Agent Memory

agent-memory: the long-term memory runtime for AI agents

Invariants · Skill · Issues

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An agent that closes its session forgets everything it learned in it. agent-memory is the
runtime that fixes that, for any agent — not only coding ones. Markdown files in one store are
the single source of truth, the SQLite index beside them is a cache you can delete at any time,
and Claude Code, Codex CLI, Muse Code, and anything else that can run a shell command share
that store.

Retrieval is local and ranked, and it answers with paths rather than pasted text — the agent
opens each hit only as deep as the task needs. Writes do not wait for the agent to remember to
make them: they fire at conversation boundaries. A sleep-time pass then consolidates and
forgets by value, on its own clock. None of it needs an API key.

Two lines, one store

Agent memory has grown along two architectural lines. One builds a retrieval engine —
embeddings, a knowledge graph, a ranking pipeline — which finds the right thing, but hands the
agent an opaque chunk it cannot inspect and a store it cannot migrate off. The other hands the
agent a filesystem — markdown it reads directly, browsable with ls and grep — which is
legible and costs nothing to run, but does not rank, and stops scaling the moment the tree
outgrows a listing.

agent-memory is the two of them in one store: the retrieval engine indexes a filesystem the
agent can also just read. Relations live as links inside the memories, a local index ranks
them, and every hit resolves to a whole markdown file on disk. Recall gains the precision of a
graph and a vector search without giving up a plain directory an agent can walk — and it stays
fast, because nothing in the read path calls a model or crosses a network.

Retrieve by path, then read by…