- Developers who want a vendor-neutral, extensible AI agent with MCP support
- Teams that prefer desktop app interfaces alongside CLI workflows
- Engineers building custom agent workflows with composable MCP extensions
openagent @ goose ~ $
cat README.md
Goose
Open-source AI agent from Block (now Linux Foundation) that automates engineering tasks via CLI and desktop app, with native MCP integration and any-LLM support.
$
brew install gooseInstalling Goose...
Goose ready
$
goose --helpReading goose configuration & environment...
●
Waiting for input... # core strengths
What makes Goose different
◆
MCP-first architecture
Goose was one of the earliest and deepest adopters of the Model Context Protocol, with 70+ documented extensions covering GitHub, Google Drive, databases, browsers, and custom APIs.
MCP-native design means Goose can connect to virtually any tool or service without waiting for vendor-specific integrations.◆
Multi-LLM provider support
Works with 15+ providers including Anthropic, OpenAI, Google, Mistral, Ollama, OpenRouter, Azure, and Bedrock. Use API keys or existing subscriptions.
Vendor-neutral design means teams can switch models based on task, cost, or privacy requirements without changing workflows.◆
Rust-based performance
Core engine built in Rust for fast startup, low latency tool calls, and efficient resource usage.
Rust gives Goose a performance advantage over Node.js or Python-based agents, especially for long-running autonomous sessions.◆
Desktop + CLI + API
Available as a native desktop app, full CLI, and embeddable API — all from the same codebase.
Developers can choose the interface that fits their workflow, from GUI exploration to scripted automation. # quick start
Your first command
$
brew install goose●
Ready. Run --help to explore. # use cases
How developers use Goose
01
Autonomous code generation and refactoring
Describe a feature in natural language and let Goose plan, implement, test, and iterate on the code autonomously.
02
CI/CD and DevOps automation
Use Goose's CLI and MCP extensions to automate build pipelines, deployment scripts, and infrastructure management.
03
Multi-step engineering workflows
Chain together MCP extensions for GitHub, databases, and cloud providers to automate complex engineering pipelines.
# comparison
How Goose compares
Choose Goose for extensibility and vendor neutrality vs Cline
Goose offers deeper MCP integration and multi-platform support. Cline has richer IDE integration and browser automation.
# faq
Questions
Q: What should I check before using Goose?
Start with one safe workflow for Goose. Inspect official setup instructions, required credentials, execution logs, approval points, and failure recovery before expanding from a sandbox task into production automation.
Q: Is Goose open source?
Goose is listed with Apache-2.0 based on the official source links in this profile. Re-check the repository, model card, or docs before production use.
Q: Who maintains Goose?
Goose was originally built by Block and is now governed by the Agentic AI Foundation (AAIF) under the Linux Foundation.
Decision brief
JSON
Should you use Goose?
- Users who need deep IDE integration over standalone desktop/CLI tooling
- Teams looking for cloud-hosted managed agent services
- Verified 2026-06-04
- License: Apache-2.0
- Repo: block/goose
- Open-source signal
Deployment
cloud
Permission surface
shell/files, external services
Decision signals
MCP
Agent packet
Structured decision data for Goose
This packet is the compact machine-readable view agents should use before following source links or taking action.
Capabilities
mcp, workflow orchestration
Constraints
open source, mcp compatible
Deployment
cloud
Permission surface
shell/files, external services
Recommended workflows
Coding agent workflow, Connector or protocol layer
Overview
What Goose does
What it is
Goose is an open-source AI agent built in Rust that automates engineering tasks through a CLI, desktop app, and API. It supports 15+ LLM providers and 70+ MCP extensions for connecting to external tools and services.
Why it matters
The Linux Foundation governance ensures Goose will remain open and community-driven long-term. Its MCP-first architecture sets a standard for extensibility, and its Rust-based engine delivers performance advantages over agent tools built in interpreted languages.
How to evaluate it
Start with one safe workflow for Goose. Inspect official setup instructions, required credentials, execution logs, approval points, and failure recovery before expanding from a sandbox task into production automation.
Facts
Known metadata and operating surface
These fields are separated from editorial interpretation so agents can reason over facts and missing checks.
Resource type agent
Category Agents
Maturity active
Difficulty Unknown
License Apache-2.0
Pricing open source
Verified 2026-06-04
Source confidence high
Risk level elevated
Fit matrix
Where Goose fits in an agent stack
strong
Coding agent workflow
Goose has multiple signals for coding agent workflow, including matching tags, capabilities, category, or positioning.
- Run a small repository change and inspect the diff, tests, and rollback path.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
strong
Connector or protocol layer
Goose has multiple signals for connector or protocol layer, including matching tags, capabilities, category, or positioning.
- Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Evaluation and observability
Goose has at least one signal for evaluation and observability, but should be checked against a real task before adoption.
- Add one repeatable test case and confirm results can run again in review or CI.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Local or private AI stack
Goose has at least one signal for local or private ai stack, but should be checked against a real task before adoption.
- Verify hardware requirements, data path, storage, and whether all calls stay in your environment.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Reusable skill workflow
Goose has at least one signal for reusable skill workflow, but should be checked against a real task before adoption.
- Run one skill end to end and check whether it produces evidence or structured output.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak
Browser automation
Goose is not primarily positioned for browser automation in the current metadata.
- Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
Inputs and outputs
What an agent should inspect
Likely inputs
- Repositories, files, issues, terminal output, and test results
- Tool schemas, API requests, service resources, and auth scopes
- Prompts, messages, documents, images, or model inputs
- Official setup instructions and a small real workflow
Likely outputs
- Diffs, commits, explanations, test results, or review notes
- A decision on whether this resource fits the target workflow
Evidence
Sources, claims, and missing checks
Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.
GitHub github
Repository source for code, license, issues, releases, and implementation details.
Homepage homepageOfficial or project-controlled source for this resource profile.
Docs docsDocumentation source for setup, API shape, and operational behavior.
Goose is listed as open source.
License metadata: Apache-2.0Goose has a recorded GitHub repository: block/goose.
Resource facts and GitHub source link.Goose supports these recorded deployment modes: cloud.
OpenAgent decision signal metadata.Goose is tagged with mcp, workflow orchestration capabilities.
OpenAgent capability taxonomy.- Repository freshness has not been recorded.
Next action
How to start evaluating Goose
Inspect repository
Check license, recent activity, issues, examples, and security-sensitive code paths.
Open sourceOpen Homepage
Start from the official source before adopting third-party instructions.
Open sourceRead setup docs
Use docs as the source of truth for installation and supported interfaces.
Open sourceInstall Goose via Homebrew
Install via Homebrew on macOS, then run 'goose' to start the CLI. Also available as a desktop app for macOS, Linux, and Windows.
brew install goose Compare
Alternatives and nearby resources
Use related resources to compare category fit, license, deployment model, and first-workflow behavior.
FAQ
Common questions about Goose
What should I check before using Goose?
Start with one safe workflow for Goose. Inspect official setup instructions, required credentials, execution logs, approval points, and failure recovery before expanding from a sandbox task into production automation.
Is Goose open source?
Goose is listed with Apache-2.0 based on the official source links in this profile. Re-check the repository, model card, or docs before production use.
Who maintains Goose?
Goose was originally built by Block and is now governed by the Agentic AI Foundation (AAIF) under the Linux Foundation.
Can Goose run fully offline?
Yes. Goose can be configured with local models via Ollama for fully offline operation, with no data leaving your machine.