How to integrate Algolia MCP with LangChain

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Introduction

This guide walks you through connecting Algolia to LangChain using the Composio tool router. By the end, you'll have a working Algolia agent that can export all records from products index, clear all objects from blog_posts index, copy index settings from staging to production, create an ab test comparing two search configs through natural language commands.

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

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

TL;DR

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

The Algolia MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Algolia account. It provides structured and secure access to your search indices, so your agent can perform actions like browsing records, managing index settings, running A/B tests, copying configurations, and tracking search events on your behalf.

  • Comprehensive index browsing and export: Easily direct your agent to retrieve and iterate through all records in any Algolia index for analysis, backup, or migration tasks.
  • Automated A/B test management: Set up, launch, and monitor A/B tests to compare search performance between different index variants or configurations—all without manual coding.
  • Index and rule duplication: Quickly copy entire indices, settings, or query rules from one index to another, streamlining your search deployment and versioning workflows.
  • Bulk index cleanup and resets: Instruct your agent to clear all objects or rules in an index while keeping configurations intact, letting you efficiently manage large data updates or reindexing processes.
  • Real-time event tracking: Capture user interactions like clicks and conversions, enabling your agent to report search analytics and optimize relevance based on actual user behavior.

Supported Tools & Triggers

Tools
Add AB TestTool to create an ab test comparing search performance between two variants.
Browse Algolia IndexTool to retrieve all records from an index.
Clear ObjectsTool to clear records of an index without affecting settings.
Clear RulesTool to delete all rules in an index.
Clicked Object IDsTool to send a click event to algolia to capture clicked items.
Clicked Object IDs After SearchTool to send a click event after a search response.
Converted Object IDsTool to send a conversion event for items outside of search context.
Copy IndexTool to copy the specified index to a new index.
Copy RulesTool to copy rules from one index to another.
Copy Index SettingsTool to copy the settings from one index to another.
Delete IndexTool to delete the specified index and all its records.
Delete Multiple RecordsTool to delete multiple records from an algolia index.
Delete RuleTool to delete the specified rule from an index.
Delete SynonymTool to delete a synonym from a specified index.
Export RulesTool to export all rules defined on an index.
Find ObjectTool to find the first object matching a query or filter in an index.
Get Object PositionTool to retrieve an object’s position in a result set.
Get multiple objectsTool to retrieve multiple records from an index.
Get Index SettingsTool to retrieve the settings of a specified index.
Index ExistsTool to check if an algolia index exists.
Init Insights API ClientTool to initialize the algolia insights api client.
List IndicesTool to list all indices and their metadata.
Partial Update ObjectsTool to partially update multiple records in the specified index.
Replace All RulesTool to push a new set of rules, erasing previous ones.
Save SynonymTool to add or update a synonym in the specified index.
Search Algolia IndexTool to perform a search on a specified algolia index.
Search Multiple IndicesTool to perform searches across multiple indices in a single call.
Search RulesTool to search for rules in the specified index.
Search SynonymsTool to search for synonyms in the specified index.
Set Index SettingsTool to update an algolia index's settings.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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

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

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.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-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 is the core agent framework
  • python-dotenv loads environment variables

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

Import dependencies

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

Initialize Composio client

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")
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 Algolia tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Algolia
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['algolia']
)

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

Configure the agent with the MCP URL

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

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Algolia 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")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts 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 ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

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

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=['algolia']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "algolia-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 Algolia 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())

Conclusion

You've successfully built a LangChain agent that can interact with Algolia 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 Algolia MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Algolia MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Algolia MCP with Claude Agent SDK

Google ADK

Google ADK

Use Algolia MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Algolia MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Algolia MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Algolia MCP with LlamaIndex

CrewAI

CrewAI

Use Algolia MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Algolia MCP?

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

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

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

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