# How to integrate Vectorshift MCP with LangChain

```json
{
  "title": "How to integrate Vectorshift MCP with LangChain",
  "toolkit": "Vectorshift",
  "toolkit_slug": "vectorshift",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/vectorshift/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/vectorshift/framework/langchain.md",
  "updated_at": "2026-03-29T06:54:33.673Z"
}
```

## Introduction

This guide walks you through connecting Vectorshift to LangChain using the Composio tool router. By the end, you'll have a working Vectorshift agent that can trigger the lead qualification chatbot workflow, get status of the sales pipeline automation, update knowledge base with latest product faq through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Vectorshift account through Composio's Vectorshift MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Vectorshift with

- [OpenAI Agents SDK](https://composio.dev/toolkits/vectorshift/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/vectorshift/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/vectorshift/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/vectorshift/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/vectorshift/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/vectorshift/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/vectorshift/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/vectorshift/framework/cli)
- [Google ADK](https://composio.dev/toolkits/vectorshift/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/vectorshift/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/vectorshift/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/vectorshift/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/vectorshift/framework/crew-ai)

## TL;DR

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

The Vectorshift MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Vectorshift account. It provides structured and secure access so your agent can perform Vectorshift operations on your behalf.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `VECTORSHIFT_CREATE_CHATBOT` | Create Chatbot | Tool to create a new chatbot. Chatbots are conversational AI interfaces built on pipelines. Use when you need to create a new chatbot with a specific pipeline configuration. |
| `VECTORSHIFT_DELETE_CHATBOT` | Delete Chatbot | Tool to delete a chatbot by its ID. Permanently removes the chatbot from the account. Use when you need to remove a chatbot that is no longer needed. |
| `VECTORSHIFT_GET_CHATBOT` | Get Chatbot | Tool to fetch an existing chatbot by its ID or name. Returns chatbot configuration and metadata. Use when you need to retrieve details about a specific chatbot. Either chatbot ID or name must be provided. |
| `VECTORSHIFT_GET_KNOWLEDGE_BASE` | Get Knowledge Base | Tool to fetch an existing knowledge base by its ID or name. Returns knowledge base configuration and metadata. Use when you need to retrieve details about a specific knowledge base. |
| `VECTORSHIFT_GET_PIPELINE` | Get Pipeline | Tool to fetch an existing pipeline by its ID or name. Returns pipeline configuration and metadata. Use when you need to retrieve a specific pipeline's details, configuration, or metadata. |
| `VECTORSHIFT_LIST_CHATBOTS` | List Chatbots | Tool to list all available chatbots in the account. Use when you need to retrieve chatbot IDs or full chatbot details. |
| `VECTORSHIFT_LIST_KNOWLEDGE_BASES` | List Knowledge Bases | Tool to list all available knowledge bases in your VectorShift account. Use when you need to retrieve knowledge base information by id or name. |
| `VECTORSHIFT_LIST_PIPELINES` | List Pipelines | Tool to list all available pipelines in the VectorShift account. Use when you need to retrieve the catalog of pipelines. Supports filtering for shared pipelines and verbose output with full pipeline details. |
| `VECTORSHIFT_LIST_TRANSFORMATIONS` | List Transformations | Tool to list all available transformations in the account. Use when you need to retrieve transformation IDs or complete transformation objects. |
| `VECTORSHIFT_RUN_PIPELINE` | Run Pipeline | Tool to run a VectorShift pipeline with the given inputs. Use when you need to execute a pipeline and get its results or run_id for asynchronous execution. Returns the pipeline execution status, run_id, and outputs if execution completed synchronously. |
| `VECTORSHIFT_RUN_PIPELINE_IN_BULK` | Run Pipeline in Bulk | Tool to run a VectorShift pipeline in bulk with multiple sets of inputs. Use when you need to batch process multiple pipeline executions in a single API call. Returns the overall status and an array of outputs with run_id for each execution. |
| `VECTORSHIFT_TERMINATE_PIPELINE_EXECUTION` | Terminate Pipeline Execution | Tool to terminate a running pipeline execution. Use when you need to stop a pipeline run by its run_id. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Vectorshift MCP server is an implementation of the Model Context Protocol that connects your AI agent to Vectorshift. It provides structured and secure access so your agent can perform Vectorshift operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

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

### 3. Set up environment variables

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
```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
```

### 4. Import dependencies

No description provided.
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
```

```typescript
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();
```

### 5. Initialize Composio client

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 Vectorshift tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
```

```typescript
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()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Vectorshift 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 Vectorshift tools as needed
```python
# Create Tool Router session for Vectorshift
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['vectorshift']
)

url = session.mcp.url
```

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

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "vectorshift-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
```

```typescript
const client = new MultiServerMCPClient({
    "vectorshift-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 });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

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

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Vectorshift 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);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

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

## Complete Code

```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['vectorshift']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "vectorshift-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Vectorshift related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
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: ['vectorshift']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "vectorshift-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 Vectorshift 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 Vectorshift 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.

## How to build Vectorshift MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/vectorshift/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/vectorshift/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/vectorshift/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/vectorshift/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/vectorshift/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/vectorshift/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/vectorshift/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/vectorshift/framework/cli)
- [Google ADK](https://composio.dev/toolkits/vectorshift/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/vectorshift/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/vectorshift/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/vectorshift/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/vectorshift/framework/crew-ai)

## Related Toolkits

- [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly.
- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place.
- [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations.
- [Asana](https://composio.dev/toolkits/asana) - Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals.
- [Google Tasks](https://composio.dev/toolkits/googletasks) - Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem.
- [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity.
- [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.
- [Jira](https://composio.dev/toolkits/jira) - Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. It helps teams organize work, prioritize tasks, and deliver projects efficiently.
- [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place.
- [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time.
- [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.
- [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.
- [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time.
- [Agiled](https://composio.dev/toolkits/agiled) - Agiled is an all-in-one business management platform for CRM, projects, and finance. It helps you streamline workflows, consolidate client data, and manage business processes in one place.
- [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows.
- [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application.
- [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities.
- [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization.
- [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Vectorshift MCP?

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

### Can I use Tool Router MCP with LangChain?

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 Vectorshift tools.

### Can I manage the permissions and scopes for Vectorshift while using Tool Router?

Yes, absolutely. You can configure which Vectorshift 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.

### How safe is my data with Composio Tool Router?

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 Vectorshift data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)