Use when the user wants to drive cognee from the terminal with cognee-cli — remember/recall/forget/improve memory commands, managing datasets and config, or database migrations.
日本語の概要は準備中です。原文の説明を表示しています。
topoteretes/cognee3.2万2026年10月10日 更新
無料GitHub で公開
cognee-integrations
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.
含まれるファイル（1）
- SKILL.md3.9 KB
SKILL.md（原文）
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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.
Databases
- Relational (
DB_PROVIDER): sqlite (default) or postgres (cognee[postgres]; host/port/user/password/name viaDB_*vars). - Vector (
VECTOR_DB_PROVIDER): lancedb (default), pgvector (cognee[postgres], needsVECTOR_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 withuse_vector_adapterbefore use; settingVECTOR_DB_PROVIDERalone 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 pointDATA_ROOT_DIRECTORY/SYSTEM_ROOT_DIRECTORYats3://paths. - Session cache:
CACHE_BACKEND= sqlite (default) | postgres | redis | fs | tapes. - Ontologies:
ONTOLOGY_FILE_PATHto an OWL file, resolver/matching viaONTOLOGY_RESOLVER/MATCHING_STRATEGY.
MCP server (IDE integration)
docker compose --profile mcp up starts the MCP server on port 8001
(Streamable HTTP at http://localhost:8001/mcp), 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.
レビュー
まだレビューはありません。使ってみた感想をお寄せください。
同じリポジトリのスキル
概要と使いどころ
Use when the user needs something that ships outside cognee core — community database adapters (Qdrant, Milvus, Weaviate, Redis, Pinecone, FalkorDB, Memgraph, DuckDB, NetworkX, …), data-source connectors (Slack, Gmail, Notion, Confluence, Google Drive), custom tasks/pipelines/retrievers (Exa, ScrapeGraph, codify), Keywords AI observability — or wants to contribute a package to the cognee-community repo.
日本語の概要は準備中です。原文の説明を表示しています。
topoteretes/cognee3.2万2026年10月10日 更新
Use when defining the shape of cognee's knowledge graph with graph_model= — writing DataPoint node classes, choosing identity and index fields so nodes merge and are searchable, declaring typed Edge fields and FromIdentity references, building a model from a JSON schema, or debugging duplicated nodes, missing edges, or InvalidReferenceTypeError.
日本語の概要は準備中です。原文の説明を表示しています。
topoteretes/cognee3.2万2026年10月10日 更新
Use when building your own cognee processing — writing custom tasks, chaining them into a pipeline with run_custom_pipeline or the lightweight run_pipeline (from cognee.pipelines import run_pipeline), storing custom DataPoints with add_data_points, running custom extraction/enrichment over the existing graph with memify, checking pipeline run status, or debugging how data flows between tasks (batch_size, data_per_batch, ctx, Drop, enriches).
日本語の概要は準備中です。原文の説明を表示しています。
topoteretes/cognee3.2万2026年10月10日 更新
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.
日本語の概要は準備中です。原文の説明を表示しています。
topoteretes/cognee3.2万2026年10月10日 更新
Use when removing data from cognee memory with forget() in the SDK, HTTP API, or CLI — finding which dataset and document hold the content to delete (listing datasets and data items, reading raw content), choosing between deleting one document, a whole dataset, or only the graph/vector memory, and doing it safely.
日本語の概要は準備中です。原文の説明を表示しています。
topoteretes/cognee3.2万2026年10月10日 更新