v0.9.7 · open source · MIT

The AI memory layer for engineering teams.

Verbatim turns meeting transcripts into a structured, queryable record of every commitment, decision, open question, and blocker — each one sourced to an exact verbatim quote. Slack, GitHub, Linear, Jira, daemon mode, local LLMs. Free forever.

~/work — verbatim
$ verbatim ingest standup.txt

 Saved 4 items from standup.txt
  VRB-a1b2c3d4  commitment  Qat → ship CULA prototype  (Friday)
  VRB-e5f6a7b8  decision    use Kafka for replay cache
  VRB-9c0d1e2f  question    who owns the m2w domain config?
  VRB-3a4b5c6d  blocker     waiting on Cyren tier-3 JWT

$ verbatim show a1b2c3d4

VRB-a1b2c3d4  commitment  open  high-confidence
Actor:     Qat
Deadline:  Friday
Quote:     "I'll have the CULA prototype shipped by Friday."
Source:    standup.txt · line 47 · 2026-05-18

Four entity types. One verbatim quote each.

Every extraction must point at the exact words that produced it. No hallucinated action items. No summarized "the team agreed to…" with no receipt.

Commitment

Who owes what, by when

Actor, deliverable, deadline. Open → confirmed → resolved status flow.

Decision

What was settled

The choice and the alternative it ruled out. Searchable later.

Question

Open threads

What was asked and not answered. Auto-promotes to a follow-up.

Blocker

What's stuck on what

Item, blocked-by, owner. Visible across surfaces until cleared.

Where it lives

Verbatim runs locally as a CLI, daemon, MCP server, or web UI — and bridges into the tools you already use.

CLI & web UI

Linear-style three-pane browser, dark / light theme, full-text and entity-type filters.

Slack HITL

Confirm, dismiss, edit, reassign — straight from a Block Kit card in the channel.

Daemon mode

Watches a folder, ingests new transcripts, posts a digest. Set and forget.

MCP server

Plug Verbatim's memory into Claude, Claude Code, or any MCP-aware agent.

Projections

Sync commitments to GitHub Issues, Jira, or Linear with one command.

Local LLM

Anthropic Claude by default — or any tool-calling model via Ollama. $0, air-gapped.

$ Cost guardrails

Per-model spend tracking, daily budget caps, dry-run mode before any paid call.

Ingest anywhere

Slack live API, Slack export ZIP, GitHub PR discussions, plain transcripts.

Get started

Python 3.10+. One pip install, then run the init wizard.

# install
pip install verbatim-ai

# interactive setup — picks model, API keys, default surfaces
verbatim init

# extract from a transcript
verbatim ingest meeting.txt

# browse the web UI on localhost:8765
verbatim web

# or run fully local on Ollama, no API key required
verbatim ingest meeting.txt --model ollama:llama3.1:8b