goose vs maka
General-purpose local AI agent in Rust — native desktop app, full CLI and embeddable API — over 15+ providers and 70+ MCP extensions. Block's goose, now an Agentic AI Foundation project. — versus — Apache-incubating agent workspace where the append-only run log IS the runtime: desktop, TUI, CLI and Eval are thin clients of one host, and every harness benchmark ships with per-task records.
Both are local, bring-your-own-model agents with a desktop app and a CLI. Goose is Block's general-purpose Rust agent with a big extension ecosystem; Maka is the Apache-incubating harness built around an append-only run log and published per-task evals.
| goose | maka | |
|---|---|---|
| Stars | 54k | 4.8k |
| Forks | 6.2k | 453 |
| Language | Rust | TypeScript |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | 2 days ago | today |
| Topics | agents, coding | coding, agents |
| Curated connections | 6 | 4 |
goose — the curator's take
The default answer when someone wants a real agent on their own machine and does not want to assemble one. Rust, three surfaces (desktop app, CLI, API), 15+ providers including Ollama and your existing Claude/ChatGPT/Gemini subscriptions over ACP, 70+ MCP extensions, and custom distributions if you want to ship your own branded build. Now governed under the Linux Foundation's AAIF rather than one vendor, which matters if you are betting a product on it. It is deliberately general — code, research, writing, data — so it has no opinion about your SDLC: no verification gates, no worktree isolation, no PR pipeline. If you want an agent that must prove its work, or a spec-to-PR factory, layer that on or pick a purpose-built harness. Note the repo moved from block/goose; old links and forks still point at the old org.
maka — the curator's take
Pick Maka if you want a coding-agent harness that treats measurement as the product: every model message, tool call, permission and termination is a RuntimeEvent; the UI, the next prompt and crash recovery are projections of that log; and the project publishes per-task results against other harnesses on the same model with the official verifier. Sessions stay local, you bring the model (API, local, or a gateway). NOT a release yet — no ASF release exists, nightlies are unsigned previews — and NOT the lightest: the discipline is the point. If you want a 100-line agent to read, that's elsewhere on the map.