projectmem

> ## 🚀 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

>

> ```bash

> pip install -U projectmem

> ```

>

> 2. Find the projects you already have

>

> ```bash

> 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:

>

> ```bash

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

> ```

>

> 3. Register them

>

> ```bash

> pjm doctor --fix

> ```

>

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

>

> ```json

> "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

>

> ```bash

> 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:

>

> ```bash

> 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

> First time here? → **The complete setup guide**

> walks the whole path end to end: install, connecting Claude Desktop, Claude Code,

> Cursor, Codex or Antigravity, checking it with `pjm doctor`, and reading your

> memory back through the dashboards — with the real terminal output at every step.

Three commands to a project that remembers:

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 `: 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=""`, or you set a default with `pjm project use `. `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.

Pin this server to a single repo instead

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

A pinned server serves exactly that repository and refuses to write anywhere else, even when asked — the stricter choice if you want a hard boundary. `pjm init --mcp-config-single` prints this form.

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 `/.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 ` 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//.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 
─────────────────────────────────────────────
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}
}

License

MIT — free for personal, commercial, and enterprise use forever.

Help Us Reach More Developers

We don't need money. We need you.

`projectmem` is built by one developer for the open-source community. Every star, every share, and every contribution helps the project survive and grow.

• **Star the repo** — takes one click, helps massively with discovery
• Share on X / LinkedIn — tell other devs they don't have to keep paying AI to relearn their codebase
• **Open an issue** — bug, feature request, or just feedback
• **Contribute code** — PRs welcome, see contributing guide
• Using `projectmem` at work or in a commercial product? Reach out to support@projectmem.dev so we know who's shipping with us. It's free — we just love hearing about it.

*Stars and shares matter more than money — but if you really want to:* sponsor on GitHub →