How to integrate Prisma MCP with LangChain

This guide walks you through connecting Prisma to LangChain using the Composio tool router. By the end, you'll have a working Prisma agent that can create a new postgres database in your project, run a sql query to list all users, delete a database connection by name through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Prisma account through Composio's Prisma MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Prisma Data Platform is a suite of database tools for managing workspaces, projects, and PostgreSQL environments. It simplifies data access, query optimization, and secure platform automation.

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Introduction

This guide walks you through connecting Prisma to LangChain using the Composio tool router. By the end, you'll have a working Prisma agent that can create a new postgres database in your project, run a sql query to list all users, delete a database connection by name through natural language commands.

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

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

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TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Prisma project to Composio
  • Create a Tool Router MCP session for Prisma
  • Initialize an MCP client and retrieve Prisma tools
  • Build a LangChain agent that can interact with Prisma
  • 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 Prisma MCP server, and what's possible with it?

The Prisma MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Prisma account. It provides structured and secure access to your database management, so your agent can perform actions like creating projects, managing databases, executing SQL queries, and handling API keys on your behalf.

  • Automated project and database provisioning: Instantly create new Prisma projects and managed PostgreSQL databases in your workspace, complete with connection strings and API keys for fast onboarding.
  • On-demand SQL execution and analysis: Have your agent run SQL commands or select queries for reporting, data inspection, or schema changes—without manual intervention.
  • API key and connection management: Programmatically generate, rotate, or revoke database API keys, ensuring secure and controlled access for all your applications.
  • Workspace and resource monitoring: Retrieve detailed information about your workspaces, projects, and databases, allowing your agent to validate deployments or monitor status in real time.
  • Safe resource cleanup and deletion: Direct your agent to delete databases, projects, or specific connections—helping you maintain a tidy, secure, and cost-effective data platform.

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 Prisma 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 Prisma 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: ['prisma']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Prisma 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 Prisma tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "prisma-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 Prisma MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Prisma 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 Prisma 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 Prisma 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: ['prisma']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "prisma-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 Prisma 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 Prisma 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 Prisma action and event your agent gets out of the box.

Create Database Connection

Create new API key connection for database access.

Create Project Database

Create new postgres database in an existing Prisma project.

Create Prisma Project

Create new Prisma project with managed postgres database.

Delete Database Connection

Permanently delete database connection and revoke API key access.

Delete Prisma Database

Permanently delete Prisma database and all stored data.

Delete Prisma Project

Permanently delete Prisma project and all associated resources.

Execute SQL Command

Execute SQL commands that modify database data or structure.

Execute SQL Query

Execute SQL SELECT queries against Prisma Postgres databases.

Get Prisma Database

Retrieve specific Prisma database by ID.

Get Database Usage Metrics

Retrieve usage metrics for a specific Prisma database.

Get Prisma Project

Retrieve specific Prisma project by ID.

Inspect Database Schema

Inspect database schema structure and table information.

List Prisma Accelerate Regions

Retrieve all available regions for Prisma Accelerate.

List Database Backups

Retrieve list of available backups for a specific database.

List Database Connections

Retrieve paginated list of connections for a specific database.

List Project Databases

Retrieve paginated list of databases for a specific Prisma project.

List Prisma Postgres Regions

Retrieve all available regions for Prisma Postgres.

List Prisma Projects

Retrieve paginated list of Prisma projects accessible to authenticated user.

List Workspace Integrations

Retrieve paginated list of integrations for a specific Prisma workspace.

List Prisma Workspaces

Retrieve paginated list of Prisma workspaces accessible to authenticated user.

Restore Database Backup

Restore database backup to new database instance.

Transfer Prisma Project

Transfer Prisma project ownership to another user's workspace.

FRAMEWORKS

How to build Prisma MCP Agent with another framework

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OpenAI Agents SDK

Use Prisma MCP with OpenAI Agents SDK

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Claude Agent SDK

Use Prisma MCP with Claude Agent SDK

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Claude Code

Use Prisma MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Prisma MCP with Claude Cowork

Codex logo

Codex

Use Prisma MCP with Codex

OpenClaw logo

OpenClaw

Use Prisma MCP with OpenClaw

Hermes logo

Hermes

Use Prisma MCP with Hermes

CLI logo

CLI

Use Prisma MCP with CLI

Google ADK logo

Google ADK

Use Prisma MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Prisma MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Prisma MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Prisma MCP with LlamaIndex

CrewAI logo

CrewAI

Use Prisma MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Prisma MCP server, the agents and LLMs can only access a fixed set of Prisma tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Prisma 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 Prisma tools.

Yes, absolutely. You can configure which Prisma 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 Prisma data and credentials are handled as safely as possible.

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