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Orchestrate multiple AI CLIs with retries, failover, and resilient task routing.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "cli-orchestrator-mcp" yet — see the docs or source repo.
Route this development task to the most suitable CLI model: first ask Claude to refactor this Python code; if it fails, retry twice automatically, then fall back to Codex; return the final code, failure logs, and execution summary. Code: …
Returns the refactored code, call and retry logs, and a summary of which model completed the task.
Use multi-CLI orchestration to generate a README for this repository: prefer Gemini to summarize the project structure and installation steps; if the response fails, trigger the circuit breaker and switch to Claude; keep execution status for each stage. Repository details: …
Outputs a usable README draft along with model switching and circuit breaker status details.
Perform batch root-cause analysis on these terminal logs: send them to Claude first, and if repeated failures occur, enable the circuit breaker and continue with Gemini; finally summarize results, error causes, and retry counts for each batch. Logs: …
Provides a batch-organized analysis report including fallback paths and execution statistics.
Connect multiple AI coding agents to collaborate on development tasks efficiently.
Enable Claude Code to perform coding tasks through the OpenAI Codex CLI.
Connect coding agents to multiple models for in-chat consultation and code review.
Orchestrate multi-agent coding with local Claude Code workers for execution and review.
Control Claude Code sessions in tmux with fine-grained orchestration and efficient extraction.
Orchestrate multi-agent workflows with parallel tasks, pipelines, scheduling, and peer review.