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Community-maintained integrations are adapters built and maintained by the Cognee community. These extend Cognee’s functionality with additional providers and services.
Community integrations are maintained separately from the core Cognee package. For issues or contributions, visit the cognee-community repository.
Everything installable lives under packages/ in that repository. The repository’s experimental/ directory (n8n nodes, dlt demos, bauplan, tower) holds demos, not published packages.

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Available Integrations

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Vector Stores

  • Qdrant — High-performance vector search engine
  • Redis — Fast vector similarity search via Redis Search module
  • Milvus — Cloud-native vector database (docs coming soon)
  • Pinecone — Managed vector database
  • Weaviate — Open-source vector search engine (docs coming soon)
  • Azure AI Search — Azure cognitive search service (docs coming soon)
  • OpenSearch — OpenSearch vector engine (docs coming soon)
  • Turbopuffer — High-performance vector database
  • MOSS (docs coming soon)
  • openGauss (docs coming soon)
  • SingleStore (docs coming soon)
  • Valkey (docs coming soon)

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Hybrid Stores

Hybrid adapters back both the graph and the vector store with a single database.
  • DuckDB — In-process analytical database (docs coming soon)
  • FalkorDB — Graph database with vector support (docs coming soon)
  • ArcadeDB (docs coming soon)
  • HelixDB (docs coming soon)

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Graph Stores

  • Memgraph — In-memory graph database
  • NetworkX — Python graph library adapter (docs coming soon)
  • ArcadeDB (docs coming soon)
  • pggraph — Graph store on Postgres (docs coming soon)
  • Spanner (docs coming soon)
  • Turbopuffer — Turbopuffer also ships a graph adapter (docs coming soon)
  • TuringDB (docs coming soon)

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Connectors (data sources)

Connectors expose a dlt source you pass straight to remember(), reusing Cognee’s DLT ingestion path — so snapshot sync and forget-on-delete work without any core changes. Give each connector its own dataset. See the dlt integration guide for setup, credentials, and ingestion patterns.
  • Slack
  • Gmail
  • Notion
  • Confluence
  • Google Drive

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Tasks, Pipelines and Retrievers

  • codify_tasks, codify_pipeline, code_retriever — code graph extraction and retrieval
  • exa_tasks — Exa search tasks
  • scrapegraph_tasks — ScrapeGraphAI scraping tasks

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Observability

  • KeywordsAI — LLM monitoring and analytics; enable with MONITORING_TOOL=keywordsai and KEYWORDSAI_API_KEY (docs coming soon)

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Installing a community adapter

Community packages generally publish to PyPI as cognee-community-<family>-<kind>-<name> and import under the same name with underscores — for example cognee-community-vector-adapter-qdrant installs the module cognee_community_vector_adapter_qdrant. A few packages deviate slightly (connectors drop the kind, e.g. cognee-community-connector-slack), so check the package’s pyproject.toml or README for the exact name. Installing is only half the job: the provider name is not valid until the package registers itself, which happens through the package’s register module. Registration lives in process memory, so it must run in every process, before Cognee touches any engine.
Under the hood, register calls use_vector_adapter(name, AdapterClass) or use_graph_adapter(...) to add the provider to Cognee’s registry. Setting VECTOR_DB_PROVIDER or GRAPH_DATABASE_PROVIDER to a community name without registering raises:
Hybrid adapters register as both a graph and a vector adapter, so set both configurations to the same provider name.
Multi-tenancy caveat. With ENABLE_BACKEND_ACCESS_CONTROL=true (the default), both the graph and the vector backend must have a dataset-database handler, or Cognee raises an EnvironmentError. Core ships handlers only for its in-tree backends, so a community adapter works in this mode only if it registers its own handler via use_dataset_database_handler in its register.py.Community adapters that ship a handler today: Qdrant, MOSS, SingleStore, Turbopuffer (both its vector and its graph adapter), FalkorDB, ArcadeDB (the hybrid package — the graph-only adapter ships none), and HelixDB. Every other community adapter requires ENABLE_BACKEND_ACCESS_CONTROL=false. The list moves as packages are updated — the definitive check is grepping the adapter’s register.py for use_dataset_database_handler.When an adapter does ship a handler, select it by its registered name — vector_dataset_database_handler in set_vector_db_config() (or VECTOR_DATASET_DATABASE_HANDLER), and the graph equivalent. See dataset database handlers for how handlers are selected and registered.

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Verifying an install

Many packages under packages/ ship an examples/example.py plus a tests/ directory that goes beyond the example. Where the package provides one, run the example from the package directory to confirm your install and configuration end to end:
An LLM API key is still required for these runs — LLM_API_KEY, OpenAI by default — because the example exercises the full ingestion and retrieval flow, not just the adapter.

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Contributing

To contribute a new community integration, work in the cognee-community repository:
  1. Branch from main. Unlike the core Cognee repo, cognee-community has no dev branch.
  2. Follow the existing package layout: a directory under packages/<family>/<name>/ with pyproject.toml, a README.md covering install and usage, examples/example.py, and tests/.
  3. For a new database adapter, implement VectorDBInterface or GraphDBInterface from core, expose a register.py, and run the shared conformance suite in packages/shared/contract_suite/ (vector_contract.py / graph_contract.py).
  4. Register a dataset-database handler with use_dataset_database_handler(...) if your backend can isolate per user + dataset — that is what makes it usable with access control enabled.
  5. Name the package cognee-community-<family>-<kind>-<name> and add it to the tables in the repository README. Lint with the repo-root ruff.toml.
  6. Open a pull request with your integration and its documentation.
The vector and graph adapter guides walk through these steps in detail.

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Support

For community integration support:
  • Check the integration’s README in the repository
  • Open issues in the cognee-community repository
  • Join the Discord community for help

Vector Stores

Official vector store providers

Setup Overview

Configuration overview