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How to integrate Fireflies MCP with LangChain

Connect LangChain to Fireflies MCP. Transcribe this uploaded meeting audio file, summarize your last five recorded calls, and more using natural language, with authentication handled for you.

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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.

26 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 search and fetch transcripts, query meetings with AskFred, review meeting analytics, upload audio, and create meeting clips 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.

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate Fireflies with

Claude CodeCursorChatGPTClaude CoworkAntigravityOpenAI Agents SDKClaude Agent SDKMuseCodexGrok BuildVS CodeOpenCodeOpenClawHermesAtomic AgentGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAIPydantic AIAutoGen

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 meeting transcripts, analytics, participants, topics, channels, user groups, and AskFred conversations, so your agent can search conversations, analyze meetings, review attendance, explore topics, and retrieve AskFred discussions on your behalf.

  • Transcript search: Have your agent search meeting titles and spoken content for keywords to find relevant conversations.
  • Meeting insights and analytics: Let the agent review conversation metrics, sentiment, team trends, and individual performance data from your meetings.
  • Participants and attendance: Direct your agent to retrieve meeting participants and review their attendance details.
  • Topics and AskFred conversations: Instruct your agent to identify extracted topics, keywords, and meeting types, or retrieve AskFred threads with their full history.
  • Team organization and roles: Have your agent list accessible channels and user groups, review group membership, or update a team member's role.

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

Starts a new AskFred conversation with a natural-language question about one meeting or multiple meetings using filters.

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 Meeting Analytics

Retrieves comprehensive meeting analytics including team-level conversation metrics, sentiment analysis, and individual user performance data.

Get Meeting Participants

Retrieves the list of participants who attended a specific Fireflies meeting along with their attendance details.

Get Meeting Topics

Retrieves AI-extracted topics, keywords, and meeting type for a specific Fireflies transcript.

Get Transcript by ID

Fetches details for a specific Fireflies transcript ID, including sentences, attendees and summary when available.

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

Executes an authenticated, read-only Fireflies GraphQL query and returns validated data, errors, and extensions when available.

List Channels

Fetches a list of all channels accessible to the authenticated user, including both public channels and private channels where the user is a member.

Search Transcripts

Search transcripts using keyword-based full-text search across meeting titles and/or spoken content.

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

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Claude Code

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ChatGPT

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Claude Cowork

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Antigravity logo

Antigravity

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OpenAI Agents SDK logo

OpenAI Agents SDK

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Claude Agent SDK logo

Claude Agent SDK

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Muse logo

Muse

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Codex logo

Codex

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Grok Build logo

Grok Build

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VS Code logo

VS Code

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OpenCode logo

OpenCode

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OpenClaw logo

OpenClaw

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Hermes logo

Hermes

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Atomic Agent logo

Atomic Agent

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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

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CrewAI logo

CrewAI

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Pydantic AI logo

Pydantic AI

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AutoGen logo

AutoGen

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FAQ

Frequently asked questions

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