Exploring opencode

I started using opencode around May of 2026, and it truly felt like a milestone in incorporating AI into my daily routine. I mentioned in my May monthly recap that I intended to write a dedicated post about the experience, this is that post.

Some context first. I started using AI coding agents (or harnesses, as I prefer to call them) around August of 2025, with Cline on VSCode, and then moved on to Claude Code in January of 2026. In both cases this happened at work, for purely professional purposes, coding and other software engineering tasks. It was only when I began experimenting with these tools on my personal computer, for a much broader range of tasks, that I eventually discovered opencode. It was a spark that changed a lot of how I think about and use harnesses, for a few reasons.

What Sets It Apart

Install And Start Using

The first thing that struck me is how simple the setup is. With every other harness I have used or looked into (Cline, Claude Code, Codex, etc.), there is a whole ritual before you can do anything: create an account, choose a subscription plan, log in through the tool, and so on. With opencode, you install it and start using it. That is the entire onboarding. It sounds like a small thing, but it completely changes the feeling of picking up the tool for the first time.

Free Access To Reasonably Good Models

The second reason is that opencode currently offers free access to reasonably good models, without any severe limits. The obvious caveat: it is impossible to know for how long this will be sustainable, and opencode might well start limiting usage or requiring a paid subscription in the future. The slightly less obvious caveat: every session needs to be treated as likely shared with the companies that created the model, as the simplest benefit a company can get from giving access to their models for free is data for future training... so, best not to share any personal or sensitive information. Regardless, right now it makes opencode a perfect tool for experimentation.

By lowering the cost of exploration to basically zero, it allows everyone to try the harness for all sorts of things, not just coding. These tools have access to terminal commands, beyond reading files and accessing the web - which means that anything that can be done on a computer can be explored through AI with them. And a huge amount of our society runs on computers, so the potential is enormous. To give a few examples of things I have done: searching for a public, free web radio station from any country (Argentine rock forever!) and playing it directly through the terminal, looking up lyrics and other info about new music I found, and debugging and fixing a broken game installation by letting the agent look at the actual configuration files. Without the burden of knowing you have a certain limit of tokens to spend, you feel free to explore all sorts of random things that an AI agent might do with a computer.

I am even considering making opencode the very first thing I install, the next time I setup a brand new OS installation (likely Linux, as it is my default OS). I find it intriguing to think what an AI-based distro would look like, though I would not want to go to the extreme yet. In my mind just being able to bootstrap the OS with opencode, and then using it to install everything else that I need (as it would have access to the package manager of the distro) by just talking with the computer feels really cool.

Open And Provider-Agnostic

The third reason is that opencode is a fully detached, open source agentic harness that can work with models from several providers. Most tools in this category fail at least one of these criteria: they are IDE-first (Cline), closed source, or tied to the provider that created them (Claude Code with Anthropic, Codex with OpenAI). Pi is an alternative that is also detached, open source and provider-agnostic, but it is far less complete when it comes to features and capabilities.

Being provider-agnostic also means opencode can be used with local models. So even if the free access to hosted models disappears in the future, it will still be possible to run it for free with local ones. That is a kind of insurance that no closed, single-provider tool can offer.

Limitations

Despite all the positive aspects, there are some limitations as well:

Model Quality

While the free hosted models are good, they do not reach the same level of quality as frontier models. For more complex or nuanced tasks, they often do not achieve the best results. This can be worked around with more guidance and handholding from the user, but it takes more work. For the absolute best quality, proprietary models (which usually work better with their own company's harness) are still the way to go.

Context Management

Context management is often a problem. Most of the free models offered have a context size of at most 128k tokens, which gets consumed very quickly - the tool seems to be quite aggressive about how much it puts into the context. It does perform automatic summarization when it gets close to filling the available context, which is good, but more often than not the summarization loses some information that is essential to the task at hand. In practice, this means the quality of the results degrades very quickly, making opencode not very suitable for long-running tasks.

Closing Thoughts

For me, the real significance of opencode is not any single feature, but what the combination enables: a tool that anyone can install and immediately start using to explore what AI agents can do with a computer, at no cost. The frontier of quality still belongs to the proprietary models and their harnesses, and the limitations around context are real. But as a gateway for experimentation, and as an open, provider-agnostic foundation that will keep working regardless of what any single company decides, it has earned a permanent place in my toolbox. I expect to keep exploring what these agents can do well beyond coding, and I am sure some of those experiments will end up here on the blog.