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

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 usevectoradapter(name, AdapterClass) / usegraphadapter(...). Setting VECTORDBPROVIDER/GRAPHDATABASEPROVIDER 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 ENABLEBACKENDACCESSCONTROL=true (the default), both backends must have a dataset-database handler or cognee raises EnvironmentError. Community adapters that ship one (registered via usedatasetdatabasehandler in their register.py): qdrant, moss, singlestore, turbopuffer (vector + graph), falkordb, arcadedb, helixdb. All other community adapters need ENABLEBACKENDACCESS_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 (LLMAPIKEY, 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/contractsuite/ (vectorcontract.py / graph_contract.py).

• Add a handler via usedatasetdatabase_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.