What each product actually is
Goose — Agentic AI Foundation local agent (Desktop, CLI, API)
Goose is a general-purpose AI agent that runs on your machine. Per goose-docs.ai and the aaif-goose/goose README, it ships as a native desktop app (macOS, Linux, Windows), a full CLI for terminal workflows, and an API to embed it elsewhere. It is built in Rust and positioned for code, research, writing, automation, data analysis, and other local agent work — not only autocomplete-in-the-editor.
Goose connects to 15+ model providers (Anthropic, OpenAI, Google, Ollama, OpenRouter, Azure, Bedrock, and more), including API keys or existing Claude / ChatGPT / Gemini subscriptions via ACP. Extensions use the Model Context Protocol (MCP); docs and README cite 70+ extensions. Recipes capture workflows as portable YAML. Goose is part of the Agentic AI Foundation (AAIF) at the Linux Foundation.
Catalog places Goose in terminal CLI tools, AI CLI agents, and AI coding assistants. It is not marked recommended. Alternatives already point Goose buyers toward opencode, crush, pi-coding-agent, codex, droid, and claude-code. Live pages: Goose, Goose Review. Official docs: goose-docs.ai. Source: github.com/aaif-goose/goose (Apache-2.0 LICENSE on main).
OpenCode — open-source coding agent (terminal, IDE, desktop)
OpenCode is an open-source AI coding agent. Per opencode.ai and opencode.ai/docs, it is available as a terminal-based interface, a desktop app, and an IDE extension. The product thesis is a coding-agent loop: project init (AGENTS.md), plan/build modes, multi-file edits, undo/redo, shareable sessions, and any LLM provider — including an optional OpenCode Zen gateway of curated models.
Installation paths documented by the vendor include the install script, npm/opencode-ai, Homebrew (anomalyco/tap/opencode), Scoop, Chocolatey, and platform packages. The GitHub repo lives at anomalyco/opencode with an MIT LICENSE on master.
Catalog places OpenCode in terminal CLI tools, AI CLI agents, and AI coding assistants. It is not marked recommended. Alternatives already point OpenCode buyers toward codex, goose, claude-code, and pi-coding-agent. Live pages: OpenCode, OpenCode Review. Official: opencode.ai.
Same intent, different emphasis
Both answer “run an open agent that edits and executes.” Shared intent is why this comparison exists. Goose leans local general-purpose agent + MCP depth + AAIF governance. OpenCode leans coding-agent UX across terminal / IDE / desktop with any-provider convenience. Prefer fit over a one-point overall Score gap.
Current surfaces (per vendor pages)
Surfaces below come from live Goose and OpenCode docs/GitHub as of 2026-09-24. They support fit decisions; they are not a substitute for the locked Score table.
Goose
Desktop + CLI + API. Install docs cover Goose Desktop downloads (macOS / Linux / Windows) and CLI install via https://github.com/aaif-goose/goose/releases/download/stable/download_cli.sh or Homebrew (block-goose / block-goose-cli). Desktop and CLI share provider, model, and extension configuration.
Providers and local models. First-run configure flows cover API keys, ChatGPT subscription login, OpenRouter, Tetrate Agent Router, and manual providers. Local Ollama is part of the model-agnostic story on docs and README.
MCP extensions, recipes, subagents, ACP. Docs and marketing pages describe 70+ MCP extensions, recipes as portable YAML workflows, subagents for parallel work, security controls (permissions / sandbox language on the home page), and ACP server / ACP provider paths for editor and subscription bridges.
Licensing. Apache License 2.0 — confirmed via LICENSE on aaif-goose/goose main and the README badge. Software fee $0; model cost is BYOK, subscription bridge, or local inference.
Sources: goose-docs.ai · Installation · Recipes · github.com/aaif-goose/goose · LICENSE
OpenCode
Terminal + IDE + desktop. Docs describe a terminal UI as the core path, plus desktop app and IDE extension. Desktop builds are listed on releases / download pages (macOS, Windows, Linux packages); Homebrew cask opencode-desktop appears in the README.
Any provider + Zen. Docs walk /connect for providers and recommend OpenCode Zen as a curated model gateway for newcomers. Site copy emphasizes 75+ providers via Models.dev, including local models, plus login paths for GitHub Copilot and ChatGPT Plus/Pro accounts.
Coding-agent loop. Project /init creates AGENTS.md. Plan vs Build modes, @ file search, undo/redo, and /share session links are documented on the getting-started docs. Multi-session and LSP-enabled coding assistance appear on the product site.
Licensing. MIT License — confirmed via LICENSE on anomalyco/opencode master. Software fee $0; model cost is BYOK, Zen usage, or local inference.
Sources: opencode.ai · opencode.ai/docs · github.com/anomalyco/opencode · LICENSE
Scoreboard (from existing reviews)
Overall: Goose (tested 2026-03-29) 84; OpenCode (tested 2026-09-05) 83.
Speed: Goose (tested 2026-03-29) 80; OpenCode (tested 2026-09-05) 81.
Privacy: Goose (tested 2026-03-29) 95; OpenCode (tested 2026-09-05) 90.
Developer experience: Goose (tested 2026-03-29) 78; OpenCode (tested 2026-09-05) 86.
These figures come from the existing aicoolies reviews for each tool. Prefer fit and current product surfaces; use the table as supporting Score references with the dates above.
Overall is a near tie (84 vs 83). Goose leads privacy (95 vs 90). OpenCode leads developer experience (86 vs 78) and edges speed (81 vs 80). That split matches the product-shape story: Goose’s privacy niche for a local, extensible agent; OpenCode’s DX niche for a polished coding-agent loop.
Neither axis alone settles the buy. A team optimizing for MCP recipes and local agent governance may still pick Goose despite the DX gap. A team optimizing for terminal/IDE/desktop coding-agent feel may still pick OpenCode despite the one-point overall gap.
Workflow, licensing, and privacy
Daily loop
Choose Goose when the daily loop is a local agent you configure once and extend with MCP tools — Desktop for interactive sessions, CLI for terminal/CI-friendly runs, recipes for repeatable workflows, and providers that can stay on your keys or on Ollama. Privacy 95 (tested 2026-03-29) supports that local / BYOK story.
Choose OpenCode when the daily loop is a coding agent inside a project: TUI or IDE extension, AGENTS.md project memory, plan/build iteration, undo/share, and any-provider models (or Zen). DX 86 (tested 2026-09-05) supports that coding-agent feel.
Licensing and billing
Goose is Apache-2.0 open source at aaif-goose/goose. Software is $0; you pay model providers or run local models. AAIF / Linux Foundation posture is part of the governance story on the project README.
OpenCode is MIT open source at anomalyco/opencode. Software is $0; you pay providers directly, use Zen usage billing if you choose that gateway, or run local models.
Both are OSS + BYOK/local cost shapes. The commercial contrast with Claude Code or Devin is not the fork here — the fork is Goose’s local MCP-agent platform vs OpenCode’s coding-agent multi-surface UX.
Privacy posture on these Scores
On the published Score table, Goose leads privacy (95 vs 90). Both scores are strong relative to many commercial hosted agents on aicoolies. Read Goose’s lead as review evidence for a local-agent / BYOK / Ollama-friendly posture, and OpenCode’s 90 as still privacy-forward for an open coding agent. Neither Score is a certificate that every deployment is air-gapped.
Who should pick which
Pick Goose if you want:
An Agentic AI Foundation (AAIF) local agent with Desktop + CLI + API
Deep MCP extension ecosystem and recipe-based workflows
Model-agnostic BYOK or local Ollama with ACP bridges to subscriptions
The privacy niche on these Scores (95, tested 2026-03-29)
A slight overall edge in a near-tie scoreboard (84 vs 83)
Pick OpenCode if you want:
An open coding agent across terminal, IDE extension, and desktop
Project init / AGENTS.md, plan/build modes, undo/share, LSP-aware coding loops
Any-provider convenience (and optional Zen gateway) with MIT licensing
The stronger DX profile on these Scores (86, tested 2026-09-05)
Editorial winner: goose — fit-first privacy-forward local MCP-agent default in a near-tie overall (84 vs 83). State OpenCode’s DX niche explicitly so the page does not read like a landslide.
How this fits the Goose / OpenCode neighborhood
On aicoolies, both tools already sit in the AI CLI agents / terminal CLI / coding assistants shelf next to Claude Code, Codex, Amp, Aider, and Grok Build comparisons. Catalog alternatives already cross-link Goose ↔ OpenCode. This page closes the direct gap.
Related live peers include claude-code-vs-goose, claude-code-vs-opencode, opencode-vs-codex, opencode-vs-cursor, opencode-vs-aider, and amp-vs-claude-code. Use those for commercial vs OSS or other OSS corners. Use this page when the fork is specifically Goose’s AAIF local agent vs OpenCode’s open coding agent.
Decision guide — five practical forks
1. Do you need MCP-first extensibility and recipes as the core workflow? If yes — databases, browsers, internal APIs, portable YAML recipes — Goose is the natural fit. OpenCode can still use providers and tools, but Goose’s documented MCP depth and recipes are the product emphasis.
2. Do you need a coding-agent TUI / IDE / desktop loop first? If the job is “live in a coding agent across surfaces with plan/build and project memory,” OpenCode is the natural fit. Goose Desktop/CLI can still code; the UX thesis differs.
3. Do you need Apache-2.0 + AAIF governance vs MIT? Both are OSS. Goose’s Apache-2.0 + Agentic AI Foundation / Linux Foundation posture may matter for org open-source policy. OpenCode’s MIT is a familiar permissive coding-tool license. Confirm LICENSE files linked above for compliance review.
4. Is privacy the primary Score niche? Goose leads privacy 95 vs 90 (tested 2026-03-29 vs 2026-09-05). If local/BYOK posture is the buying constraint, Goose has the clearer Score niche — still choose on deployment reality, not Score alone.
5. Is DX the primary Score niche? OpenCode leads DX 86 vs 78. If interactive coding-agent feel is the buying constraint, OpenCode has the clearer Score niche — still choose on product shape, not Score alone.
What “good” looks like for each buyer
Goose buyer profile
A strong Goose buyer wants an agent that lives on their machine and grows with MCP extensions. They may use Desktop for interactive work and CLI for scripted or CI-adjacent runs. They care that providers are swappable and that recipes can capture repeatable workflows for a team. They often arrive from AAIF / Block lineage awareness or from Goose’s Discord/docs, and they shortlist OpenCode, Claude Code, and Codex as peers.
OpenCode buyer profile
A strong OpenCode buyer wants a coding agent that feels native in the terminal (and optionally IDE/desktop). They care about project memory (AGENTS.md), plan/build iteration, undo/share, and connecting whatever model subscription or API keys they already have — including Zen if they want a curated gateway. They often arrive from the OpenCode site or npm/Homebrew install path, and they shortlist Goose, Claude Code, and Codex as peers.
Shared intent, divergent constraints
Both answer “open agent that edits and runs.” Shared intent is why the comparison exists. Divergent constraints — local MCP-agent platform vs coding-agent multi-surface UX — are why the winner is fit-first rather than “always pick the higher overall Score.”
Feature surfaces that matter in practice
Context and project conventions
OpenCode’s documented AGENTS.md init path is a practical convention for project-scoped coding agents. Goose’s recipes and shared Desktop/CLI config emphasize portable workflows and extension settings more than a single markdown project file. Pick the convention that matches how your team stores agent instructions.
Tools and extensibility
Goose’s MCP extension catalog and recipe system are first-class. OpenCode’s strength is the coding-agent interaction model (plan/build, file search, undo/share) plus broad provider connectivity. For the latest extension and provider lists, see the vendor pages in Sources.
Surfaces: Desktop vs IDE vs CLI
Goose: Desktop + CLI + API (docs). OpenCode: terminal + IDE extension + desktop (docs/site). Both cover more than a pure CLI, but the default “home surface” differs — Goose’s home page leads with native local agent; OpenCode’s docs lead with the terminal coding agent.
Models
Both are model-agnostic rather than single-vendor locked. Goose documents 15+ providers plus ACP subscription bridges and Ollama. OpenCode documents any-provider setup, Copilot/ChatGPT login paths, local models, and optional Zen. Neither Score table is a model-quality league table.
Cost and procurement notes
Goose cost shape. Software $0 (Apache-2.0). Budget API keys, subscription bridges, or local GPU/Ollama ops. Free-credit offers mentioned in install docs (for example Tetrate first-time credits) are promotional — treat as time-bounded vendor offers, not permanent pricing.
OpenCode cost shape. Software $0 (MIT). Budget provider tokens, optional Zen usage, or local inference. Site FAQ language emphasizes free software with model costs elsewhere — confirm opencode.ai if finance needs a one-pager.
Total cost of ownership is not overall Score. A near-tie overall (84 vs 83) means packaging and workflow fit matter more than chasing one Score point.
Privacy and data-path framing
On published aicoolies Scores, Goose leads privacy (95 vs OpenCode 90). Both are strong. Practical questions:
Where do prompts and repository context go by default for your chosen provider?
Can you keep inference local (Ollama) or on an approved gateway?
Can security review the agent runtime source (Apache-2.0 vs MIT)?
Do MCP extensions or IDE plugins introduce additional data paths?
Goose answers (2)–(3) with a local-agent + MCP story and scores 95 privacy (tested 2026-03-29). OpenCode answers with open coding-agent software and privacy-first site claims, scoring 90 privacy (tested 2026-09-05).
Speed and DX — what the leads mean
OpenCode leads DX (86 vs 78) and slightly leads speed (81 vs 80) on the locked Score table. In buyer language: the coding-agent loop felt smoother in the OpenCode review.
Goose’s lower DX Score is not a claim that Goose is unusable. It marks a different optimization: extensible local agent platform vs polished coding-agent UX. If your rubric weights MCP/recipes/local governance above TUI/IDE feel, Goose can still win the purchase.
Overall 84 vs 83 is a near tie. Fit-first still applies.
Migration and coexistence patterns
From Goose to OpenCode. Teams sometimes want a more coding-agent-centric TUI/IDE loop after prototyping with Goose’s MCP platform. Migration is workflow and config, not a Score transfer.
From OpenCode to Goose. Teams sometimes need richer MCP recipes, Desktop+CLI shared config, or AAIF governance after starting with OpenCode’s coding agent. Again: workflow choice.
Coexistence. Some orgs keep OpenCode for product-engineering coding sessions and Goose for automation/recipes/MCP-heavy ops. That can be coherent. The editorial winner on this page remains goose for the default privacy-forward local-agent recommendation; coexistence is a team topology choice, not a winner flip.
Providers, local models, and governance
Provider flexibility without a commercial CLI lock-in
Goose and OpenCode both reject single-vendor model lock-in at the product layer. Goose’s install and configure docs walk API keys, OpenRouter, Tetrate Agent Router, ChatGPT subscription login, and manual providers, with Ollama as the local path. OpenCode’s docs walk /connect for many providers and recommend Zen for newcomers who want a curated gateway, while still allowing direct keys and local models.
That shared BYOK reality is why this page is not “which model is smarter.” It is which agent runtime and surface you want around those models. Claude Code remains the commercial Anthropic CLI peer on other comparison pages; here both tools stay open-source runtimes.
Governance and foundation posture
Goose’s README and docs emphasize the Agentic AI Foundation (AAIF) at the Linux Foundation — vendor-neutral, community-governed open source. For buyers whose security or open-source review asks “who steers this project,” AAIF is a concrete answer with a public GitHub org at aaif-goose.
OpenCode’s live home is the anomalyco/opencode repository under MIT. Governance is the typical open-source maintainer model rather than a Linux Foundation project umbrella. Neither posture is “better” in absolute terms; they are different compliance and contribution stories.
What this means for procurement
If your legal team prefers Apache-2.0 + foundation-backed projects for agent runtimes, Goose is easier to explain. If your team already standardizes on MIT coding tools and wants the coding-agent UX first, OpenCode is easier to explain. Software cost is $0 either way; model spend still needs a budget owner.
When the near-tie scoreboard still helps
Overall 84 vs 83 is not a mandate. It still helps in three narrow ways:
Privacy niche clarity. Goose 95 vs OpenCode 90 (tested 2026-03-29 / 2026-09-05) matches the local-agent emphasis without turning the page into a scare story about OpenCode.
DX niche clarity. OpenCode 86 vs Goose 78 matches the coding-agent feel emphasis without pretending Goose cannot code.
Anti-landslide framing. A one-point overall gap should never be sold as a wipeout. Fit-first goose is a default recommendation for privacy-forward local MCP agents, not a claim that OpenCode buyers are wrong.
Speed is effectively tied (80 vs 81). Ignore speed as a deciding axis on these Scores unless your own benchmarks say otherwise.
Practical evaluation checklist
Before you standardize on either tool, run the same short checklist on a real repository:
Install path. Can every engineer install Desktop or CLI without a ticket maze? (Goose: docs installers / Homebrew. OpenCode: install script / npm / Homebrew tap.)
Provider path. Does your approved model path work on day one — API key, subscription login, or Ollama?
Extension path. Do you need MCP recipes and internal tools on day one (Goose strength) or a tight coding loop first (OpenCode strength)?
Review path. Can security read the LICENSE and skim the GitHub org you will depend on (aaif-goose/goose vs anomalyco/opencode)?
Team path. Will the team live in Desktop, terminal, IDE, or a mix? Match the surface to how people already work.
Use aicoolies Scores as a second opinion with the published dates — not as a substitute for that checklist.
FAQ-style clarifications
Is Goose the same as Block’s old branding only? Goose is documented as part of the Agentic AI Foundation (AAIF) at the Linux Foundation, with the live GitHub org aaif-goose/goose. Older block/goose URLs redirect there.
Is OpenCode still an SST project? The live GitHub repository as of 2026-09-24 is anomalyco/opencode. Older sst/opencode URLs redirect to anomalyco/opencode.
Does the overall Score mean Goose always wins? No. Goose is the fit-first winner in a near tie (84 vs 83). OpenCode remains the better pick when DX / coding-agent multi-surface fit matters more.
Can both use Claude or GPT models? Yes — both are model-agnostic via BYOK / providers. That does not turn either into Claude Code.
Are the Scores from the same day? No. Goose Score tested 2026-03-29; OpenCode Score tested 2026-09-05.
