Meetings
Shared spoken notes from customer calls, one-on-ones, and team meetings.
This is where decisions, questions, and follow-ups show up.
Agent memory docs
Local spoken context your agent can actually use.
Transcripted turns meetings, dictation, and audio files into durable local artifacts: Markdown for people, JSON for tools, and a corpus index for agents.
Short answer
Transcripted is a local memory layer for spoken work. It records or imports audio on your Mac, transcribes it locally, and saves the result as Markdown transcripts, JSON sidecars, and a transcripted.json index that agents can search later.
What becomes memory?
Dictations
Private voice notes from quick thoughts, recaps, and post-meeting follow-ups.
This is where you save what you think happened and what to do next.
Together
Meetings and dictations give your agent both the shared record and your own notes.
Transcripted starts with meetings and gets better as you add the voice notes that connect the dots.
The artifact model
Each capture is useful to humans and machines at the same time.
Readable transcript
A clean transcript with timestamps and speaker names.
JSON sidecar
The same recording in JSON, ready for agents and scripts.
transcripted.json index
One local index so agents can find meetings and dictations fast.
Persistent speaker identity
The same person can stay linked across meetings.
What the files look like
These examples are intentionally plain. Agents do better when the interface is boring, stable, and readable.
Markdown transcript
# Customer onboarding call
Date: 2026-04-22
People: Maya Chen, Justin Betker
## Transcript
[00:01:14] Maya Chen:
The onboarding checklist is still where teams slow down.
[00:03:48] Justin Betker:
So the next step is a shorter first-run flow and a better export.JSON sidecar
{
"id": "meeting_2026_04_22_customer_onboarding",
"kind": "meeting",
"title": "Customer onboarding call",
"startedAt": "2026-04-22T15:00:00-05:00",
"participants": [
{ "id": "person_maya_chen", "name": "Maya Chen" },
{ "id": "person_justin_betker", "name": "Justin Betker" }
],
"utterances": [
{
"speakerId": "person_maya_chen",
"start": 74,
"text": "The onboarding checklist is still where teams slow down."
}
]
}transcripted.json index
{
"version": 1,
"items": [
{
"id": "meeting_2026_04_22_customer_onboarding",
"kind": "meeting",
"title": "Customer onboarding call",
"path": "meetings/2026-04-22-customer-onboarding.md",
"sidecar": "meetings/2026-04-22-customer-onboarding.json",
"people": ["person_maya_chen", "person_justin_betker"]
}
]
}Four access modes
Start simple. Add structure only when it helps.
Files first
Point Claude, Cursor, Obsidian, or any file-reading tool at your local Transcripted folder.
Starter prompt
Transcripted can generate a simple prompt that tells your agent where the files live.
Read-only MCP
On supported agents, the local MCP server lets you search, recap, and read meetings or dictations.
CLI and automation
Developers can script against the same local files with the CLI and automation.
Starter prompt
A clear prompt helps an agent treat the folder as a corpus instead of a pile of random files.
You have access to my local Transcripted corpus.
Use the Markdown transcripts for readable context.
Use JSON sidecars when you need speaker, timestamp, or structure.
Use transcripted.json as the index of meetings, dictations, and audio files.
When answering, cite the meeting title and date when possible.
Prefer decisions, owners, open questions, and follow-ups over generic summaries.Questions this unlocks
Why this is different from cloud notes
Private by default
Audio stays on your Mac.
No meeting bot
Transcripted records from your Mac. It does not join as a bot.
Open source and free
MIT licensed, no subscription, no paid tier, and no account required.
Built for repeated context
Speaker identity makes old meetings more useful over time.
Fast on Apple Silicon
The local pipeline is designed for M-series Macs and keeps transcription fast enough to fit into normal workflows.
Yours to keep
Your transcripts, sidecars, and index stay in files you control.
Next steps
FAQ
What is agent memory in Transcripted?
Agent memory in Transcripted is a local spoken-context corpus made from meetings, dictations, and audio files. It includes Markdown transcripts, JSON sidecars, and a transcripted.json index.
How do agents access Transcripted data?
Agents can read the local files directly, use a starter prompt, connect through the read-only local MCP server on supported clients, or use CLI and script workflows.
Does Transcripted require cloud storage for agent memory?
No. Transcripted starts with local files on your Mac. You can choose to sync or share them, but cloud custody is not required for the core workflow.
Why does persistent speaker identity matter?
Persistent speaker identity lets agents reason about the same person across meetings instead of treating every transcript as an isolated conversation.