The author built a lightweight AI agent harness in pure Go using BubbleTea, inspired by opencode and other tools, to address issues like bloatware and poor task tracking, with plans to release it soon.
Why built this? I tried the grok build I really like the UI of it, I even tried to add the configs as suggested by opencode to launch the subagents with deepseek and master agent as grok, but it didn't work also there was lots of bloatware with skills (which any harness) 1.Opencode is really good, But I have noticed that most of the times it was not tracking my current tasks nor updating them properly (in the todos, I have to tell the agents to update the todo always) 2. sometimes the subagents take too much time 3. The database was around like 11 GB for the logs storage 4. If we have like long chat history it is sometimes difficult to what was the earlier prompt you gave 5. Subagents layout I have similar to the codex app, like a drawer, where you can see the history of the subagents 6. Planning to add the diff viewer, using the arrow keys and similar to the codex drawer 7. Keeping it simple similar to the PI Would you guys be interested in trying out my opencode alternative ? Will be releasing it over the next weekend probably Over the weekend Just randomly watching PI harness guy interview on the weekend, thought why not build our own harness, Took the database storage idea from the opencode UI inspiration xD/ clone from grok build + codex Overall idea of keeping things simple with the PI agent harness Build this in pure golang (BubbleTea lib) over the last 2 days binary is very small around 20mbs, have disabled the most harmful things first lmao starting with rm rf xD Currently doing some optimization, benchmarks, security checks (especially around rm rf, and other destructive commands) also the auto compactions, once done will share more info on this ! Just wanted the feedback, if anyone is interested will provide the GitHub link, if you have any request feel free to raise the issue as feature request !
The author built TinyHarness, a low-memory-footprint AI harness compatible with Ollama, Llama.cpp, and vllm, aiming to compete with tools like pi and opencode.
A technical blog post that walks through building a production-grade agentic harness around a basic LLM loop, covering typed tools, plan DAGs, tiered memory, verification hierarchies, budgets, and tracing.
This article discusses an opinionated harness for encoding domain-specific structure into agent systems, with code demonstrations showing performance gains.
The author questions the efficiency of monolithic agentic coding tools like Claude Code, suggesting that a custom harness with per-phase model routing could cut costs without sacrificing quality, and asks the community for their experiences and recommendations.
As AI coding models become commoditized, the agent harness—the control loop managing tools and workflows—emerges as the key differentiator. This guide maps the field of harnesses, weighing vendor-native performance against the portability of model-agnostic workflows.