Context7
Up-to-date docs for AI agents
Beschreibung vom Entwickler (englisches Original)
Open, local-first engram memory for AI agents: read, correct, delete; shared over MCP.
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": {
"plur": {
"command": "npx",
"args": [
"-y",
"@plur-ai/mcp"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add plur -- npx -y @plur-ai/mcpIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"plur": {
"command": "npx",
"args": [
"-y",
"@plur-ai/mcp"
]
}
}
}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": {
"plur": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@plur-ai/mcp"
]
}
}
}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 7. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto plur-ai 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 (0.23.0) kam vor 7 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.
Persistent, open memory for AI agents — local-first, zero-cost, shared across MCP tools (Claude Code, Codex, Cursor, OpenCode, Hermes, OpenClaw). Your agent's memory is plain-text engrams you can read, correct, and delete — not weights you can't.
plur.ai · Benchmark · Engram Spec · npm · Comparisons
PLUR is memory, not just retrieval — so we measure it on more than one axis, on the full corpus, and we publish the harness so you can reproduce every number.
Retrieval recall — full LongMemEval-S (N=500), R@5, fully local:
| Stack | R@5 | Notes |
|---|---|---|
| BM25 only | 92.2% | no embedder — fully airgapped |
| Hybrid (BGE-small, shipping default) | 95.6% | local embedder; one-time model download |
| + BGE-reranker-v2-m3 | 97.6% | local cross-encoder, max quality — opt-in, ≈5s p50 on CPU |
Numbers come from plur-ai/plur-bench, which is the source of truth for every benchmark figure PLUR publishes. Where an in-repo number and a plur-bench number disagree, plur-bench wins — it is the reproducible harness, and it is what CI regression-checks.
Chunk granularity, canonical-doc scoring, corpus SHA256 pinned — reproduce it in plur-ai/plur-bench. No cloud call is required for any of these numbers (an *optional* cloud embedder, openai-3-large, reaches 97.0% hybrid). A faster reranker — ms-marco-minilm-l6 (p50≈245ms vs BGE's ≈5s on CPU) — trades a little recall for sub-second latency.
Run it yourself — and tell us what you get. The harness is plur-ai/plur-bench: CPU-runnable, no API key needed for the local path, corpus auto-fetched and SHA-verified. If you run it, we'd genuinely love to see your numbers — open an issue or discussion with your results, especially if they don't match ours. Independent reproduction is worth more than any number we publish, and we'll gladly credit you.
Retrieval ≠ answer accuracy — and we report them separately, never conflated. End-to-end (LLM-judge) answer accuracy with the reranker stack is 60.5%, versus 52.0% for dumping full context into the prompt and 5.5% with no memory at all.
Agent-task impact — same task, with memory vs without: Haiku + PLUR outperforms Opus *without* it at roughly 10× less cost; house rules 12–0 across Haiku, Sonnet, and Opus.
Operational — local-first, zero-cost search, data-sovereign by design.
*More in progress: LoCoMo, agentic task suites, cross-tool portability, decay / contradiction correctness.* Full methodology →
You correct your agent's coding style on Monday. On Tuesday, it makes the same mistake. You explain your architecture in Cursor. That night, Claude Code has no idea.
PLUR fixes this. Install it once, and corrections, preferences, and conventions persist — across sessions, tools, and machines. Your memory is stored as plain YAML on your disk. No cloud, no API calls, no black box.
The interesting part: in our tool-routing and local-knowledge benchmark, Haiku with PLUR memory outperformed Opus without it — 2.6x better on tool routing, at roughly 10x less cost. Turns out the bottleneck isn't model intelligence. It's context.
The model is rented; your memory is owned. Swap Haiku for Opus for whatever ships next month — the reasoning is a commodity you don't control. The part that's *yours* — everything the agent has learned about your work, your corrections, your conventions — shouldn't live in someone else's cloud or be baked into weights you can't read. PLUR keeps it in plain files on your disk, in an open format you can inspect, correct, and delete. That's what owning your intelligence actually means.
Requires Node.js 20 or newer. Install both packages so editor configurations can use the installed MCP server:
npm install -g @plur-ai/cli@latest @plur-ai/mcp@latest
plur init plur doctor
Restart your editor after setup. plur doctor checks the configuration and MCP handshake, and downloads the search model if it is missing and downloads are allowed. Search runs locally after that download; without the model it falls back to keyword search.
Set up PLUR memory for me: install @plur-ai/cli@latest and @plur-ai/mcp@latest globally, run `plur init`, then `plur doctor`. Show me any checks that fail.
plur init creates the local store at ~/.plur/, registers the user-scoped MCP server in ~/.claude.json, and writes global hooks to ~/.claude/settings.json. A project .mcp.json is a separate project-scoped configuration; ~/.claude/mcp.json is not the user-scoped registration path. The hooks inject memory and close session state when Claude Code emits SessionEnd. Run init again after upgrading PLUR to refresh its configuration.
For multi-project setups, use domain/scope to separate knowledge:
cd ~/projects/my-app
plur init --domain myapp --scope project:my-app
This creates a .plur.yaml in the project with defaults that hooks apply automatically. Engrams learned in that project are tagged; recall filters by scope but always includes global knowledge.
Set scope per engram, by content. Scope is not a once-per-session setting — every plur_learn call takes its own scope, chosen from what the engram is about. Team/shared knowledge goes to a team scope (e.g. group: / , used by PLUR Enterprise); project details to project: ; personal preferences stay local. Don't let team-relevant knowledge fall back to global by omitting scope — global leaks into every project and (with a team store configured) never reaches the team. plur_session_start lists the remote scopes a token can write to.
npm install -g @plur-ai/cli@latest @plur-ai/mcp@latest
plur init plur doctor
Restart the editor so it launches the updated MCP server.
From your project root:
plur init --cursor
This writes .cursor/mcp.json, .cursor/hooks.json and .cursor/rules/plur-memory.mdc. Cursor uses the reduced cursor tool profile: core tools are direct, and other operations are available through plur_admin. Call plur_admin with { "action": "help" } to discover their current names and arguments.
plur init --codex
Registers a new MCP server via codex mcp add in ~/.codex/config.toml, writes lifecycle hooks to ~/.codex/hooks.json (CODEX_HOME overrides the directory), and adds a PLUR section to AGENTS.md. Auto-detected when ~/.codex/ exists.
To upgrade an existing Codex installation:
npm install -g @plur-ai/cli@latest @plur-ai/mcp@latest
plur init --codex plur doctor --codex
Init updates recognized older PLUR launch pins in place, preserving environment settings, timeouts, tool policies and comments. It preserves newer pins and custom or HTTP servers. Use --keep-codex-mcp to retain an intentional older launch entry. Restart Codex after changing its configuration.
For remote stores using plur remote --token-env NAME, both remote and init --codex add the required variable names to Codex's env_vars; they never copy token values into its config. The variable must also be available to the process that launches Codex. A variable exported in PowerShell does not prove that a separately launched desktop app inherited it.
plur doctor --codex starts the MCP command from the Codex user config with Codex-style environment filtering, checks its reported version against the CLI, and reports missing token forwarding or values. It does not check project/profile overrides, another running application's environment, hook trust, or a remote write. --no-handshake reports the runtime as unverified and exits nonzero. Verify saving and recalling a test memory in the actual Codex session before treating desktop setup as complete. Native Windows and WSL2 have separate home directories and registrations; run setup in each environment you use.
Injection uses hybrid search (BM25 + embeddings) with an automatic BM25 fallback if the embedder is slow or unavailable. Set PLUR_HOOK_HYBRID=0 to force BM25 (applies to the Antigravity hooks too; PLUR_CODEX_HYBRID is honoured as an alias). PLUR_HOOK_HYBRID_DEADLINE_MS tunes the fallback deadline — keep it below your harness's hook timeout (Codex 25s, Antigravity 20s).
One manual step after install: open Codex, run /hooks, and trust the PLUR entries. Codex fingerprints every hook and refuses to run untrusted ones — *silently*, with no warning and a zero exit code. Until you trust them, memory simply never loads. plur doctor says so too.
plur init --opencode
plur doctor
This adds the memory plugin and MCP tools to ~/.config/opencode/opencode.json, or to the existing opencode.jsonc when no .json exists. OPENCODE_CONFIG_DIR takes precedence; otherwise XDG_CONFIG_HOME/opencode overrides the default directory.
The MCP command uses Node and the installed @plur-ai/mcp entry when available. Init updates an older exact @plur-ai/opencode pin to the plugin version shipped with the CLI while preserving comments, formatting and unrelated settings. Newer pins, tags and unpinned entries remain unchanged. Use --keep-opencode-plugin to retain an intentional old pin. Restart OpenCode to load the changed configuration. Plugin details.
Every MCP client can call PLUR's tools. Only some have an *adapter* — the hooks and always-on context that make memory load automatically instead of waiting for the agent to think of it. Without one, recall and learning depend entirely on the model choosing to call the tools, which degrades badly under context pressure.
| Harness | Tools | Auto-injection + enforcement |
|---|---|---|
| Claude Code | ✅ | ✅ hooks + CLAUDE.md |
| Codex | ✅ | ✅ hooks + AGENTS.md (trust /hooks once) |
| Cursor | ✅ | ✅ hooks + rules |
| OpenCode | ✅ | ✅ memory plugin + MCP tools |
| OpenClaw | ✅ | ✅ ContextEngine plugin |
| Hermes | ✅ | ✅ plugin |
Antigravity CLI (agy) | ✅ | ✅ hooks + AGENTS.md |
| Windsurf, Gemini CLI, other MCP clients | ✅ | ❌ tools only |
If your harness is in the last row, paste the PLUR section from CLAUDE.md into its own context file (AGENTS.md, GEMINI.md, …) as an interim measure — that restores the instruction layer, though not automatic injection.
plur init --antigravity
Writes hooks and the MCP server into agy's global config (~/.gemini/config/) and adds a PLUR section to AGENTS.md. Auto-detected when ~/.gemini/antigravity-cli/ exists. No trust step — agy runs
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