by riponcm
Local-first memory layer for AI coding agents. Captures issues, attempts, decisions, and cross-project library gotchas — your AI starts experienced, not amnesiac. Native MCP server verified across Claude Desktop, Cursor, Antigravity, and Codex. 100% local · no cloud · no telemetry · MIT.
# Add to your Claude Code skills
git clone https://github.com/riponcm/projectmemGuides for using ai agents skills like projectmem.
Last scanned: 6/27/2026
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"issues": [],
"status": "PASSED",
"scannedAt": "2026-06-27T06:53:17.787Z",
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}| Doc | What's in it |
|---|---|
| TUTORIAL.md | 15-minute step-by-step walkthrough — set up projectmem on your own project, watch the lifecycle, see the pre-commit warning fire. |
| CHANGELOG.md | Release history. Latest: v0.2.0 — the workspace release: the cross-project pjm dashboard (serverless + live --serve), code structure & relations with failure-heat overlay, and the plan.md intent file. |
| Research paper (arXiv:2606.12329) | PROJECTMEM: A Local-First, Event-Sourced Memory and Judgment Layer for AI Coding Agents — the peer-readable version: design, Memory-as-Governance framing, capability comparison, and the 207-event dogfooding study. |
| LICENSE | MIT |
Every new AI session starts from zero. Claude, Cursor, Aider — they all forget yesterday's decisions, repeat failed debugging attempts, and burn millions of tokens reconstructing context from raw source files.
The model isn't the problem. The architecture is. Stateless models need a memory cortex.
projectmem is the local-first memory + judgment layer that sits above your AI tools. It captures every failed attempt, decision, and gotcha — then injects that experience back into future AI sessions. Git tracks what changed. projectmem tracks why it changed, what was tried, and what failed.
pip install projectmem
cd your-project
pjm init
That's it. pjm init installs three git hooks (pre-commit warnings, post-commit classification, post-merge tracking), auto-starts a real-time file watcher, inherits cross-project memory if available, and creates .projectmem/. Capture is active from minute one.
The canonical command is
projectmem. Apjmalias is installed for speed.
0.1.6 made one project's memory something you could watch. 0.2.0 lifts that to your whole workspace — and closes the gap between what happened (memory) and what your code is (structure).
pjm dashboard is one page over every project you've pjm init-ed: total issues captured, fixes confirmed, dead-ends prevented, tokens saved, a grade per project, and a "needs attention" list. Click any card to open that repo's own dashboard, generated fresh. It's a global view, not a global store — each repo's .projectmem/ is aggregated at read time and never leaves its folder. Default is serverless (a static snapshot); add --serve for a tiny, ephemeral live server where the Refresh button re-reads your files — no background daemon, Ctrl+C stops it.pjm map --build (run automatically at pjm init) walks your codebase and, for Python, resolves imports into a real dependency graph. The Project Map's Graph and Flow views now render actual files and the import edges between them. The cache (structure.json) is derived from code, gitignored, and never committed — code is only ever read.plan.md — a new editable intent file: ideas and plans, what you mean to do — deliberately not the event log. events.jsonl → summary.md records what happened; plan.md records what you intend. The AI reads it at session start and edits it directly; a plan never becomes an event. pjm plan / pjm plan "idea" / MCP get_plan().Everything stays 100% local — the global dashboard is a read-time aggregate, never a central honeypot of your code's history.
Your project's memory is also something you can watch — and share.
.webm clip of the animation, rendered 100% locally with a "made with projectmem" badge. Your debugging story, ready for a tweet or a standup.PROJECT → DIRECTORIES → FILES → WHAT HAPPENED → MEMORY. Files with repeated failures glow red along their path, every file shows its outcome chips, and everything flows into the events.jsonl cylinder. Tree and Graph views are one click away.pjm precheck warns you before you commit if you're about to repeat a failed approach, modify a high-churn file, or touch an unresolved issue. No other AI tool does this — it requires the memory layer underneath. The warning now lists the dead ends themselves ("What already failed here: ✗ tried CSS contain:layout"), and pjm precheck --snooze 2h silences it politely — the snooze is itself logged, so even the silence is audited.pjm decision "new way" --supersedes <id>: the old event stays in the log, tagged, forever.pjm brief answers "where waprojectmem is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by riponcm. Local-first memory layer for AI coding agents. Captures issues, attempts, decisions, and cross-project library gotchas — your AI starts experienced, not amnesiac. Native MCP server verified across Claude Desktop, Cursor, Antigravity, and Codex. 100% local · no cloud · no telemetry · MIT. It has 192 GitHub stars.
Yes. projectmem passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/riponcm/projectmem" and add it to your Claude Code skills directory (see the Installation section above).
projectmem is primarily written in Python. It is open-source under riponcm on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh projectmem against similar tools.
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