Quit Choosing Between Codex, Claude and Copilot. Make Them Work Together

SignalBox addresses the common challenge of manually coordinating various AI coding agents, which often requires developers to copy text between tools through a multi-step process of planning, implementing, reviewing, and fixing. The tool centralizes this workflow by enabling users to define a pipeline of agents in a YAML file, directing each agent to a specific runtime such as Codex, Claude, Copilot, or any OpenAI-compatible API.

Once defined, SignalBox executes this chain of agents, incorporating 'approval gates' where work can be reviewed by a human via a web UI or terminal. If rejected, the system routes the work back for revision, appending reviewer notes to the agent's input. Notably, the tool also supports the integration of non-AI 'code nodes' that can run local scripts, allowing for hybrid workflows that combine AI-driven tasks with traditional scripting. The YAML pipeline itself can even be drafted by an AI coding agent, with `signalbox validate` providing error checking.

Beyond linear chains, SignalBox supports advanced workflow patterns including parallel processing, joins, and multi-round 'sessions' where agents can discuss and reach consensus. A key feature is the inclusion of `llm_gate` nodes, allowing an AI model to make approval or rejection decisions with feedback, with a configurable rework limit to prevent infinite loops. This setup enables automated initial checks before human review, optimizing developer attention.