Goose

Open-source extensible AI agent now governed by the Agentic AI Foundation (AAIF) at the Linux Foundation. Started inside Block (formerly Square). 25+ provider support including Ollama, Ramalama, Docker Model Runner. Best for terminal-native workflows where you want shell + file + multi-step orchestration in one place.

Install via npm: npm install -g @gooseai/goose or via pip: pip install goose-ai. Requires Node.js 18+ or Python 3.10+. Goose is an agentic CLI tool from Block (Square) that executes multi-step engineering tasks using LLMs. Authenticate with your provider: set ANTHROPIC_API_KEY or OPENAI_API_KEY. Run: goose run "Add a health-check endpoint to src/server.ts". Goose starts a session, reads relevant files, plans changes, and executes them — editing files, running commands, and iterating on results. It supports multiple MCP (Model Context Protocol) servers for extended capabilities: database access, API integration, browser control. Configure MCP servers in ~/.goose/config.yaml. First run downloads the tooling (~30 seconds) and begins task execution. Verify: goose run "What version of Node.js is installed?" — Goose runs node --version in a shell and reports the result. Goose is designed for UNIX-like environments (macOS, Linux, WSL2); native Windows support is limited. Time-to-first-action: ~10 seconds including model API latency. Works with Anthropic, OpenAI, and Google Gemini providers.

Best for: multi-tool orchestration tasks where the agent needs to coordinate across databases, APIs, filesystems, and code in a single workflow, MCP-server-heavy environments where you've already built tool interfaces for your infrastructure, DevOps and platform engineering tasks (check CI status, update config, deploy preview), developers comfortable with terminal-native agents who want extensibility beyond code editing. Not suited for: code-only pair programming (use Aider or Claude Code), Windows-native development (Goose targets UNIX-like environments), teams without MCP infrastructure investment (Goose's value compounds with MCP tool investment), lightweight autocomplete or chat in IDE (use Continue or GitHub Copilot).

Use Goose when you want an extensible agentic CLI tool with MCP server integration — connect to databases, APIs, filesystems, and browsers through MCP servers that Goose orchestrates. The MCP ecosystem is Goose's differentiator: you can give it read access to production databases or your calendar, and it can coordinate across them. Switch to Claude Code for a more polished terminal agent with better reasoning for complex code tasks — Goose is more about orchestration across tool interfaces. Use Aider for git-native pair programming with code-focused edits — Goose is a general agent, Aider is code-specific. Use Cline for VS Code-integrated agentic coding — Goose is terminal-native. Use Open Interpreter for an interactive Python/R/Shell agent. Goose's unique value is MCP — if you need an agent that can query your database, check your CI status, and edit code in a single workflow, Goose is the right tool.

Problem: Error: MCP server connection refused when Goose tries to use an MCP tool. Fix: The MCP server process must be running and accessible. Check ~/.goose/config.yaml for the correct server command and ensure the server binary/npm module is installed. Start MCP servers manually first to debug: run the command from config.yaml directly and verify it starts without errors. Goose launches MCP servers as child processes — any stdout noise can confuse the MCP protocol handshake. Problem: Goose makes changes without asking permission. Fix: Goose's default permission model varies by version. In ~/.goose/config.yaml, set require_approval: true to gate file writes and terminal commands. Goose's MCP tool access is controlled per-server — restrict write-access servers to read-only in config if you want consultative use. Problem: "Tool call failed: unexpected token" with local models via Ollama. Fix: Goose's tool-use format is provider-specific. When using Ollama, Goose sends tool descriptions in a format that some models (especially smaller ones) handle poorly. Use a tool-calling-aware model like Llama 3.1 8B with tool support or Mistral 7B v0.3. Verify the local model responds correctly to tool-calling prompts by testing with ollama run <model> --format json.