_USERNAME": os.environ.get("NEO4J_USERNAME"),
            "NEO4J_PASSWORD": os.environ.get("NEO4J_PASSWORD"),
            "NEO4J_DATABASE": os.environ.get("NEO4J_DATABASE", "neo4j")
        }
    }
}

Resources