Use and contribute cognee-community packages

Community-maintained plugins live in a separate monorepo: https://github.com/topoteretes/cognee-community. Everything installable is under packages/; experimental/ holds demos (n8n nodes, dlt demos, bauplan, tower) that are not published packages. Each package publishes to PyPI as cognee-community-<family>-<kind>-<name> and imports as the same name with underscores.

Package families

Family	Packages
Vector adapters	azureaisearch, milvus, moss, opengauss, opensearch, pinecone, qdrant, redis, singlestore, turbopuffer, valkey, weaviate
Graph adapters	arcadedb, memgraph, networkx, pggraph, spanner, turbopuffer, turingdb
Hybrid (graph+vector in one DB)	arcadedb, duckdb, falkordb, helixdb
Connectors (data sources)	confluence, gmail, google-drive, notion, slack
Tasks / pipelines / retrievers	codify_tasks, codify_pipeline, code_retriever, exa_tasks, scrapegraph_tasks
Observability	keywordsai (MONITORING_TOOL=keywordsai + KEYWORDSAI_API_KEY)

Using a database adapter

Install, then import the package's register module before cognee touches any engine — registration is what makes the provider name valid:

uv pip install cognee-community-vector-adapter-qdrant

import cognee
from cognee import config
from cognee_community_vector_adapter_qdrant import register  # noqa: F401

config.set_vector_db_config(
    {
        "vector_db_provider": "qdrant",
        "vector_db_url": "http://localhost:6333",
        "vector_db_key": "...",
        "vector_dataset_database_handler": "qdrant",  # only if the adapter ships one
    }
)

The register.py calls use_vector_adapter(name, AdapterClass) / use_graph_adapter(...). Setting VECTOR_DB_PROVIDER/GRAPH_DATABASE_PROVIDER to a community name without the register import raises "Unsupported vector database provider". Hybrid adapters (e.g. falkordb) register as both graph and vector — set both configs to the same provider name.

Multi-tenancy caveat: with ENABLE_BACKEND_ACCESS_CONTROL=true (the default), both backends must have a dataset-database handler or cognee raises EnvironmentError. Community adapters that ship one (registered via use_dataset_database_handler in their register.py): qdrant, moss, singlestore, turbopuffer (vector + graph), falkordb, arcadedb, helixdb. All other community adapters need ENABLE_BACKEND_ACCESS_CONTROL=false.

Using a connector

Connectors expose a dlt source you hand straight to remember(); they reuse core's DLT ingestion path, so snapshot sync and forget-on-delete work with no core changes:

from cognee_community_connector_slack import slack_export_source

await cognee.remember(
    slack_export_source("/path/to/slack-export"),
    dataset_name="team-slack-export",  # use a dedicated dataset
    max_rows_per_table=0,
)

Same shape for gmail ("ask my inbox"), notion, confluence, and google-drive (incremental, forget-on-delete). Each package README documents its credentials; always give a connector its own dataset.

Verifying an install

Every package has examples/example.py (run uv run python examples/example.py from the package dir) and a tests/ directory. An LLM API key is still required (LLM_API_KEY, OpenAI by default).

Contributing a package

• Branch from main — unlike the core repo, cognee-community does not use a dev branch.
• Follow the existing structure: package dir under packages/<family>/<name>/ with pyproject.toml, a README.md (install + usage), examples/example.py, and tests/ that go beyond the example.
• New DB adapters implement VectorDBInterface / GraphDBInterface from core, expose a register.py, and should run the shared conformance tests in packages/shared/contract_suite/ (vector_contract.py / graph_contract.py).
• Add a handler via use_dataset_database_handler(...) if the backend can isolate per user+dataset — that's what makes it work with access control on.
• Name it cognee-community-<family>-<kind>-<name> and add it to the tables in the repo README. Lint config is the repo-root ruff.toml.