Technical breakdown of open source Granola AI alternatives

Granola’s popularity has led to the rise of self-hosted and open source desktop meeting recorders. In this blog, I’ll examine the features, tradeoffs and arguments surrounding the self-hosted and open source approach to meeting recording.

This blog is for developers and product managers building AI-powered desktop meeting recorders like Granola who want to understand the infrastructure and features necessary.

A desktop meeting recorder can generate meeting artifacts such as summaries, key insights, action items and transcripts. The recent popularity of this form factor has made it a part of many users’ everyday workflows.

You can build your own desktop meeting notetaker in days using Recall.ai’s Desktop Recording SDK.

Evaluating Granola AI alternatives: features and post-meeting artifacts

Users expect desktop meeting notetakers to support both Mac and Windows, an easy setup process, automatic meeting detection, and high-quality post-meeting artifacts, all of which are supported by products like Granola. I evaluated the open source applications with this in mind.

The three applications I evaluated are Speakr, Meetily, and Steno AI. I chose these three projects because they are open sourced and have gained traction on GitHub, as reflected by their repository stars. I ran all three applications during the same meeting and compared the resulting user experience and post-meeting artifacts generated. But firstly, I evaluated the extent to which the applications supported both macOS and Windows.

Support for different operating systems and devices

Since users are typically distributed across both operating systems, cross-platform support is important. The applications also need to account for users running on older devices and outdated operating systems, since users may not be on the most updated device.

Across all applications, platform support is more mature on macOS. Testing was conducted on a device running macOS version 26.5.

The table below summarizes the operating system across each application, based on project documentation rather than hands-on validation:

Installation experience

To install the three applications, I followed the setup instructions in each repository’s README, which were easy to follow. With the exception of Speakr, they could either be downloaded directly as desktop applications or cloned from GitHub.

Speakr requires additional setup, including configuring OpenAI API keys for LLM-based summarization and installing Docker to run the application. It also needs to be accessed through localhost rather than as a standalone desktop application, making it less accessible for non-technical users.

Since not all end users are technical, the ideal installation flow mirrors the experience provided by Granola where users can simply download and run a desktop application without needing to clone a GitHub repository or do additional configuration.

Automatic meeting detection abilities

Automatic meeting detection is a feature that allows a desktop recorder to automatically start recording a meeting without requiring the user to open the app. This capability has become a baseline expectation for meeting recording and is supported by products like Granola.

While Meetily and StenoAI have automatic meeting detection abilities, Speakr doesn’t have this feature, so users need to manually start and stop recordings. This creates the risk of users forgetting to record meetings or accidentally continuing to record after the meeting has ended, potentially capturing private conversations. Users become responsible for managing the recording workflow. All three applications support manual in-person recording, but automatic meeting detection remains the more important feature because it ensures important meeting data is always recorded.

Automatic meeting detection is included out of the box with Recall.ai’s Desktop Recording SDK.

Availability, completeness, and quality of post-meeting artifacts

Users now expect meeting recorders to generate summaries, transcripts, and action items automatically after a meeting ends. I examined the quality and completeness of the post-meeting artifacts they generated, and whether they offered additional outputs.

Based on this criteria, Speakr currently offers the least complete meeting notetaking experience. After recording has ended, Speakr did not automatically generate summaries, transcripts, or any other post-meeting artifacts. While Speakr markets itself as a tool for turning audio into searchable notes, the current experience feels closer to a voice recorder app than a production-ready meeting assistant.

Both Meetily and StenoAI provide meeting transcripts and summaries, with Meetily generating additional post-meeting action items. However, the overall quality of the transcripts and action items generated for Meetily and StenoAI need improvement, due to the lack of reliable speaker attribution in the transcripts.

Transcripts generated by Meetily and StenoAI lack reliable speaker attribution, making them difficult to follow because they often read like walls of text. This becomes especially challenging in meetings with more than two participants and reduces the reliability of downstream workflows.

Downstream workflows like action item assignment heavily depend on accurately identifying who said what during a meeting. Without this context, it becomes difficult to assign tasks to the correct participant. In contrast, meeting summaries are more tolerant of missing speaker attribution because they rely more on overall conversation meaning. However, even meeting summaries can improve with more accurate speaker attribution.

Why you need speaker names in your transcript

Recall.ai provides speaker attribution with real speaker names rather than placeholder labels (for example, “John Smith” rather than “Speaker 1”), across both its Desktop Recording SDK and Meeting Bot API. With Recall.ai’s Calendar API, you can also get speaker emails.

Many successful AI meeting products go beyond basic note-taking by using meeting conversations as the foundation for workflows such as action item generation, email drafting, real-time coaching, and conversation analysis. These workflows depend heavily on accurately identifying who said what. Without speaker attribution, it becomes much harder to build differentiated and reliable meeting products beyond summaries.

Recall.ai handles much of the required infrastructure and production complexity, and provides features that help meeting products stand out. This includes access to high-quality meeting audio and video recordings, which developers can leverage to build richer meeting features. However, because these features depend on Recall.ai’s infrastructure, products built on top of Recall.ai cannot be fully self-hosted.

Limitations of open-source and self-hosted Granola AI alternatives

Open source and self-hosted meeting applications appeal to end users who want more control over their meeting data. However, this does not guarantee a more secure, reliable or better end user experience.

The infrastructure behind many open source meeting applications has not been proven due to limited testing at production scale and lack of sustained engineering effort. Many advanced downstream workflows rely on external APIs, LLMs, and transcription infrastructure that are difficult to run entirely on a user’s device.

This is why many developers building desktop meeting recorders choose Recall.ai. Recall.ai has a proven track record across startups and Fortune 500 companies, giving teams access to reliable meeting infrastructure so they can focus on product differentiation rather than data security and infrastructure maintenance.

The infrastructure needed to power an AI meeting recorder

Recall.ai’s Desktop Recording SDK handles many of the infrastructure edge cases and end-user behavior edge cases involved in building a production-ready meeting recorder. It supports both Windows and macOS, and accounts for real-world user behaviors such as devices shutting down mid-meeting or laptops closing immediately after a meeting ends, ensuring meeting data is preserved.

Using Recall.ai’s Desktop Recording SDK saves developers months of engineering work beyond building meeting recording infrastructure. This includes solving reliable system and microphone audio capture where platform-specific restrictions become a challenge. Recall.ai also handles production-ready audio features like echo cancellation, microphone selection and mute state detection out of the box (see Appendix), which directly impacts transcription accuracy.

Advanced features like meeting video recording is also a significant engineering challenge. Frameworks such as ScreenCaptureKit can be used to build the video capture feature, but they come with platform-specific complexity. Recall.ai has dealt with all of these challenges, giving developers the building blocks needed to create production-ready meeting recorders that stand out in the market.

If you are simply looking for meeting summarization features as an end user, existing off-the-shelf tools may already be sufficient. However, if you are building a meeting recorder for end users or looking to migrate your existing meeting infrastructure, you can try Recall.ai for free or book a demo.

Appendix

FAQ

What are some alternatives to Granola AI?

Alternatives to Granola include Cluely, Mem, and Fireflies. Many AI meeting notetakers also build on top of infrastructure providers such as Recall.ai to power meeting recording and post-meeting workflows.

What does it mean for a meeting recorder to be open source?

Open source means the code for the application can be inspected, modified, or self-hosted by users. However, being open-sourced does not automatically guarantee stronger security, better privacy, or a more reliable product experience.

What are some open-source Granola AI alternatives?

Projects such as Speakr, Meetily, and StenoAI position themselves as open source alternatives to Granola.

Are open-sourced alternatives safer for data privacy?

Not necessarily. While open source applications allow users to inspect the code, that alone does not guarantee better privacy or stronger security.

Who should build their own desktop meeting recording app?

Building your own desktop meeting notetaker is best suited for developers who want to build products that rely on conversation data to power downstream workflows such as action item generation, CRM updates, coaching insights, or conversation analysis. Developers looking to build or migrate production-grade meeting infrastructure can explore Recall.ai’s Desktop Recording SDK.

What can I use to make my own Granola AI?

If you want to build your own desktop meeting recorder like Granola AI, you can use Recall.ai. We power the meeting infrastructure behind startups and Fortune 500 companies building products on top of meeting and conversation data. With Recall.ai, developers can get to production in days. Sign up for free today.