AI Council – multi-model deliberation that runs in the browser
Multi-model deliberation mixing Ollama + OpenAI + Anthropic with zero backend, runs offline.
AI Council is a self-hosted web app that runs a structured multi-model deliberation process
Multi-stage deliberation with role-specific prompts beats simple multi-model queries.
Analysts, consultants, researchers, teams seeking multi-perspective reasoning
Anthropic's prompt caching for ensemble reasoning · Mixture-of-Experts routing patterns
Law Firm — Litigator + Corporate Counsel + Compliance Officer + Junior Associate, synthesized by a Senior Partner Hospital Team — GP + Specialist + Pharmacist + Medical Ethicist, synthesized by Chief of Medicine Editorial Team — Reporter + Editor + Legal Reviewer + SEO lead, synthesized by Editor-in-Chief Corporate — CFO + CTO + CMO + Legal, synthesized by CEO Startup — Founder + Engineer + Designer + Growth Lead, synthesized by Investor Consulting — Strategy + Operations + Finance + Risk, synthesized by Senior Partner
Each persona has a role-specific system prompt tuned to how that function actually thinks — the CFO talks in EBITDA and burn rate, the Junior Associate flags the clause the partners missed. Also shipped in v2:
Temperature slider per run (Precise → Balanced → Creative) Follow-up questions — council carries the full prior verdict as context Abort mid-run with partial result preservation Export MD / PDF Import/export council config as JSON Webhook after every completed session (works with Zapier, n8n, Make)
Still zero backend. Still runs entirely in the browser. API keys never leave your machine.
GitHub: https://github.com/prijak/Ai-council
Livelink: https://council.gameinghub.com/
Genuinely curious: has anyone found multi-model deliberation actually useful for a specific domain, or does it mostly just produce longer wrong answers?
Multi-model deliberation mixing Ollama + OpenAI + Anthropic with zero backend, runs offline.
Product Algebra routing plus an explicit 'dharma' pipeline (no-self regularization, entropy/mindfulness metrics, compassion and ethos scores) is a strikingly specific approach — it moves beyond cost/capability heuristics into cross-modal interaction scoring and reputation-driven incentives. There's real engineering here (1s perception loop, SQLite memory, Telegram UX, multi-provider SDK support), but the repo reads young and claim-heavy: I want reproducible benchmark artifacts, links from the code to the cited 439-model experiments, and clearer deployment/security guidance before trusting it for critical workloads.
CLI agents with repo access debate, not just API calls.
Perplexity automation + council synthesis is clever, but loop drivers already exist elsewhere.
Debate mode where models change minds is novel, but model comparison tools already exist.
Escalates uncertain queries to cloud, then caches answers locally for free reuse.