How to integrate Circleci MCP with LangChain

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Introduction

This guide walks you through connecting Circleci to LangChain using the Composio tool router. By the end, you'll have a working Circleci agent that can trigger a new pipeline on main branch, list all pipelines for backend service, get test results from last successful build through natural language commands.

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

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

Also integrate Circleci with

TL;DR

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

The Circleci MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Circleci account. It provides structured and secure access to your Circleci projects and pipelines, so your agent can trigger builds, fetch job artifacts, monitor workflows, and analyze test results on your behalf.

  • Automated pipeline triggering and management: Let your agent start new builds for specific branches or tags, enabling continuous integration workflows without manual intervention.
  • Workflow and job status monitoring: Ask your agent to fetch detailed information about jobs and workflows, including status, timing, and execution environment, to stay on top of your CI/CD processes.
  • Artifact and test result retrieval: Have the agent collect job artifacts or extract comprehensive test metadata and failure messages for easier debugging and reporting.
  • Pipeline and runner insights: Get your agent to list all pipelines for a project or enumerate available self-hosted runners, making it simple to manage and audit your Circleci resources.
  • User and configuration access: Retrieve user profile details or fetch pipeline YAML configurations as needed for documentation, troubleshooting, or workflow optimization.

Supported Tools & Triggers

Tools
Create ContextTool to create a new context in CircleCI.
Create Context (GraphQL)Tool to create a new CircleCI context using the GraphQL API.
Create Context RestrictionTool to create a context restriction in CircleCI.
Create Organization Orb AllowlistTool to create a new URL Orb allow-list entry for an organization.
Create Organization ProjectTool to create a new project within a CircleCI organization.
Create Organization GroupTool to create a group in an organization.
Create Project Environment VariableTool to create a new environment variable for a CircleCI project.
Create Usage Export JobTool to create a usage export job for a CircleCI organization.
Delete Context (GraphQL)Tool to delete a CircleCI context by its UUID using GraphQL API.
Delete Context RestrictionTool to delete a context restriction by its ID.
Delete Namespace and Related OrbsTool to delete a CircleCI registry namespace and all its associated orbs.
Delete Namespace AliasTool to remove a namespace alias by name in CircleCI.
Delete Organization Orb Allowlist EntryTool to remove an entry from the organization's URL orb allow-list.
Delete Organization GroupTool to delete a group from a CircleCI organization.
Delete ProjectTool to delete a CircleCI project and its settings.
Delete Project Environment VariableTool to delete an environment variable from a CircleCI project.
Get ContextTool to retrieve a context by its unique ID.
Get Current UserTool to retrieve information about the currently authenticated user.
Get Flaky TestsTool to get flaky tests for a project.
Get Job ArtifactsRetrieves artifacts (output files like test results, logs, build binaries, reports) produced by a CircleCI job.
Get Job DetailsTool to fetch details of a specific job within a project.
Get Orb DetailsTool to query detailed information about a CircleCI orb using the GraphQL API.
Get Orb VersionTool to retrieve detailed information about a specific CircleCI orb version via GraphQL.
Get OrganizationTool to retrieve organization details from CircleCI using GraphQL query.
Get Organization GroupTool to retrieve a group in an organization.
Get Pipeline ConfigTool to fetch pipeline configuration by ID.
Get Pipeline DefinitionTool to retrieve a pipeline definition by project and definition ID.
Get ProjectTool to retrieve a CircleCI project by its slug.
Get Project WorkflowsTool to get summary metrics for all workflows of a project.
Get Test MetadataTool to fetch test metadata for a specific job.
Get Usage Export JobTool to retrieve a usage export job by organization ID and job ID.
Get User InformationTool to retrieve information about a CircleCI user by their unique ID.
Get Workflow SummaryTool to get metrics and trends for a workflow.
List Context Environment VariablesTool to list all environment variables for a specific context.
List Insights BranchesTool to get all branches for a project from CircleCI Insights.
List Insights SummaryTool to get summary metrics with trends for the entire organization and for each project.
List Namespace OrbsTool to list orbs in a CircleCI registry namespace with pagination support.
List Orb CategoriesTool to retrieve all CircleCI orb categories with pagination support.
List OrbsTool to list CircleCI orbs with pagination support via GraphQL API.
List Organization GroupsTool to list all groups in a CircleCI organization.
List Pages SummaryTool to get summary metrics and trends for a project across its workflows and branches.
List Pipeline DefinitionsTool to list all pipeline definitions for a specific project.
List PipelinesTool to get a list of pipelines for an organization.
List Pipelines for ProjectTool to list all pipelines for a specific project.
List Project Environment VariablesTool to list all environment variables for a CircleCI project.
List Project SchedulesTool to list all schedules for a specific project.
List Self-Hosted RunnersList self-hosted runners in CircleCI.
List User CollaborationsTool to retrieve organizations where the authenticated user has access.
List Workflows by Pipeline IDTool to list all workflows associated with a specific pipeline.
List Workflows Jobs WorkflowsTool to get summary metrics for a project workflow's jobs.
List Workflows Test MetricsTool to get test metrics for a project's workflows.
Query ContextTool to retrieve a CircleCI context by its UUID using GraphQL API.
Query Namespace ExistsTool to determine if a namespace exists in the CircleCI registry.
Query Orb Category IDTool to fetch the unique category ID for a CircleCI orb category by its name.
Query Orb ExistsTool to check if an orb exists in CircleCI registry and retrieve its privacy status.
Query Orb IDTool to fetch an orb's ID and optionally its namespace ID by orb name.
Query Orb Latest VersionTool to fetch the latest published version of a CircleCI orb.
Query Orb SourceTool to retrieve source code of a specific CircleCI orb version via GraphQL.
Query Plan MetricsTool to query plan metrics including credit usage by project and organization for a date range.
Remove Context Environment Variable (GraphQL)Tool to remove an environment variable from a CircleCI context using GraphQL API.
Rename NamespaceTool to rename a CircleCI namespace by its UUID identifier.
Store Environment VariableTool to store an environment variable in a CircleCI context using GraphQL mutation.
Trigger PipelineTriggers a new CI/CD pipeline run for a specified CircleCI project.
Upsert Context Environment VariableTool to add or update an environment variable in a CircleCI context.
Validate Orb ConfigTool to validate CircleCI orb YAML configuration using the orbConfig GraphQL query.

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

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 Circleci 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 Circleci tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

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

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "circleci-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 Circleci MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Circleci 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 Circleci 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 Circleci 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=['circleci']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "circleci-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 Circleci 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 Circleci 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.

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FAQ

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

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

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

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

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