As for context size and harnesses I did make a trivial bash agent based on this "agent in 50 lines" tutorial[0] recently, and found that for trivial work, it was about an order of magnitude cheaper and faster.

I haven't tested it on anything bigger but it doesn't seem to do the kind of proactive testing, that they do in bigger harnesses.

Codex at least has a system prompt that tells it not to consider a feature a complete until it has verified it. I'm not sure about Claude Code.

I suppose I could add that one line to the prompt, and it would get me much closer to agi :) I think Fable does this proactively even without a prompt, but I haven't tested that yet.

If Fable in my own harness is significantly cheaper than Claude Code, that would be very appealing. (I could actually afford to use it for most things!) But I think most of the cost comes from the testing it does. So we'll have to see.

[0] https://minimal-agent.com/

Fable's subscription inclusion theoretically ends EOD today. Anthropic put a wishy-washy "if we have capacity we'll continue it" thing, and given how competitive GPT 5.6 Sol is, and it is included in OpenAI's subscription, I fully expect Anthropic to extend Fable or they will have a serious exodus on their hands.

Competition is good.

I have just re-analysed most common failed tool-calls and adjusted the tool so that it works. I have a manual repair step on failure that programmatically attempts to fix some things. On failure, the harness reports the error, the repaired function, and the result. Overall, seems to work fine. But it's very model-specific. Most commonly the model fails on shell commands where it hallucinates some programs. If it does it often enough, I just promote those to commands in the PATH. Over time, it has happened less.

> and on-device model combination right now

That would depend entirely on what your device is. This sounds likely not to be an issue with the harness, but the capabilities of the models you've tried.

I experience almost no tool call failure using my nothing-special harness and DSv4 Flash.

You can't afford the best model. What are your specs and what models + quants have you tried?

Qwen 3.6 35B A3B and Qwen 3.6 27B can both do reliable tool calls on Pi at Q4_K_M using llama.cpp

Pi.dev requires some plugins to work well. Using Qwen3.6-27B/35B locally at Q8, I was quite frustrated with failed tool calls and tried many things.

Ultimately this combo worked:

1. https://pi.dev/packages/pi-tool-guard —- corrects key name synonyms and common structure errors, so tool calls succeed automatically (e.g if the model hallucinates old_str instead of oldText). It also wraps top level oldText/newText in an edits array if the tool didn’t do it.

2. https://pi.dev/packages/@aboutlo/pi-smart-edit - white-space-tolerant edits, as Qwen would sometimes add a fifth space to a four space indent

Hashline edit tools didn’t work well for me at all, they confused the model and it still failed to edit correctly. Also line removals would invalidate the rest of the file requiring re-reads. I tried pi-hashline-edit-pro, though I see it now keeps a database of hashes to help keep them stable across edits. Regardless Qwen kept thinking that the hashline prefixes were part of the source.

OpenCode, Crush and Pi do have the ability to fork a conversation. But cache reuse is up to the provider and not guaranteed. At some point you need to forward the cache to a more recent checkpoint, and you have a finite (unknown) number of parallel cached chats.