riponcm/projectmem

the summary stays small as your project ages, commits are filed under the code they change, and stale warnings no longer depend on your time zone.
See what changed →
We don't make AI smarter. We make it experienced.
Coding agent memory — the local-first memory + judgment layer for AI coding agents. One MCP server for every project. Stop re-reading your project every session. Stop repeating yesterday's bug.
Website •
Guide •
Demo •
Changelog •
Paper
pjm visualize — every case your project solved, what failed on the way, and what it saved. Generated locally from .projectmem/events.jsonl .
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> ## 🚀 Start here — five minutes, once
>
> *Five minutes if you follow along here. Want to be shown instead — every
> command, the exact output it prints back, and the dashboards at the end?
> Take **the complete setup guide**.*
>
> **New to projectmem, or upgrading from 0.1.x / 0.2.x?** Since 0.3.0 one MCP
> server serves every project, so this is the last time you configure anything.
>
> **1. Install or update**
>
>
>
> **2. Find the projects you already have**
>
>
>
> It looks where code lives — ~/Developer, ~/code, ~/projects, your cloud
> folders, and every drive on Windows — and lists projects with memory that
> aren't registered yet. Anything it missed, add by hand:
>
>
>
> **3. Register them**
>
>
>
> **4. Point your AI at all of them with one config**
>
>
>
> No --root, no cwd — that's what makes it serve everything. Per-client
> instructions (Claude Desktop, Claude Code, Cursor, Antigravity, Codex) are in
> MCP Integration; pjm init prints this block
> with your own Python path filled in. **Then fully restart the client** — MCP
> servers only load on a cold start.
>
> **5. Check your work**
>
>
>
> Add --online if you also want it to tell you when a newer projectmem is out
> — projectmem makes no network calls otherwise, and --auto turns that into a
> once-a-day check if you prefer.
>
> Run it again after editing the config. It flags any client still pinned to a
> single repo — the most common reason a new project is invisible to your agent.
>
> **All green? You're done.** From here on it is one command per repo:
>
>
>
> Your agent reads what the project already learned instead of rediscovering it,
> and writes down what it finds. Fewer tokens, no repeated dead ends, memory that
> outlives the session.
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What is coding agent memory?
**Coding agent memory is a persistent record of what happened while building a
project** — the issues hit, the approaches attempted, the fixes that worked and
the decisions made — stored so an AI coding agent can read it at the start of a
new session. Without it every session begins from zero.
projectmem is an open-source **agent memory** layer built for that job. It is
**local-first**: memory lives in a plain .projectmem/ directory inside your
repository, with no cloud, no account and no telemetry — the only network call
it can make is an update check you turn on yourself. A native **MCP server**
exposes 17 tools to Claude Code, Claude Desktop, Cursor, Antigravity and Codex,
so your agent reads memory and logs its work on its own.
Unlike chat-history memory tools, projectmem stores **typed events** — issues,
attempts, fixes, decisions, notes — which is what makes its signature feature possible: a **pre-commit warning**
that fires *before* you repeat an approach that already failed in this project.
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🎬 Watch the demo
Full screen-recorded tutorial- watch on YouTube
📚 Docs
| Doc | What's in it |
|---|---|
| **Complete setup guide** | The full walkthrough on the web — install, MCP setup per client, pjm doctor, your first logged issue, and both dashboards. Every terminal output is captured from a real run. |
| **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: v0.3.4 — fixes for three community reports, a bounded summary, and a time-zone regression from 0.3.3. |
| **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 |
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The Problem
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.