{"id":46781717,"title":"Show HN: Honcho – Open-source memory infrastructure, powered by custom models","points":8,"user":"vvoruganti","time":1769529523,"time_ago":"3 days ago","type":"link","content":"

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","url":"https://github.com/plastic-labs/honcho","domain":"github.com","comments":[],"comments_count":0}