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Cognee Forget

OrganizationPopular
topoteretes
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 doing it safely.

Overview

Publishertopoteretes
Repositorycognee
Skill namecognee-forget
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 Forget 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-forget
  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-forget .claude/skills/cognee-forget
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cognee Forget 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 Forget 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 Forget 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.

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

python
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

python
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:
python
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

GoalCallWhat remains
One documentforget(data_id=..., dataset_id=...) (or dataset="name")Nothing of that document; shared entities stay while another document still references them
A whole datasetforget(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 laterforget(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 onlyforget(dataset="name", data_id=..., memory_only=True)That document's raw file and row
Every dataset the user can deleteforget(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.

Frequently asked questions

What does the Cognee Forget AI skill do?

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.

Why use Cognee Forget on TypingMind?

Because you install it once and use it with any model. Cognee Forget 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 Forget in TypingMind?

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

Which AI models can use Cognee Forget?

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 Forget?

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

Is the Cognee Forget 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.

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