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.