LLM Council

The idea of this repo is that instead of asking a question to your favorite LLM provider (e.g. OpenAI GPT 5.1, Google Gemini 3.0 Pro, Anthropic Claude Sonnet 4.5, xAI Grok 4, eg.c), you can group them into your "LLM Council". This repo is a simple, local web app that essentially looks like ChatGPT except it uses OpenRouter to send your query to multiple LLMs, 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. I'm not going to support it in any way, it's provided here as is for other people's inspiration and I 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 (base, no memory):

uv sync

Backend (with persistent memory):

uv sync --extra memory

Frontend:

cd frontend
npm install
cd ..

2. Configure API Key

Create a .env file in the project root:

OPENROUTER_API_KEY=sk-or-v1-...

Get your API key at openrouter.ai. Make sure to purchase the credits you need, or sign up for automatic top up.

3. Configure Models (Optional)

Edit backend/config.py to customize the council:

COUNCIL_MODELS = [
    "openai/gpt-5.1",
    "google/gemini-3-pro-preview",
    "anthropic/claude-sonnet-4.5",
    "x-ai/grok-4",
]

CHAIRMAN_MODEL = "google/gemini-3-pro-preview"

Running the Application

Option 1: Use the start script

./start.sh

Option 2: 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.

Memory

Each completed council turn is automatically saved into a local MemPalace vector store at data/palace/. On every new query the palace is searched for semantically similar past exchanges, and the top results are injected into the Stage 1 and Stage 3 prompts as a RELEVANT MEMORY block. This gives every council member long-term memory that spans across separate conversation sessions.

To disable memory (reverts to vanilla behaviour): set MEMORY_ENABLED = False in backend/config.py.

Tunable settings in backend/config.py:

Setting	Default	Purpose
MEMORY_ENABLED	True	Master on/off switch
MEMORY_PALACE_PATH	data/palace	Where ChromaDB stores its files
MEMORY_TOP_K	5	Number of past exchanges to retrieve
MEMORY_MAX_ANSWER_CHARS	500	Truncation limit for injected answers

The palace lives entirely on disk — no third-party services, no API keys required beyond OpenRouter.

First run note: MemPalace uses the all-MiniLM-L6-v2 sentence-transformer model for embeddings. On the very first startup it will download ~90 MB of model weights and cache them locally. Subsequent starts are instant.

Tech Stack

• Backend: FastAPI (Python 3.10+), async httpx, OpenRouter API
• Frontend: React + Vite, react-markdown for rendering
• Storage: SQLite (data/council.sqlite) + MemPalace vector store (data/palace/)
• Package Management: uv for Python, npm for JavaScript