Context Graphs vs Vector RAG vs Raw Context - A benchmark for agent memory

Retrieval is essential for AI agents to perform tasks accurately, requiring effective methods to extract and connect relevant information from memory. The text explores different memory methodologies, including raw context, vector retrieval-augmented generation (RAG), context graphs, and a hybrid approach, benchmarking their effectiveness in addressing multi-hop questions where facts are not directly linked. A context graph, which stores extracted facts as nodes and relationships, excels in chaining multi-hop facts but struggles with paraphrased queries, where vector RAG performs better due to its semantic understanding. The hybrid model, combining the strengths of both context graphs and vector RAG, achieves the highest accuracy by leveraging the precise fact-chaining ability of graphs and the paraphrase handling of vectors. The analysis highlights the importance of semantic search and robust fact extraction to improve the performance of context graphs in real-world applications, suggesting that context graphs are suited for complex, multi-step tasks in environments where long-term memory and fact resolution are critical.

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