Projectmem

🎉 v0.3.1 is out — one MCP server for every project, and a security fix worth upgrading for. 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. Save up to 50%+ of AI tokens. 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.}

🚀 Start here — five minutes, once

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

pip install -U projectmem

2. Find the projects you already have

pjm doctor

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:

pjm project register "/Users/you/Developer/repos/ossdrop"

3. Register them

pjm doctor --fix

4. Point your AI at all of them with one config

"mcpServers": {
  "projectmem": {
    "command": "/absolute/path/to/python",
    "args": ["-m", "projectmem.mcp_server"]
  }
}

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

pjm doctor

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:

pjm init

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.

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 the one thing no other tool does possible: a pre-commit warning that fires before you repeat an approach that already failed.

pip install projectmem
cd your-project && pjm init

🎬 Watch the demo

Full screen-recorded tutorial- watch on YouTube

📚 Docs

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.

The Solution

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.

Install

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. A pjm alias is installed for speed.

✨ New in 0.3.1 — know when to upgrade

Both dashboards now show which version generated the page, with a check for updates link beside it. The page makes no request until you click — PyPI's public JSON is fetched straight from your browser and nothing about your machine is sent. On the command line, pjm doctor --online checks once and pjm doctor --auto remembers to check daily; both are off unless you ask.

✨ New in 0.3.0 — one server, many projects

Until now an MCP config was tied to one repository: eleven projects meant eleven server entries and eleven restarts. 0.3.0 serves every registered project from a single server. Paste the config once; every repo you pjm init afterwards is reachable from it.

pjm project list          # what this server can reach
pjm project use ossdrop   # the default when a call names no project

log_issue(summary="stars come back empty", project="ossdrop")
→ Logged issue #0019 → ossdrop: stars come back empty

Every write names the project it landed in — in a shared server, the dangerous failure is not "nothing works", it is a write that succeeds against the wrong repo. Existing --root configs keep working untouched, and a pinned server now refuses to write anywhere else even when asked.

Also in 0.3.0:

• Fixed: the MCP server was broken on fresh installs. mcp 2.0 renamed FastMCP and left the old import path raising — since 2026-07-28 every new pip install projectmem got a server that died at import. Caught and fixed by @VIVAAN-DHAWAN.
• Security: stored XSS in pjm visualize. Event summaries reached the DOM unescaped, and git commit messages become event summaries — so a crafted commit in a branch you pulled could run script in your dashboard. Every sink is escaped now.
• A rebuilt dashboard — a shareable Memory Card, case files with the full issue → attempt → fix chain, an effort treemap, per-file dossiers, and a global view that opens with where you left off.

Registry migration is automatic: the 0.2.x list of paths is converted on first read, with a .bak kept beside it.

✨ New in 0.2.0 — the workspace release

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).

• 🌐 Global dashboard — 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.
• 🧬 Structure & relations — 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.
• 🔥 Failure heat on structure (the combo) — the one view a pure code-grapher can't draw and a pure memory tool can't either: files with repeated failed attempts glow red, laid directly over the real import graph. Structure comes from the code, heat comes from your memory, and they meet only in the renderer.
• 🗂️ 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.

Global Dashboard — every pjm init-ed project in one view: grades, issues, savings, and a "needs attention" list, aggregated at read time. Each card opens that repo's own dashboard.

The visualization suite (shipped in 0.1.6)

Your project's memory is also something you can watch — and share.

• 🎬 Showoff — a dashboard tab with three animated story scenes, all rendered from your real event log: Story Replay (watch your project's history build itself, node by node), Orbit (files orbit the project, events orbit their file), and Universe (your project as a rotating galaxy — every bright star is a real issue, attempt, fix, or decision; click one for its full details).
• ⏺ Built-in recorder — hit REC (10–60 s) and Showoff downloads a .webm clip of the animation, rendered 100% locally with a "made with projectmem" badge. Your debugging story, ready for a tweet or a standup.
• 🗺️ Flow — the Project Map's default view: a layered flowchart reading 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.
• 🧵 Time Spine — the Timeline's default view: a real-time axis you scroll, with problems branching left (issues, failed attempts) and knowledge branching right (fixes, decisions, notes). Hover any card and its whole issue thread lights up. The classic list remains as "Details".

Showoff · Universe — every bright star is a real event from this project's memory

Project Map · Flow — what happened, file by file, flowing into append-only memory

Timeline · Time Spine — problems on the left, knowledge on the right, real time down the middle

Why You'll Love It

• Pre-Commit Warnings — 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.
• Stale-Memory Detection (new in 0.1.4) — other memory tools silently decay or delete old memories; projectmem never deletes. Every decision that cites a file is cross-checked against that file's git history — when the file has moved on, the memory is flagged ("predates 7 commits to auth.py — confirm or supersede") and a human decides. Retire it cleanly with pjm decision "new way" --supersedes <id>: the old event stays in the log, tagged, forever.
• Session-Start Briefing (new in 0.1.4) — pjm brief answers "where was I?" in one screen: active warnings, possibly-stale memories, open issues, recent decisions, stack gotchas, and your prevention score with a week-over-week delta.
• Memory for agents without MCP (new in 0.1.4) — pjm export --claude-md compiles live decisions, gotchas, and a "Do NOT retry — these already failed" list into a marked block in CLAUDE.md (or .cursorrules). Copilot, plain Claude, any agent that reads the file inherits your project's judgment.
• Smart Context Injection — pjm wrap claude (or cursor/aider) injects a token-budgeted memory block into your AI before the session opens. Your AI starts experienced, not blank.
• Provable ROI Score — pjm score outputs a letter grade (A+ → F) backed by concrete numbers — debugging hours saved, tokens prevented, dollars protected. CI-friendly JSON output and shields.io badge for your README.
• Cross-Project Memory — Lessons learned in one repo follow you forever. Library gotchas, decisions, and patterns live in ~/.projectmem/global/ and auto-inherit into every new project that matches your stack.
• Real-time File Watcher — Background daemon detects rapid edits to the same file (debugging sessions) between commits. Battery-aware, gitignore-aware, auto-started by pjm init.
• Native MCP Server — Plugs into Claude Desktop, Cursor, Antigravity, Codex, and any MCP-compatible tool. 15 native tools force the AI to read context, check files for known failures, read your plan.md, and log work automatically. Verified end-to-end against all four clients.
• Interactive Dashboard (expanded in 0.1.6) — pjm visualize opens a six-tab local dashboard: Overview, Story Map (failure heatmap with collapse/focus controls), ROI Dashboard, Project Map (Flow / Tree / Graph, now over your real code structure), Timeline (Time Spine / Details), and Showoff — animated story scenes with a built-in video recorder.
• One MCP server for every project (new in 0.3.0) — configure your client once instead of once per repository. Calls name their project (project="ossdrop"), or fall back to the active one; every write reports which repo it landed in, and a pinned --root server refuses to write outside its own. Existing single-project setups are untouched.
• Global Dashboard (new in 0.2.0) — pjm dashboard is one cross-project view over every repo you've pjm init-ed: grades, issues, savings, and per-project drill-in. A global view, never a global store — each repo's memory is aggregated at read time and never leaves its folder. Serverless by default; --serve for an ephemeral live server (Ctrl+C to stop).
• Code Structure + Judgment (new in 0.2.0) — pjm map --build reads your codebase into a real import graph, and the Project Map overlays failure heat from your event log on top: the files that keep breaking, glowing red over the structure that actually connects them. The structure cache is derived from code and gitignored — never committed.
• Intent, separate from memory (new in 0.2.0) — plan.md holds ideas and plans (what you mean to do), kept deliberately apart from the append-only event log (what happened). pjm plan, or the MCP get_plan(); the AI edits it directly and a plan never becomes an event.
• 100% Local — No cloud, no telemetry, no accounts. Your code, your memory, your machine.

How It Compares

_{✅ yes · 🟡 partial · ❌ no — snapshot June 2026; design capabilities, not benchmark results. claude-mem runs a background worker (port 37777) and enables telemetry by default (v13.5+); agentmemory down-ranks and prunes old memories via decay, mem0 rewrites facts on update, Letta's memory blocks self-edit in place — projectmem never deletes: it flags staleness and lets you decide. Letta requires a running server (Postgres or cloud).}

_{† There is no database and nothing you have to keep running: the MCP server is a stdio subprocess your AI client spawns, and everything else is plain files. The only server anywhere is the optional pjm dashboard --serve, an ephemeral local viewer you start and stop with Ctrl+C — never a background service.}

🚧 Upcoming

• Import your existing memory — pjm import (planned for 0.3.3) will migrate history from mem0, agentmemory, Letta, and Claude session logs into projectmem. It maps only to the core event vocabulary — issues, attempts, fixes, decisions, notes — so signal comes in and another tool's clutter stays out. Your judgment history moves with you.

Want a source supported? Open an issue and tell us what you're migrating from.

How AI Reads Your Memory (Token Efficiency)

The architecture is built around one rule: AI reads small, distilled files. Tools generate them from the big raw log.

AI never reads events.jsonl directly. That file is for tools (pjm score, pjm context, pjm wrap). Tools distill the raw log into compact AI-readable summaries.

One server, many projects

Since 0.3.0 a single MCP server serves every project you have registered. Paste the config once and every repo you pjm init afterwards is reachable from it — no second entry, no restart.

pjm project list          # what this server can reach
pjm project use ossdrop   # the default when a call names no project
pjm project alias ossdrop od

Your agent picks the project per call:

log_issue(summary="stars come back empty", project="ossdrop")
→ Logged issue #0019 → ossdrop: stars come back empty

Every write says where it landed. That echo is the point: in a one-project setup a misconfigured server simply fails, but a shared server can succeed against the wrong repository, which corrupts two audit trails at once. If the name in the reply is not the project you meant, stop.

How a call is routed, highest first:

Client roots outrank the active project on purpose: the root is where you are now, the active project is a mode you set days ago. When they disagree, the stale one is the wrong answer.

Single-repo setups are untouched — pjm init --mcp-config-single still prints the pinned config, and an existing --root entry keeps working exactly as before.

MCP Integration (Recommended)

For: Claude Desktop, Cursor, Antigravity, Codex — and any tool with native MCP support. The MCP server forces the AI to read memory and log every action automatically.

Since 0.3.0 you configure this once, not once per repository. The block below has no --root: the server serves every project you have registered, and each call resolves its own. Paste it, and every repo you pjm init from then on is reachable — no second entry, no restart.

"mcpServers": {
  "projectmem": {
    "command": "/opt/anaconda3/bin/python",
    "args": ["-m", "projectmem.mcp_server"]
  }
}

Upgrading with projects you already have? The registry only ever recorded projects you ran pjm init on since it existed (0.2.0), so anything older is missing — and global mode routes through the registry. One command sorts it out:

pjm doctor          # what's unregistered, what's stale, what's still pinned
pjm doctor --fix    # register what it found

It looks in the places code actually lives — ~/Developer, ~/code, ~/src, ~/projects and friends, plus every fixed drive on Windows, where projects sit on D:\ and E:\ as often as under your home folder. To point it somewhere specific:

pjm doctor --path ~/work --path /Volumes/ssd --fix
pjm project scan D:\ E:\ --depth 3     # the same walk, without the other checks

Nothing is scanned until you run it, and nothing is written without --fix. After an upgrade the CLI mentions pjm doctor once — a wheel install can't run code, so the first command you type is the only place to say it.

With one project registered, that is the whole setup — there is only one place a call can go. With several, your AI passes project="<name>", or you set a default with pjm project use <name>. pjm init prints this block with your own Python path already filled in.

Upgrading from 0.2.x? Your existing --root entry keeps working exactly as before, and a pinned server now refuses to write outside its own repo even if asked. Replace it with the block above when you want one server for everything.

The 3-minute workflow (let your AI do the setup)

1. Install + init. pip install projectmem, then cd into your project and run pjm init — or simply ask your AI to run it.
2. Ask your AI to set up the projectmem MCP server for you — it can edit the client's config file itself. (It needs permission to do that: use Auto / accept-edits mode, or approve the file edit when asked. The exact config per client is in the sections below if you'd rather paste it by hand.)
3. Restart the AI tool so the MCP server loads, then start your session with this prompt:

Hi — I use projectmem as this project's memory. Before anything else,
call get_instructions(), then get_summary(), then get_project_map() to
load what we already know. As we work, log issues, attempts
(failed/worked), fixes, decisions, and notes with the projectmem tools,
and call precheck_file(path) before you edit a file. Ideas and plans go
in plan.md via get_plan() — never as events.

Strictly speaking this prompt is optional — with the MCP server installed correctly the AI discovers the memory on its own. But saying it makes capture noticeably more consistent, so we recommend it.

• Repeat for every project: pjm init + the same kickoff prompt.
• Coming back after closing the window? Open with a one-line reminder — "Reminder: we use projectmem as memory here." — and the whole setup carries on where you left off.

Prefer to wire it up by hand? The exact, verified config for each client follows.

Claude Desktop

Easiest — open the config from the UI:

• macOS: Claude menu → Settings… → Developer tab → Local MCP servers → Edit Config.
• Windows / Linux: same path expected (Settings → Developer → Edit Config) — open an issue if your platform differs and we'll update this.

If you prefer the raw file path: ~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows, ~/.config/Claude/claude_desktop_config.json on Linux (or $XDG_CONFIG_HOME/Claude/ if you have moved it). pjm init prints the right one for the machine you run it on.

Paste this block:

"mcpServers": {
  "projectmem": {
    "command": "/opt/anaconda3/bin/python",
    "args": ["-m", "projectmem.mcp_server"]
  }
}

Two things to know about this block:

• Use the absolute path to python (e.g. /opt/anaconda3/bin/python, or run which python to find yours). Claude Desktop subprocesses don't inherit your shell PATH, so bare "python" often fails.
• You no longer need the cwd field, and you never could rely on it. Claude Desktop's current build (with the Epitaxy / Cowork workspace system) silently ignores cwd — the server ends up running with cwd=/ and can't find .projectmem/. That is why older releases needed --root. The registry replaces it: the server finds projects by name, not by where it happens to be running.

"mcpServers": {
  "projectmem": {
    "command": "/opt/anaconda3/bin/python",
    "args": [
      "-m", "projectmem.mcp_server",
      "--root", "/absolute/path/to/your/project"
    ]
  }
}

Then fully quit Claude Desktop (Cmd+Q on Mac) and reopen — MCP servers only initialize on cold start.

Cursor

Two ways to register the MCP server — pick whichever fits your workflow:

1. Global (recommended): Cursor menu → Settings… → left sidebar Tools & MCPs → Installed MCP Servers → Add Custom MCP. Paste the JSON below.
2. Per-project: drop the JSON into <project-root>/.cursor/mcp.json — only active when that project is open.

{
  "mcpServers": {
    "projectmem": {
      "command": "/opt/anaconda3/bin/python",
      "args": ["-m", "projectmem.mcp_server"]
    }
  }
}

Two things to know about this block (same gotchas as Claude Desktop):

• Use the absolute path to python (run which python to find yours). Cursor subprocesses don't reliably inherit your shell PATH.
• Don't bother with the cwd field. Cursor — like Claude Desktop — silently ignores it: the server ends up running with cwd=~. Since 0.3.0 that no longer matters, because projects are found by name in the registry rather than by where the server runs.

Registered globally, one entry covers every project. Per-project .cursor/mcp.json still works if you prefer the server to exist only when that repo is open — add "--root", "/absolute/path/to/your/project" to args there to pin it.

Then fully quit Cursor (Cmd+Q on Mac) and reopen. projectmem also auto-discovers .projectmem/ by walking up from CWD (like git does for .git/), and honors PROJECTMEM_ROOT and a --root <path> CLI argument.

Antigravity

Antigravity (Google's AI IDE) speaks standard MCP.

Easiest — open the config from the UI:

1. Open the Agent window (the chat panel on the right).
2. Click the ⋯ Additional Options button in the panel header.
3. Choose MCP Servers → Manage MCP Servers → Add new (or Edit Config).

The raw file is at ~/.gemini/antigravity/mcp_config.json if you prefer editing it directly.

Paste this block:

{
  "mcpServers": {
    "projectmem": {
      "command": "python",
      "args": ["-m", "projectmem.mcp_server"]
    }
  }
}

Antigravity does honor the cwd field, so adding "cwd": "/absolute/path/to/your/project" works — but it ties the server to that one repo. Leave it out and the same entry serves every registered project.

Then fully quit Antigravity (Cmd+Q on Mac) and reopen — MCP servers only initialize on cold start. All 17 projectmem tools register identically to Claude Desktop / Cursor.

Codex

Codex stores MCP config as TOML (not JSON) in ~/.codex/config.toml. There's a UI form at Settings → MCP Servers → Add MCP Server, but during cross-client verification the form's Save button didn't reliably persist — the file-edit path is faster and more reliable.

Easiest — edit ~/.codex/config.toml directly:

Append this block (preserves any existing config):

[mcp_servers.projectmem]
command = "/opt/anaconda3/bin/python"
args = ["-m", "projectmem.mcp_server"]
cwd = "/absolute/path/to/your/project"

Three things to know about this block:

• Use the absolute path to python (run which python to find yours). Codex subprocesses don't reliably inherit your shell PATH.
• You no longer need --root or cwd. Earlier releases passed --root as defense in depth (the cwd field does appear to work in Codex, unlike Claude Desktop and Cursor). Since 0.3.0 the registry makes both unnecessary — add "--root", "/absolute/path/to/your/project" to args only if you want this server locked to a single repo.
• Set your reasoning effort to medium or higher. On low-reasoning Codex skips get_instructions from the session-start trio, which can cause the AI to miss the Setup Mode workflow rules. Medium+ honors the full trio automatically.

Validate the TOML:

python -c "import tomllib; tomllib.load(open('/Users/<you>/.codex/config.toml','rb')); print('OK')"

Should print OK. If not, the parser tells you the offending line.

Then fully quit Codex (Cmd+Q on Mac) and reopen. Same cold-start rule as every other MCP client. Codex MCP servers spawn lazily on the first tool call in a chat session — if you don't see the process in ps aux right after reopening, send any message to a Codex chat and check again.

Reasoning-effort note: Codex's mode selector is at the bottom of the chat input. Set it to medium (not low) for the full session-start trio behavior. Once set, it persists per-session.

First-run permission prompts

On first use in any MCP-capable client (Claude Desktop, Cursor, Antigravity, Codex), your AI will ask permission before each projectmem tool call. This is expected security behavior — MCP clients require explicit consent for every new tool. Approve each tool once and the prompt won't reappear for that session.

Other MCP Tools

Any MCP-compatible client works — point your tool at python -m projectmem.mcp_server and either set cwd to your project root or rely on the parent-walk auto-discovery.

MCP Tools Exposed

All 17 tools your AI can call. Every repo tool takes an optional project argument — see One server, many projects:

Read-side (10 tools):

Write-side (5 tools):

CLI Reference

Core memory

Intelligence layer

Projects (global MCP)

Visualization & utility

Use --at "file.py:42" with any logging command to attach precise location metadata.

plan.md — intent, kept separate from memory

pjm init scaffolds a .projectmem/plan.md: your ideas and plans — what you mean to do, in plain Markdown (Ideas · Active plans · Next · Someday · Shipped). It's the one file that is deliberately not the event log:

• events.jsonl → summary.md records what happened (append-only, never rewritten).
• plan.md records what you intend — and you (or the AI) edit it directly, like PROJECT_MAP.md.

Your AI reads it at session start via get_plan() and updates it in place: adding ideas, checking items off, moving finished work down to Shipped. A plan is never logged as an event, so intent stays cleanly out of your memory's audit trail. pjm plan prints it; pjm plan "auto-batch the exporter" appends an idea. It's committed (not gitignored) so intent is shared with your team.

Example: Pre-Commit Warnings in Action

$ git commit -m "switch auth to JWT"

projectmem: Pre-Commit Check
─────────────────────────────────────────────
  src/auth/middleware.py
    WARN  What already failed here (2 attempts):
           ✗ tried switching to JWT middleware (2d ago)
           ✗ patched session timeout to 60min (5d ago)
    WARN  HIGH CHURN: 5 changes in last 30 days
    WARN  1 possibly-stale memory cites this file
           decision [evt_9db5a3f8…] "auth uses session
           cookies, 30min timeout" — predates 7 commits
           Confirm it still holds, or retire it:
           pjm decision "..." --supersedes <id>
─────────────────────────────────────────────
3 warning(s). Review before committing.

~30 min re-debugging just saved.

Need it quiet for a refactor sprint? pjm precheck --snooze 2h — warnings pause, the pause itself is logged, and every commit shows one dim line so silence is never mistaken for a clean check.

Privacy & Security

By default, projectmem commits the distilled files (summary.md, PROJECT_MAP.md, AI_INSTRUCTIONS.md, issues/) and gitignores the raw log + runtime files (events.jsonl, watch.pid, watch.log). This means your teammate's AI inherits your team's knowledge automatically — just git clone and the AI already knows what your team learned.

Want total privacy? Add a single line .projectmem/ to your .gitignore. Nothing leaves your machine.

Full security policy and threat model: SECURITY.md · Privacy & Security guide

Design Principles

• Local-first — No network calls, no cloud, no telemetry. Your data never leaves your machine.
• Project-scoped — Memory lives in the repo. When the code moves, the memory moves.
• AI-tool-agnostic — Works natively via MCP, or universally via Markdown instructions. Any AI tool, any workflow.

Built With

projectmem stands on the shoulders of these excellent open-source projects:

• Typer — the CLI framework that makes pjm feel ergonomic
• Model Context Protocol — Anthropic's open spec that lets AI agents talk to local tools
• watchdog — cross-platform filesystem event monitoring (the heart of pjm watch)
• D3.js — the interactive visualizations in pjm visualize

Research & Citation

projectmem is described in a peer-readable research paper:

PROJECTMEM: A Local-First, Event-Sourced Memory and Judgment Layer for AI Coding Agents Ripon Chandra Malo, Tong Qiu — University of Utah arXiv:2606.12329 · cs.SE (cross-list cs.AI)

The paper introduces the Memory-as-Governance framing — memory that doesn't merely answer the agent but acts on its next action — and reports the design, the deterministic pre-commit judgment gate, a capability comparison against 12 contemporary memory systems, and a two-month, 207-event dogfooding study across 10 real projects.

If projectmem is useful in your research or writing, please cite:

@misc{malo2026projectmem,
  title         = {PROJECTMEM: A Local-First, Event-Sourced Memory and
                   Judgment Layer for AI Coding Agents},
  author        = {Malo, Ripon Chandra and Qiu, Tong},
  year          = {2026},
  eprint        = {2606.12329},
  archivePrefix = {arXiv},
  primaryClass  = {cs.SE},
  url           = {https://arxiv.org/abs/2606.12329}
}

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