Honcho

Honcho is a memory platform built for AI agents. Instead of just storing facts and retrieving them later, Honcho actively learns from every interaction. It uses custom reasoning models to figure out what context actually matters, so agents don't waste tokens on irrelevant data. The result is smarter, more stateful agents that remember the right things without ballooning your token budget.

Under the hood, Honcho runs on Neuromancer, a family of reasoning models that achieve state-of-the-art scores on benchmarks like LongMem, LoCoMo, and BEAM. The system claims 60–90% token savings compared to naive retrieval approaches. It integrates directly with popular agent frameworks — Claude Code, OpenAI Codex, OpenClaw, Hermes Agent, and DeepSeek Harness — so you can plug in persistent memory with a few commands. There's also a CLI and a quickstart guide to get going fast.

Honcho is best suited for developers building autonomous agents, chatbots, or any system that needs long-term context without blowing through API costs. If you're tired of cramming entire conversation histories into prompts, Honcho offers a more efficient way to give agents the memory they actually need.