Cognee is an AI memory platform that stores information in a self-hosted knowledge graph so agents can retain context across sessions. It ingests data, connects related information, and helps agents retrieve it for reasoning and actions. The catalogue includes skills and instructions that extend agent workflows around Cognee.
Borrowing it
Nothing to install: this file belongs to topoteretes/cognee. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/topoteretes/cognee/main/.agents/skills/cognee-forget/SKILL.mdgit clone --depth 1 https://github.com/topoteretes/cogneeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/topoteretes/cognee/cognee-forget)<a href="https://agentmods.dev/skills/topoteretes/cognee/cognee-forget"><img src="https://agentmods.dev/badge/skills/topoteretes/cognee/cognee-forget/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/topoteretes/cognee/cognee-forget"><img src="https://agentmods.dev/badge/skills/topoteretes/cognee/cognee-forget.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00070 | $0.02420 |
| Opus 5.5 | $0.00028 | $0.00968 |
| Sonnet 5.5 | $0.00014 | $0.00484 |
| Haiku 4.5 | $0.00007 | $0.00242 |
Grade A, and why
cognee-forget scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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}/rawreturns 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")
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago Changed · +25 lines ea46a4168f19
- 1mo ago First seen · 159 lines · 70 tokens per session scan A e7011313def0
cognee-forget is a skill published in the GitHub repository topoteretes/cognee (33,094 stars, last pushed today), licensed Apache-2.0. It adds 70 tokens to every session and 2,420 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-30.
cognee-forget compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| cognee-forgettopoteretes/cognee | Skill | 33k | today | Apache-2.0 | A | 70 | Claude Code, Codex |
| openmemoryCaviraOSS/OpenMemory | Skill | 4.6k | 19d ago | Apache-2.0 | A | 96 | Claude Code, Codex |
| memseek-remembermemseekai/memseek | Skill | 11 | yesterday | Apache-2.0 | A | 24 | Claude Code |
| memseek-explainmemseekai/memseek | Skill | 11 | yesterday | Apache-2.0 | A | 25 | Claude Code |
| memseek-searchmemseekai/memseek | Skill | 11 | yesterday | Apache-2.0 | A | 22 | Claude Code |
| surface-learningsterrizoaguimor/celiums-memory | Skill | 23 | 2mo ago | Apache-2.0 | A | 60 | Claude Code |
| context-recoveryterrizoaguimor/celiums-memory | Skill | 23 | 2mo ago | Apache-2.0 | A | 37 | Claude Code |