Context7
Up-to-date docs for AI agents
Beschreibung vom Entwickler (englisches Original)
Token-lean memory for AI agents: a knowledge graph searched cheaply. Drop-in for server-memory.
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.
MEMORY_FILE_PATHDu 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": {
"memory-lean": {
"command": "npx",
"args": [
"-y",
"memory-lean"
],
"env": {
"MEMORY_FILE_PATH": "your-value"
}
}
}
}Führe das in deinem Terminal aus
claude mcp add memory-lean --env MEMORY_FILE_PATH=your-value -- npx -y memory-leanTrag die leeren Werte ein, bevor du ihn startest.
In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"memory-lean": {
"command": "npx",
"args": [
"-y",
"memory-lean"
],
"env": {
"MEMORY_FILE_PATH": "your-value"
}
}
}
}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": {
"memory-lean": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"memory-lean"
],
"env": {
"MEMORY_FILE_PATH": "your-value"
}
}
}
}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.
MEMORY_FILE_PATH envDiese 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 zamansheikh 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 (1.5.0) kam vor 21 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Node.js 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.
memory-lean
Long-term memory for AI coding agents that doesn't burn the context window.
A knowledge graph your agent searches cheaply, and a web UI where you can see and edit all of it.
Quick start · Web UI · How it works · Tools · Setup for 12 agents · Contributing
memory-lean is an MCP server that gives Claude Code, Codex, Cursor, MiMo Code and any other MCP client a memory that lasts between sessions: which projects exist, what depends on what, which version shipped, what is still pending, and which rules were learned the hard way. It is a drop-in replacement for the reference memory server (@modelcontextprotocol/server-memory), with the same file and tool names, but its answers are about 40 times smaller.
About 40× smaller answers. Search returns a ranked list of names and the lines that matched, not every entity in full, so the agent can afford to check its memory before every task.
A web UI for people. See the whole graph grouped by type, follow relations, and add, edit or delete anything. Agent writes show up live.
Drop-in replacement. Same memory.jsonl file and the same nine tools as the reference server. Switch in, and switch back whenever you like.
Works with every agent. Copy-paste setup for Claude Code, MiMo Code, Codex, Cursor, Copilot, Gemini CLI, Zed, JetBrains and more, plus the instructions that teach the agent to use it.
Stays small by itself. Short facts, a cap per entity, and an archive for the overflow, so the graph never grows into a wall of text.
Private and safe. Plain local files, daily backups, a write lock for parallel sessions, and no network access. One server file, zero dependencies.
Measured on a real graph of 237 entities, 355 relations and 1,160 facts (tokens ≈ characters ÷ 4):
| Call | Reference server | memory-lean | |
|---|---|---|---|
read_graph | ~77,800 tokens | ~2,000 tokens | 40× smaller |
search_nodes, average of 5 common words | ~31,000 tokens | ~770 tokens | 40× smaller |
A narrow search that matches one or two entities saves less, because there is little to trim. To measure your own graph, run node tools/bench.mjs from a clone; it is read-only and prints sizes, never content.
Claude Code: install the plugin. It adds the server, the instructions as a skill, a short reminder at the start of each session, and /memory-ui.
/plugin marketplace add zamansheikh/memory-lean
/plugin install memory-lean@memory-lean
Any other agent: add the server to its MCP config, then give it the instructions.
{
"mcpServers": { "memory": { "command": "npx", "args": ["-y", "memory-lean"] } } }
npx -y memory-lean --protocol >> AGENTS.md # into the rules file your agent reads
Just looking? Open the web UI on a made-up example graph:
npx -y memory-lean --ui --demo
Setup for each agent
| Agent | Server config | Instructions |
|---|---|---|
| Claude Code | plugin | included in the plugin |
| Xiaomi MiMo Code | mimocode.jsonc | skill or AGENTS.md |
| opencode | opencode.json | AGENTS.md |
| OpenAI Codex CLI | codex mcp add | AGENTS.md |
| Cursor | .cursor/mcp.json | AGENTS.md or a rule |
| VS Code (Copilot) | .vscode/mcp.json | copilot-instructions.md |
| Gemini CLI | settings.json | GEMINI.md |
| Windsurf | mcp_config.json | a rule |
| Cline | cline_mcp_settings.json | .clinerules/ |
| Zed | settings.json | AGENTS.md or .rules |
| JetBrains IDEs | AI Assistant settings | a rule or guidelines |
| Claude Desktop | claude_desktop_config.json | project instructions |
The graph is created on the first write at ~/.claude/memory-graph/memory.jsonl, whichever agent writes it, so all your agents share one memory. Set MEMORY_FILE_PATH to keep it somewhere else.
npx -y memory-lean --ui
This opens your memory in the browser. Everything an agent can do, you can do here:
Follow the connections. Select an entity to see its facts and relations. Find anything. Search names, types and facts at once.
Edit in place. With the same 300-character limit the agents have. Light or dark. It follows your system, or pick one.
The page runs on your machine only (127.0.0.1) and needs the one-time key in the link it prints, so other websites cannot read or change your memory. Nothing is uploaded anywhere.
The reference server answers a search with every matching entity in full, as pretty-printed JSON. memory-lean answers with a ranked list of names and the lines that matched:
> search_nodes { "query": "deploy" }
57 match "deploy", top 10 shown (narrow the query or raise limit). open_nodes for full entities.
The agent then opens only what it needs:
> open_nodes { "names": ["shop-backend"] }
shop-app -talks_to-> shop-backend PENDING shop-backend 2.4.0 -blocks-> shop-app
| Limit | What it means |
|---|---|
| Small answers | search_nodes returns at most 10 names with snippets, read_graph returns an index of names, and only open_nodes returns full entities. |
| Short facts | An observation longer than 300 characters is rejected, with a message telling the agent to split it or put the detail in the repo. |
| A cap per entity | An entity keeps 15 observations. Past that, the oldest dated ones move to archive/ .md. Nothing is deleted: they can be listed and restored, and undated facts (the identity facts) always stay. |
| Search that narrows | Every word must match somewhere in the entity, so adding a word narrows the result. The reference server looks for the query as one exact phrase. |
| Safeguard | What it means |
|---|---|
| Safe to share | Writers take a lock file, so several agent sessions can use one graph without overwriting each other. Every write replaces the file atomically. |
| Daily backups | A copy goes to backups/ once a day; the last 7 are kept. |
| Damaged lines are kept | A line that cannot be read is moved to memory.jsonl.unreadable on the next write and reported, never silently dropped. |
| Plain files | The graph is JSON lines, archives are Markdown, backups are copies. You can read, grep and edit all of it by hand. |
| Tool | What it returns |
|---|---|
search_nodes | Ranked list of entity names with counts and up to two matching snippets each. Every word must match the name, type, an observation, or the type of a relation touching the entity. |
open_nodes | The full entities you name, as compact text, with every relation touching them and their archive count. Unknown names come back with "did you mean" suggestions. |
read_graph | An index: entity names grouped by type, with observation counts. Never the observations. |
create_entities | Creates entities; existing names are skipped. |
create_relations | Creates relations between existing entities; duplicates are skipped. |
add_observations | Adds facts to an entity, enforcing the length limit and the per-entity cap. |
delete_observations | Removes observations by exact text. |
delete_relations | Removes specific relations. |
delete_entities | Removes entities and every relation touching them. |
rename_entity | Renames an entity, keeping its observations, relations and archive. *Not in the reference server.* |
| archived_observations | Lists what was archived for an entity, optionally filtered, and restores chosen fa
Nur der Anfang wird gezeigt; den Rest findest du im Repo