LLM Council

The idea of this repo is that instead of asking a question to a single LLM, you can group multiple LLMs into your "LLM Council". This repo is a simple, local web app that essentially looks like ChatGPT except it sends your query to multiple LLMs via an OpenAI-compatible API (Ollama, vLLM, TGI, etc.), it then asks them to review and rank each other's work, and finally a Chairman LLM produces the final response.

In a bit more detail, here is what happens when you submit a query:

1. Stage 1: First opinions. The user query is given to all LLMs individually, and the responses are collected. The individual responses are shown in a "tab view", so that the user can inspect them all one by one.
2. Stage 2: Review. Each individual LLM is given the responses of the other LLMs. Under the hood, the LLM identities are anonymized so that the LLM can't play favorites when judging their outputs. The LLM is asked to rank them in accuracy and insight.
3. Stage 3: Final response. The designated Chairman of the LLM Council takes all of the model's responses and compiles them into a single final answer that is presented to the user.

Vibe Code Alert

This project was 99% vibe coded as a fun Saturday hack because I wanted to explore and evaluate a number of LLMs side by side in the process of reading books together with LLMs. It's nice and useful to see multiple responses side by side, and also the cross-opinions of all LLMs on each other's outputs. You're not going to support it in any way, it's provided here as is for other people's inspiration and you don't intend to improve it. Code is ephemeral now and libraries are over, ask your LLM to change it in whatever way you like.

Setup

1. Install Dependencies

The project uses uv for project management.

Backend:

uv sync

Frontend:

cd frontend
npm install
cd ..

2. Configure Ollama Server

LLM Council requires an OpenAI-compatible API server. The easiest way to get started is with Ollama running locally or on a remote server.

For local Ollama:

1. Install and start Ollama: https://ollama.ai
2. Pull some models:

ollama pull llama3.2:3b
ollama pull qwen2.5:3b
ollama pull gemma2:2b

3. Configure Environment

Create a .env file in the project root with your configuration:

For local Ollama:

USE_LOCAL_OLLAMA=true
COUNCIL_MODELS=llama3.2:3b,qwen2.5:3b,gemma2:2b
CHAIRMAN_MODEL=llama3.2:3b
MAX_TOKENS=1024
LLM_MAX_CONCURRENCY=1

For remote Ollama or other OpenAI-compatible server:

OPENAI_COMPAT_BASE_URL=http://your-server:11434
COUNCIL_MODELS=llama3.2:3b,qwen2.5:3b,gemma2:2b
CHAIRMAN_MODEL=llama3.2:3b
MAX_TOKENS=2048
LLM_MAX_CONCURRENCY=1

Optional timeout configuration:

LLM_TIMEOUT_SECONDS=120.0          # Default timeout for LLM queries
CHAIRMAN_TIMEOUT_SECONDS=180.0     # Timeout for chairman synthesis
TITLE_GENERATION_TIMEOUT_SECONDS=120.0  # Timeout for title generation
OPENAI_COMPAT_TIMEOUT_SECONDS=300.0    # Timeout for OpenAI-compatible server
OPENAI_COMPAT_CONNECT_TIMEOUT_SECONDS=10.0  # HTTP connection timeout
OPENAI_COMPAT_WRITE_TIMEOUT_SECONDS=10.0    # HTTP write timeout
OPENAI_COMPAT_POOL_TIMEOUT_SECONDS=10.0    # HTTP pool timeout

See .env.example for all available configuration options. Alternatively, you can edit backend/config.py directly to set defaults.

Running the Application

Option 1: Use the start script

./start.sh

Option 2: Use Makefile

make dev

Option 3: Run manually

Terminal 1 (Backend):

uv run python -m backend.main

Terminal 2 (Frontend):

cd frontend
npm run dev

Then open http://localhost:5173 in your browser.

Option 4: Test setup with pre-configured conversation

# Set in .env:
# TEST_MESSAGE="Your message"
# TEST_DOCS="doc1.md,doc2.md"
make test-setup

This creates a new conversation with today's date/time, uploads documents, and pre-fills the message in the UI (it does not auto-send).

Frontend theme default (optional)

By default, the UI theme is persisted in localStorage. If there is no saved theme yet, you can set a default theme via a Vite env var:

# Example (starts in dark mode if there's no localStorage value yet)
VITE_DEFAULT_THEME=dark make dev

Using Ollama on a Remote Server

If you have Ollama running on a remote server or VM:

1. In your project .env, set:

OPENAI_COMPAT_BASE_URL=http://your-server-ip:11434
COUNCIL_MODELS=llama3.2:3b,qwen2.5:3b,gemma2:2b
CHAIRMAN_MODEL=llama3.2:3b
MAX_TOKENS=2048
LLM_MAX_CONCURRENCY=1

2. Verify connectivity from your machine:

curl http://your-server-ip:11434/api/tags

Using Other OpenAI-Compatible Servers (vLLM, TGI, etc.)

If you're running vLLM, TGI, or another OpenAI-compatible server:

1. Ensure your server exposes:

   • POST /v1/chat/completions
   • GET /v1/models

2. In your project .env, set:

OPENAI_COMPAT_BASE_URL=http://your-server:port
COUNCIL_MODELS=your-model-1,your-model-2,your-model-3
CHAIRMAN_MODEL=your-model-1
MAX_TOKENS=2048
LLM_MAX_CONCURRENCY=1

# (optional) if your server requires auth:
# OPENAI_COMPAT_API_KEY=...

3. Verify connectivity:

curl http://your-server:port/v1/models

Documentation

• Architecture - System architecture and design
• Deployment Guide - How to deploy with remote GPU VM
• Deployment Recommendations - Professional deployment options

Tech Stack

• Backend: FastAPI (Python 3.10+), async httpx, OpenAI-compatible API
• Frontend: React + Vite, react-markdown for rendering
• Storage: JSON files in data/conversations/
• Package Management: uv for Python, npm for JavaScript
• LLM Backend: Ollama, vLLM, TGI, or any OpenAI-compatible server