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Redis published benchmark results showing its memory system outperforms other production systems on LongMemEval, a standard test for long-term agent memory. The report also flags that published memory benchmarks often don't replicate when re-tested independently.
- Redis' Remis + Instruct strategy achieved 86.5% task-averaged accuracy on LongMemEval, the highest among production systems tested on GPT-4o, at lower cost than competitors
- Oracle's managed service scored slightly higher (within 0.5 points) but uses a stronger answer model
- Vertex Memory Bank's actual performance (42.4%) significantly underperformed its published results (~70%), suggesting benchmark results don't always hold up in real-world setups
Memoryfield proposes treating agent memory as a simple data format (Markdown files plus optional vector index) rather than a complex pipeline system. It avoids the lock-in, overhead, and retrieval problems of existing memory systems by letting AI agents write prose directly and search semantically instead of traversing knowledge graphs.
- Existing agent memory systems fail because they're vendor lock-in platforms, unnecessarily complex (requiring multiple databases), or strip context from facts—memory should be data, not process
- Semantic search with parallel reads beats graph traversal: jumping directly to relevant pages requires 2 tool calls instead of N+1, finds information more reliably, and reduces noise in context windows
- The format scales with model improvements because agents can natively work with Markdown, SQLite, and bash rather than navigating fixed APIs, so better models automatically discover more creative uses
- Memoryfield is transport-agnostic (zip, local files, S3, GitHub, HTTP) and spec-based to prevent vendor lock-in and keep memories portable as agents and models change
This article describes an Elasticsearch-based system for long-term agent memory, dividing data into episodic, semantic, and procedural indices with tailored aging and update rules. It uses hybrid retrieval—BM25 plus dense vectors fused by RRF and refined with a cross-encoder reranker—alongside per-user DLS, supersession, decay, and an LLM-driven consolidation process to keep memory accurate and scalable.
- Splitting agent memory into episodic, semantic, and procedural indices (each with its own decay/update rules) avoids one-size-fits-all tradeoffs in write rate and aging logic.
- Hybrid retrieval (BM25 + Jina v5 embeddings fused via RRF, then reranked with a Jina v2 cross-encoder over 160 candidates) hit 0.89 recall@10 across 168 test queries.
- Contradictions are resolved via supersession with time-decay ranking rather than deletion, preserving an audit trail while surfacing current facts.
- Document-level security enforced per-user isolation with zero cross-tenant leaks, all within a single Elasticsearch cluster exposed as an MCP-compatible tool.