Hey HN, It’s Vineeth from Plastic Labs. We've been building Honcho, an open-source memory library for stateful AI agents. Most memory systems are just vector search—store facts, retrieve facts, stuff into context. We took a different approach: memory as reasoning. (We talk about this a lot on our blog) We built Neuromancer, a model trained specifically for AI-native memory. Instead of naive fact extraction, Neuromancer does formal logical reasoning over conversations to build representations that evolve over time. Its both cheap ( $2/M tokens ingestion, unlimited retrieval), token efficient and SOTA: LongMem (90.4%), LoCoMo (89.9%), and BEAM. On BEAM 10M—which exceeds every model's context window—we hit 0.409 vs prior SOTA of 0.266, using 0.5% of context per query. Github: https://github.com/plastic-labs/honcho Evals: https://evals.honcho.dev Neuromancer Model Card: https://plasticlabs.ai/neuromancer) Memory as Reasoning Approach: https://blog.plasticlabs.ai/blog/Memory-as-Reasoning Read more about our recent updates: https://blog.plasticlabs.ai/blog/Honcho-3 Happy to answer questions about the architecture, benchmarks, or agent memory patterns in general |