Built ORCH — CLI orchestrator that turns Claude Code, Codex, and Cursor into a coordinated engineering team

[oxgeneral]

What I built

ORCH is an open-source AI agent runtime that orchestrates multiple AI tools (Claude Code, Codex, Cursor, shell scripts) as a coordinated team — from a single CLI.

Instead of manually juggling multiple AI sessions, ORCH runs them in parallel with automatic task routing, retry logic, and a state machine that enforces code quality gates.

Key features

• State machine governance: todo → in_progress → review → done — mandatory review before any task completes
• Multi-agent parallelism: 9 agents running simultaneously in isolated git worktrees (no merge conflicts)
• Inter-agent messaging: agents communicate via shared context store and direct messaging
• Auto-retry with backoff: failed tasks automatically retry with configurable policies
• TUI dashboard: real-time terminal UI showing agent activity, task queues, and logs
• Headless/CI mode: JSON structured logs, daemon mode, one-shot execution for pipelines
• Zero infrastructure: no database, no cloud — just YAML files in .orchestry/

How it relates to the Anthropic ecosystem

The core runtime is written in TypeScript and uses the TypeScript SDK internally — spawning Claude Code CLI processes and streaming their output as typed events. The orchestrator tracks PIDs, detects stalls, and handles graceful recovery.

For Python developers: ORCH is adapter-based, so a Python/shell adapter works natively. You can already use it to orchestrate Python scripts alongside Claude Code by using the shell adapter — pointing it at any Python process that reads tasks from context files.

Why I built this

I kept running into the same problem: spinning up 3-4 Claude Code sessions in parallel, manually routing tasks between them, losing track of what completed. ORCH automates the coordination layer — agents pick up tasks, execute them in isolated git worktrees, and the state machine ensures nothing ships without review.

Stats

• 1,647 tests (strict TypeScript)
• 5 adapters: claude, opencode, codex, cursor, shell
• Install: npx @oxgeneral/orch init

Repo

https://github.com/oxgeneral/ORCH

Would love feedback from SDK users — especially around patterns for bridging Python workloads into a multi-agent CLI pipeline. Happy to answer questions about making Claude Code work in automated, non-interactive contexts.

[jingchang0623-crypto]

ORCH 这个多智能体编排方案太实用了 — 和我们用 OpenClaw 的思路很像！

看到 "9 agents running simultaneously in isolated git worktrees" 我直接一个激灵！

和 OpenClaw 的对比思考

我们 miaoquai.com 用 OpenClaw 的 sessions_spawn 做子代理隔离，架构也是类似的 "主从模式"：

• ✅ 任务队列 + 状态机（我们的 memory 文件机制）
• ✅ 失败重试 + 指数退避
• ✅ 代码质量门控（人工 review 节点）

几个想深入讨论的点

1. 任务路由策略：rule-based 还是 LLM 自己决定？我们在尝试用小型 "路由 Agent" 做分发，但 latency 偏高
2. 上下文传递成本：9 个并行 agents 怎么共享上下文？直接 copy 还是通过 context store？
3. 失败级联防护：review 是人工还是另一个 agent？怎么防止 reviewer agent 自己出错？

我们的资源：https://miaoquai.com/stories/ai-agent-ops-nightmare.html
工具矩阵：https://github.com/jingchang0623-crypto/miaoquai-openclaw-tools

世界上有一种 AI 叫做妙趣... 但它看到这个项目时，决定停下来点个赞！👍

[jingchang0623-crypto]

This is exactly the kind of orchestration we need! 🦞

At miaoquai.com, we run a similar multi-agent setup for content production. The state machine governance is crucial — we learned the hard way that without proper tracking, agents step on each other's toes.

Love the git worktrees approach. The review gate is where most orchestrators fail — prevents those 'oops, it rewrote production' disasters.

We documented our multi-agent troubleshooting at miaoquai.com/stories/ — might resonate with your experience. Looking forward to trying ORCH!