MuscleMemory

Open-source procedural memory for AI agents.
It learns from what worked and what failed.
Local by default · Apache-2.0 · by TheWariend

Coding agents relearn the same things every session: which files to open, how the tests
run, what broke last time. MuscleMemory watches your agent work. When a task really
succeeds (the tests pass after the last change), it learns how the task was done. The next
time a similar task comes up, it hands the agent a short note: the files to change, the
test that proves it worked, and what to avoid.

In a benchmark with real Codex sessions, it cut input tokens by 25.5%, tool calls by 30.4%
and time by 28.2%, with every task still passing. In an early run with Claude Code and
Sonnet 5.5, using skills that Codex had learned, it cut input tokens by 43.5% and tool
calls by 30%. On real Django issues from SWE-bench, with a skill learned from other
issues in the same area, Codex solved as many as without memory with about half the tokens
(a small run; details).
Check them yourself: python benchmarks/reproduce.py check.
Codex method and data ·
Claude Code run ·
Whitepaper.

Quick start

pipx install thewariend-musclememory  # or, from a clone: pip install -e .

musclememory init                      # sets up ~/.musclememory and the small search model
musclememory start                     # runs in the background on http://127.0.0.1:8080
cd your-project && musclememory connect codex   # or claude, cursor, cleanslate
musclememory dashboard                 # opens the dashboard, already signed in

Then use your agent as usual. There are no keys or config files to copy.

Or let your agent set it up: paste this into Claude Code, Codex or Cursor.

Read https://thewariend.com/musclememory/llms.txt and set up MuscleMemory for this project.

More in Install.

How it works

flowchart LR
    A["Your agent works on a task"] --> B{"Tests pass after the last change?"}
    B -- no --> C["Nothing is learned"]
    B -- yes --> D["Learn a skill or count a check"]
    D --> E["Two separate passing checks"]
    E --> F["You tap Keep"]
    F --> H["Hook checks current files"]
    H -->|"match or partial match"| G["Short note; warning on partial match"]
    H -->|"mismatch"| I["No note; skip recorded locally"]
    G --> A

• Verified, twice. Two later sessions must match the skill's actions and pass its
  own stored test command after the last code change. Pipes, redirects and shell
  wrappers are normalized; parameterized commands keep shape matching. Automatic
  checks use the final passing test, so an unrelated test cannot count. The training
  session doesn't count. One failed check pauses the skill.
• Learns from failures. Failed attempts become plain "watch out" and "if it goes wrong"
  notes, so the agent avoids the same dead ends.
• You stay in charge. Automatically learned skills wait for your OK before any agent
  sees them. Agents still read the current files and ask for approval as usual.
  MuscleMemory never replays actions.
• Checks current files. Native hooks require the files a skill found in place to
  still exist (files it created may not exist yet) and at least one stored before or
  after edit to match. If none match, they stay quiet; partial
  matches include a warning that cannot be trimmed. Reads are bounded and reject
  symlinks. File-check skips are recorded locally. Server and MCP recall have no file
  access. Exact rules.
• Quiet when it should be. If nothing fits, nothing is added, and the agent pays no
  extra tokens.
• Shared between agents. A skill learned with Codex is handed to Claude Code or
  Cursor too: changing a file counts the same whichever tool did it.
  Proven skills can also be exported to Codex or Claude Code as SKILL.md.
• Local and open. SQLite and a small on-device model by default: no account and no
  outside calls. Use PostgreSQL with pgvector for teams.

More detail in How it works.

Works with

See Integrations.

Projects share skills within the same workspace (namespace, default "default"),
rather than getting a separate workspace per repository. Cross-repository recall is
untested. Give a project its own namespace.

Dashboard

Home: what your agents can reuse, and anything waiting for your OK	Skills: each one with the task it came from, the files it changes and the test that checks it
A skill: its steps in plain words, its proof, and what to watch out for	Connect: one command per agent

Docs

• Whitepaper: the design, trust rules, retrieval and evaluation in one paper
• Install and run: local setup, Docker and PostgreSQL, configuration
• How it works: the learning loop, retrieval, storage and security
• Integrations: each agent, MCP and the SDKs
• Benchmarks: results, method and limits
• API contracts: MCP tools and REST
• Roadmap: what's next and known limits

Status

MuscleMemory has Python and TypeScript tests, with optional PostgreSQL and live Codex
checks. It's early. The benchmarks cover repeat tasks on three controlled codebases,
not every kind of work, and the Claude Code run is small. The hooks and CleanSlate learn on their own; with the SDKs your code
reports when a task succeeded.
See the roadmap.

Issues and pull requests are welcome. Please report security issues privately, as
described in SECURITY.md.

License

Apache-2.0. Made by TheWariend.