Use when querying cognee memory with recall() (or search()) — picking a search type, understanding auto-routing, scoping to datasets, node sets or sessions, getting context or citations instead of an
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add topoteretes/cognee --skill cognee-recall -a cursor -y
Use when querying cognee memory with recall() (or search()) — picking a search type, understanding auto-routing, scoping to datasets, node sets or sessions, getting context or citations instead of an answer, reading the results, or debugging empty or unexpected results.
Use when querying cognee memory with recall() (or search()) — picking a search type, understanding auto-routing, scoping to datasets, node sets or sessions, getting context or citations instead of an answer, reading the results, or debugging empty or unexpected results.
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 searches every dataset the user can read. Pass
datasets=["research"] (names) or dataset_ids=[...] (UUIDs, which win) to
narrow it; that is also faster.
query_type=SearchType.X always wins.CHUNKS (plain vector search).auto_route=True, the default). It is two regex
rules, first match wins, no LLM call:
"quoted phrase" → CHUNKS_LEXICALCODING_RULESHYBRID_COMPLETION.A routed type (never a pinned one) that the backend rejects, or a routed
CHUNKS_LEXICAL / CODING_RULES that returns nothing, is retried once as
HYBRID_COMPLETION. The router never picks CYPHER.
from cognee import SearchType
await cognee.recall("What changed in v2?", query_type=SearchType.GRAPH_COMPLETION)
The full list is cognee/modules/search/types/SearchType.py; the
type-to-retriever table is cognee/modules/retrieval/README.md.
| Type | LLM? | Use for |
|---|---|---|
HYBRID_COMPLETION (default) | yes | General questions: document passages plus entity neighbourhoods, then an answer |
GRAPH_COMPLETION | yes | Answers from graph relationships |
GRAPH_COMPLETION_COT, _CONTEXT_EXTENSION, _DECOMPOSITION | yes | Harder multi-hop questions (more LLM calls) |
GRAPH_SUMMARY_COMPLETION | yes | Summarizes the retrieved graph edges at query time (extra LLM call), then answers |
RAG_COMPLETION | yes | Classic chunk RAG |
TRIPLET_COMPLETION | yes | Subject-predicate-object facts (needs triplet embedding) |
TEMPORAL | yes | Time questions; reads the Timestamp nodes the default pipeline extracts |
CHUNKS | no | Raw passages by vector similarity |
CHUNKS_LEXICAL | no | Keyword / exact-phrase match |
SUMMARIES | no | Document summaries |
CODE | no | Code-graph operations via code_query={...}; needs scope="code" in recall |
SKILLS | no | Discover skill playbooks; exactly one dataset |
CYPHER | no | Raw Cypher. On by default; ALLOW_CYPHER_QUERY=false disables it. It can write, so only pass user-authored queries deliberately |
NATURAL_LANGUAGE | yes | LLM writes Cypher, then runs it (same flag) |
GRAPH_REPORT | partly | Graph insight report: hubs, cross-set links, suggested questions |
FEELING_LUCKY | yes | An LLM picks the type |
AGENTIC_COMPLETION | yes | Multi-step loop with skills/tools; exactly one dataset. Use search() for its parameters |
scope is one of, or a list of: graph, session, session_first,
trace, session_context, all, tools, code. all means graph +
session + trace + session_context; tools and code are never included
implicitly.
When scope is omitted:
| You pass | Sources |
|---|---|
session_id only | Session first; a session hit skips the graph |
session_id + datasets | Session and graph both contribute |
session_id + query_type | Graph only — pinning a type drops the session |
no session_id | Graph only |
Session and trace search is keyword overlap, not embeddings.
top_k=15: per dataset, not in total. The default HYBRID caps each lane
at min(top_k, 10); set chunks_top_k / entities_top_k /
facts_top_k in retriever_specific_config to go higher.node_name=["AI"] (+ node_name_filter_operator="OR"|"AND"): restricts
graph/chunk/completion types to data remembered with that node_set.
SUMMARIES, CHUNKS_LEXICAL, GRAPH_REPORT, CYPHER, NATURAL_LANGUAGE, CODE and
SKILLS ignore it; CODING_RULES treats it as the rules node-set name.system_prompt / system_prompt_path: change the answering prompt.response_model=MyPydanticModel: structured answer, on r.structured.include_references=True: attach the document chunks that support each
graph edge used (needs EDGE_EVIDENCE_ENABLED=true, the default).only_context=True: return what the LLM would have received instead of
an answer. r.text is the rendered user prompt, r.system_prompt the
system prompt. Pin query_type when you use it.retriever_specific_config={...}: retriever-only options. For the
agentic extras (skills, tools, max_iter) and node_type, call
cognee.search() instead, which takes them as parameters.Each item is a Pydantic model with a source discriminator: graph,
session, trace, session_context, code, tools, skills, or
system. Graph items carry text (always renderable), search_type,
kind, score, dataset_id / dataset_name, metadata, raw, and
structured. A system item is a status
marker, not data (see "memory warming up" below).
When a query sounds procedural ("how do I…", "runbook", "steps to…") and
exactly one dataset is targeted, recall also runs a small SKILLS lookup
and appends hits with source="skills". Disable with
SKILL_GATE_ENABLED=false.
datasets, recall
searches only datasets the user can read, so a user without grants gets
one source="system" memory_warming_up marker from a graph-only recall
(with only_context=True or RECALL_WARMUP_SHORTCIRCUIT=false it gets
[]; with session sources included only the graph lane is empty, and
session and trace hits still come back). Asking for a dataset id the user cannot read raises
PermissionDeniedError (HTTP 403). Dataset names resolve only among
the user's own datasets, so a name that is not theirs (even one shared
with them) raises DatasetNotFoundError; use dataset_ids for shared
datasets. See the cognee-permissions skill.only_context) returns one source="system" item
with status="memory_warming_up" (or "build_failed" plus
error_message) instead of results; a multi-source recall just returns
no graph results. Wait for the
remember to finish, or check why it failed.neighborhood_depth or feedback_influence > 0 (including a nonzero
DEFAULT_FEEDBACK_INFLUENCE), or the chunk collection is missing.
node_name stays on hybrid, which filters to that node set. search()
also defers for a custom node_type or node_name with node_type=None;
recall() has no node_type. wide_search_top_k and triplet_distance_penalty
with hybrid raise InvalidHybridSearchConfig; pin
GRAPH_COMPLETION to use them.SKILLS and AGENTIC_COMPLETION need exactly one dataset. For
SKILLS, search() raises unless exactly one dataset is given.
recall() runs SKILLS per dataset with access control on (zero datasets
gives [], not an error) and raises only with access control off and not
exactly one dataset. For
AGENTIC_COMPLETION only search() checks it up front, so call it through
search() with one dataset.code_query without scope="code" raises, and scope="tools" also
needs TOOL_CALLS_ENABLED=true.max_iter, default 6) make more. NATURAL_LANGUAGE makes one (no answer call) and retries only on
an empty or failed query, up to 3 attempts. With CACHING and
AUTO_FEEDBACK on (defaults), each answered turn adds one analysis call,
even without a session_id. Set
AUTO_FEEDBACK=false for low-latency reads (see the cognee-performance
skill).Use recall(). Drop to cognee.search() only for agentic parameters
(skills, tools, max_iter, node_type) as first-class arguments, raw
SearchResult objects, or a pinned type with no router. search() never
searches the session cache; its session_id only adds conversation history
to the prompt. Full guide: docs/recall-vs-search.md.
recall() resolves scope and search type, then calls the same authorized
search search() uses: datasets resolve through the permission layer with
read, one search per dataset runs concurrently, and results are
normalized and tagged.
cognee/api/v1/recall/recall.pycognee/api/v1/recall/query_router.pycognee/api/v1/recall/skill_gate.pycognee/modules/recall/types/RecallResponse.py,
SearchResultItem.pycognee/memory/entries.py:normalize_scopeRECALL_WARMUP_*): cognee/modules/recall/config.pycognee/modules/search/methods/search.pycognee/modules/search/methods/hybrid_deferral.pycognee/modules/search/methods/get_search_type_retriever_instance.pyExamples in examples/guides/: recall_core.py,
hybrid_retrieval_recall.py, references_example.py, temporal_recall.py,
sessions.py.
Adding a search type, per cognee/modules/retrieval/README.md:
cognee/modules/retrieval/ (subclass
BaseRetriever or a completion base).SearchType member and its search_core_registry entry.cognee/tests/unit/modules/retrieval/retriever_readme_index_test.py)
fails if the table and registry disagree.SEARCH_TYPE_CHOICES in cognee/cli/config.py for
the CLI, or a regex rule to query_router.py for auto-routing. Never
route a type that can write.