LibreAI / AI Agents / agent-memory
// agents · tigerless-labs/agent-memory

agent-memory

Long-term memory runtime for AI agents — plain Markdown as truth, local ranked retrieval, no API key

★ 2.6k stars⑂ 142 forksMITupdated 2026-10
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
// project facts

Project facts

agent-memory is an open-source ai agents project by tigerless-labs (tigerless-labs/agent-memory on GitHub). It has 2,646 GitHub stars and 142 forks, is released under the MIT license, and was last updated on GitHub on 7 October 2026.

GitHub stars
2,646
Forks
142
License
MIT
Last updated on GitHub
7 October 2026
Category
AI Agents
Listing
Full install guide
// how it works

How it works

  1. Clarify your goal

    Decide which agents should share long-term memory and where the store should live (default ~/agent-memory-store).

  2. Get agent-memory

    Clone the repo and build the mem, mem-mcp and mem-hook binaries with uv sync --all-packages.

  3. Install and configure

    Run mem init, then mem setup --host claude-code / codex / muse-code to merge hooks, skills and MCP entries safely into each host.

  4. Run your first memory

    Record a decision with mem record, recall it with mem recall, then delete .index and mem rebuild to prove nothing was lost.

// features

Why people love it

Markdown is the truth

One memory = one Markdown file with frontmatter (abstract, type, validity interval, links, provenance) — greppable, git-able and portable.

Ranked local retrieval

Recall returns an L0 list of paths/abstracts/scores (via BM25 FTS5, optionally fused with vectors), so the agent opens only the depth it needs.

Automatic writes

Distillation triggers at conversation boundaries (SessionStart/Stop/SessionEnd hooks) without the agent having to remember to write.

Sleep-time Manage layer

An independent consolidation pass on its own clock can add and update; deletions only ever arrive as proposals you confirm, and supersede keeps history (recall --as-of).

One store, many hosts

Claude Code, Codex CLI and Muse Code share one store through the same pipeline; the CLI is the universal fallback for anything that can run a shell command.

No keys, no network in the read path

There is no LLM client inside the library — judgement is borrowed from the host agent's own CLI, keeping writes visible in your transcript.

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// run it

Run it yourself

  1. Clone and build

    Clone agent-memory and build its binaries with uv (Python 3.12+, no PyPI release yet).

    git clone https://github.com/tigerless-labs/agent-memory.git
    cd agent-memory
  2. Initialize a store

    Create the default store at ~/agent-memory-store (or set AGENT_MEMORY_STORE).

    mem init
  3. Wire it into your agent

    Probe and configure a host, then record and recall a first memory.

    mem setup --host claude-code
    mem record --type decision --field project=agent-memory --abstract "Markdown files are the single source of truth" --body "Indexes are rebuildable caches."
// use cases

What you can build

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// faq

Questions, answered

Who maintains agent-memory?

tigerless-labs maintains it on GitHub.

Is it free?

Yes, MIT-licensed, and it runs entirely locally.

Does it need a paid API key?

No API key at all — the README stresses none of it needs one; distillation borrows the host agent's existing login/model.

How does it compare to Memary or Memobase?

Those are service/graph or profile systems. agent-memory is a local runtime whose store is plain Markdown files with a rebuildable index, shared across agent hosts.

How do I get started?

Clone, uv sync --all-packages, mem init, mem setup --host <your agent>, then mem record / mem recall.

// keep exploring

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// alternatives

Alternatives in AI Agents

The most-starred other AI Agents projects in the LibreAI catalogue — the realistic shortlist if agent-memory is not the right fit.

Get agent-memory

Free and open — grab the source, star the repo, and start creating.