Best Open Source AI Meeting Tools

Why Open Source Meeting Tools Matter

Cloud-based meeting assistants process your audio on remote servers operated by third parties. For many individuals and teams, this is perfectly acceptable. But for organizations handling sensitive information, whether legal discussions, medical consultations, financial negotiations, or classified government work, sending meeting audio to a third-party server creates unacceptable risk.

Open source meeting tools address this by running the entire processing pipeline locally. Audio never leaves the user's device or the organization's infrastructure. There is no subscription to cancel, no terms of service to negotiate, and no risk that the vendor will change its privacy policy or shut down the service. The source code is inspectable, so security teams can audit exactly what the software does with meeting data.

The tradeoff is setup complexity and hardware requirements. Running speech recognition models locally requires a reasonably powerful computer, and the initial configuration is more involved than signing up for a cloud service. Organizations also take on the responsibility of maintaining and updating the software themselves. For many privacy-conscious users, this tradeoff is worthwhile.

Meetily

Meetily is the most mature open source AI meeting assistant available. Licensed under MIT, it provides a complete meeting documentation pipeline that runs entirely on the user's device. The project has accumulated over 11,600 GitHub stars and more than 180,000 users, making it the most widely adopted open source option in this category.

The transcription engine uses Parakeet and Whisper models optimized for real-time processing. Meetily claims 4x faster transcription than standard Whisper implementations, which is critical for live meeting capture where processing needs to keep pace with the conversation. Speaker diarization identifies who said what throughout the meeting, and the results are formatted into a readable transcript with speaker labels and timestamps.

Summarization is handled by Ollama, which runs large language models locally. After transcription, the full transcript is processed through a local LLM to generate structured summaries, action items, and key points. Because Ollama runs on-device, the summarization step also avoids cloud processing. The quality of summaries depends on which LLM model you choose to run and your hardware's capability to run it effectively.

Meetily supports macOS and Windows, with a desktop application that provides a clean interface for recording, reviewing transcripts, and browsing past meetings. The app captures system audio through a virtual audio device, which means it works with any conferencing platform including Zoom, Google Meet, Teams, Webex, and others. No bot joins the call since all capture happens at the operating system level.

Hardware requirements are the main consideration. Running both transcription and summarization models locally requires a machine with at least 8GB of RAM and a modern CPU. GPU acceleration through NVIDIA CUDA or Apple Silicon significantly improves performance. On capable hardware, Meetily delivers a smooth real-time experience. On older or less powerful machines, there may be noticeable lag between speech and transcription.

Anarlog

Anarlog positions itself as an open source alternative to Granola, the popular commercial meeting assistant. It has accumulated over 18,000 users and emphasizes a philosophy of minimal data footprint. Audio, transcripts, and notes live as files on your device rather than in a database, which means they are accessible through standard file management tools and can be backed up, moved, or deleted using normal file operations.

The tool captures audio without appearing in the meeting participant list, similar to Krisp's approach. Participants do not see any additional attendees or recording indicators unless the user explicitly enables them. Transcription can be processed locally or through an AI provider of the user's choosing, giving flexibility between full local processing and cloud-assisted accuracy.

Anarlog's approach of storing everything as plain files appeals to users who distrust application-specific databases and want their meeting data in a format they can control independently. Notes can be edited in any text editor, transcripts are stored as plain text, and audio files are standard formats that work with any audio player.

The feature set is more limited than Meetily. Anarlog focuses on capture and transcription rather than advanced analytics or deep integrations. For users who want a lightweight, privacy-respecting meeting recorder without the complexity of a full-featured platform, it fills that niche well.

WhisperX

WhisperX is a research-grade transcription engine that extends OpenAI's Whisper model with forced alignment and speaker diarization. It is not a meeting assistant in itself but rather a powerful transcription backend that can be integrated into custom meeting workflows or used to process recorded meeting audio files.

The forced alignment feature maps transcribed words precisely to their timestamps in the audio, achieving word-level accuracy. This means you can locate the exact point in a recording where a specific word was spoken, which is significantly more precise than the sentence-level timestamps most tools provide. Speaker diarization adds labels identifying which speaker said each segment.

WhisperX runs locally and supports GPU acceleration for real-time or near-real-time processing. It handles multiple languages and can detect language automatically within the audio. The output format is structured JSON that can be parsed by downstream tools for summarization, analysis, or integration with other systems.

For technical teams that want to build custom meeting intelligence pipelines, WhisperX provides the transcription foundation. It requires command-line familiarity and Python knowledge to set up and use, which makes it unsuitable for non-technical users but ideal for developers building tailored meeting tools for their organization.

Whishper

Whishper packages Whisper transcription into a Docker-deployed web application with a drag-and-drop interface. Teams can deploy it on a shared server and access it through a web browser, which makes it accessible to non-technical users without requiring them to install anything locally.

The workflow is simple: upload an audio or video file, and Whishper processes it using Whisper models running on the server. The resulting transcript includes timestamps and can be exported as subtitles or plain text. The web UI supports managing multiple transcription jobs, reviewing results, and editing transcripts before export.

Whishper is primarily a batch processing tool rather than a real-time meeting assistant. Users record their meetings through their conferencing platform's built-in recording feature, then upload the recording to Whishper for transcription. This two-step workflow is less convenient than tools that record automatically, but it keeps the transcription entirely within the organization's infrastructure.

For teams that need a shared transcription service without individual desktop installations, Whishper provides a practical self-hosted solution. The Docker deployment makes installation straightforward for anyone familiar with containerized applications, and the server can be sized to handle the organization's transcription volume.

Vibe

Vibe is a cross-platform desktop application that transcribes audio and video files using Whisper.cpp, the C++ port of OpenAI's Whisper model. It provides a clean graphical interface for file-based transcription on macOS, Windows, and Linux. The focus is on simplicity: drag a file onto the window, select the language, and Vibe produces a transcript.

Because Vibe uses Whisper.cpp rather than the Python implementation, it runs efficiently on consumer hardware without requiring a GPU. The tradeoff is that it supports only file-based processing rather than real-time capture. Like Whishper, users need to record their meeting first using another tool, then transcribe the recording through Vibe.

Vibe works well as a personal transcription tool for users who already record meetings through their platform's native recording feature and want local, private processing of those recordings. Its minimal interface makes it accessible to non-technical users who need occasional transcription without the overhead of a full meeting assistant platform.

Choosing the Right Open Source Tool

For a complete, real-time meeting assistant that runs locally, Meetily is the strongest option. It handles capture, transcription, diarization, and summarization in a single application with a polished desktop interface. For a lighter-weight recorder that stores everything as plain files, Anarlog is the simpler alternative.

For teams that need shared transcription infrastructure, Whishper provides a web-based service deployable via Docker. For developers building custom meeting intelligence systems, WhisperX offers the most capable transcription engine to build upon. For casual personal transcription of recorded files, Vibe provides the simplest experience.

All of these tools share one fundamental advantage over commercial alternatives: your meeting data never leaves your control. No terms of service, no vendor lock-in, no data processing agreements to negotiate. For organizations where data sovereignty is non-negotiable, open source is the only approach that fully delivers on that requirement.