I also wish the discussion around Pi did not always use cost/token count as the metric. It's amazingly token efficient, but how does it stack up again opencode and others if you don't care about token count?
My experience is that the harness is mainly polish preventing failed tool calls, bad edits, stuff like that, but doesn't make much difference to the overall "intelligence". But that opencode seems slightly more robust against stupid errors than out of the box Pi due to the additional context it forces through every thread.
The harness becomes more and more important, the smaller the model is as you need to offload context management as well as memory to the harness. The big models basically just need a bash prompt tooling and you let the model manage everything inside its own context.
At this point when all the models have been trained on all available data with the similar algorithm,
1. either you get more data which is not feasible,
2. or get a better algorithm - a possibility ,
3. or write a more targeted harness.
Harnesses for legal, medicine and all are the ones which are getting focus for this reason. Writing benchmarks for these targeted harnesses would be a catching task
Tell a horny monkey not to jerk off. How the fuck … would that even be possible? God, only God can stop this godless train.
There is no sincere discussion to be had here. HN has been a cesspool for marketing and it reeks in here lately.
Edit: I am not punching down, it’s gotta be crooks from those companies down-voting.
E.g. for editing files with Claude models you should use Edit(file_path, old_string, new_string, replace_all) but with GPT models you should use apply_patch_call(patch) (where patch is a custom patch string with custom grammar).
It appears newer models are better at narive harness tool calls and worse at custom tools that look similar to default tools.
https://lucumr.pocoo.org/2026/7/4/better-models-worse-tools/
This is true. For example, the other day I was doing some testing with DeepSeek-V4-Flash-0731, and I gave it a tool called "EditFile" with "old_content" argument. It ignored the tool definition and called it with "old_string" instead, presumably because it was trained on a bunch of Claude Code traces.
So either make sure your tools look nothing like the tools in the popular harnesses, or make them similar.
identical. similar is what you tried and it didn't work.
Or is that just why my model likes to break out of the sandbox, going strait to exec shell command and editing files using python on the cli?
not RegEx ... more like a "fuzzy" DSL.
I can understand this take 4-5 years ago but I have no idea how that's your position in 2026
We are still witnessing the same underlying problems of transformers.
[0] Think back to all the publicity stunts like the Hugging Face. They are meant to convince you that the agents have somehow progressed past the transformer limitations when those publicity stunts are actually expressions of transformer limitations.
Having a coding harness is critical but the differences between them are overstated.
Personally, I’ve replaced OpenCode with a thin wrapper around Pydantic-AI as the pythonic analogue to Pi-Agent for headless use via Hermes
They’d all do the job - I just prefer to compartmentalize for access control.
Keeping the harness’ surface area tiny had the added benefit of preserving my understanding and being able to adapt it to my preferred workflow effortlessly
It's the killer feature from me personally, often when wanting to troubleshoot for example things like Kubernetes workloads. LLMs are now really good at it, but we doesn't want them to like delete a pod.
Other harnesses like Codex have often on static rules, like the allow/deny of claude code, that can filter out based on regex. It's quite good already, but sometimes the model can find a way to write something that wasn't anticipated, or in a convoluted way.
After, I guess it's something that can be added in an open-source harness like Pi, and add like this new Jev model or something else equivalent
Personally, I’m not convinced that a cascade of LLM judges lets me sleep soundly at night.
My agents stay in their sandbox and escalating changes to that env or prod is solely in the (human) operator’s domain.
Obviously this is less convenient but I think it’s a necessary tradeoff for security and correctness.
With coding agents, what I’ve noticed is that a simple task can often be handled with a fairly simple harness. But the hidden cost is really around context. One of the more interesting things I’ve seen is that two different harnesses can make a similar number of model calls while consuming a very different amount of context.
I think I recently came across a paper comparing Claude Code and Pi that touched on this. More context, more tooling, focused context, simpler loops, all of these can lead to very different costs and performance, even when the number of model calls looks similar.
Optimal harnesses use concurrent execution + subagents and are not stuck on one model. Cost and performance are impacted GREATLY by these tactics, regardless of the native agent context (instruction). This kind of single-harness analysis is shallow and misleading, although the finding that "Provider-specific optimization does not guarantee the best pairing" is probably correct, depending on how you measure.
It is a starting point.
This is interesting. We built our own harness (CodeBot, an agent for Delphi) and it currently uses OpenAI models; we tuned it for specific behaviours and patterns and I find its behaviour better than Codex. Same models underneath.
Do harnesses and therefore sending the queries directly to the LLM providers have caching and other benefits that OpenRouter does not provide? Would I get any of those benefits if I simply proxied any requests to the major providers' harnesses through OpenRouter? Or only if the requests go straight from the harness to the provider's API?
As per your response it sounds like at least caching would happen for any provider regardless of the request's origin.
In my experience if you're using OpenAI/Claude models and paying API costs, almost every other harness beats Claude Code/Codex in cost.
In the case of DeepSeek-V4-Flash-0731 on deepinfra.com there was little difference when both used high reasoning. In the case of that same model on together.ai there was a substantial difference between the two (high reasoning for both again). When using together.ai with Kilo Code the LLM was having a lot of trouble making successful edits. In some cases that meant a lot tries at using the tools and in others it worked around by running scripts. Meanwhile it used the tools from my harness just fine. I've specifically tried to make my tools easy for all of the open weight LLMs to use correctly. That was inspired by getting some errors from Kilo Code at the beginning of the year telling me that the model was having trouble and I should use a smarter model.
"Why pay the waste disposal tax? Dumping into the ocean is free!"
Pi actively omits any sort of guardrails and sandboxing in the name of speed and simplicity, so it is not shocking that it is faster and simpler.
Doubling the cost of something in the name of vague security is standard operating procedure for big enterprises, maybe even quite cheap.
(I do think Pi should ship with more than zero builtin sandboxing though)
Funny to me how stuff is described these days. Seems like some people lack the full understanding of all these different systems and really overcomplicate their harness setups or use super bloated all in one tooling.
I'm looking at changing my AI harness and am definitely interested in this
They add a --tools flag, which can only add tools onto the built-in tools, so if you wanted to sandbox pi.dev by adding sandboxed versions of the existing tools you will have to run pi.dev via
pi --no-tools --tools tool1, tool2, tool3
So if you want to sandbox your agent, guess what you'll have to do? Yep, you have to supply --no-extensions.
Here is how to run pi.dev under its most minimal configuration under a sandbox:
pi --no-tools --no-extensions --tools tool1, tool2, tool3 -e ./your-sandboxing-extension
Um, no? Have you actually read Claude Code's system prompt and tools?
Here's Fable's system prompt: https://github.com/navanchauhan/agent-autopsy/blob/6d9c00e54...
There are a few lines of what I would call "for security and alignment purposes", but it's not the majority. And the majority of the bloat is from the tool descriptions.
The system prompt in this case has pretty much nothing to do with the built-in guard rails, that is: you cannot sidestep Claude's guard rails by editing/replacing the system prompt (which you officially can with Claude Code's `--system-prompt-file`).
Two examples from daily use on a small business's systems. Telling it to batch browser calls in the system prompt didn't hold; a hook that denies the seventh consecutive single call did. Telling it to record which machine did a piece of work didn't hold either; having the CLI stamp the hostname did.
Both are nearly free in tokens, and that layer is where the reliability actually came from. The prompt-size argument skips straight past it.