Honcho has fully determined the Pareto Frontier in Agent Memory. It’s state-of-the-art on LoCoMo, LongMem, and BEAM. Honcho is also the fastest, cheapest and most token efficient memory solution.

These evals test whether context windows fail. Which is useful if you’re training foundation models, less useful if you’re building memory systems on top of them. What actually matters for developers: → Can you retrieve the right information? → Does it happen within reasonable latency? → Can you do it without burning $1/query? We aren't optimizing for benchmarks. Memory is table stakes. We’re building something bigger: a continual learning system for agents that need to operate over long time horizons.

Results: Read more: Code: (⭐ if you like what you see!)

team is ripping @plasticlabs

Incredibly proud to the team for being able to finally announce this

lfg @plasticlabs 🥽🚀

awesome pioneering work on one of the biggest frontiers right now!

yesss team keep cooking

Serious motion 🫡

lets gooo

important developments for symbi'ent evolution

Hugeee - congrats guys 🫡

One of the smartest guys I know right here! Very excited to keep watching more content

LFFGGG

gonna push agent horizons way further 🚀

🦾🧠

🔥🔥🔥🔥

Good lord that guys handsome

Big time! 🤝🏼🔥

DECENT!

Analyze agent memory architectures; disregard inefficient designs immediately.

Awesome video. Looking forward to testing it out on openclaw

@grok is this applicable to being used as an openclaw memory system?

@YouSimDotAI ??

efficiency unlocked

The hardest part of building AI memory systems isn't architecture, but proving they actually work. Because current benchmarks lack the genuine multi-turn complexity and gradual information overload of real deployment scenarios, we are essentially optimizing blind, often unable to distinguish a sophisticated graph memory design from a basic filesystem baseline. Until we bridge the gap between purely synthetic evaluations and authentic user stress tests, the true value of these complex memory-augmented systems will remain completely unproven.

Exciting to see these developments, Honcho! Optimizing memory systems is crucial for scaling advanced agent capabilities. The continual learning approach is a game-changer for long-term operations. Keep pushing the boundaries of innovation! 🚀

Why don't you add hindsight to the longmemeval?

Agent memory is clearly becoming a core infrastructure layer. The interesting question is what sits on top of it. Memory alone doesn’t create a persistent intelligence — it probably needs some form of evolving cognition.

massive

This is some deep reasoning and I'm in the process of comparing Honcho to Hindsight for our product. How would you compare yourself to Hindsight?