| name | cognee-integrations |
| description | Use when the user wants to connect cognee to external services — switching LLM or embedding providers (OpenAI, Azure, Gemini, Anthropic, Ollama, OpenRouter), changing databases (Postgres, PGVector, Neo4j, Neptune, Turso), S3 storage, or the MCP server for IDE integration. |
Set up cognee integrations
All integration config is environment variables (.env). The authoritative,
always-current list with commented examples is .env.template at the repo
root — check it before inventing variable names. Install the matching extra
before switching a backend (e.g. pip install cognee[postgres]).
LLM providers
Default is OpenAI (LLM_API_KEY is all you need). To switch, set
LLM_PROVIDER, LLM_MODEL, LLM_API_KEY, and (where relevant)
LLM_ENDPOINT / LLM_API_VERSION:
- Azure OpenAI:
LLM_PROVIDER=azure, LLM_MODEL=azure/gpt-4o-mini, endpoint + api version required.
- Gemini (no extra needed):
LLM_PROVIDER=gemini, LLM_MODEL=gemini/gemini-2.0-flash-exp.
- Anthropic (
cognee[anthropic]): LLM_PROVIDER=anthropic, model e.g. claude-3-5-sonnet-20241022.
- Ollama, local (
cognee[ollama]): LLM_PROVIDER=ollama, LLM_ENDPOINT=http://localhost:11434/v1, and set the embedding block + HUGGINGFACE_TOKENIZER too.
- Custom / OpenRouter / vLLM:
LLM_PROVIDER=custom with the provider's OpenAI-compatible endpoint.
- AWS Bedrock (
cognee[aws]): LLM_PROVIDER=bedrock + AWS credentials/region.
The classic trap: LLM and embeddings are configured independently
(EMBEDDING_PROVIDER, EMBEDDING_MODEL, EMBEDDING_ENDPOINT,
EMBEDDING_API_KEY). Configuring only one leaves the other on OpenAI —
either keep a valid OpenAI key or configure both.
Databases
- Relational (
DB_PROVIDER): sqlite (default) or postgres
(cognee[postgres]; host/port/user/password/name via DB_* vars).
- Vector (
VECTOR_DB_PROVIDER): lancedb (default), pgvector
(cognee[postgres], needs VECTOR_DB_URL), neptune_analytics
(cognee[neptune]), turso (cognee[turso]). Anything else (ChromaDB,
Qdrant, Weaviate, Milvus, …) lives in community adapters — install from
https://github.com/topoteretes/cognee-community and register with
use_vector_adapter before use; setting VECTOR_DB_PROVIDER alone raises
"Unsupported vector database provider".
- Graph (
GRAPH_DATABASE_PROVIDER): ladybug (default), neo4j
(cognee[neo4j], bolt URL + credentials), neptune (cognee[neptune]),
ladybug-remote, postgres (no raw Cypher / natural-language search).
The repo docker-compose.yml ships ready-to-use postgres (pgvector) and
neo4j profiles with matching default credentials. From a container, reach
host services with DB_HOST=host.docker.internal.
Storage, cache, and the rest
- S3 storage (
cognee[aws]): STORAGE_BACKEND=s3 + bucket/credentials,
and point DATA_ROOT_DIRECTORY/SYSTEM_ROOT_DIRECTORY at s3:// paths.
- Session cache:
CACHE_BACKEND = sqlite (default) | postgres | redis | fs | tapes.
- Ontologies:
ONTOLOGY_FILE_PATH to an OWL file, resolver/matching via
ONTOLOGY_RESOLVER / MATCHING_STRATEGY.
MCP server (IDE integration)
docker compose --profile mcp up starts the MCP server on port 8001
(SSE transport), built from cognee-mcp/. Point Cursor / Claude Desktop /
Claude Code at it to use cognee memory from the IDE. Configure its DB_* env
to match the main service so both see the same data.
After changing providers mid-project
Embeddings from different models are not comparable — after switching the
embedding provider or model, reset local state (cognee-cli forget --all or
await cognee.forget(everything=True)) and re-ingest with remember().
To drop just the graph and vectors while keeping the ingested files, use
await cognee.forget(dataset="my_project", memory_only=True) — the dataset can
then be rebuilt under the new embedding model without re-uploading anything.
