How to integrate Fireflies MCP with LangChain

This guide walks you through connecting Fireflies to LangChain using the Composio tool router. By the end, you'll have a working Fireflies agent that can transcribe this uploaded meeting audio file, summarize your last five recorded calls, list all transcripts involving the marketing team through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Fireflies account through Composio's Fireflies MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Fireflies.ai is an AI-powered meeting assistant that records, transcribes, and analyzes voice conversations. It helps teams capture call notes automatically and search or summarize meetings effortlessly.

21 Tools1 Triggers

Introduction

This guide walks you through connecting Fireflies to LangChain using the Composio tool router. By the end, you'll have a working Fireflies agent that can transcribe this uploaded meeting audio file, summarize your last five recorded calls, list all transcripts involving the marketing team through natural language commands.

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

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

Also integrate Fireflies with

ChatGPTOpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenCodeCursorVS CodeOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

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

The Fireflies MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Fireflies account. It provides structured and secure access to your voice conversations, so your agent can perform actions like transcribing meetings, summarizing discussions, searching transcripts, and managing audio files on your behalf.

  • Automated meeting transcription: Instantly upload audio files or add the Fireflies bot to live meetings so your agent can generate accurate transcripts for later review.
  • AI-powered conversation summarization: Let your agent fetch concise, actionable summaries of calls and meetings to help you quickly catch up or share insights with your team.
  • Transcript search and retrieval: Ask your agent to find specific transcripts or extract key segments from past conversations using keywords, dates, or participant names.
  • Audio file management: Effortlessly upload, organize, or delete audio files and transcripts right from your agent, keeping your conversation library up to date.
  • User and team insights: Enable your agent to fetch user details or team-wide meeting data, so you can stay on top of collaboration and engagement.

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 Fireflies 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 Fireflies 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: ['fireflies']
    }
);

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

Configure the agent with the MCP URL

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

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "fireflies-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 Fireflies 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 Fireflies 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 & TRIGGERS

Supported Tools and Triggers

Every Fireflies action and event your agent gets out of the box.

Add to Live Meeting

The AddToLive Action allows you to add the Fireflies.

Continue AskFred Thread

Tool to continue an existing AskFred conversation thread with follow-up questions.

Create AskFred Thread

Tool to start a new AskFred conversation thread with a question about meetings.

Create Bite

Tool to create a bite (short video or audio clip) from a transcript segment.

Delete Transcript by ID

Permanently delete a transcript from the Fireflies account by its unique ID.

Fetch AI App Outputs

Tool to fetch AI App outputs for specific apps or transcripts.

Get AskFred Thread

Tool to get a specific AskFred conversation thread with full history.

Get AskFred Threads

Tool to retrieve a summary of all AskFred conversation threads for the current user.

Get Bite by ID

Fetches details for a specific bite by ID.

Get Transcripts

Fetches a list of bites (highlights) against input arguments.

Get Transcript by ID

Fetches details for a specific Fireflies transcript ID.

Get Transcripts

Fetches a list of transcripts against input filters.

Get User by ID

The GetUser Action is designed to fetch details associated with a specific user id.

Get User Groups

Tool to fetch a list of all user groups within the team with information about user groups including their members.

Get Users

Fetches a list of all users within the team, including their full email addresses.

Execute GraphQL Query

Execute an authenticated, read-only Fireflies GraphQL operation (query) and return the full raw GraphQL response (data+errors) for reliable fallback and debugging.

Set User Role

Tool to update a user's role within a team.

Update Meeting Channel

Tool to batch update channel assignments for 1-5 meeting transcripts.

Update Meeting Privacy

Tool to update the privacy setting of a meeting transcript.

Update Meeting Title

Tool to update the title of a meeting transcript.

Upload Audio

The UploadAudio Action allows you to upload audio files to Fireflies.

FRAMEWORKS

How to build Fireflies MCP Agent with another framework

ChatGPT logo

ChatGPT

Use Fireflies MCP with ChatGPT

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Fireflies MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Fireflies MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Fireflies MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Fireflies MCP with Claude Cowork

Codex logo

Codex

Use Fireflies MCP with Codex

Cursor logo

Cursor

Use Fireflies MCP with Cursor

OpenClaw logo

OpenClaw

Use Fireflies MCP with OpenClaw

Hermes logo

Hermes

Use Fireflies MCP with Hermes

CLI logo

CLI

Use Fireflies MCP with CLI

Google ADK logo

Google ADK

Use Fireflies MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Fireflies MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Fireflies MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Fireflies MCP with LlamaIndex

CrewAI logo

CrewAI

Use Fireflies MCP with CrewAI

OpenCode logo

OpenCode

Use Fireflies MCP with OpenCode

VS Code logo

VS Code

Use Fireflies MCP with VS Code

FAQ

Frequently asked questions

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

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

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