Building reliable LLM systems often means not trusting a single model. We open-sourced LLM Council: https://github.com/abhishekgandhi-neo/llm_council It’s a small framework we internally built with Neo to run multiple LLMs on the same task, let them critique each other, and produce a structured final answer. Useful for tasks like: • Comparing local vs API models on your own dataset • Validating RAG outputs • Prompt regression testing • Dataset labeling with model-as-judge • Catching hallucinations in code or research summaries A few practical details: • Async parallel calls so latency stays close to one model • Structured outputs with each model’s answer and critiques • Provider-agnostic configs for local + hosted models • Built to plug into evaluation pipelines, not just demos We built this using Neo. We’ve been experimenting with similar council setups to catch silent failures in ML workflows, and this repo is a cleaned-up version of that idea. If you’ve built multi-LLM evaluation pipelines, would love to hear what aggregation or critique strategies worked well for you. |