opencode VS goose

Dax, the creator of OpenCode — an open-source coding agent, of all the people one might expect to disagree — put the case for hand-coding cleanly in a recent interview about Spec-Driven Development:

Pipa is our agent for studio operations at Lunch Pail Labs. She lives in Slack, is powered by E2B sandboxes, and uses OpenCode for the harness.

One reason I liked OpenSpec more than others, such as Kiro and SpecKit, is that OpenSpec is highly portable between coding assistants, codebases and stages of development. I've plugged OpenSpec into all sorts of projects, from infrastructure to Python libraries. I also work on diverse codebases, with a variety of AI friends that OpenSpec supports: OpenCode, Codex and Claude Code.

Beyond the big names, I would also keep an eye on OpenCode and T3 Code. OpenCode is a solid open-source, terminal-first option if you want a model-agnostic agent and bring your own provider setup. T3 Code is interesting for the opposite reason: it gives you an open-source GUI on top of the agents you may already pay for, like Claude Code, Codex CLI, OpenCode, or Cursor.

Brought to you by the infamous creator of OpenCode, who will exfiltrate all your data, then feign ignorance:

https://github.com/anomalyco/opencode/issues/10416

Source: https://github.com/aaif-goose/goose

Maybe check out Goose. It is the standard agent harness being developed by The Linux Foundation under the AAIF. Under active development and the implementation seems to have a good leg up on the other popular agents.

https://github.com/aaif-goose/goose

https://goose-docs.ai/

As a maintainer of goose, an open source AI agent, I have spent 2025 and 2026 sifting through pull requests. I do not want to limit who can contribute. I want everyone to be able to contribute. The goal is to understand the context of a PR at a fast pace, even if an external contributor uses an agent to build it. Open source needs infrastructure built to support both human-authored and agent-authored work.

Goose is an open-source AI agent that can act as an MCP Client and has a visual form to configure the extension name, type (STDIO), and the command it runs every time it starts. Internally, it stores config in a YAML file you never touch.

I had 55 pages of documentation to write, 59 screenshots to capture, and a product that was still shipping features and being rebranded weeks before release. I did it in four days with Goose, an open-source AI agent by Block, part of the Linux Foundation, and I want to walk you through exactly how. Not the polished version. The real one: how I built it, how it works, everything that broke along the way, and what I learned from it.

These are already available. Mistral’s Vibe CLI[1] is open source. Tools like goose[2]are API agnostic.

[1] https://github.com/mistralai/mistral-vibe

[2] https://goose-docs.ai/

Much of my previous work with GitHub and Block (goose) was focused on using agents to build faster. Recently joining the team at Entire has pushed me to think more about the next layer: making agentic work durable and accountable.

you can run https://block.github.io/goose/ in headless mode (I work on goose)