Agenten stärken
Installation vom Herausgeber. Kein PlugMCP-Endpunkt.
Host a copy with our keys and logs.
Noch kein paketiertes Release in der Registry. Folgen Sie der README des Repositorys.
Remote-URL oder ein Befehl auf Ihrem Rechner? Worin sich die beiden Installationen unterscheiden
Auf PlugMCP ausführen
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
StartenVom Herausgeber
A shared, persistent memory service for AI agents.
memini gives any MCP-capable agent (Claude Code, Cursor, Codex, opencode, Hermes, OpenClaw, Open WebUI) one place to remember and , with retrieval quality that compounds over time. It runs as a single Go binary, boots with zero configuration, and scales from an embedded SQLite file on a laptop to Postgres in Kubernetes.
Give any MCP-capable agent persistent memory: remember/recall over a tiered store with hybrid vector + keyword retrieval. Single Go binary, SQLite or Postgres, embedded admin UI.
recallEverything is indexed in docs/.
memini draws on three earlier projects:
agentmemory). See docs/tiers.md for what each tier means and how memories move between them.fsck consistency checker (after mnemory).Hybrid results are re-ranked by a composite of relevance, access recency, and importance rather than similarity alone, and near-duplicates are collapsed at recall time.
An LLM is optional. With one configured, writes are stored immediately and then deduplicated and contradiction-resolved in the background, and each fresh episodic capture is distilled into durable semantic facts at write time, so a fact stated once is durable immediately. Without one, marker heuristics run the same lifecycle, so durable knowledge still accumulates in an embedder-only deployment.
memini boots with zero configuration in its embedded SQLite mode. Vector search is the one thing it cannot invent, so point it at any OpenAI-compatible embeddings endpoint:
export MEMINI_EMBED_BASE_URL=http://localhost:8081/v1
export MEMINI_EMBED_MODEL=bge-m3
export MEMINI_EMBED_DIMS=1024 # must match the model
mise run run
curl -s localhost:8080/healthz
MEMINI_EMBED_DIMS has to match the model you point at. That is the most common setup mistake, and it corrupts the store rather than failing cleanly.
For the full walk-through, including wiring it into an agent, see Solo laptop. For Docker, Compose and Kubernetes, see Deployment.
Most integrations read MEMINI_BASE_URL for the server and MEMINI_API_KEY for the token. MEMINI_URL and MEMINI_TOKEN are removed: clients warn once at session start and otherwise ignore them. The Codex and Cursor plugins are the exception: their bundled MCP config uses a fixed local URL with static env-based headers; see the remote override recipe below. Where an integration has its own config (opencode options, Open WebUI Valves, openclaw.json), that config wins over the environment.
Claude Code:
/plugin marketplace add eleboucher/memini
/plugin install memini
opencode: add the plugin to opencode.json (or ~/.config/opencode/opencode.json):
{
"plugin": ["@eleboucher/opencode-memini"]
}
Hermes: hermes plugins install eleboucher/memini-hermes
OpenClaw: openclaw plugins install clawhub:@eleboucher/memini
Open WebUI: paste filter/memini_memory.py into Admin Panel, Functions, and optionally tools/memini_tools.py into Workspace, Tools.
Codex:
codex plugin marketplace add eleboucher/memini
codex plugin add memini@memini
Start the local server first (memini, default http://localhost:8080), set MEMINI_API_KEY when authentication is enabled, review and trust the bundled hooks with /hooks, then start a new thread. Remote and custom-server setup is covered in integrations/codex/.
Cursor:
ln -s "$PWD/plugin" ~/.cursor/plugins/local/memini
Reload the window (Developer: Reload Window) and confirm under Customize → Plugins; teams can instead import the repo as a marketplace (Dashboard → Plugins → Import from Repo). The bundled MCP server targets a fixed http://localhost:8080/mcp; authentication and remote setup (a ~/.cursor/mcp.json override) are covered in plugin/README.md.
Full details, edge cases and every client-side setting live in integrations/ and plugin/README.md. If a variable seems to have no effect, check server vs client variables: four names mean different things on each side.
memini speaks the Model Context Protocol, over two transports:
http://<host>:8080/mcpmemini mcpThe nine tools an agent sees, their parameters, and the standing policy the server sends every client on connect are in MCP tools. Ready-to-paste client configs are in integrations/.
memini's hybrid retrieval beats agentmemory's published LongMemEval-S numbers on the same model, dataset and metric (98.4% recall@5 against 95.2%), and reranking adds double-digit gains on the turn-level sets where base recall still has headroom.
mise run bench
Full tables, per-leg and per-category breakdowns, the tune and held-out split, parameter sweeps, methodology, caveats, and the LoCoMo comparison against mem0 and Letta are in bench/README.md.
See CONTRIBUTING.md for the dev loop, the generated-code drift gates, and the docs conventions. The short version: the reference under docs/reference/ is generated from the code (mise run docs), and CI fails if it has drifted. If you add a setting, a CLI command or an MCP tool, run mise run docs and commit the result. The generator refuses to run when a new setting has no doc comment or belongs to no section, which is deliberate: that is how a knob ships undocumented.
Give any MCP-capable agent persistent memory: remember/recall over a tiered store with hybrid vector + keyword retrieval. Single Go binary, SQLite or Postgres, embedded admin UI.
Tools
Noch keine Tool-Liste. Remote-Server erhalten eine nach dem ersten Health-Check.
FAQ
Kopieren Sie auf dieser Seite die stdio-Konfiguration für Cursor, Claude Desktop, Claude Code oder VS Code. Sie nutzt die Installation des Herausgebers, keinen PlugMCP-Proxy.
In diesem Eintrag ist keine erforderliche Umgebungsvariable aufgeführt.
Memini läuft lokal über stdio.
Zuletzt geprüft: 2026-10-28. Give any MCP-capable agent persistent memory: remember/recall over a tiered store with hybrid vector + keyword retrieval. Single Go binary, SQLite or Postgres, embedded admin UI.
