MRAgent: Graph Memory for LLM Agents

MRAgent is a retrieval-augmented QA system that builds a graph-structured episodic memory from long, multi-session dialogues instead of using simple vector retrieval. It operates in two phases: first, it rewrites dialogue turns into self-contained sentences (resolving pronouns, converting dates), extracts keywords, and stores everything as a graph with nodes for key entities, episodes, topics, and personal facts; second, it answers questions by running a tool-calling reasoning loop where the LLM uses seven specialized graph query tools (e.g., by topic, time, personal info, event context) to retrieve relevant memory and produce an answer. Evaluated on LoCoMo and LongMemEval benchmarks, the system uses OpenRouter for LLM access, caches intermediate results to avoid redundant work, and includes an LLM-as-judge evaluation script—treating memory as a reconstructed graph rather than retrieved chunks for more structured, context-aware querying in long-form conversational AI. 

https://github.com/Ji-shuo/MRAgent

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