goose: an open-source agent harness
goose is an open-source AI agent that wraps an LLM in a loop of tool calls, so the model can actually do things rather than only describe them. Its own docs put it this way:
goose, an open source AI Agent, builds upon the basic interaction framework of Large Language Models (LLMs), which primarily functions as a text-based conversational interface. It processes text input and generates text output. This “text in, text out” approach is enhanced with tool integrations, which allows the AI agent to complete tasks, creating goose.
In other words, goose is a harness: the layer that sits between you and the model, carrying requests out to tools and results back in.
How it works
- Human request. The loop starts and ends with you — a question, command, or problem to solve.
- Provider chat. goose sends your request along with the list of available tools to the LLM provider you’ve connected. The provider processes it and, if needed, emits a tool call as part of its response.
- Extension call. The model can request a tool call but not execute it — that’s goose’s job. It takes the JSON-formatted tool call, runs it, and collects the results.
- Response to model. goose sends the results back to the model. If more extensions are needed, these steps repeat.
- Context revision. goose drops old or irrelevant information so the model stays focused on what matters, which also keeps token usage in check.
- Model response. Once the tool calls are done, the model sends its final response back to you, and the loop restarts when you reply.
Interesting features
The context-engineering side of goose is, in my opinion, particularly well designed.
Split providers for planning and coding. You can point planning at one provider and code generation at another via configuration variables:
GOOSE_PLANNER_PROVIDER— which provider to use for planningGOOSE_PLANNER_MODEL— which model to use for planning
Recent models are converging on both capabilities, so you may not need two, but it’s a nice lever to have.
Subagents. You can ask goose to spawn subagents and delegate work — “create the login and logout page in parallel with two agents”. Agent creation can be constrained with Recipes: YAML files specifying the system prompt, inputs, and a timeout after which an agent that hasn’t finished is shut down.
Scheduled agents. goose has built-in scheduling — essentially cron for agent runs. It’s a minor feature, but a genuinely useful one that other harnesses like Claude Code and Codex don’t offer out of the box.
Pre-built extensions. Extensions are add-ons that connect goose to the applications and tools already in your workflow — adding features, accessing data, or integrating with other systems. They’re built on the Model Context Protocol (see What is MCP?), so goose plugs into a wide ecosystem of existing capabilities.