llm-council

To generate bias-resistant implementation plans, you can orchestrate multiple CLI planning agents to produce independent outputs, anonymize their sources, and merge them into a single auditable final plan.

Multi-model orchestration for software planning coordinates different CLI agents like Codex and Claude Code to generate parallel plans, which are then anonymized, judged, and merged to ensure robust decision-making.

You can merge plans from Codex, Claude Code, and Gemini by running a Python-based orchestrator that anonymizes and randomizes their independent outputs, judging them to produce a single structured JSON output.

Yes, you can use custom CLI agents for multi-model planning by configuring agent definitions within the orchestrator, allowing you to generate independent plans suitable for auditing.

Yes, multi-model planning handles retry and failure management across distributed agents, ensuring robust execution when coordinating multiple CLI tools to generate and merge implementation plans.

Anonymizing source providers when merging implementation plans hides which agent generated which output, reducing model bias during the judging phase to produce a fair, auditable final plan.