Schema Inventory

get_schema_inventory() summarizes your knowledge graph by semantic type instead of rendering every node. It returns deterministic per-type instance counts, a bounded set of representative sample names, and the relationship distribution between types — useful for understanding the shape of a graph at a glance or for driving custom dashboards. Before you start:

Complete Quickstart to understand basic operations
Have some remembered data or any existing knowledge graph

Code in Action

import cognee

# Data must be remembered/loaded into cognee before building the inventory,
# e.g. via cognee.remember() or cognee.add() + cognee.cognify().
inventory = await cognee.get_schema_inventory(
    dataset=None,           # optional dataset UUID to scope the graph databases
    samples_per_type=5,     # max sample instance names per type (default 5)
    sort="count",           # "count" (default, descending) or "none" (discovery order)
)

Each list entry is a dict describing one semantic type:

{
    "type": "Person",           # semantic type name
    "count": 42,                 # total instances of this type
    "samples": ["Carlos", ...],  # up to samples_per_type representative names
    "sample_size": 5,            # number of names actually returned
    "relationships": [           # aggregated edges involving this type
        {"to_type": "Broker", "relation": "works_at", "count": 12},
        # incoming edges are shown with a "← " prefix on the relation name
    ],
}

How types are resolved

Extracted entities are grouped under their resolved semantic type (the EntityType reached via the is_a edge) rather than the generic "Entity" label, and the internal EntityType taxonomy nodes are not surfaced as their own group. Passing a negative samples_per_type, or a sort value other than "count"/"none", raises ValueError.

Over HTTP

The same projection is available at GET /api/v1/schema/inventory (query params dataset_id, samples_per_type, sort). The endpoint is caller-scoped: it returns 403 when the caller is not authorized to read the dataset, and 409 if the inventory cannot be built.

Full Example

A richer demo builds a representative graph in code — so it runs without an LLM or a graph database — and writes a standalone HTML file with the schema side panel to your home directory:

examples/demos/schema_inventory_demo.py → ~/cognee_schema_demo.html

The complete flow — remember data, then summarize the graph by type is in the following example:

Schema inventory

import asyncio
import cognee


async def main():
    # Prune data and system metadata before running, only if we want "fresh" state.
    await cognee.forget(everything=True)

    # Data must be remembered/loaded into cognee before building the inventory.
    await cognee.remember(
        ["Alice knows Bob.", "NLP is a subfield of CS."],
        self_improvement=False,
    )

    inventory = await cognee.get_schema_inventory(samples_per_type=5, sort="count")

    for entry in inventory:
        print(f"{entry['type']}: {entry['count']} instance(s), samples={entry['samples']}")
        for rel in entry["relationships"]:
            print(f"  {rel['relation']} -> {rel['to_type']} ({rel['count']})")


if __name__ == "__main__":
    asyncio.run(main())

Graph Visualization

Render your knowledge graph to an interactive HTML file

Memory Provenance

Visualize the ownership and data-flow story behind your memory

Getting Started

Core Concepts

Setup Configuration

Guides

Examples

CLI

Rust SDK

TypeScript SDK

OSS

Code in Action

How types are resolved

Over HTTP

Full Example

Graph Visualization

Memory Provenance

​Code in Action

​How types are resolved

​Over HTTP

​Full Example

Graph Visualization

Memory Provenance

Code in Action

How types are resolved

Over HTTP

Full Example