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-recall/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-recall)<a href="https://agentmods.dev/skills/topoteretes/cognee/cognee-recall"><img src="https://agentmods.dev/badge/skills/topoteretes/cognee/cognee-recall/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-recall"><img src="https://agentmods.dev/badge/skills/topoteretes/cognee/cognee-recall.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 | $0.00059 | $0.03242 |
| Opus 5.5 | $0.00024 | $0.01297 |
| Sonnet 5.5 | $0.00012 | $0.00648 |
| Haiku 4.5 | $0.00006 | $0.00324 |
Grade A, and why
cognee-recall 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Query memory with recall()
recall() is cognee's query API. It picks a search strategy, searches the
graph and (with a session) the session cache, and returns a list of tagged
results.
import cognee
results = await cognee.recall("Where was Einstein born?")
for r in results:
print(r.source, r.text) # e.g. "graph", "Einstein was born in Ulm."
Without datasets it covers every dataset the user can read, but dataset
routing (always on, no off switch) scores those datasets
against the query on their global context index and searches only the best
DATASET_ROUTING_TOP_K (default 1), plus any dataset that has no index yet.
A dataset farther than DATASET_ROUTING_DISTANCE_CUTOFF (cosine distance,
default 0.8) is skipped, and when none is within it all are searched; datasets
within DATASET_ROUTING_TIE_MARGIN (default 0.001) of the last routed one are
searched too. A routed search that finds nothing is widened to the rest. The
search log prints every dataset's distance. Routing assumes
one embedding model across datasets; after an EMBEDDING_MODEL change it
logs a warning and every readable dataset is searched. Pass
datasets=["research"] (names) or dataset_ids=[...] (UUIDs, which win) to
pin the scope yourself; an explicit scope is never routed.
Use it
How the search type is picked
- An explicit
query_type=SearchType.Xalways wins. - With no usable LLM key nothing can write an answer, so on both paths
HYBRID_COMPLETION,GRAPH_COMPLETIONandRAG_COMPLETIONrun withonly_context=True(the assembled prompt instead of an answer), and a routedTEMPORALorBROADbecomesHYBRID_COMPLETIONwithonly_context=True; a routedCODEneeds no LLM and runs as it is. - Without an explicit type, the router (
auto_route=True, the default). It is five regex rules, first match wins, no LLM call:- the whole query is one
"quoted phrase"→CHUNKS_LEXICAL - mentions coding rules/standards/conventions or code-review guidelines
→
CODING_RULES - code phrasing right next to an identifier (
what calls parse_config,where is CodeRetriever defined,callers of foo()) →CODE, running itsaskoperation over the identifiers - opens with
how many/total number of→BROAD - an absolute date behind a time preposition (
in 2019,before 1900,on 7 November 1867) →TEMPORAL
- the whole query is one
- Everything else →
HYBRID_COMPLETION.
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 · +21 lines 13994db1ddde
- 21d ago Changed · +1 lines 812cea30729b
- 1mo ago Changed 299065113099
- 1mo ago First seen · 212 lines · 59 tokens per session scan A 2a245a9f99b4
cognee-recall is a skill published in the GitHub repository topoteretes/cognee (33,094 stars, last pushed today), licensed Apache-2.0. It adds 59 tokens to every session and 3,242 once invoked, about $0.0002 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-recall compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| cognee-recalltopoteretes/cognee | Skill | 33k | today | Apache-2.0 | A | 59 | Claude Code, Codex |
| openmemoryCaviraOSS/OpenMemory | Skill | 4.6k | 19d ago | Apache-2.0 | A | 96 | Claude Code, Codex |
| yesmem-configcarsteneu/yesmem | Skill | 46 | 3d ago | Apache-2.0 | A | 82 | Claude Code, Codex |
| goodmem:helpPAIR-Systems-Inc/goodmem-claude-code-plugin | Skill | 9 | 2d ago | MIT | A | 46 | Claude Code |
| 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 |