{"slug":"graphiti","name":"Graphiti","domain":null,"verdict":"As of 2026-08-10, ChatGPT, Claude, Gemini, Grok collectively rank Graphiti first for graph memory stores for multi-agent systems. Source: https://modelsagree.com/product/graphiti (modelsagree.com, CC BY 4.0).","best_rank":1,"categories":1,"entries":[{"slug":"best-graph-memory-stores-for-multi-agent-systems","title":"Best graph memory stores for multi-agent systems","rank":1,"of":6,"score":20,"appearances":4,"modelRanks":{"ChatGPT":1,"Claude":1,"Gemini":1,"Grok":1},"reason":"Best overall balance of merit and value: open-source, bi-temporal facts, automatic invalidation of superseded relationships, and hybrid vector/full-text/graph retrieval; near-tied with Zep, but wins for practitioners who can self-host","reasons":[{"model":"ChatGPT","reason":"Best overall balance of merit and value: open-source, bi-temporal facts, automatic invalidation of superseded relationships, and hybrid vector/full-text/graph retrieval; near-tied with Zep, but wins for practitioners who can self-host"},{"model":"Claude","reason":"Purpose-built temporal knowledge-graph engine for agent memory; bi-temporal model tracks both event time and ingestion time, and it does incremental entity/edge updates without recomputing the whole graph, which matters when many agents write concurrently. Open-source, backend-agnostic (Neo4j or FalkorDB), and designed for real-time read/write rather than batch GraphRAG. Assumes the practitioner needs relationship- and time-aware recall, not just semantic vector lookup."},{"model":"Gemini","reason":"Purpose-built for dynamic multi-agent memory by structuring context into temporal knowledge graphs with automatic entity resolution, bi-directional edge updates, and time-based fact invalidation; assumes tracking how facts evolve over time is the main memory bottleneck in multi-agent systems (near-tie with Mem0 for overall agent adoption)."},{"model":"Grok","reason":"Purpose-built temporal knowledge graph engine for agent memory with bi-temporal validity windows on every edge, hybrid semantic+BM25+traversal retrieval, native multi-tenant group isolation, and explicit multi-agent shared-memory patterns; backends include Neo4j/FalkorDB and it underpins production Zep deployments with strong LongMemEval temporal scores"}],"fixes":[{"model":"ChatGPT","fix":"You must operate a graph backend and build your own authorization, tenancy, and production governance"},{"model":"Claude","fix":"LLM-driven extraction adds per-write latency and token cost, and you still operate a graph backend yourself — overkill for a single agent that a plain vector store would serve."},{"model":"Gemini","fix":"Incurs high LLM API costs and extraction latency during graph construction, making it poorly suited for high-frequency, real-time raw data ingestion."},{"model":"Grok","fix":"Self-host requires operating a graph DB and extraction pipeline; not the lightest drop-in for simple per-user vector needs"}],"updated":"2026-08-10","rank_history":{"days":["2026-08-03","2026-08-10"],"ranks":[1,1]},"api":"https://modelsagree.com/api/v1/best/best-graph-memory-stores-for-multi-agent-systems.json"}],"page":"https://modelsagree.com/product/graphiti","check":"https://modelsagree.com/check?q=Graphiti","updated":"2026-08-10T18:18:45.051Z","attribution":"modelsagree.com, CC BY 4.0"}