Cognee Memory
Builds persistent agent memory and knowledge graphs with graph-based retrieval.
Install
npx skills add topoteretes/cognee --skill cognee -g -a claude-code -y- Repository
- topoteretes/cognee33k
- Category
- Data and ML
- Updated
- 3d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- Yes, review before use
What it does
Use this skill whenever the user asks about Cognee, AI memory, persistent agent memory, self-improving agents, agents learning from feedback, knowledge graphs, graph-based RAG, long-term memory for agents, short-term memory for agents, personalization, personas, temporal search, temporal knowledge graphs, ontology-based extraction, ontology grounding, feedback, Cypher search, natural-language graph search, chunk search, RAG search, cross-session memory, session feedback, feedback loops, session based memory, redis based memory, knowledge promotion. Also use when the user describes the workflow such as: "turn documents into a knowledge graph", "build memory from files", "search my graph", "extract entities and relations", "sync data into a graph", "update graph memory", "store memories for an agent", "help my agent learn over time", "visualize a knowledge graph built from documents", "let the agent learn", "adaptive agents", "personalized agents", "session based personalization", "find important ontologies", "find custom pydantic models", "isolate agentic behaviour", "add permission control to retrieval", "reduce context bloating".
Cognee
Use this skill for Cognee-specific Python API help and for mapping user goals to the right Cognee workflow.
When to apply this skill
Apply this skill whenever the user wants to do any of the following with Cognee:
- ingest text, files, URLs, repos, or datasets
- build or rebuild a knowledge graph
- search documents, chunks, summaries, triplets, or graph context
- choose a
SearchType - enrich an existing graph with
improve - define custom graph extraction models or
DataPointtypes - run custom task pipelines
- configure LLM, graph DB, vector DB, or storage settings
- tag and scope memory with
node_set/ NodeSets - build persistent memory for agents across sessions
- create feedback loops or self-improving agent workflows
- work with temporal extraction, ontologies, Cypher, or natural-language graph queries
- manage datasets, sessions, feedback, deletion, updates, or visualization
If the user's intent is "store information in memory and query it later", prefer Cognee's memory API: remember -> recall (plus improve to enrich and forget to delete).
Core workflow
import cognee
# Store: ingest + build the graph (+ improve, because self_improvement=True by default)
await cognee.remember(
"Your text, file path, URL, or list of inputs",
dataset_name="main",
)
# Query: recall picks a search strategy automatically (rule-based, no LLM call)
results = await cognee.recall("What are the key insights?", datasets=["main"])
for r in results:
print(r.source, r) # each result is tagged "graph" / "session" / ...
remember() runs add() + cognify() and then improve() underneath. recall() wraps
search() and adds routing, session memory as a source, and source-tagged results.
Default guidance
When helping with Cognee:
