ORCH
ORCH is an open-source command-line interface runtime that enables developers to orchestrate multiple AI coding assistants—specifically Claude Code, OpenAI Codex, and Cursor—as typed agent teams. It introduces a state machine to manage agent interactions, ensuring coherent, goal-driven collaboration. The tool includes a terminal user interface (TUI) for real-time monitoring and control. By assigning specific roles and states, ORCH allows teams of AI agents to work on complex software engineering tasks such as code generation, refactoring, debugging, and review. The goal-oriented architecture helps break down high-level objectives into manageable steps, with state transitions that reflect progress. This approach aims to improve reliability and output quality compared to ad-hoc use of individual AI tools. ORCH is designed to run locally, giving developers full control over their environment and data. It is open source, allowing customization and community contributions. While it depends on external services like Claude and Codex, the orchestration logic runs on the user's machine. The project is actively developed on GitHub, aiming to streamline AI-assisted development workflows. Its typed agent approach ensures that each agent operates within defined boundaries, reducing errors and improving synergy. The TUI provides insights into the state machine, enabling developers to understand and debug the collaboration process.
- Category
- AI Agents
- Pricing
- Open Source
Problem solved
ORCH solves the challenge of coordinating multiple AI coding assistants (Claude Code, Codex, Cursor) to work together reliably on complex software development tasks. It provides a state-machine-driven orchestration layer with goal management and a terminal UI, enabling structured, multi-agent collaboration that goes beyond the capabilities of individual tools.
Use cases
- Coordinating multi-agent teams for large-scale code refactoring
- Automating end-to-end feature implementation using specialized AI roles
- Running parallel code generation with different models for comparison
- Debugging complex issues with multiple AI perspectives
- Orchestrating code review processes across an agent team
Audience
- Developers
- ML Engineers
- Data Scientists
- Researchers
Pros
- Open source and free
- Typed agent teams with state machine coordination
- Goal-driven execution enhances reliability
- Terminal UI for interactive monitoring
- Local execution preserves data privacy
Cons
- Limited to specific AI tools (Claude Code, Codex, Cursor)
- Requires setup and API keys for each tool
- CLI-only may not suit all users
- Dependent on external AI services
- Steep learning curve for state machine concepts
Official website: https://github.com/oxgeneral/ORCH
Canonical LambdaBase page: https://www.lambdabase.com/tools/orch