How to integrate Neo4J MCP with LangChain

This guide walks you through connecting Neo4J to LangChain using the Composio tool router. By the end, you'll have a working Neo4J agent that can run cypher queries on customer graph, find shortest paths between accounts, create nodes for new transactions through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Neo4J account through Composio's Neo4J MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

Get started for freeGet a demo
Neo4J logoNeo4J
Oauth2BasicApi KeyS2s Oauth2

Neo4J is a graph database platform for storing and querying connected data. Use it to model relationships, run Cypher queries, and power real-time graph analytics.

21 Tools

Introduction

This guide walks you through connecting Neo4J to LangChain using the Composio tool router. By the end, you'll have a working Neo4J agent that can run cypher queries on customer graph, find shortest paths between accounts, create nodes for new transactions through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Neo4J account through Composio's Neo4J MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Neo4J with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Neo4J project to Composio
  • Create a Tool Router MCP session for Neo4J
  • Initialize an MCP client and retrieve Neo4J tools
  • Build a LangChain agent that can interact with Neo4J
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

What is the Neo4J MCP server, and what's possible with it?

The Neo4J MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Neo4J account. It provides structured and secure access to your Neo4J Aura resources, so your agent can manage database instances, handle snapshots, review projects and users, monitor Graph Data Science sessions, and configure IP filters on your behalf.

  • Aura instance management: Have your agent list, create, inspect, update, or pause Neo4J Aura instances based on your infrastructure needs.
  • Snapshot and recovery operations: Direct your agent to create snapshots, review available backups, inspect snapshot details, or restore an Aura instance from a snapshot.
  • Project and user oversight: Let the agent list Aura projects, inspect project details, review project users, or retrieve information about an organization user.
  • Graph Data Science planning: Instruct your agent to list active Graph Data Science sessions or estimate session size from node counts, relationship counts, and algorithm categories.
  • Network access and agent review: Have your agent list Aura agents, review organization IP filters, or update an existing IP filter.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step10 STEPS
1

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • @composio/langchain provides Composio integration for LangChain
  • @langchain/mcp-adapters enables MCP client connections
  • @langchain/core is the core agent framework
  • dotenv/config loads environment variables
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models
5

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Neo4J functionality through MCP
6

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Neo4J tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['neo4j']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Neo4J tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Neo4J tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "neo4j-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Neo4J MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Neo4J tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Neo4J related question or task to the agent.\n");

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

Here's the complete code to get you started with Neo4J and LangChain:

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['neo4j']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "neo4j-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Neo4J related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

Conclusion

You've successfully built a LangChain agent that can interact with Neo4J through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.
TOOLS

Supported Tools

Every Neo4J action and event your agent gets out of the box.

Aggregate Directors Count

Executes a directorsAggregate GraphQL query to count directors in Neo4j.

Create Neo4j Aura Instance

Creates a new Neo4j Aura instance with the specified configuration.

Create Neo4j Aura Snapshot

Create an on-demand snapshot of a Neo4j Aura instance.

Estimate GDS Session Size

Estimate the size of a new GDS (Graph Data Science) session based on node/relationship counts and algorithm categories.

Get Neo4j Aura Instance Details

Retrieves details for a specific Neo4j Aura instance using its unique instance ID.

Get Neo4j Aura Organization User

Retrieves details of a specific user in a Neo4j Aura organization.

Get Neo4j Aura Project Details

Retrieve details of a specific Neo4j Aura project (tenant).

Get Neo4j Aura Project Details (v1beta5)

Retrieve details of a specific Neo4j Aura project (tenant) using the v1beta5 API.

Get Neo4j Aura Snapshot Details

Retrieves details of a specific snapshot for a Neo4j Aura instance.

List Neo4j Aura Agents

List all agents belonging to a specific Neo4j Aura project.

List GDS Sessions

List all Graph Data Science (GDS) sessions for the authenticated Neo4j Aura account.

List Neo4j Aura Instances

Returns a list of Neo4j Aura instances.

List IP Filters

Returns a list of IP filters configured for a Neo4j Aura organization.

List Aura Projects

List all Aura projects (tenants) available to the authenticated user.

List Project Users

List all users in a specific Neo4j Aura project.

List Neo4j Aura Snapshots

Lists available snapshots for a Neo4j Aura instance.

Pause Neo4j Aura Instance

Pauses a Neo4j Aura instance.

Restore Neo4j Aura Instance from Snapshot

Restore a Neo4j Aura instance from a snapshot.

Update Neo4j Aura Instance

Updates a Neo4j Aura instance configuration.

Update Neo4j Aura Instance

Edits the configuration of a Neo4j Aura instance.

Update IP Filter

Updates an existing IP filter for a Neo4j Aura organization.

FRAMEWORKS

How to build Neo4J MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Neo4J MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Neo4J MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Neo4J MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Neo4J MCP with Claude Cowork

Codex logo

Codex

Use Neo4J MCP with Codex

OpenClaw logo

OpenClaw

Use Neo4J MCP with OpenClaw

Hermes logo

Hermes

Use Neo4J MCP with Hermes

CLI logo

CLI

Use Neo4J MCP with CLI

Google ADK logo

Google ADK

Use Neo4J MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Neo4J MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Neo4J MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Neo4J MCP with LlamaIndex

CrewAI logo

CrewAI

Use Neo4J MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Neo4J MCP server, the agents and LLMs can only access a fixed set of Neo4J tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Neo4J and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. LangChain fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Neo4J tools.

Yes, absolutely. You can configure which Neo4J scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Neo4J data and credentials are handled as safely as possible.

Start with Neo4J.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Neo4J tool your agent needs.Free to start.

Start building
Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREEBOOK A DEMO
agent.aiZoomLettaGleanHubSpotAWSWabi

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati
Harsha Gaddipati
Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya
Abhi Arya
Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave
Nirman Dave
CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu
Ryan Yu
Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola
Tomisin Jenrola
Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche
Jerome Leclanche
Co-Founder, Ingram Technologies