本文へ移動
cccskills
無料GitHub で公開

cognee-migrations

Use when dealing with cognee database migrations — understanding when they run automatically, checking or repairing migration state with cognee-cli upgrade/downgrade/stamp/current, a write blocked by a failed migration, authoring a new Alembic (relational schema) revision or a graph/vector data migration, or moving data between systems (relational DB import, memory export/import).

インストール方法を見る

含まれるファイル(1)

  • SKILL.md10.2 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。

Database migrations

cognee has two migration chains, run together:

ChainChangesLives inRevision stored in
Relational schema (Alembic)Tables and columns of the relational DB (users, datasets, ACLs, pipeline runs, …)cognee/alembic/ (alembic.ini is in cognee/)alembic_version table
Graph/vector dataCross-store data rewrites (re-keying node ids, adding graph columns) across graph DB, vector DB and relational ledgercognee/modules/migrations/ (registry.py, versions/)Per dataset: dataset_database.migration_revision (access control on). Globally: global_database_version.global_migration_revision (access control off)

Use it

They run by themselves

run_migrations() applies the relational chain first, then the data chain. It runs:

  • at API server startup (and in the Docker entrypoint.sh before gunicorn);
  • on the first write in an SDK or CLI process (remember, add, cognify, improve, memify, memory imports), once per process;
  • when you call await cognee.run_migrations().

A fresh database is built by running the whole chain (no stamping). At head, the first run in each process still does a no-op Alembic upgrade plus a scan of the per-database revision rows; later calls in the same process are skipped by an in-memory flag. ENABLE_AUTO_MIGRATIONS=false turns off all automatic runs; then run cognee-cli upgrade yourself.

Concurrent processes are serialized by a migration lock: a Postgres advisory lock (works across hosts) or a file lock next to the SQLite DB (one host only).

Check and repair

cognee-cli current                   # stamped revision per database, and the last failure
cognee-cli history                   # the data chain, newest first
cognee-cli upgrade                   # relational to head, then data chain to head
cognee-cli upgrade <slug>            # data chain up to and including <slug>
cognee-cli upgrade --alembic <rev>   # pin the relational target
cognee-cli downgrade <slug|base> [--dataset UUID ...] [--alembic REV] [--force]
cognee-cli stamp <head|base|slug> [--dataset UUID ...] [--force]
  • The positional revision is always a data-chain slug; relational targets go through --alembic.
  • downgrade rewrites data and asks for confirmation. It only reverts spans where every migration defines a down(), and leaves the relational schema alone unless you pass --alembic.
  • stamp changes only the stored data-chain revision, without running anything. Use stamp base --dataset <id> and then upgrade when a database's data drifted from its stamp (for example after restoring a backup); the chain is idempotent and converges it.
  • upgrade runs even with ENABLE_AUTO_MIGRATIONS=false.

A write is blocked

If a dataset's data migration failed, writes to that dataset are refused until it succeeds (with access control off, any failure blocks all writes). The server still starts. Run cognee-cli current to see the error, fix the cause, then cognee-cli upgrade. A failed run is retried on the next start or write.

Moving data between systems (not schema migrations)

GoalUse
Turn an existing relational database into a graphmigrate_relational_database(graph_db, schema) (cognee/tasks/ingestion/migrate_relational_database.py), with the source DB set by MIGRATION_DB_PROVIDER / _PATH / _NAME / _HOST / _PORT / _USERNAME / _PASSWORD. Examples: examples/demos/ingestion_and_migration/
Back up a dataset, or move it to another cognee instanceA COGX archive, see below
Export a dataset's graph for other toolsawait cognee.export(dataset, format=...): "json", "graphml" or "cypher" write a file (one way: cognee can't import them back); "pydantic" (default) returns typed DataPoint objects in memory
Import from another memory system (Mem0, Zep/Graphiti, Letta, LangMem)Build a MemorySource (cognee/modules/migration/sources/) and pass it to await cognee.remember(source, dataset_name=...)

There is no tool that moves a whole deployment from one database backend to another.

COGX archives

COGX (Cognee eXchange, cognee/modules/migration/cogx.py) is cognee's portable memory format and the only export format cognee can import back. An archive is a directory with a manifest.json (COGX version, source system, the dataset's data-migration revision) and one JSONL file per record kind (documents, episodes, entities, facts, memories, memory_blocks), plus nodes.jsonl with the raw graph nodes. The Mem0, Zep, Letta and LangMem importers also translate into COGX records first.

Use it to back up and restore a dataset, or to copy one to another cognee instance:

from cognee.migration import COGXArchiveSource

await cognee.export("my_dataset", format="cogx", destination="backup_cogx")
await cognee.remember(COGXArchiveSource("backup_cogx"), dataset_name="my_dataset")
  • A restore defaults to mode="preserve": the archived graph is written back as-is, with no LLM calls. mode="hybrid" also re-cognifies the raw content; mode="re-derive" ignores the archived graph and extracts again (costs LLM tokens).
  • cognee.push() / cognee-cli push does the same to Cognee Cloud: it exports to COGX, packs it as a .cogx.tar.gz and uploads it, and the receiving instance restores it (preserve mode unless you pass mode=).
  • export(..., include_permissions=True) also writes permissions.json with the dataset owner and ACL grants, including password hashes, so the restore recreates working accounts. Treat that archive as a secret.
  • An archive written by a newer major COGX version is rejected with a ValueError; upgrade cognee on the importing side.

Pitfalls

  • Never regenerate cognee/alembic/frozen_schema.py. It is the certified base schema the initial revision builds from, pinned by cognee/tests/unit/test_frozen_schema_seal.py. Schema changes ship as new revisions at head.
  • Never hand-type an Alembic revision id. Hand-typed patterns (a1b2c3d4e5f6, …) already collided with a downstream chain that vendors this one (b2c3d4e5f6a7). Generate ids with alembic revision.
  • Never rename, remove, or reorder a data-chain entry. The slug is what deployed databases store; an unknown stored slug disables the chain for that database.
  • A model change and its migration land together. CI's "Migration/Model Lockstep Guard" fails otherwise.
  • The relational schema cannot be downgraded below the data-bookkeeping revisions unless the data chain goes to base in the same call.
  • alembic.ini is always the packaged one; COGNEE_ALEMBIC_PATH or --alembic-path only changes the scripts directory (for vendored chains).

How it works

run_migrations() (cognee/modules/migrations/startup.py) takes the migration lock, decides fresh vs existing (a users or alembic_version table exists), runs Alembic in-process on a worker thread, then walks the data chain per database with runner.run_database_migrations, stamping after every step. After the chain, it syncs vector-adapter storage when the recorded cognee_version differs from the library's (versions/adapter_storage_migration.py, not a chain entry).

  • Relational: cognee/alembic.ini, cognee/alembic/env.py, cognee/alembic/versions/, cognee/alembic/frozen_schema.py
  • Data chain: cognee/modules/migrations/ (README.md is the authoring contract; registry.py, migration.py, runner.py, startup.py, versions/)
  • CLI: cognee/cli/commands/migrate_command.py

Extending it

A new Alembic revision

cd cognee                              # the directory with alembic.ini
uv run alembic revision -m "add foo to data"

The DB URL comes from the live relational engine, so your normal .env settings apply. Follow the recent revisions (for example versions/e7f9a1c3d5b8_add_data_dataset_created_index.py):

  • Idempotent and guarded: inspect first (sa.inspect(op.get_bind())) and skip when the table is missing or the column/index already exists.
  • Branch on dialect (conn.dialect.name == "postgresql") for Postgres-only SQL. For Postgres enums use postgresql.ENUM(..., create_type=False) and create the type up front with checkfirst.
  • SQLite cannot drop or alter columns in place: use op.batch_alter_table(...).
  • Indexes: plain CREATE INDEX IF NOT EXISTS inside the migration transaction, not CONCURRENTLY (which releases the version-row lock and lets concurrent workers into the same build). Repair invalid Postgres indexes via pg_index.indisvalid.
  • A new model module outside the usual import path must be imported in cognee/alembic/env.py, or autogenerate will not see it.

A new data migration

Read cognee/modules/migrations/README.md first. In short:

  1. Write a module in cognee/modules/migrations/versions/ with async def migrate(context) (and optionally async def downgrade(context)); step 2 registers them as up= / down=.
  2. Append Migration(slug=..., cognee_version=..., up=..., down_revision=<previous slug>, down=...) to MIGRATIONS in registry.py. The chain is validated at import (linear, unique slugs).
  3. Make it idempotent and cheap on empty stores, and crash-safe: re-key derived stores (vectors, ledger) first and rename in the graph last.
  4. Freeze private copies of any logic you depend on; never import live models that may change later.

Tests

  • cognee/tests/e2e/migrations/test_migration_model_lockstep.py (+ schema_baseline.json): the lockstep CI job.
  • cognee/tests/unit/test_run_migrations.py: single Alembic head, startup behaviour.
  • cognee/tests/unit/test_frozen_schema_seal.py: frozen schema fingerprint.
  • cognee/tests/unit/modules/migrations/: data-chain unit tests.
  • cognee/tests/migrations/test_migration_lockstep.py: data chain against real stores (seed, down, up, verify).

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 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13日 更新

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13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13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13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13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13日 更新

topoteretes のスキルをすべて見る

このスキルの問題を報告する