Cognee Integrations
Use when the user wants to connect cognee to external services, switching LLM or...
Install
npx skills add topoteretes/cognee --skill cognee-integrations -g -a claude-code -y- Repository
- topoteretes/cognee33k
- Category
- AI and agents
- Updated
- 3d ago
- License
- Apache-2.0
- Context cost
- Lightabout 921 tokens
- Runs scripts
- No, instructions only
What it does
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_TOKENIZERtoo. - Custom / OpenRouter / vLLM:
LLM_PROVIDER=customwith 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.
