---

name: cognee-docker description: Use when the user wants to run cognee with Docker or docker compose — trying it out from the prebuilt image, starting the API server in a container, or bringing up the full stack (UI, MCP, Postgres, Neo4j) with compose profiles.

Start cognee from the Docker image

Fastest path: prebuilt image, one file

For a local try-out, do NOT clone or build anything. Follow docs/minimal-docker-compose.md: save this as docker-compose.yml in an empty directory:

services:
  cognee:
    image: cognee/cognee:main
    ports:
      - "8000:8000"
    environment:
      LLM_API_KEY: ${LLM_API_KEY:?set LLM_API_KEY to your OpenAI API key}
      # Single-user try-out: no auth, shared local databases.
      ENABLE_BACKEND_ACCESS_CONTROL: "false"

Then:

export LLM_API_KEY="sk-..."   # OpenAI key (default LLM + embedding provider)
docker compose up
curl http://localhost:8000/health

Interactive API reference: http://localhost:8000/docs. First requests:

echo "Cognee turns documents into AI memory." > note.txt
# remember = ingest + build the graph in one call (multipart form)
curl -X POST http://localhost:8000/api/v1/remember -F "data=@note.txt" -F "datasetName=main_dataset"
# recall = query it (JSON)
curl -X POST http://localhost:8000/api/v1/recall -H "Content-Type: application/json" \
  -d '{"query": "What does Cognee do?", "datasets": ["main_dataset"]}'

/api/v1/recall takes the question as query. Omit search_type (or pass null) and the query is auto-routed by the same rule-based router the SDK recall() uses, with HYBRID_COMPLETION as the fallback; pass a value such as "search_type": "GRAPH_COMPLETION" to pin a strategy. The rule table is in docs/recall-vs-search.md.

Request DTOs accept both snake_case and camelCase for every field (alias_generator=to_camel + populate_by_name in cognee/api/DTO.py), so search_type and searchType are equally valid.

The legacy /api/v1/add + /api/v1/cognify + /api/v1/search endpoints still exist and are what remember/recall call underneath; use them only when you need a single stage on its own. /api/v1/improve and /api/v1/forget complete the memory API.

Data lives inside the container by default. To persist it, set DATA_ROOT_DIRECTORY=/cognee-data/data and SYSTEM_ROOT_DIRECTORY=/cognee-data/system and mount a named volume at /cognee-data (full example in docs/minimal-docker-compose.md).

Full stack from the repo

The repository's docker-compose.yml builds from source and adds opt-in profiles. From the repo root (needs a .env with at least LLM_API_KEY; copy .env.template):

docker compose up                                  # API server only, port 8000
docker compose --profile ui up                     # + frontend on port 3000
docker compose --profile mcp up                    # + MCP server on port 8001
docker compose --profile postgres --profile neo4j up   # + databases

Postgres profile: pgvector/pg17, user/password/db cognee/cognee/cognee_db on 5432. Neo4j profile: neo4j/pleaseletmein on 7474/7687. When cognee runs in a container and the database on the host, use DB_HOST=host.docker.internal.

Gotchas

• With ENABLE_BACKEND_ACCESS_CONTROL unset (defaults to true), every API call requires authentication — the single-user try-out sets it to false.
• The image defaults to OpenAI for both LLM and embeddings; configuring only one of them leaves the other on OpenAI, so keep a valid OpenAI key or configure both (see the cognee-integrations skill).