--- 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:

```yaml 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:

```bash 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:

```bash 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`. It defaults `search_type` to `GRAPH_COMPLETION` for backward compatibility — pass `"search_type": null` to opt into auto-routing (the SDK `recall()` default). The difference is real: `{"query": "Why does X?"}` answers with `GRAPH_COMPLETION`, while the same query with `"search_type": null` routes to `GRAPH_COMPLETION_COT`.

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`):

```bash 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).

--- 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:

```yaml 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:

```bash 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:

```bash 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`. It defaults `search_type` to `GRAPH_COMPLETION` for backward compatibility — pass `"search_type": null` to opt into auto-routing (the SDK `recall()` default). The difference is real: `{"query": "Why does X?"}` answers with `GRAPH_COMPLETION`, while the same query with `"search_type": null` routes to `GRAPH_COMPLETION_COT`.

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`):

```bash 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).

--- 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:

```yaml 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:

```bash 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:

```bash 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`. It defaults `search_type` to `GRAPH_COMPLETION` for backward compatibility — pass `"search_type": null` to opt into auto-routing (the SDK `recall()` default). The difference is real: `{"query": "Why does X?"}` answers with `GRAPH_COMPLETION`, while the same query with `"search_type": null` routes to `GRAPH_COMPLETION_COT`.

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`):

```bash 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).

--- 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:

```yaml 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:

```bash 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:

```bash 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`. It defaults `search_type` to `GRAPH_COMPLETION` for backward compatibility — pass `"search_type": null` to opt into auto-routing (the SDK `recall()` default). The difference is real: `{"query": "Why does X?"}` answers with `GRAPH_COMPLETION`, while the same query with `"search_type": null` routes to `GRAPH_COMPLETION_COT`.

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`):

```bash 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).