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