Open-source, extensible AI agent that goes beyond code suggestions to install, execute, edit, and test with any LLM, available as a desktop app, CLI, and API with 70+ MCP extensions.
Open the source and read safety notes before installing.
Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.
Decision playbook
Signals are present but mixed. Use the checklist below to confirm the source and operational safety for your environment.
0
63
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No baseline selected
No major trust-signal divergence detected in the current selection.
Confirm ownership and provenance before trusting install instructions.
Source link availableRequired
Open the canonical repository and verify ownership.
Source provenance statusRequired
Marked as source-backed.
Metadata reviewed
No reviewed flag detected in metadata.
Validate risk disclosures before installation or API wiring.
Safety notes presentRequired
Review the listed safety guidance before running commands.
Privacy notes presentRequired
Review data handling notes before connecting accounts or secrets.
Trust level risk gateRequired
Trust level does not block evaluation.
Check package metadata and artifact integrity signals.
Install payload available
Install or copy payload is available for review.
Package verification flag
No package verification flag provided.
Checksum metadata
No checksum provided for downloaded artifact.
Use compare context to validate trade-offs before adoption.
Compare tray has multiple entries
Add at least one more entry to compare trust differences.
Baseline comparison available
No baseline peer selected yet.
Diverging trust signals identified
No major trust-signal divergence found.
Setup at a glance
Copy-ready — paste the snippet to get started.
Adoption plan
Current risk score 24/100. Use staged verification before broader rollout.
Validate source and review signals before any execution.
Confirm source provenanceRequired
Source URL/provenance metadata is present.
Confirm metadata review state
No review metadata found; increase manual validation.
Verify install payload
Install/config payload exists and can be inspected.
Confirm safety, privacy, and package integrity signals.
Review safety notesRequired
Safety notes are present.
Review privacy notesRequired
Privacy notes are present.
Verify package integrity metadata
No package verification/checksum metadata.
Adopt in controlled steps based on the selected plan.
Run in isolated sandbox firstRequired
Use a constrained sandbox and observe behavior across multiple tasks.
Roll out graduallyRequired
Roll out to a small cohort before wider usage.
Set monitoring and fallback
Define rollback path and monitor errors after adoption.
Evidence readiness
Missing required evidence: Metadata review. Risk score 31.
Source repository/provenance is listed.
Required in this preset
Review metadata is missing.
Required in this preset
Safety notes are present.
Required in this preset
Privacy notes are present.
Optional in this preset
Package integrity metadata is missing.
Optional in this preset
Install payload is available.
Required in this preset
Required gaps: Metadata review
Decision timeline
Blocking gaps: Check metadata review status. Risk 28.
triage
Source/provenance metadata is available.
triage
Review metadata is missing.
verify
Safety notes are available.
verify
Privacy notes are available.
verify
Package integrity metadata is missing.
rollout
Install payload is available.
Blockers: Check metadata review status
Prerequisite readiness
3 prerequisites to line up before setup. Have accounts and credentials ready first.
Safety & privacy surface
4 safety and 3 privacy notes across 5 risk areas. Review closely: credentials & tokens, permissions & scopes, third-party handling.
Disclosure: editorial
## Overview
Goose is an open-source, extensible AI agent that goes beyond code suggestions —
it installs, executes, edits, and tests with any LLM. It runs as a native
desktop app, a CLI, and an API, and is used for research, writing, automation,
data analysis, and code tasks.
It is released under the Apache-2.0 license and is primarily Rust. The project
moved from `block/goose` to the Agentic AI Foundation (AAIF) at the Linux
Foundation, and its current home is `aaif-goose/goose`.
## Features
- Works as a desktop app, CLI, and API.
- Provider flexibility across 15+ providers including Anthropic, OpenAI, Google,
Ollama, OpenRouter, Azure, and Bedrock.
- Connects to 70+ extensions via the Model Context Protocol (MCP).
- ACP integration to use existing Claude, ChatGPT, or Gemini subscriptions.
- General-purpose automation beyond code generation.
## Use Cases
- Run a local AI agent that can execute and test changes, not just suggest them.
- Use your preferred provider or an existing subscription via ACP.
- Extend the agent with MCP extensions for tools and data sources.
- Automate research, writing, and data tasks alongside coding.
## Installation
Install the desktop app or CLI from the official installation guide at
[goose-docs.ai](https://goose-docs.ai/docs/getting-started/installation). Choose
your platform build and configure an LLM provider before first use.
## Disclosure
Editorial listing. No paid placement or affiliate relationship. Goose is open
source under Apache-2.0 and hosted by the Agentic AI Foundation; using it
requires your own LLM provider access.Goose is an open-source, extensible AI agent that goes beyond code suggestions — it installs, executes, edits, and tests with any LLM. It runs as a native desktop app, a CLI, and an API, and is used for research, writing, automation, data analysis, and code tasks.
It is released under the Apache-2.0 license and is primarily Rust. The project
moved from block/goose to the Agentic AI Foundation (AAIF) at the Linux
Foundation, and its current home is aaif-goose/goose.
Install the desktop app or CLI from the official installation guide at goose-docs.ai. Choose your platform build and configure an LLM provider before first use.
Editorial listing. No paid placement or affiliate relationship. Goose is open source under Apache-2.0 and hosted by the Agentic AI Foundation; using it requires your own LLM provider access.
Show that Goose is listed on HeyClaude. Paste this Markdown into your README — it renders the badge and links back to this page.
[](https://heyclau.de/entry/tools/goose)Goose side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.
1 trust signal differ across this comparison (Submitter).
Next steps differ across entries — use the actions in the table below to copy install commands and source links per resource.
| Field | Open-source, extensible AI agent that goes beyond code suggestions to install, execute, edit, and test with any LLM, available as a desktop app, CLI, and API with 70+ MCP extensions. Open dossier | Open-source framework for building internal coding agents that accept tasks via Slack, Linear, or GitHub, execute code changes in isolated cloud sandboxes, and open draft pull requests automatically. Open dossier | Open-source status companion for Claude Code and Codex with live local session state, your-turn alerts, usage views, and native macOS and Windows applications. Open dossier | Open-source CLI and web ecosystem for recording, replaying, live streaming, sharing, and embedding terminal sessions as lightweight asciicast files. Open dossier |
|---|---|---|---|---|
| Next stepsDiffers | ||||
| Trust | ||||
| Review status | Not reviewed | Not reviewed | Not reviewed | Not reviewed |
| Package trust | Package not verified | Package not verified | Package not verified | Package not verified |
| Source provenance | Source-backed | Source-backed | Source-backed | Source-backed |
| SubmitterDiffers | JPette1783 | JPette1783 | tristan666666 | oktofeesh1 |
| Install risk | Review first | Review first | Review first | Review first |
| Notes | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ |
| Brand | — | |||
| Category | tools | tools | tools | tools |
| Source | Source-backed | Source-backed | Source-backed | Source-backed |
| Author | Agentic AI Foundation | LangChain | Tristan Tang | asciinema |
| Added | 2026-06-05 | 2026-06-05 | 2026-07-15 | 2026-06-03 |
| Platforms | ||||
| Harness | ||||
| Source repo | 49.4k repo stars | — | — | — |
| Safety notes | ✓Goose installs, executes, edits, and tests code and runs commands locally, so it can change files and system state on your machine. It connects to 70+ MCP extensions; each extension adds capabilities and its own integration risk, so enable only those you trust. Review actions and generated code before allowing changes to important repositories or systems. Because it works across 15+ providers, confirm which provider and model a session uses before sending sensitive context. | ✓Each task runs in an isolated cloud Linux sandbox (Modal, Daytona, Runloop, or LangSmith) to prevent production impact. The agent executes shell commands, file operations, web fetches, and HTTP requests inside the sandbox without confirmation prompts — review sandbox provider permissions before deployment. GitHub operations are performed through a GH_TOKEN proxy; scope token permissions to the minimum required repositories. Subagent orchestration can spawn parallel child agents — set appropriate step limits and monitor LangSmith traces to prevent runaway execution. AGENTS.md or CLAUDE.md at the repository root is injected into the system prompt; review this file to control agent behavior and conventions. | ✓Agent Island reads local Claude Code and Codex session files to determine session state; review the requested filesystem access before use. The macOS release is ad-hoc signed rather than Apple-notarized, so first launch requires right-clicking the app and choosing Open. Windows packages are distributed through GitHub Releases, Scoop, and winget; verify the release source before installation. | ✓Terminal recordings can capture secrets, prompts, commands, file paths, hostnames, account names, API output, and generated code, so review and redact casts before publishing or embedding them. Live streaming exposes terminal output as it happens; use it only in approved environments and avoid production shells, private repositories, credentials, customer data, and destructive commands. Optional input and environment capture should be configured deliberately because it can record keystrokes, command context, and metadata that are more sensitive than the visible terminal output. |
| Privacy notes | ✓Your code and prompts are sent to whichever LLM provider you configure; data handling follows that provider's policies. API keys and provider credentials should be stored securely and never committed to source control. MCP extensions can access local files and external services depending on their scope; review what each extension can reach. | ✓Repository code, Linear issue history, and Slack thread history are sent to the configured model provider API. Sandbox providers (Modal, Daytona, Runloop, LangSmith) process task execution data according to their own privacy policies. LangSmith tracing, when enabled, logs full agent traces including tool inputs and outputs — configure retention and access controls in your LangSmith organization. GitHub OAuth tokens and model API keys should be stored as secrets and never committed to the repository. | ✓Session monitoring is local and the project states that the app has no Agent Island account and no product telemetry. Usage views may call provider usage APIs with existing local credentials; those requests remain subject to the provider's privacy terms. Local transcript files and provider credentials can contain sensitive data and should not be included in public screenshots, issues, or logs. | ✓Local `.cast` files can contain terminal output, timing data, window size, command metadata, titles, captured environment fields, and optionally keyboard input. Uploading or streaming through asciinema.org sends recordings or live terminal output to the public asciinema service unless visibility and sharing settings are reviewed first. Self-hosted asciinema servers still need access control, retention, backup, log, database, and upload-storage policies for recorded terminal sessions. |
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| Claim | Unclaimed | Unclaimed | Unclaimed | Unclaimed |
Self-hosted coding agent framework built on LangGraph — trigger via Slack, Linear, or GitHub and get automatic PRs from isolated sandboxes.
Monitor local Claude Code and Codex sessions from a native desktop companion and get notified when an agent needs your attention.
Source-backed guides for putting this to work.
Secure open-source repos for Claude Code agentic workflows with MCP policy, permissions, review gates, and contributor safety practices.
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