Context7
Up-to-date docs for AI agents
Beschreibung vom Entwickler (englisches Original)
Coding agent memory — one local MCP server for every project. Warns before repeating failed fixes.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"projectmem": {
"command": "uvx",
"args": [
"projectmem"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add projectmem -- uvx projectmemIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"projectmem": {
"command": "uvx",
"args": [
"projectmem"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"projectmem": {
"type": "stdio",
"command": "uvx",
"args": [
"projectmem"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 6. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto riponcm bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.3.5) kam vor 6 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Python (uv) auf deinem Computer.
In derselben Kategorie: Context7, Codebase Memory, Exa
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
🎉 v0.3.5 is out — precheck recognises a file however its path is spelled, and the summary stays small even when entries are long. 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
https://github.com/user-attachments/assets/e055988e-7253-4ba9-92fc-95d3d4bcd763
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
*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](https://projectmem.dev/blog/projectmem-complete-guide-ai-coding-agent-memory).*
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, nocwd— that's what makes it serve everything. Per-client
instructions (Claude Desktop, Claude Code, Cursor, Antigravity, Codex) are in
MCP Integration;
pjm initprints 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
--onlineif you also want it to tell you when a newer projectmem is out
— projectmem makes no network calls otherwise, and
--autoturns 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.
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.
pip install projectmem
cd your-project && pjm init
Full screen-recorded tutorial- watch on YouTube
| Doc | What's in it |
|---|---|
| [Complete setup guide](https://projectmem.dev/blog/projectmem-complete-guide-ai-coding-agent-memory) | 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](https://github.com/riponcm/projectmem/blob/main/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](https://github.com/riponcm/projectmem/blob/main/CHANGELOG.md) | Release history. Latest: v0.3.5 — precheck matches every spelling of a path, and long entries no longer bloat the summary. |
| [Research paper (arXiv:2606.12329)](https://arxiv.org/abs/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](https://github.com/riponcm/projectmem/blob/main/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.
First time here? → [The complete setup guide](https://projectmem.dev/blog/projectmem-complete-guide-ai-coding-agent-memory)
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. Apjmalias is installed for speed.
Precheck recognises a file however its path is spelled. Events store project-relative paths, so asking about ./src/auth.py, an absolute path, a path through a symlinked folder or a Windows C:\ path matched nothing, and precheck called a file with known failures "Safe to modify". Every spelling now gets the same warnings, and checking is faster on large logs. get_issue("1") and get_issue("#1") find issue 0001. (#26)
Long entries no longer bloat the summary. 0.3.4 capped how many entries the summary shows, not how long each one is. Records are now clipped at 300 characters, and a fixed issue takes one line. On a real 238-event project the summary went from 8,974 tokens to 3,458. (#29, #30)
Upgrading? Run `pjm init` once in each project. The instructions now ask agents for shorter records, and pjm init refreshes AI_INSTRUCTIONS.md, keeping your previous copy as AI_INSTRUCTIONS.md.bak.
The #26 fixes were contributed by @vishalhabib99, and #29 was reported and fixed by @bigfree.
The summary no longer grows forever. Notes were capped, but Recent issues and Decisions listed everything ever logged, so on an aged project reading the memory cost more than reading the code. Every open issue is still shown — the newest 20 in full, older ones by id — while fixed issues are capped at the newest 10 and decisions at the newest 15, each with a line saying what was left out and where to find it. On a test project with 25 issues and 60 decisions the summary went from about 2,900 tokens to about 1,100. (#19)
Commits are filed under the code they change. Auto-capture used the first changed path in git's sort order, which was usually the regenerated .projectmem/summary.md — so valid memories were flagged stale and the real file lost its history. The location is now the source file the commit is about, with deletions, renames, merges and a repository's first commit handled. (#20)
Stale warnings no longer depend on your time zone. A 0.3.3 speed-up compared git's local-time commit dates with UTC event times as text: west of UTC, recent stale memories were never flagged; east of UTC, fresh ones were flagged at once. Both sides are compared in UTC now, and events already filed under .projectmem/ stop warning without your log being rewritten.
*
Nur der Anfang wird gezeigt; den Rest findest du im Repo