claw-code vs oh-my-openagent
Two of the top coding agents and assistants, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
claw-code
Rust CLI coding agent harness, built from source
oh-my-openagent
Terminal coding agent that runs parallel sub-agents and keeps git-based memory
Verdict
claw-code ranks higher, 77 to 76. Of the 5 score parts we measure for both, claw-code leads on 1, oh-my-openagent on 2 and 2 are even.
- Adoptionclaw-code leads by 23 points, 96 to 73: a wide gap.
- FreshnessEven at 100.
- Maintenanceoh-my-openagent leads by 10 points, 85 to 75.
- Easy to runEven at 50.
- Agent-readyoh-my-openagent leads by 15 points, 85 to 70: a wide gap.
Pick claw-code if you are
Rust-comfortable developers inspecting or building a CLI coding agent harness
- Rust workspace builds to a single `claw` binary with a `doctor` check
- Supports local OpenAI-compatible backends: Ollama, llama.cpp, vLLM
Pick oh-my-openagent if you are
Developers who want a terminal agent that orchestrates many models
- Native binary installs via curl script, bun or npm; no Docker needed
- Logs in with Claude, ChatGPT, Kimi and GLM subscriptions
| What we compare | claw-code | oh-my-openagent |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 96, widely used | 73, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 75, fair | 85, healthy |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 70, partly | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 195k | 70k |
| License | MIT (permissive) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Rust | TypeScript |
| Docker | Yes | No |
| GPU | Not stated | Not stated |
| arm64 or Apple Silicon | Mentioned | Mentioned |
claw-code
Claw Code is a Rust implementation of the `claw` CLI agent harness, with the workspace in `rust/` and commands such as `claw prompt`, an interactive session, and `claw doctor` as a health check. It authenticates with API keys (Anthropic, OpenAI, others), and the docs cover local OpenAI-compatible providers such as Ollama, llama.cpp and vLLM. The README describes the repo as an agent-managed exhibit and points users who want to run work to LazyCodex or Gajae-Code.
Who it is for: Rust-comfortable developers inspecting or building a CLI coding agent harness
Strengths
- Rust workspace builds to a single `claw` binary with a `doctor` check
- Supports local OpenAI-compatible backends: Ollama, llama.cpp, vLLM
- MIT license; PowerShell-first Windows install docs alongside Linux and macOS
- Mock parity harness and workspace test suite via `cargo test --workspace`
Weaknesses
- Build from source only; `cargo install claw-code` installs a deprecated stub
- README says it is not the serious production project
- Claude subscription login unsupported; API key required
- No ACP/Zed daemon yet; `claw acp serve` only returns status
- Docker + Compose
- Needs Rust toolchain (cargo), API key (Anthropic, OpenAI or compatible provider)
- Models: Anthropic API, OpenAI API, OpenAI-compatible local servers (Ollama, llama.cpp, vLLM)
oh-my-openagent
OmO ships a single `omo` command, a native binary built on senpi, the project's fork of pi. It takes a prompt and plans, runs and checks the work. Adding `ultrawork` or `mass ulw` makes it fan the job out to many agents, each on a model chosen for that step. Memory is stored as markdown files in a git repository, and `/login` supports Claude, ChatGPT, Kimi and GLM subscriptions.
Who it is for: Developers who want a terminal agent that orchestrates many models
Strengths
- Native binary installs via curl script, bun or npm; no Docker needed
- Logs in with Claude, ChatGPT, Kimi and GLM subscriptions
- Memory is plain markdown in a git repo, so it can be inspected
- `omo setup` migrates keys, MCP servers and skills from the OpenCode edition
Weaknesses
- License is SUL-1.0, not a standard OSI license; check terms before commercial use
- Install is a curl-pipe-to-bash script
- Computer use is documented as experimental
- Hardware needs and supported model list are not stated in the README
- Models: Claude, ChatGPT, Kimi, GLM
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