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
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