Agenten stärken
Die eigene URL des Herausgebers. Kein PlugMCP-Endpunkt.
Host a copy with our keys and logs.
https://mcp.kortexio.io/{appId}/mcpDie Claude-Schaltfläche oben öffnet Claude mit ausgefülltem Namen und ausgefüllter URL. Sie bestätigen dort noch.
claude mcp add --transport http io.kortexio-memory https://mcp.kortexio.io/{appId}/mcpRun in a terminal.
{ "mcpServers": { "io.kortexio-memory": { "command": "npx", "args": [ "-y", "mcp-remote", "https://mcp.kortexio.io/{appId}/mcp" ] } }}claude_desktop_config.json
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
git clone https://github.com/Kortexio/ContextMemory.git && cd ContextMemory
docker compose -f docker-compose.yml -f docker-compose.llamacpp.yml up --build -d # CPU; GPU: docker-compose.vllm.yml
./scripts/aha-chat.sh # Windows: .\scripts\aha-chat.ps1
sends two requests in the same session. The second one carries the new question:
Hosted ambient memory for AI agents. Retrieve context and save notes via MCP tools with OAuth.
aha-chat==> Turn 1: "Remember this for later: our staging database host is postgres-staging-01."
==> Turn 2: "What is our staging database host?" (no history in the request body)
AHA OK — the client sent no history; the gateway remembered 'postgres-staging-01'.
The same check runs in CI against a real llama-server on every push (e2e-llamacpp). First start downloads a ~2 GB GGUF; pick another model with LLAMACPP_HF_MODEL (Engines). Admin UI: http://localhost:5200.
ContextMemory is the open-source agentic memory gateway behind Kortexio.
Your app (or Cursor/Claude) keeps talking to a normal chat API. The gateway:
chat.completions response (streaming supported)Your client (OpenAI SDK / Cursor MCP / curl)
│
▼ POST /v1/chat/completions
┌────────────────────────────────────────────┐
│ ContextMemory (.NET 9) │
│ Auth · session wiki · Global Wiki tool │
│ Agentic loop · skills · guardrails · HITL │
│ LLM backend (per app — BYO engine) │
└───────┬──────────────────┬─────────────────┘
▼ ▼
sandbox-runtime mcp-runtime / MCP servers
(shell/python/node) (HTTP + stdio, OAuth)
or Azure ACA sessions
Honest boundaries: this is a gateway + server-side harness, not a client agent framework (LangGraph/CrewAI) and not an agent OS (Letta). You keep your OpenAI client; the loop runs on the server.
LLM engines: ContextMemory does not ship or lock to one inference stack. Per tenant you pick any OpenAI-compatible /v1 host — Ollama, vLLM, LM Studio, ExLlamaSharp, OpenAI, Azure-compatible, LiteLLM, custom. Compose ships llama.cpp and vLLM overrides; swap engines per app in Admin → Config → LLM.
How we compare (Mem0 / Zep / Letta / why we are not RAG): docs/compare.md.
Full detail: docs/architecture-and-features.md · Admin: docs/admin-ui.md · HITL: docs/hitl.md.
The gateway talks OpenAI-compatible /v1 to the engine (and Ollama native /api/chat when you need num_ctx). Change the engine anytime in Admin → Config → LLM or PATCH /admin/apps/{id}/config.
docker run --rm -p 5100:8080 \
-v contextmemory-data:/app/data \
-e ContextMemory__MasterKey=cm_master_dev_key_change_me \
-e ContextMemory__Apps__demo-dev__ApiKey=cm_live_dev_key_change_me \
-e ContextMemory__Apps__demo-dev__LlmBackend=openai-compatible \
-e ContextMemory__Apps__demo-dev__LlmModel=local-model \
-e ContextMemory__Apps__demo-dev__LlmEndpoint=http://host.docker.internal:8080 \
--add-host=host.docker.internal:host-gateway \
ghcr.io/kortexio/contextmemory:latest
Host-level default for all apps: ContextMemory__LlmEndpoint. Ollama on the host works too (ContextMemory__LlmEndpoint=http://host.docker.internal:11434). Engine flags that matter (llama.cpp --jinja, vLLM tool parser): Engines. Full stack (API + Admin + MCP + sandbox): docs/self-host.md.
git clone https://github.com/Kortexio/ContextMemory.git
cd ContextMemory/mcp-server && npm install && node print-mcp-config.mjs
Paste into Cursor → Settings → MCP (or ~/.cursor/mcp.json). Same snippet works for Claude Desktop. Details: mcp-server/README.md.
Then, in two separate chats:
Same flow without Cursor (wiki API, no LLM): ./scripts/aha-demo.sh or .\scripts\aha-demo.ps1.
curl -X POST http://localhost:5100/v1/chat/completions \
-H "Content-Type: application/json" \
-H "X-App-Id: demo-dev" -H "X-User-Id: user-42" -H "X-Session-Id: sess-abc" \
-H "Authorization: Bearer cm_live_dev_key_change_me" \
-d '{"model":"local-model","messages":[{"role":"user","content":"Hello"}]}'
Thin header helpers (not full SDKs): @kortexio/contextmemory · kortexio-contextmemory
Website: kortexio.io · Email: [email protected]
AGPL-3.0 for this open-source core — self-host it freely, including commercially. Need to embed it in a closed-source product without AGPL obligations? Use Kortexio Cloud or a commercial license. See docs/license-and-support.md.
Hosted ambient memory for AI agents. Retrieve context and save notes via MCP tools with OAuth.
{ "mcpServers": { "io.kortexio-memory": { "url": "https://mcp.kortexio.io/{appId}/mcp", "headers": {} } }}Tools
Noch keine Tool-Liste. Remote-Server erhalten eine nach dem ersten Health-Check.
FAQ
Kopieren Sie auf dieser Seite die http-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.
Kortexio Memory wird über HTTP erreicht.
Zuletzt geprüft: 2026-11-10. Hosted ambient memory for AI agents. Retrieve context and save notes via MCP tools with OAuth.