Kortexio Memory

[Kortexio ContextMemory — Memory you can open as a wiki]

Try it (3 commands) · Engines · Docs · vs Mem0 / Zep / Letta

[License: AGPL-3.0] [.NET 9] [Docker GHCR] [Docker CI] [Tests] [e2e: llama.cpp]

Self-hosted memory gateway for your llama.cpp / vLLM server.

Put one OpenAI-compatible /v1 URL in front of your engine. Your client sends only the new message; the gateway keeps session memory as markdown you can open, edit, and diff. No vector DB, no client rewrite.

Try it in three commands

git clone https://github.com/Kortexio/ContextMemory.git && cd ContextMemorydocker 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

aha-chat sends two requests in the same session. The second one carries only the new question:

==> 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.

What is ContextMemory?

ContextMemory is the open-source agentic memory gateway behind Kortexio.

Your app (or Cursor/Claude) keeps talking to a normal chat API. The gateway:

1. Authenticates the tenant and attaches session wiki + history
2. Runs an agentic tool loop when tools are enabled (wiki search, sandbox, MCP, …)
3. Applies skills, guardrails, validators, and optional HITL before destructive actions
4. Returns a standard OpenAI-shaped 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.

What it gives developers

Capabilities (summary)

Full detail: docs/architecture-and-features.md · Admin: docs/admin-ui.md · HITL: docs/hitl.md.

[ContextMemory Admin dashboard]

[LLM backend picker]   [Admin Playground]

More ways to run

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.

Engine already running (llama-server, vLLM, LM Studio, …) — API image only

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.

Cursor / Claude memory (MCP)

git clone https://github.com/Kortexio/ContextMemory.gitcd 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.

No GPU, no ops: Kortexio Cloud

Guides: Cloud · Self-host

Chat drop-in

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

Documentation & support

Website: kortexio.io · Email: [email protected]

License

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.