claw-code vs Open SWE
Two of the top projects in 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 using Anthropic and OpenAI-compatible APIs
Open SWE
LangChain coding agent that plans, implements and reviews pull requests
Verdict
claw-code ranks higher, 83 to 80. Of the 5 score parts we measure for both, claw-code leads on 1, Open SWE on 3 and 1 is even.
- Adoptionclaw-code leads by 45 points, 99 to 54: a wide gap.
- FreshnessEven at 100.
- MaintenanceOpen SWE leads by 1 point, 100 to 99: close to even.
- Easy to runOpen SWE leads by 17 points, 67 to 50: a wide gap.
- Agent-readyOpen SWE leads by 15 points, 85 to 70: a wide gap.
Pick claw-code if you are
Developers who want to inspect or build a Rust CLI coding agent
- `claw doctor` checks API key, model access and tool configuration after build
- Docs cover local OpenAI-compatible providers: Ollama, llama.cpp, vLLM
Pick Open SWE if you are
Engineering teams building an internal coding-agent service
- Covers build, review, investigate and operate flows, with subagents for parallel work
- Durable execution and thread state via LangGraph; sandboxes persist per thread
| What we compare | claw-code | Open SWE |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 99, widely used | 54, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 99, healthy | 100, healthy |
| Easy to run | 50, easy | 67, easy |
| Agent-ready | 70, partly | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 194.9k | 11k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Rust | Python |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
claw-code
Claw Code is a Rust implementation of the `claw` command-line coding agent, built from source with cargo. It runs one-shot prompts or interactive sessions using an API key, and the docs cover local OpenAI-compatible servers such as Ollama, llama.cpp and vLLM. The README describes the repo as an agent-managed exhibit, not the production project, and points to LazyCodex and Gajae-Code for real work.
Who it is for: Developers who want to inspect or build a Rust CLI coding agent
Strengths
- `claw doctor` checks API key, model access and tool configuration after build
- Docs cover local OpenAI-compatible providers: Ollama, llama.cpp, vLLM
- Build steps documented for macOS, Linux and Windows PowerShell
- MIT license; workspace test suite and deterministic mock-service parity harness
Weaknesses
- README says it is not the serious production project and points elsewhere
- Build-from-source only; `cargo install claw-code` installs a deprecated stub
- Requires an API key; Claude subscription login is not supported
- No ACP/Zed daemon yet; `claw acp serve` only returns status
- no GPU
- Docker + Compose
- Needs Rust toolchain (cargo), LLM API key
- Models: Anthropic API, OpenAI-compatible APIs, Ollama, llama.cpp, vLLM
Open SWE
LangGraph-based agent that investigates a repository, implements changes in a per-thread Linux sandbox, validates them and opens a pull request, then reviews PRs and watches CI with /baby-sit. Work starts from a dashboard, GitHub issues or PR comments, Slack or Linear. Deploys into your infrastructure with a backend, dashboard, GitHub and Slack apps, for teams building an internal coding-agent service.
Who it is for: Engineering teams building an internal coding-agent service
Strengths
- Covers build, review, investigate and operate flows, with subagents for parallel work
- Durable execution and thread state via LangGraph; sandboxes persist per thread
- Configurable models, reasoning effort, skills, MCP integrations and sandbox providers
- Push approvals gate detected git pushes; PR chat excludes mutation tools
Weaknesses
- Production standalone Agent Server deployments require a license key
- Maintainers are not accepting issues or contributions; breaking changes expected
- LangSmith is the default sandbox and tracing provider; alternatives need configuration
- CLI mode executes commands locally as you, with no sandbox isolation
- no GPU
- Docker + Compose
- Needs LangSmith (default sandbox and tracing), GitHub App, Slack app (optional), model provider credentials
- Models: configurable LLM providers
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