Vector RAG Isn’t Enough — I Built a Context Graph Layer for Multi-Agent Memory

Standard memory architectures like raw history and vector search fail in long-running multi-agent conversations, especially when recalling decisions made by different agents. A proposed solution is a context graph that stores information as structured entities and relationships rather than as flat text chunks. This graph structure allows the system to answer complex queries that require combining multiple, separately-stated facts, a task where other methods struggle. In a benchmark test, the context graph demonstrated significantly higher accuracy and lower token consumption compared to both a raw history dump and a vector-only RAG pipeline.