Fireflies Meeting Assistant
Turn meetings into actionable intelligence insights
Rounak Show
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27-01-2026
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The Problem
Teams waste a huge amount of time manually reviewing meeting recordings and transcripts to find decisions, tasks, and important discussions. Critical action items often get missed, knowledge gets lost across meetings, and valuable insights remain buried inside unstructured text data. This creates inefficiency, poor follow-ups, and information overload.
The Solution
The project solves this problem by introducing a conversational AI layer on top of Fireflies.ai meeting data. Instead of users manually reading long transcripts, the system allows them to interact with meetings using natural language.
About This Project
It is a meeting intelligence assistant built using LangChain, MCP, and large language models, designed to make meeting data instantly usable. The project integrates a Streamlit-based chat interface with a LangChain agent that connects directly to Fireflies.ai through MCP servers. Users can ask questions about past meetings, generate summaries, extract action items, or search across multiple discussions using natural language. The agent handles conversation flow, model selection (OpenAI GPT-5-mini or Google Gemini), and intelligent tool invocation, while Fireflies MCP servers provide structured access to transcripts and meeting metadata. This project demonstrates an agentic AI architecture where LLMs are not just generating text, but actively reasoning, selecting tools, and orchestrating data retrieval. FireMind AI showcases how unstructured meeting content can be transformed into structured knowledge—enabling faster follow-ups, better decision-making, and improved team productivity.