Use when the user wants to run the cognee API server (and optional UI) on their own machine — starting it, checking it's healthy, connecting the SDK or other clients to it, and choosing the right auth
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add topoteretes/cognee --skill cognee-server -a cursor -y
Use when the user wants to run the cognee API server (and optional UI) on their own machine — starting it, checking it's healthy, connecting the SDK or other clients to it, and choosing the right auth posture.
Use when the user wants to run the cognee API server (and optional UI) on their own machine — starting it, checking it's healthy, connecting the SDK or other clients to it, and choosing the right auth posture.
cognee-cli -ui
launches the full local stack: FastAPI backend on and the UI on . Needs in the environment or . The interactive API reference is at , health at .
LLM_API_KEY.env/healthFor API-only serving via Docker instead, use the cognee-docker skill (the
prebuilt cognee/cognee:main image or docker compose up from the repo).
ENABLE_BACKEND_ACCESS_CONTROL decides everything:
true (default): multi-tenant — auth required on every API call, per
user+dataset database isolation.false: single-user local mode — no auth, shared local databases. Right
choice for a personal dev server; never for anything exposed.REQUIRE_AUTHENTICATION=false is ignored while access control is on; to turn
auth off you must set ENABLE_BACKEND_ACCESS_CONTROL=false.
SDK / CLI against the server (instead of embedded local mode):
cognee-cli serve --url http://localhost:8000 # local instance
cognee-cli serve # cognee cloud (device flow)
cognee-cli serve --logout # disconnect
In Python: await cognee.serve(url="http://localhost:8000").
HTTP: main routes live under /api/v1/ — the memory API is remember
(plus remember/entry), recall, improve, forget; sessions covers
session memory; datasets, users, visualize handle the rest. The legacy
add, cognify, search, memify, and delete routes still exist and are
what the memory routes call underneath (see cognee/api/client.py for the
registered routers, or GET /openapi.json on a running server).
Note there is no /api/v1/feedback route — feedback exists as a CLI
command and in the SDK, but is not exposed over HTTP.
from cognee.api.v1.visualize import visualization_server
shutdown = visualization_server(port=8080) # synchronous; returns a shutdown callable
"8080:8000").ENABLE_BACKEND_ACCESS_CONTROL=false and restart.recall/search returns [] instead of erroring → permission-filtered
result; check dataset access rights for the calling user.