Definition
Graph memory is an agent memory architecture that stores information as nodes and edges in a knowledge graph, enabling agents to recall entity relationships, track state across sessions, and reason over connected data more effectively than flat context windows.
In Depth
Traditional agent memory uses vector stores or conversation buffers, which lose relational context between entities. Graph memory stores facts as triples (subject-predicate-object) in a knowledge graph, preserving how entities relate to each other. When an agent encounters new information, it updates the graph by adding nodes and edges rather than appending text. This enables queries like 'what companies has this person worked at' or 'which products compete with X' that are difficult with flat retrieval. Graph memory is especially valuable for research agents that build understanding over multiple search sessions. When combined with search APIs like Scavio, agents can populate their knowledge graph with structured data from Google, Amazon, and YouTube, then reason over the accumulated relationships to generate insights that span multiple data sources and sessions.
Example Usage
A competitive intelligence agent uses graph memory to store relationships between companies, products, and pricing data collected from Scavio API calls over weeks. When asked 'how has competitor X changed pricing relative to Y,' it traverses the graph to find pricing nodes across time rather than re-searching everything.
Platforms
Graph Memory for AI Agents is relevant across the following platforms, all accessible through Scavio's unified API:
- YouTube
- Amazon
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