Everything within your agent’s ken.
A curated memory your AI coding agent reaches over MCP, so it searches before it solves and keeps the lessons it learns. AI proposes; you curate.
Agents keep solving the same problems twice.
Solved work gets re-derived
Every session starts cold. An agent rediscovers the same fix it found last week, burning tokens and time on problems that were already solved.
Hard-won lessons evaporate
The subtle fix, the race condition, the config gotcha, lives in one chat log and then vanishes the moment the context window closes.
A flat memory dump goes stale
Dump everything into one file and it rots. Wrong answers sit beside right ones with nothing to tell them apart, and the agent trusts both.
A search-first loop, every session.
Ken runs the same loop on every task, so knowledge compounds instead of resetting.
Warm up
The agent loads recent, relevant context before it starts: what has been touched and what is fresh.
Search first
Before writing a fix, it searches Ken with the real symptoms and error text, not a guess at keywords.
Use and record
It applies what it finds and records the outcome: helped, did not apply, or was wrong.
Save or enhance
A new lesson becomes a saved draft or an enhanced entry. Ken self-curates from those outcome signals.
AI authors. A human curates.
This is what keeps Ken trustworthy. Agents can read, draft, and propose, but only a person decides what becomes canonical.
AI proposes
Agents save drafts and propose revisions. They can never curate.
A human promotes
You review a proposal and promote it to the curated head. Only a person makes it canonical.
Append-only lineage
Every change is a new revision. Nothing is overwritten; a wrong answer is flagged stale, not deleted.
Built to be owned, not rented.
Self-host in minutes.
Point any MCP client at your Ken server and start curating.
Yours to run, read, and shape.
Ken is open source under AGPL-3.0. Self-host it, read every line, and send patches.