Fireflies
Good transcript input, if you don't trust the webhook alone
Most reviews compare it to Otter as a note-taker. We read its output with code, and that changes what matters.
The verdict
Fireflies is a reasonable transcript source for an automation pipeline, provided you poll the API as a backstop instead of trusting the webhook to fire every time.
Almost every Fireflies review compares it to Otter as a meeting assistant a person opens after a call. That is not how we use it. We consume its output with code. Different questions matter then: does the webhook fire reliably, is the transcript clean enough to feed an LLM without pre-processing, and what do you have to verify yourself.
The transcript quality is good enough to skip a cleanup step before summarization. The delivery mechanism is the part that needs a second check built into the pipeline.
In short
5 things that decide this
- 01Fireflies transcribes meetings automatically and exposes the result through a GraphQL API and a webhook on transcript completion.
- 02Transcript text is clean enough to feed straight to an LLM for summarization, with speaker labels already attached.
- 03The completion webhook does not fire on every transcript, so a pipeline that depends on it alone will silently drop meetings.
- 04The GraphQL API covers transcript retrieval well; anything past that, like bulk historical export, takes more calls than you expect.
- 05It is priced and built for a person opening notes after a call, not for high-volume programmatic polling, and that shows in the rate limits.
For someone who has not used it
Fireflies joins a video call as a bot, records it, and returns a transcript with speakers separated and an AI-generated summary. Most buyers treat it as a note-taking app: it sits next to Zoom or Google Meet and saves someone from typing minutes.
It also exposes that same transcript through an API and a webhook, which is what turns it from a note-taker into a data source. Few reviews look at it from that angle, because few teams build on it that way.
How it holds up as a building block
Good at
- Transcript text arrives with speaker labels already separated, so an LLM summarization step needs little pre-processing.
- The GraphQL API returns transcript, summary and metadata in one call, which keeps an integration simple to write.
- Meeting capture itself is reliable. The bot joins and records without the dropouts we have seen from some competitors.
Weak at
- The completion webhook is not guaranteed to fire for every transcript, so a pipeline that only listens for it will lose meetings without warning.
- Bulk or historical transcript export is not a single call. Pulling more than a handful of past meetings means paging through the API yourself.
- Rate limits are built around a person checking notes occasionally, not a service polling on a schedule, and you hit them faster than the docs suggest.
What we built with it
Little Tree Confections, an artisan bakery, recorded every meeting in Fireflies but rarely read the transcripts back. We built an n8n pipeline that pulls the transcript once a meeting finishes. It refines the text with OpenAI, then creates routed ClickUp tasks and Notion docs for the right department.
We did not wire the pipeline to the completion webhook alone. A scheduled check against the API catches any transcript the webhook missed, so a meeting never silently disappears. That backstop is the one piece of production hardening the docs do not tell you to add.
Common questions
01Fireflies or Otter for building automation on top?
Otter is built for the same note-taking use case, and its API surface is comparably thin for programmatic use. Neither was designed as a data pipeline first. Pick whichever your team already uses for meetings, then plan to poll rather than trust the webhook alone in either case.
02Can I pull every past transcript from an account in one call?
No. The GraphQL API returns transcripts individually or in small pages. A bulk historical export means writing a loop that pages through results rather than one request.
03Does the webhook fire reliably enough to trigger a workflow directly?
Not on its own. We saw transcripts complete with no webhook event following. A scheduled poll against the API as a backstop is what makes a pipeline built on Fireflies actually dependable.
04Is the transcript clean enough to summarize without pre-processing?
Yes. Speaker labels and timestamps arrive attached, and we feed the raw transcript straight into an LLM summarization step with no cleanup pass in between.
Fireflies transcripts, read programmatically, in production
Related
- n8n in production →The workflow tool that runs the pipeline Fireflies feeds.
- business process automation →How we build automation that runs unattended.
- connecting an AI agent to your CRM →The same webhook-versus-poll question, applied to CRM data.
- meeting notes that become tracked actions →The build this review draws on.
