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claw-code-go

claw-code-go

Experimental fork — ingesting features from claw-code (Rust) into claw-code-go (Go)


Warning: This is an ongoing experiment and is not tested. Use at your own risk.


What is this?

This repo is a fork of daolmedo/claw-code-go, a Go reimplementation of Claude Code — Anthropic's agentic coding assistant.

On top of the upstream codebase, this fork ingests features from ultraworkers/claw-code — a Rust port of Claude Code that emerged after the source leak. The goal is to bring the more complete Rust implementation's capabilities into the Go codebase: hooks, plugins, MCP lifecycle, sandbox, permissions engine, runtime session management, apikit with multi-provider routing, prompt caching, and much more.

This is an experiment, not a product. The ported code has not been manually tested or validated beyond automated build checks.

How was this done?

The Rust-to-Go feature porting was orchestrated by Iterion — a workflow engine for complex multi-agent LLM pipelines, using Claude Code, Codex, and other backends.

This repo serves as the real-world example that drove Iterion's development. The full workflow configuration and writeup are available:

Run stats

Metric Value
Refinement runs 4 (workflow iterated alongside the engine)
Outer loop iterations 25+ batches across all runs
Commits added 48 (47 by Iterion, 1 by Claude Code)
Go code generated 37,995 lines across 173 files + 96 test files
New Go packages 30+ (hooks, plugin, apikit, worker, lane, policy, recovery, sandbox, lsp, task, team...)
Feature parity 100% (37/37 features)
Final run 100% parity in 41 min, single batch, zero fix loops, zero interventions
Longest autonomous stretch 2h25m without human intervention
Dual-judge verdicts 30+ across all runs (Claude + Codex)

How the workflow operates

The workflow breaks the porting into dependency-ordered batches. Each batch goes through: plan → implement → simplify → commit → test → parity scan → dual-judge review → fix loop. Session continuity (fork/inherit) preserves KV cache across related phases. A human gate pauses for high-risk batches and auto-approves routine ones.

See the full writeup for details on convergence strategies, stagnation detection, and model allocation.

Status

Experimental. The code compiles and passes go vet, but has not been manually tested. The latest commit (Anthropic prompt caching with cache_control breakpoints) was done directly with Claude Code outside the Iterion workflow.

Contributions, testing, and feedback are welcome.

Credits

License

MIT

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