Open-source alternatives to Fireflies AI Notetaker
Fireflies is a hosted AI teammate for meetings. If you are searching for an open-source alternative, you are usually looking for one specific thing — a local meeting app, a transcription engine you can embed, or a library you can script. Pick your requirement and this page will send you to the right project instead of a generic list.
Licenses and setup steps verified 2026-10-13 from each project's own repository. Not affiliated with Fireflies.ai or any project listed here.
What are you actually replacing?
Nothing here is ranked by score, because these projects are not competing on the same axis. Choose the shape of solution you need.
Why people search for an alternative at all
Fireflies describes its product as an AI teammate that transcribes, summarizes, and searches meetings across a growing list of integrations, with enterprise security claims on its homepage. That is a genuinely large surface area, and most people do not leave it because the transcription is bad.
The recurring themes in public discussion are different. In a long-running r/Zoom thread about a notetaker bot appearing in meetings, the practical questions were about how the bot got there and how to keep it out — calendar access, platform integrations, and waiting-room controls. In App Store reviews for the same product, users praised accuracy and privacy posture while complaining about onboarding, editing limits on mobile, and account/calendar hygiene. Those are product-fit and control issues, not accuracy issues.
So before you migrate, name the actual problem. "I want recordings to never leave my hardware" is a solvable engineering requirement. "The mobile app is annoying" is not solved by self-hosting anything.
The three open-source routes
These projects are listed by what they are, not by a score. Read the license line before you build on any of them.
Route A · Application
Meetily
License: MIT. Runs: macOS, Windows, Linux. A privacy-first AI meeting assistant that captures, transcribes in real time, and summarizes locally, using open models instead of paid APIs. Summaries can run through a local provider such as Ollama, or through a hosted provider if you choose one.
Its repository also describes a paid "Meetily PRO" tier for enhanced accuracy, advanced exports, and team features, so the open-source edition and the paid edition are not the same product.
License: MIT. A dependency-free C/C++ implementation of the Whisper speech-recognition model, with first-class Apple Silicon support and CPU-only inference on ordinary hardware. It is a transcription engine, not a meeting app — you supply capture, storage, summarization, and the interface.
Its CLI transcribes 16-bit WAV files, so you convert other audio formats first with ffmpeg.
License: MIT. A general-purpose speech-recognition model with a Python package and a command-line tool, covering multilingual transcription, translation to English, and language identification. Installs with pip and needs ffmpeg on the system.
The widest scripting surface of the three, and the easiest to drop into an existing Python pipeline. It is a model and library, not an application.
The fastest way to waste a week is to install a transcription engine expecting a meeting assistant. This table separates what each project actually ships.
Based on each project's own repository documentation, checked 2026-10-13. Recheck licenses and dependencies before you ship.
Project
What it is
License
Ships with
You still have to build
Meetily
Local meeting application
MIT
Live capture, real-time transcription, local summaries, desktop installers
Any calendar-bot automation, team library, or CRM sync you relied on before
whisper.cpp
Transcription engine
MIT
Model inference and a CLI that transcribes WAV files
Recording, audio conversion, summarization, storage, and a user interface
OpenAI Whisper
Model and Python library
MIT
Python package, command-line transcription, language detection
Everything above the model: capture, queueing, summary, review workflow
Nothing to build — but you are trusting a vendor with your meeting audio
One caution about the word "open source" in this category. A hosted tool that offers a free tier is not open source, and an open-source transcription engine is not a meeting assistant. Some projects also publish a paid edition alongside an open-source one; the MIT license covers the code in the repository, not the paid tier.
Self-host transcription in three steps
This is the smallest useful version of Route B — offline transcription of a recording you already have, no meeting bot and no vendor account.
Build the engine
Clone the repository, download a model, and compile. whisper.cpp's documented quick start uses CMake and a downloaded model file.
Convert your audio
Its CLI expects 16-bit WAV. Convert a meeting recording from MP3 or M4A to 16 kHz mono WAV with ffmpeg before you transcribe.
Transcribe and keep the output
Run the CLI against the WAV file, then move the transcript somewhere your team can review. Storage and review are your job in this route.
Model size is the main tradeoff: the tiny and base models run almost anywhere and are the least accurate; the large model is the most accurate and needs the most memory and time. Choose deliberately rather than defaulting to the largest file you can download.
If you would rather not assemble a pipeline, install Meetily instead — it packages capture, transcription, and summarization into a desktop application and is the closer match to what a hosted notetaker gave you.
What you give up when you leave the hosted tool
The calendar bot. The convenience of a notetaker that joins every meeting automatically is exactly what you are rebuilding, and it is the part open-source projects deliberately leave to you.
The team library. Searchable, shared transcripts across a company are a product feature, not a model feature. Self-hosting means deciding where transcripts live and who can read them.
Compliance paperwork. A vendor's SOC 2 or HIPAA claims are an audit artifact. Replacing the vendor does not produce those documents; if your organization needs them, that is a separate workstream.
Someone else's summaries. Summarization quality now depends on the model you point at it. A local model keeps data on-device but is a different quality tier from a frontier hosted model.
Your own consent process. Recording rules vary by jurisdiction and by who is in the room. Self-hosting removes the vendor, not the legal obligation.
What the output actually looks like
Raw transcription is not notes. The gap between a transcript and a meeting note is the work most people underestimate. Below is an illustrative example of the shape you should aim for if you build your own summary step — it is a format sketch, not a real meeting and not a sample of any tool's output.
Transcript excerpt (illustrative)
[00:14:20] Speaker 2: So the blocker is the export path, not the parser. [00:14:33] Speaker 1: Right — can we ship the parser fix this week and leave export for the next cycle? [00:14:41] Speaker 2: That works if we tell support before Thursday.
Illustrative format only
The note you want to end up with
Decision: ship the parser fix this cycle; defer the export change.
Action: notify support before Thursday — owner to be confirmed.
Open question: whether the export change needs a migration plan first.
Illustrative format only
If your post-processing produces decisions and owners rather than paragraphs of transcript, you have closed most of the gap between a self-hosted pipeline and a hosted notetaker. If it produces only transcript, you have replaced the easy half.
Sources
Meetily repository: local-first meeting assistant, platform support, model and provider options, license, and the paid PRO tier.
whisper.cpp repository: build steps, WAV requirement, offline inference, and MIT license.
What are the best open-source alternatives to Fireflies AI Notetaker?
There are three different shapes of answer, and comparing them as if they were the same product is the most common mistake. Meetily is an MIT-licensed local meeting application that captures, transcribes, and summarizes on your own machine. whisper.cpp is an MIT-licensed C/C++ transcription engine you embed in your own tool. OpenAI Whisper is an MIT-licensed Python speech-recognition library for building a pipeline. None of them is a drop-in swap for a hosted notetaker with a calendar bot.
Can I stop an AI notetaker bot from joining my meeting?
Yes, within limits. Meeting hosts can use a waiting room, require authenticated join, remove a participant, or decline admission. Reported cases where a notetaker bot appeared unexpectedly usually trace back to calendar permissions, a meeting-platform integration, or another invitee's configuration rather than to a tool choosing meetings at random. Check who has calendar and app access before you assume the bot is uninvited.
Are AI note-takers legal to record with?
This is a legal question, and the answer depends on your jurisdiction and on who is in the meeting. Recording rules differ widely — some places require only one party's consent, others require everyone's. A notetaker that runs entirely on your own machine does not exempt you from those rules. Get your own legal advice rather than relying on a vendor's compliance page.
Do open-source meeting note-takers need the cloud?
Not for transcription. whisper.cpp and Whisper run the model locally on your own hardware, and Meetily is built around on-device capture and transcription. Where the cloud can re-enter is the summary step: Meetily supports a local provider such as Ollama, but it also lets you point summaries at a hosted provider, which changes the privacy picture completely.
Can an open-source tool replace Fireflies' summaries and integrations?
Partially. Transcription is the easy part to reproduce. What you are giving up is the surrounding product: the calendar bot, the searchable team library, the CRM and project-management integrations, the admin controls, and the vendor's compliance posture. Expect to build or assemble those yourself, or to accept a lighter workflow.