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cognee-forget

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 d…

Install / Use

npx skills add topoteretes/cognee --skill cognee-forget

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

96/100

Category

Automation

Supported Platforms

Universal

Our assessment of cognee-forget

cognee-forget scores 96/100 on our quality scale, 206th of 2,893 Automation skills we index (top 8%).

Its SKILL.md is 7.5 KB long, well organised into 9 sections with 3 code examples: a thorough specification that gives an agent plenty to work with.

With 30,958 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
18/20
Description
15/15
Adoption
19/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 13 days ago, so cognee-forget is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

cognee-forget compared with similar skills

All 4 of these similar skills score higher than cognee-forget; compare them before choosing.

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Frequently asked questions

How do I install cognee-forget?
Run npx skills add topoteretes/cognee --skill cognee-forget. The install tabs above show the steps for each supported agent.
Which AI agents does cognee-forget work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is cognee-forget safe to use?
It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is cognee-forget still maintained?
The repository was last updated 13 days ago, so cognee-forget is actively maintained.

name: cognee-forget description: 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.

Remove data with forget()

forget() is cognee's one deletion API. It removes one document, a whole dataset, or only the derived memory (graph + vectors) while keeping the raw files. Deletion cannot be undone, so the workflow is always find, read, confirm, then delete.

Hard limits for agents

  • Delete only what the user asked to forget. Identify it by reading the content first; never guess from a file name alone.
  • Confirm the exact items with the user before deleting, unless they already named exact ids.
  • The widest deletion you may run is forget(everything=True), and only when the user explicitly asks to wipe all of their memory. Never use any other reset or wipe mechanism to delete data.

Use it

1. Find the dataset

import cognee

datasets = await cognee.datasets.list_datasets()  # datasets the user can read
for ds in datasets:
    print(ds.id, ds.name)

HTTP: GET /api/v1/datasets. CLI: cognee-cli datasets list.

2. List its documents

items = await cognee.datasets.list_data(dataset_id)  # all Data rows, oldest first
for item in items:
    print(item.id, item.name, item.extension, item.created_at)

HTTP: GET /api/v1/datasets/{dataset_id}/data?limit=100&offset=0 (limit up to 1000; GET .../data/count for the total; example: examples/python/dataset_data_pagination.py). CLI: cognee-cli datasets data <dataset_uuid>.

Each item has id, name, created_at, extension, mime_type, raw_data_location, dataset_id, label, external_metadata (including any node_set), and data_size.

3. Read the content before deciding

Names are often text_<hash>.txt, so read the content to find what the user means.

  • HTTP: GET /api/v1/datasets/{dataset_id}/data/{data_id}/raw returns the stored file (404 if it is gone).
  • SDK: there is no "get raw" helper; open the stored location:
from cognee.infrastructure.files.utils.open_data_file import open_data_file

async with open_data_file(item.raw_data_location, mode="rb") as f:
    preview = f.read(2000).decode("utf-8", errors="replace")

Judge matches by meaning, not only by keywords, and show the user the candidates (name + a short preview) before deleting.

4. Delete

| Goal | Call | What remains | |---|---|---| | One document | forget(data_id=..., dataset_id=...) (or dataset="name") | Nothing of that document; shared entities stay while another document still references them | | A whole dataset | forget(dataset="name") or forget(dataset_id=...) | The dataset is deleted outright: the record, its data rows, graph and vector stores, and attributed sessions | | Rebuild a dataset's graph later | forget(dataset="name", memory_only=True) | Raw files and data rows; graph, vectors, sessions and pipeline status are reset, so the data can be re-processed | | One document's memory only | forget(dataset="name", data_id=..., memory_only=True) | That document's raw file and row | | Every dataset the user can delete | forget(everything=True) | Nothing, in any dataset the user has delete on in the current tenant (shared ones included). Only on explicit request (see the hard limits) |

Return values: {"data_id", "dataset_id", "status"} for a document, {"dataset_id", "status"} for a dataset (plus data_records_reset with memory_only), {"datasets_removed", "status"} for everything (datasets_removed counts the datasets the user can read, not the delete set).

HTTP: POST /api/v1/forget with a JSON body; camelCase and snake_case keys both work: {"datasetId": "...", "dataId": "..."}, {"dataset": "name", "memoryOnly": true}, {"everything": true}. Invalid combinations return 422.

CLI: cognee-cli forget --dataset NAME | --dataset-id UUID [--data-id UUID] [--memory-only], or --everything / --all. The CLI does not ask for confirmation; confirm with the user first.

Pitfalls

  • A data_id that is not in the dataset returns success and deletes nothing. The delete path treats an unknown id as a custom-graph-model delete. Always take the id from list_data for that same dataset, and check it is still listed afterwards if it matters.
  • Pass either dataset or dataset_id, not both (ValueError). data_id and memory_only both need a dataset.
  • memory_only is ignored when everything=True in the SDK (the CLI rejects the combination). everything=True always deletes everything.
  • Not found and not allowed look the same. An unknown dataset name and one the user cannot delete both raise DatasetNotFoundError. Deleting needs the delete permission on the dataset (see the cognee-permissions skill). everything=True deletes every dataset the user has delete permission on in the current tenant, including datasets shared to them with delete rights, not only the ones they own.
  • Sessions that cited deleted data are invalidated so recall stops returning answers built on it. Agent-trace entries are not invalidated.
  • Changing a document is not a delete. To replace a document's content, use cognee.update(data_id=..., data=..., dataset_id=...), which keeps its id and re-extracts only the changed parts.
  • cognee.delete() is deprecated; use forget().

How it works

forget() resolves the dataset with the delete permission, then:

  • One document → datasets.delete_data(): takes the dataset lock, deletes the graph nodes and edges the document owns, the matching vectors and edge evidence, invalidates sessions that cited them, then deletes the Data row. Ownership is tracked per document (source-refs on graph elements), so an entity shared by two documents survives until both are deleted. Raw files are reference-counted by storage location.
  • A dataset → datasets.empty_dataset().
  • memory_only → drops the dataset's graph/vector memory and resets its pipeline status, leaving raw data for a rebuild.
  • everything → datasets.delete_all() over every dataset the user has delete on in the current tenant, plus a full prune of the session cache (when caching or usage logging is on). This wipes every user's sessions (Redis FLUSHDB / the whole fs cache / all SQL cache tables), not just this user's.

Key files:

  • cognee/api/v1/forget/forget.py (SDK), routers/get_forget_router.py (HTTP), cognee/cli/commands/forget_command.py (CLI)
  • cognee/api/v1/datasets/datasets.py (list_datasets, list_data, delete_data, empty_dataset, delete_all)
  • cognee/api/v1/datasets/routers/get_datasets_router.py (list, count, raw)
  • cognee/infrastructure/databases/provenance/source_refs.py (per-document ownership of graph elements)
  • cognee/infrastructure/files/utils/open_data_file.py

Extending it

  • Anything new that writes graph nodes or edges for a document must record its source-refs, or forget(data_id=...) cannot find and remove it.
  • Anything new that stores per-document data outside the graph (like edge evidence) needs a cleanup step in the delete path and in memory_only.
  • Unit tests for forget() are in cognee/tests/unit/api/v1/forget/ (argument validation, the HTTP endpoint, memory_only); cover both the document and the memory_only paths for new deletion behaviour.

Related Skills

View on GitHub
GitHub Stars31.0k
CategoryAutomation
Updated13d ago
Forks3.1k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions