How to integrate Fireflies MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting Fireflies to the OpenAI Agents SDK 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 OpenAI Agents SDK 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

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the necessary dependencies
  • Initialize Composio and create a Tool Router session for Fireflies
  • Configure an AI agent that can use Fireflies as a tool
  • Run a live chat session where you can ask the agent to perform Fireflies operations

What is OpenAI Agents SDK?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.

Key features include:

  • Hosted MCP Tools: Connect to external services through hosted MCP endpoints
  • SQLite Sessions: Persist conversation history across interactions
  • Simple API: Clean interface with Agent, Runner, and tool configuration
  • Streaming Support: Real-time response streaming for interactive applications

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.

Supported Tools & Triggers

Tools
Triggers
Add to Live MeetingThe AddToLive Action allows you to add the Fireflies.
Continue AskFred ThreadTool to continue an existing AskFred conversation thread with follow-up questions.
Create AskFred ThreadTool to start a new AskFred conversation thread with a question about meetings.
Create BiteTool to create a bite (short video or audio clip) from a transcript segment.
Delete Transcript by IDPermanently delete a transcript from the Fireflies account by its unique ID.
Fetch AI App OutputsTool to fetch AI App outputs for specific apps or transcripts.
Get AskFred ThreadTool to get a specific AskFred conversation thread with full history.
Get AskFred ThreadsTool to retrieve a summary of all AskFred conversation threads for the current user.
Get Bite by IDFetches details for a specific bite by ID.
Get TranscriptsFetches a list of bites (highlights) against input arguments.
Get Transcript by IDFetches details for a specific Fireflies transcript ID.
Get TranscriptsFetches a list of transcripts against input filters.
Get User by IDThe GetUser Action is designed to fetch details associated with a specific user id.
Get User GroupsTool to fetch a list of all user groups within the team with information about user groups including their members.
Get UsersFetches a list of all users within the team, including their full email addresses.
Execute GraphQL QueryExecute an authenticated, read-only Fireflies GraphQL operation (query) and return the full raw GraphQL response (data+errors) for reliable fallback and debugging.
Set User RoleTool to update a user's role within a team.
Update Meeting ChannelTool to batch update channel assignments for 1-5 meeting transcripts.
Update Meeting PrivacyTool to update the privacy setting of a meeting transcript.
Update Meeting TitleTool to update the title of a meeting transcript.
Upload AudioThe UploadAudio Action allows you to upload audio files to Fireflies.

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, make sure you have:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live Fireflies project
  • Some knowledge of Python or Typescript

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

Install dependencies

pip install composio_openai_agents openai-agents python-dotenv

Install the Composio SDK and the OpenAI Agents SDK.

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

Import dependencies

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
What's happening:
  • You're importing all necessary libraries.
  • The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Fireflies.

Set up the Composio instance

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
What's happening:
  • load_dotenv() loads your .env file so OPENAI_API_KEY and COMPOSIO_API_KEY are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.

Create a Tool Router session

# Create a Fireflies Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["fireflies"]
)

mcp_url = session.mcp.url

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only fireflies.
  • The router checks the user's Fireflies connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access Fireflies.
  • This approach keeps things lightweight and lets the agent request Fireflies tools only when needed during the conversation.

Configure the agent

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Fireflies. "
        "Help users perform Fireflies operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access Fireflies and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a HostedMCPTool that connects to the MCP server URL we created earlier.
  • The headers dict includes the Composio API key for secure authentication with the MCP server.
  • require_approval: 'never' means the agent can execute Fireflies operations without asking for permission each time, making interactions smoother.

Start chat loop and handle conversation

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
What's happening:
  • The program prints a session URL that you visit to authorize Fireflies.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using Runner.run().
  • The responses are printed to the console, and conversations are saved locally using SQLite.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with Fireflies and OpenAI Agents SDK:

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["fireflies"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Fireflies. "
        "Help users perform Fireflies operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())

Conclusion

This was a starter code for integrating Fireflies MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Fireflies.

Key features:

  • Hosted MCP tool integration through Composio's Tool Router
  • SQLite session persistence for conversation history
  • Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

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FAQ

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

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.

Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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.

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

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.

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 Fireflies data and credentials are handled as safely as possible.

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