Memory Provenance

visualize_memory_provenance() renders the ownership and data-flow story behind your memory — Tenant → User → Agent → Dataset → file, plus agent read/write access and agent-written sessions — to a self-contained HTML file. Before you start:

Complete Quickstart to understand basic operations
Have some ingested data (the projection reads the relational database, so no graph backend is required)

Code in Action

import os
import cognee

# Data must be remembered/loaded into cognee before projecting provenance,
# e.g. via cognee.remember() or cognee.add() + cognee.cognify().
dest = os.path.join(os.path.expanduser("~"), "memory_provenance.html")

await cognee.visualize_memory_provenance(
    destination_file_path=dest,
    include_memory=False,   # when True, fold in extracted entities/relationships
)

This projection is read purely from the relational database — it does not require the graph backend or an LLM, so it works even when the graph database is unavailable. Set include_memory=True to also fold in the extracted entities/relationships (from the relational nodes/edges tables) and link them back to the files they were extracted from.

Raw graph data

If you only need the projected graph data (in the same (nodes, edges) shape the renderer consumes) rather than HTML, call get_memory_provenance_graph():

nodes, edges = await cognee.get_memory_provenance_graph(include_memory=True)

Scoping in multi-tenant deployments

Both functions accept scope_tenant_ids and scope_user_ids to restrict the projection to a tenant or user. In multi-tenant deployments you must pass a scope — an unscoped read returns every tenant’s actors, datasets, and files. When neither scope is given the read is global, which is the intended behavior only for single-user / OSS installs where one user owns everything.

Over HTTP

The HTTP endpoint GET /api/v1/schema/provenance (query param include_memory) always scopes to the authenticated caller’s tenant (or user).

Full Example

A richer demo drives the production projection against representative records — so it runs without an LLM or a graph database — and writes a standalone HTML file to your home directory:

examples/demos/memory_provenance_demo.py → ~/cognee_provenance_demo.html

The complete flow — remember data, render the provenance HTML, and read the raw projection is in the following code:

Memory provenance

import asyncio
import os

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 projecting provenance.
    await cognee.remember(
        ["Alice knows Bob.", "NLP is a subfield of CS."],
        self_improvement=False,
    )

    dest = os.path.join(
        os.path.dirname(__file__), ".artifacts", "memory_provenance.html"
    )
    await cognee.visualize_memory_provenance(destination_file_path=dest)

    # Raw (nodes, edges) projection, folding in the extracted memory.
    nodes, edges = await cognee.get_memory_provenance_graph(include_memory=True)
    print(f"provenance nodes={len(nodes)}, edges={len(edges)}")


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

Graph Visualization

Render your knowledge graph to an interactive HTML file

Schema Inventory

Summarize the knowledge graph by semantic type

Getting Started

Core Concepts

Setup Configuration

Guides

Examples

CLI

Rust SDK

TypeScript SDK

OSS

Code in Action

Raw graph data

Scoping in multi-tenant deployments

Over HTTP

Full Example

Graph Visualization

Schema Inventory

​Code in Action

​Raw graph data

​Scoping in multi-tenant deployments

​Over HTTP

​Full Example

Graph Visualization

Schema Inventory

Code in Action

Raw graph data

Scoping in multi-tenant deployments

Over HTTP

Full Example