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/.claude/skills/cognee-docker/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-docker)<a href="https://agentmods.dev/skills/topoteretes/cognee/cognee-docker"><img src="https://agentmods.dev/badge/skills/topoteretes/cognee/cognee-docker.svg" alt="Measured on agentmods" 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 | $0.00060 | $0.00978 |
| Opus 5 | $0.00030 | $0.00489 |
| Sonnet 5 | $0.00012 | $0.00196 |
| Haiku 4.5 | $0.00006 | $0.00098 |
Grade A, and why
cognee-docker scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl http://localhost:8000/health How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Start cognee from the Docker image
Fastest path: prebuilt image, one file
For a local try-out, do NOT clone or build anything. Follow
docs/minimal-docker-compose.md: save this as docker-compose.yml in an
empty directory:
services:
cognee:
image: cognee/cognee:main
ports:
- "8000:8000"
environment:
LLM_API_KEY: ${LLM_API_KEY:?set LLM_API_KEY to your OpenAI API key}
# Single-user try-out: no auth, shared local databases.
ENABLE_BACKEND_ACCESS_CONTROL: "false"
Then:
export LLM_API_KEY="sk-..." # OpenAI key (default LLM + embedding provider)
docker compose up
curl http://localhost:8000/health
Interactive API reference: http://localhost:8000/docs. First requests:
echo "Cognee turns documents into AI memory." > note.txt
# remember = ingest + build the graph in one call (multipart form)
curl -X POST http://localhost:8000/api/v1/remember -F "[email protected]" -F "datasetName=main_dataset"
# recall = query it (JSON)
curl -X POST http://localhost:8000/api/v1/recall -H "Content-Type: application/json" \
-d '{"query": "What does Cognee do?", "datasets": ["main_dataset"]}'
/api/v1/recall takes the question as query. It defaults search_type to
GRAPH_COMPLETION for backward compatibility — pass "search_type": null to
opt into auto-routing (the SDK recall() default). The difference is real:
{"query": "Why does X?"} answers with GRAPH_COMPLETION, while the same
query with "search_type": null routes to GRAPH_COMPLETION_COT.
Request DTOs accept both snake_case and camelCase for every field
(alias_generator=to_camel + populate_by_name in cognee/api/DTO.py), so
search_type and searchType are equally valid.
The legacy /api/v1/add + /api/v1/cognify + /api/v1/search endpoints still
exist and are what remember/recall call underneath; use them only when you
need a single stage on its own. /api/v1/improve and /api/v1/forget complete
the memory API.
Data lives inside the container by default. To persist it, set
DATA_ROOT_DIRECTORY=/cognee-data/data and
SYSTEM_ROOT_DIRECTORY=/cognee-data/system and mount a named volume at
/cognee-data (full example in docs/minimal-docker-compose.md).
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 First seen · 89 lines · 60 tokens per session scan A a55b71226465
cognee-docker is a skill published in the GitHub repository topoteretes/cognee (30,501 stars, last pushed yesterday), licensed Apache-2.0. It adds 60 tokens to every session and 978 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
openmemory
Manage persistent memory via OpenMemory MCP. TRIGGER when: user says "remember this", "save to memory", "store this", "recall", "what do you remember about", "check memory", "forget this", "delete memory", "clean up memory", or when agent forms a stable conclusion worth persisting. DO NOT TRIGGER when: user refers to…
surface-learnings
Use when the user asks "what have you learned", "what do you remember about me", "show me memory stats", "memory state", "/reflexes", or any variant. Surfaces what the limbic engine has accumulated as patterns, not as a memory dump.
context-recovery
Use at the start of every session, and especially after context compaction or a cold restart. Fires as the first action, before engaging with the user's opening message.
decision-encoding
Use when an architectural choice is made, a library is selected, an approach is committed to, a convention is set, or any "we'll do it this way" moment occurs. Fires the moment the decision stabilizes, not after.
pre-response-recall
Use when the user asks about anything that could have context in prior sessions — names, preferences, decisions, past work, or anything starting with "remember", "what did", "have we", "did we". Fires before generating any substantive response.
salience-filtering
Use when deciding whether to store a memory. Fires as a gate before every remember() call. The default answer is "no" — biological memory is selective, not exhaustive.