How to integrate Bika.ai MCP with LangChain

This guide walks you through connecting Bika.ai to LangChain using the Composio tool router. By the end, you'll have a working Bika.ai agent that can list records from bika.ai database, create new team task record, check failed bika.ai automation runs through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Bika.ai account through Composio's Bika.ai MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Bika.ai is an AI automation database platform for managing spaces, teams, records, automations, and outgoing webhooks. It helps teams organize structured data and automate database-driven workflows in one place.

8 Tools

Introduction

This guide walks you through connecting Bika.ai to LangChain using the Composio tool router. By the end, you'll have a working Bika.ai agent that can list records from bika.ai database, create new team task record, check failed bika.ai automation runs through natural language commands.

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

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

Also integrate Bika.ai 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 Bika.ai project to Composio
  • Create a Tool Router MCP session for Bika.ai
  • Initialize an MCP client and retrieve Bika.ai tools
  • Build a LangChain agent that can interact with Bika.ai
  • 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 Bika.ai MCP server, and what's possible with it?

The Bika.ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Bika.ai account. It provides structured and secure access to your spaces, databases, schemas, and records, so your agent can discover resources, inspect database structures, retrieve records, create records, and update or delete records on your behalf.

  • Space and resource discovery: Have your agent list available spaces and find databases and automations by name, type, or location.
  • Database structure review: Let the agent inspect a database's fields, views, and metadata before working with its records.
  • Record search and retrieval: Instruct your agent to list records from a database, select relevant fields, or fetch a specific record.
  • Record creation and updates: Direct your agent to add new database records or update fields across several existing records at once.
  • Record cleanup: Have your agent permanently delete selected records that are outdated, duplicated, or no longer needed.

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 Bika.ai 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 Bika.ai 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: ['bika']
    }
);

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

Configure the agent with the MCP URL

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

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

Create Records

Create one to ten records in a Bika database in one request and return every created record ID and field value.

Delete Records

Permanently delete one to ten explicitly identified records from a Bika database and return a deletion result for each ID.

Get Database Schema

Get a Bika database's metadata, views, and field definitions, including the field IDs needed to read and write records by ID.

Get Record

Get one Bika record by ID, with field values keyed by field name or field ID.

List Nodes

List the nodes in a Bika space so an agent can discover database and automation resource IDs by name, type, and path.

List Records

Return one cursor-controlled page of records from a Bika database, optionally selecting specific fields by name or ID.

List Spaces

List spaces available to the connected Bika account and return the space IDs needed by database tools.

Update Records

Update fields on one to ten existing Bika records in one request and return the updated records.

FRAMEWORKS

How to build Bika.ai MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Bika.ai MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Bika.ai MCP with Claude Agent SDK

Claude Code logo

Claude Code

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Claude Cowork logo

Claude Cowork

Use Bika.ai MCP with Claude Cowork

Codex logo

Codex

Use Bika.ai MCP with Codex

OpenClaw logo

OpenClaw

Use Bika.ai MCP with OpenClaw

Hermes logo

Hermes

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CLI logo

CLI

Use Bika.ai MCP with CLI

Google ADK logo

Google ADK

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Vercel AI SDK logo

Vercel AI SDK

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Mastra AI logo

Mastra AI

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LlamaIndex logo

LlamaIndex

Use Bika.ai MCP with LlamaIndex

CrewAI logo

CrewAI

Use Bika.ai MCP with CrewAI

FAQ

Frequently asked questions

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

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

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