Disclosure: I am the maintainer of Tura. I am posting this to ask for candid advice about open-source communication and evaluation, not as an independent review of my own project.
Since the beginning of 2026, projects such as RTK, Caveman, and Ponytail have claimed that they can reduce token usage by 80–90%. Some gained tens of thousands of GitHub stars in a very short time.
My previous startup was building an e-commerce chatbot. The product was close to what later became the Universal Commerce Protocol (UCP). After UCP was released, I decided not to continue in that direction.
Earlier this year, I began turning the architecture we had built for e-commerce chatbot agents into an SDK. Put simply, its core idea is to use state machines and deterministic execution sequences to reduce the number of round trips to LLM providers by around 80% for the same task, thereby reducing token consumption.
I understand why tools such as RTK are ineffective in genuinely long-running tasks. I published a report on July 18, and JetBrains published a report on July 20 that reached essentially the same conclusion: token-saving plugins such as RTK and Caveman have little or no effect on real-world, long-running tasks.
I also believe I know how to achieve a real reduction of more than 80% in token consumption during long-running tasks. However, I have found it much harder to communicate and promote this open-source project than to build it.
What I think went wrong
Most coding-agent users do not care what actually drives token consumption
There is a large gap in understanding among coding-agent users, whether they have formal software-engineering training or are self-taught "vibe coders."
A long article that systematically explains the mechanism and challenges existing assumptions has little chance of spreading in forums already flooded with AI-written posts and plugins claiming 95% token savings.
Simple ideas are easier to spread. People are not purely rational; we tend to believe explanations that are easy to understand. That is also why pseudoscientific content based on incorrect causal explanations can attract much more attention than careful explanations of the underlying mathematics.
The author's arrogance
This is probably a combination of two problems.
From the perspective of someone who strongly believes in scientific testing, evaluations, and benchmarks, software-engineering claims without an evaluation can feel meaningless.
But perhaps my own arrogance—and my contempt for, or jealousy of, tools such as RTK—has made me less willing to explain and promote my work in ways that would actually be effective.
If you have read this far, I would genuinely appreciate your advice:
- How can I communicate this project more effectively without sacrificing rigorous evaluations and benchmarks?
- What evidence would make you willing to try an unfamiliar coding-agent SDK?
- Is the message too technical, too combative, or aimed at the wrong audience?
- What should I change on the repository page or in the benchmark presentation?
I know that building a coding agent with Codex or Claude is relatively easy. But I also believe there will always be people who value benchmarks and evaluation harnesses.
The project was released on July 16 and currently has a little over 400 GitHub stars:
https://github.com/Tura-AI/tura
I welcome critical feedback, including reasons you would not use it.