Cognee Migrations logo

Cognee Migrations

OrganizationPopular
topoteretes
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).

Overview

Publishertopoteretes
Repositorycognee
Skill namecognee-migrations
Stars
32K
Forks
3.3K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by topoteretes on GitHub. Read the source before you install it.

Installation

Install the Cognee Migrations AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/topoteretes/cognee/tree/main/.agents/skills/cognee-migrations
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/topoteretes/cognee.git /tmp/cognee
mkdir -p .claude/skills
cp -r /tmp/cognee/.agents/skills/cognee-migrations .claude/skills/cognee-migrations
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cognee Migrations in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cognee Migrations on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cognee Migrations is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

bash
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:

python
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

bash
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).

Frequently asked questions

What does the Cognee Migrations AI skill do?

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).

Why use Cognee Migrations on TypingMind?

Because you install it once and use it with any model. Cognee Migrations is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cognee Migrations in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/topoteretes/cognee/tree/main/.agents/skills/cognee-migrations. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cognee Migrations?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cognee Migrations?

As many as you like. As long as a model supports skills, you can use Cognee Migrations with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cognee Migrations AI skill free?

Yes. It is published on GitHub by topoteretes under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇