Python API Reference

Complete reference for the cognee Python package. All functions are accessible directly from the top-level cognee module.

import cognee

# v1.0 workflow
await cognee.remember("Your data here")
results = await cognee.recall("Your query")

Core Operations

These are the default v1.0 entry points for storing, querying, enriching, and deleting memory.

remember()

Store data as permanent graph memory or fast session memory in one call.

recall()

Query memory with auto-routing and session-aware retrieval.

improve()

Enrich an existing graph and bridge session memory into permanent memory.

forget()

Remove a data item, an entire dataset, or all memory for the current user.

serve()

Connect the SDK to Cognee Cloud or another remote Cognee instance.

push()

Upload a local dataset’s already-built graph to a remote Cognee instance.

Legacy Operations

The lower-level pipeline operations are still available when you need direct control over each step.

add()

Ingest text, files, or structured data into the knowledge base.

cognify()

Transform raw data into a structured knowledge graph.

search()

Query the knowledge graph with a chosen SearchType.

memify()

Enrich an existing knowledge graph with custom extraction and enrichment tasks.

Data Management

datasets

List, create, and delete datasets.

users

Get the default user, look up users, check existence, and create users.

agents

Create, list, inspect, and delete agents, and manage their connections.

update()

Update existing data items.

prune

Delete stored data and reset Cognee system state.

delete()

Delete data items (deprecated — use datasets).

run_migrations()

Apply pending relational and vector database schema migrations.

Session Management

Persistent Memory Across Sessions

Use session_id and inspect stored conversation history with get_session().

Feedback System

Add and clear feedback on stored session Q&A entries.

Configuration & Utilities

config

Configure LLM providers, databases, chunking, and more.

SearchType

The search modes you can pass to cognee.search().

run_custom_pipeline()

Execute custom task pipelines.

Data Models

Key types: DataPoint, Task, PipelineRunInfo, SearchResult.

Visualization

Visualization Payloads

visualize_graph_json() and friends: the graph, semantic layout, per-dataset previews, and live events as plain dicts.

get_schema_inventory()

Summarize the knowledge graph by semantic type: per-type counts, sample names, and relationships.

visualize_memory_provenance()

Render the tenant/user/agent/dataset ownership and data-flow graph (relational-only) to HTML. Pair with get_memory_provenance_graph() for raw (nodes, edges).

report()

Generate a Graph Insight Report in Markdown: hub nodes, cross-node-set connections, edge provenance, and suggested questions.

validate()

Cross-check a dataset’s graph and vector stores: orphaned edges, identity-id mismatches, and nodes missing from the vector index.

​Python API Reference

​Core Operations