It seems like the tool to solve the problem that won't last longer than couple of months and is something that e.g. claude code can and probably will tackle themselves soon.
Why would the problem ever go away? It's compression technologys have existed virtually since the beginning of computing, and one could argue human brains do their own version of compression during sleep.
Advantage of SML in between some outputs cannot be compressed without losing context, so a small model does that job. It works but most of these solutions still have some tradeoff in real world applications.
We compress tool outputs at each step, so the cache isn't broken during the run. Once we hit the 85% context-window limit, we preemptively trigger a summarization step and load that when the context-window fills up.
I guess I'm skeptical that this actually improves performance. I'm worried that the middle man, the tool outputs, can strip useful context that the agent actually needs to diagnose.
That's why give the chance to the model to call expand() in case if it needs more context. We know it's counterintuitive, so we will add the benchmarks to the repo soon.
Given our observations, the performance depends on the task and the model itself, most visible on long-running tasks
Just for anyone else who hadn't seen the announcement yet, this Anthropic 1M context is now the same price as the previous 256K context - not the beta where Anthropic charged extra for the 1M window:
As for retrieval, the post shows Opus 4.6 at 78.3% needle retrieval success in 1M window (compared with 91.9% in 256K), and Sonnet 4.6 at 65.1% needle retrieval in 1M (compared with 90.6% in 256K).
do you guys have any stats on how much faster this is than claude or codex's compression? claudes is super super slow, but codex feels like an acceptable amount of time? looks cool tho, ill have to try it out and see if it messes with outputs or not.
This company sounds like it has months to live, or until the VC money runs out at most. If this idea is good, Anthropic et. al. will roll it into their own product, eliminating any purpose for it to exist as an independent product. And if it isn't any good, the company won't get traction.
I don't want some other tooling messing with my context. It's too important to leave to something that needs to optimize across many users, there by not being the best for my specifics.
The framework I use (ADK) already handles this, very low hanging fruit that should be a part of any framework, not something external. In ADK, this is a boolean you can turn on per tool or subagent, you can even decide turn by turn or based on any context you see fit by supplying a function.
YC over indexed on AI startups too early, not realizing how trivial these startup "products" are, more of a line item in the feature list of a mature agent framework.
I've also seen dozens of this same project submitted by the claws the led to our new rule addition this week. If your project can be vibe coded by dozens of people in mere hours...
I wonder what is the business model.
It seems like the tool to solve the problem that won't last longer than couple of months and is something that e.g. claude code can and probably will tackle themselves soon.
Business model is: Get acquired
Could also be selling data to model distillers.
Why would the problem ever go away? It's compression technologys have existed virtually since the beginning of computing, and one could argue human brains do their own version of compression during sleep.
Is it similar to rtk? Where the output of tool calls is compressed? Or does it actively compress your history once in a while?
If it's the latter, then users will pay for the entire history of tokens since the change uncached: https://platform.claude.com/docs/en/build-with-claude/prompt...
How is this better?
This is a bit more akin to distill - https://github.com/samuelfaj/distill
Advantage of SML in between some outputs cannot be compressed without losing context, so a small model does that job. It works but most of these solutions still have some tradeoff in real world applications.
We do both:
We compress tool outputs at each step, so the cache isn't broken during the run. Once we hit the 85% context-window limit, we preemptively trigger a summarization step and load that when the context-window fills up.
I guess I'm skeptical that this actually improves performance. I'm worried that the middle man, the tool outputs, can strip useful context that the agent actually needs to diagnose.
That's why give the chance to the model to call expand() in case if it needs more context. We know it's counterintuitive, so we will add the benchmarks to the repo soon.
Given our observations, the performance depends on the task and the model itself, most visible on long-running tasks
How does the model know it needs more context?
Funny enough, Anthropic just went GA with 1m context claude that has supposedly solved the lost-in-the-middle problem.
Just for anyone else who hadn't seen the announcement yet, this Anthropic 1M context is now the same price as the previous 256K context - not the beta where Anthropic charged extra for the 1M window:
https://x.com/claudeai/status/2032509548297343196
As for retrieval, the post shows Opus 4.6 at 78.3% needle retrieval success in 1M window (compared with 91.9% in 256K), and Sonnet 4.6 at 65.1% needle retrieval in 1M (compared with 90.6% in 256K).
now that's major news
In addition to context rot, cost matters, I think lots of people use toke compression tools for that not because of context rot
From a determinism standpoint it might be better for the rot to occur at ingest rather than arbitrarily five questions later.
do you guys have any stats on how much faster this is than claude or codex's compression? claudes is super super slow, but codex feels like an acceptable amount of time? looks cool tho, ill have to try it out and see if it messes with outputs or not.
I can already prevent context pollution with subagents. How is this better?
This company sounds like it has months to live, or until the VC money runs out at most. If this idea is good, Anthropic et. al. will roll it into their own product, eliminating any purpose for it to exist as an independent product. And if it isn't any good, the company won't get traction.
ok, its great
I don't want some other tooling messing with my context. It's too important to leave to something that needs to optimize across many users, there by not being the best for my specifics.
The framework I use (ADK) already handles this, very low hanging fruit that should be a part of any framework, not something external. In ADK, this is a boolean you can turn on per tool or subagent, you can even decide turn by turn or based on any context you see fit by supplying a function.
YC over indexed on AI startups too early, not realizing how trivial these startup "products" are, more of a line item in the feature list of a mature agent framework.
I've also seen dozens of this same project submitted by the claws the led to our new rule addition this week. If your project can be vibe coded by dozens of people in mere hours...