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2025-11-22 14:27:53 -08:00
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--- a/.gitignore
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+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv
+
+# Keys and secrets
+.env
+
+# Data files
+data/
+
+# Frontend
+frontend/node_modules/
+frontend/dist/
+frontend/.vite/
--- a/.python-version
+++ b/.python-version
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+3.10
--- a/CLAUDE.md
+++ b/CLAUDE.md
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+# CLAUDE.md - Technical Notes for LLM Council
+
+This file contains technical details, architectural decisions, and important implementation notes for future development sessions.
+
+## Project Overview
+
+LLM Council is a 3-stage deliberation system where multiple LLMs collaboratively answer user questions. The key innovation is anonymized peer review in Stage 2, preventing models from playing favorites.
+
+## Architecture
+
+### Backend Structure (`backend/`)
+
+**`config.py`**
+- Contains `COUNCIL_MODELS` (list of OpenRouter model identifiers)
+- Contains `CHAIRMAN_MODEL` (model that synthesizes final answer)
+- Uses environment variable `OPENROUTER_API_KEY` from `.env`
+- Backend runs on **port 8001** (NOT 8000 - user had another app on 8000)
+
+**`openrouter.py`**
+- `query_model()`: Single async model query
+- `query_models_parallel()`: Parallel queries using `asyncio.gather()`
+- Returns dict with 'content' and optional 'reasoning_details'
+- Graceful degradation: returns None on failure, continues with successful responses
+
+**`council.py`** - The Core Logic
+- `stage1_collect_responses()`: Parallel queries to all council models
+- `stage2_collect_rankings()`:
+  - Anonymizes responses as "Response A, B, C, etc."
+  - Creates `label_to_model` mapping for de-anonymization
+  - Prompts models to evaluate and rank (with strict format requirements)
+  - Returns tuple: (rankings_list, label_to_model_dict)
+  - Each ranking includes both raw text and `parsed_ranking` list
+- `stage3_synthesize_final()`: Chairman synthesizes from all responses + rankings
+- `parse_ranking_from_text()`: Extracts "FINAL RANKING:" section, handles both numbered lists and plain format
+- `calculate_aggregate_rankings()`: Computes average rank position across all peer evaluations
+
+**`storage.py`**
+- JSON-based conversation storage in `data/conversations/`
+- Each conversation: `{id, created_at, messages[]}`
+- Assistant messages contain: `{role, stage1, stage2, stage3}`
+- Note: metadata (label_to_model, aggregate_rankings) is NOT persisted to storage, only returned via API
+
+**`main.py`**
+- FastAPI app with CORS enabled for localhost:5173 and localhost:3000
+- POST `/api/conversations/{id}/message` returns metadata in addition to stages
+- Metadata includes: label_to_model mapping and aggregate_rankings
+
+### Frontend Structure (`frontend/src/`)
+
+**`App.jsx`**
+- Main orchestration: manages conversations list and current conversation
+- Handles message sending and metadata storage
+- Important: metadata is stored in the UI state for display but not persisted to backend JSON
+
+**`components/ChatInterface.jsx`**
+- Multiline textarea (3 rows, resizable)
+- Enter to send, Shift+Enter for new line
+- User messages wrapped in markdown-content class for padding
+
+**`components/Stage1.jsx`**
+- Tab view of individual model responses
+- ReactMarkdown rendering with markdown-content wrapper
+
+**`components/Stage2.jsx`**
+- **Critical Feature**: Tab view showing RAW evaluation text from each model
+- De-anonymization happens CLIENT-SIDE for display (models receive anonymous labels)
+- Shows "Extracted Ranking" below each evaluation so users can validate parsing
+- Aggregate rankings shown with average position and vote count
+- Explanatory text clarifies that boldface model names are for readability only
+
+**`components/Stage3.jsx`**
+- Final synthesized answer from chairman
+- Green-tinted background (#f0fff0) to highlight conclusion
+
+**Styling (`*.css`)**
+- Light mode theme (not dark mode)
+- Primary color: #4a90e2 (blue)
+- Global markdown styling in `index.css` with `.markdown-content` class
+- 12px padding on all markdown content to prevent cluttered appearance
+
+## Key Design Decisions
+
+### Stage 2 Prompt Format
+The Stage 2 prompt is very specific to ensure parseable output:
+```
+1. Evaluate each response individually first
+2. Provide "FINAL RANKING:" header
+3. Numbered list format: "1. Response C", "2. Response A", etc.
+4. No additional text after ranking section
+```
+
+This strict format allows reliable parsing while still getting thoughtful evaluations.
+
+### De-anonymization Strategy
+- Models receive: "Response A", "Response B", etc.
+- Backend creates mapping: `{"Response A": "openai/gpt-5.1", ...}`
+- Frontend displays model names in **bold** for readability
+- Users see explanation that original evaluation used anonymous labels
+- This prevents bias while maintaining transparency
+
+### Error Handling Philosophy
+- Continue with successful responses if some models fail (graceful degradation)
+- Never fail the entire request due to single model failure
+- Log errors but don't expose to user unless all models fail
+
+### UI/UX Transparency
+- All raw outputs are inspectable via tabs
+- Parsed rankings shown below raw text for validation
+- Users can verify system's interpretation of model outputs
+- This builds trust and allows debugging of edge cases
+
+## Important Implementation Details
+
+### Relative Imports
+All backend modules use relative imports (e.g., `from .config import ...`) not absolute imports. This is critical for Python's module system to work correctly when running as `python -m backend.main`.
+
+### Port Configuration
+- Backend: 8001 (changed from 8000 to avoid conflict)
+- Frontend: 5173 (Vite default)
+- Update both `backend/main.py` and `frontend/src/api.js` if changing
+
+### Markdown Rendering
+All ReactMarkdown components must be wrapped in `<div className="markdown-content">` for proper spacing. This class is defined globally in `index.css`.
+
+### Model Configuration
+Models are hardcoded in `backend/config.py`. Chairman can be same or different from council members. The current default is Gemini as chairman per user preference.
+
+## Common Gotchas
+
+1. **Module Import Errors**: Always run backend as `python -m backend.main` from project root, not from backend directory
+2. **CORS Issues**: Frontend must match allowed origins in `main.py` CORS middleware
+3. **Ranking Parse Failures**: If models don't follow format, fallback regex extracts any "Response X" patterns in order
+4. **Missing Metadata**: Metadata is ephemeral (not persisted), only available in API responses
+
+## Future Enhancement Ideas
+
+- Configurable council/chairman via UI instead of config file
+- Streaming responses instead of batch loading
+- Export conversations to markdown/PDF
+- Model performance analytics over time
+- Custom ranking criteria (not just accuracy/insight)
+- Support for reasoning models (o1, etc.) with special handling
+
+## Testing Notes
+
+Use `test_openrouter.py` to verify API connectivity and test different model identifiers before adding to council. The script tests both streaming and non-streaming modes.
+
+## Data Flow Summary
+
+```
+User Query
+    ↓
+Stage 1: Parallel queries → [individual responses]
+    ↓
+Stage 2: Anonymize → Parallel ranking queries → [evaluations + parsed rankings]
+    ↓
+Aggregate Rankings Calculation → [sorted by avg position]
+    ↓
+Stage 3: Chairman synthesis with full context
+    ↓
+Return: {stage1, stage2, stage3, metadata}
+    ↓
+Frontend: Display with tabs + validation UI
+```
+
+The entire flow is async/parallel where possible to minimize latency.
--- a/README.md
+++ b/README.md
@@ -0,0 +1,90 @@
+# LLM Council
+
+![llmcouncil](header.jpg)
+
+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), you can group them into your "LLM Council".
+
+This is a fully local, lightweight web app. The code has to be minimal, readable, simple and not bloated with complexity. The idea is that the user runs the web app locally, and access it via a localhost url/port.
+
+UIUX wise, the app looks like ChatGPT. Meaning there is a sidebar on the left storing conversations. User can create a new conversation or access past conversations. The conversations persist.
+
+The life of a single conversation looks as follows. The user writes up a query. Then:
+
+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.
+2. Stage 2: review. Each individual LLM is given the responses of the other LLMs. 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 (the strongest model) takes all of the model's responses and compiles them into a single final answer that is presented to the user.
+
+Implementation details:
+
+- The project uses [uv](https://docs.astral.sh/uv/) for project management.
+- The project uses [OpenRouter](https://openrouter.ai/) to easily call models across all API providers with a single API key. The API key is stored in `.env` file in current repo.
+
+## Setup
+
+### 1. Install Dependencies
+
+**Backend:**
+```bash
+uv sync
+```
+
+**Frontend:**
+```bash
+cd frontend
+npm install
+cd ..
+```
+
+### 2. Configure API Key
+
+Create a `.env` file in the project root:
+
+```bash
+OPENROUTER_API_KEY=sk-or-v1-...
+```
+
+Get your API key at [openrouter.ai](https://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:
+
+```python
+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**
+```bash
+./start.sh
+```
+
+**Option 2: Run manually**
+
+Terminal 1 (Backend):
+```bash
+uv run python -m backend.main
+```
+
+Terminal 2 (Frontend):
+```bash
+cd frontend
+npm run dev
+```
+
+Then open http://localhost:5173 in your browser.
+
+## Tech Stack
+
+- **Backend:** FastAPI (Python 3.10+), async httpx, OpenRouter API
+- **Frontend:** React + Vite, react-markdown for rendering
+- **Storage:** JSON files in `data/conversations/`
+- **Package Management:** uv for Python, npm for JavaScript
--- a/backend/init.py
+++ b/backend/init.py
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+"""LLM Council backend package."""
--- a/backend/config.py
+++ b/backend/config.py
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+"""Configuration for the LLM Council."""
+
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# OpenRouter API key
+OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
+
+# Council members - list of OpenRouter model identifiers
+COUNCIL_MODELS = [
+    "openai/gpt-5.1",
+    "google/gemini-3-pro-preview",
+    "anthropic/claude-sonnet-4.5",
+    "x-ai/grok-4",
+]
+
+# Chairman model - synthesizes final response
+CHAIRMAN_MODEL = "google/gemini-3-pro-preview"
+
+# OpenRouter API endpoint
+OPENROUTER_API_URL = "https://openrouter.ai/api/v1/chat/completions"
+
+# Data directory for conversation storage
+DATA_DIR = "data/conversations"
--- a/backend/council.py
+++ b/backend/council.py
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+"""3-stage LLM Council orchestration."""
+
+from typing import List, Dict, Any, Tuple
+from .openrouter import query_models_parallel, query_model
+from .config import COUNCIL_MODELS, CHAIRMAN_MODEL
+
+
+async def stage1_collect_responses(user_query: str) -> List[Dict[str, Any]]:
+    """
+    Stage 1: Collect individual responses from all council models.
+
+    Args:
+        user_query: The user's question
+
+    Returns:
+        List of dicts with 'model' and 'response' keys
+    """
+    messages = [{"role": "user", "content": user_query}]
+
+    # Query all models in parallel
+    responses = await query_models_parallel(COUNCIL_MODELS, messages)
+
+    # Format results
+    stage1_results = []
+    for model, response in responses.items():
+        if response is not None:  # Only include successful responses
+            stage1_results.append({
+                "model": model,
+                "response": response.get('content', '')
+            })
+
+    return stage1_results
+
+
+async def stage2_collect_rankings(
+    user_query: str,
+    stage1_results: List[Dict[str, Any]]
+) -> Tuple[List[Dict[str, Any]], Dict[str, str]]:
+    """
+    Stage 2: Each model ranks the anonymized responses.
+
+    Args:
+        user_query: The original user query
+        stage1_results: Results from Stage 1
+
+    Returns:
+        Tuple of (rankings list, label_to_model mapping)
+    """
+    # Create anonymized labels for responses (Response A, Response B, etc.)
+    labels = [chr(65 + i) for i in range(len(stage1_results))]  # A, B, C, ...
+
+    # Create mapping from label to model name
+    label_to_model = {
+        f"Response {label}": result['model']
+        for label, result in zip(labels, stage1_results)
+    }
+
+    # Build the ranking prompt
+    responses_text = "\n\n".join([
+        f"Response {label}:\n{result['response']}"
+        for label, result in zip(labels, stage1_results)
+    ])
+
+    ranking_prompt = f"""You are evaluating different responses to the following question:
+
+Question: {user_query}
+
+Here are the responses from different models (anonymized):
+
+{responses_text}
+
+Your task:
+1. First, evaluate each response individually. For each response, explain what it does well and what it does poorly.
+2. Then, at the very end of your response, provide a final ranking.
+
+IMPORTANT: Your final ranking MUST be formatted EXACTLY as follows:
+- Start with the line "FINAL RANKING:" (all caps, with colon)
+- Then list the responses from best to worst as a numbered list
+- Each line should be: number, period, space, then ONLY the response label (e.g., "1. Response A")
+- Do not add any other text or explanations in the ranking section
+
+Example of the correct format for your ENTIRE response:
+
+Response A provides good detail on X but misses Y...
+Response B is accurate but lacks depth on Z...
+Response C offers the most comprehensive answer...
+
+FINAL RANKING:
+1. Response C
+2. Response A
+3. Response B
+
+Now provide your evaluation and ranking:"""
+
+    messages = [{"role": "user", "content": ranking_prompt}]
+
+    # Get rankings from all council models in parallel
+    responses = await query_models_parallel(COUNCIL_MODELS, messages)
+
+    # Format results
+    stage2_results = []
+    for model, response in responses.items():
+        if response is not None:
+            full_text = response.get('content', '')
+            parsed = parse_ranking_from_text(full_text)
+            stage2_results.append({
+                "model": model,
+                "ranking": full_text,
+                "parsed_ranking": parsed
+            })
+
+    return stage2_results, label_to_model
+
+
+async def stage3_synthesize_final(
+    user_query: str,
+    stage1_results: List[Dict[str, Any]],
+    stage2_results: List[Dict[str, Any]]
+) -> Dict[str, Any]:
+    """
+    Stage 3: Chairman synthesizes final response.
+
+    Args:
+        user_query: The original user query
+        stage1_results: Individual model responses from Stage 1
+        stage2_results: Rankings from Stage 2
+
+    Returns:
+        Dict with 'model' and 'response' keys
+    """
+    # Build comprehensive context for chairman
+    stage1_text = "\n\n".join([
+        f"Model: {result['model']}\nResponse: {result['response']}"
+        for result in stage1_results
+    ])
+
+    stage2_text = "\n\n".join([
+        f"Model: {result['model']}\nRanking: {result['ranking']}"
+        for result in stage2_results
+    ])
+
+    chairman_prompt = f"""You are the Chairman of an LLM Council. Multiple AI models have provided responses to a user's question, and then ranked each other's responses.
+
+Original Question: {user_query}
+
+STAGE 1 - Individual Responses:
+{stage1_text}
+
+STAGE 2 - Peer Rankings:
+{stage2_text}
+
+Your task as Chairman is to synthesize all of this information into a single, comprehensive, accurate answer to the user's original question. Consider:
+- The individual responses and their insights
+- The peer rankings and what they reveal about response quality
+- Any patterns of agreement or disagreement
+
+Provide a clear, well-reasoned final answer that represents the council's collective wisdom:"""
+
+    messages = [{"role": "user", "content": chairman_prompt}]
+
+    # Query the chairman model
+    response = await query_model(CHAIRMAN_MODEL, messages)
+
+    if response is None:
+        # Fallback if chairman fails
+        return {
+            "model": CHAIRMAN_MODEL,
+            "response": "Error: Unable to generate final synthesis."
+        }
+
+    return {
+        "model": CHAIRMAN_MODEL,
+        "response": response.get('content', '')
+    }
+
+
+def parse_ranking_from_text(ranking_text: str) -> List[str]:
+    """
+    Parse the FINAL RANKING section from the model's response.
+
+    Args:
+        ranking_text: The full text response from the model
+
+    Returns:
+        List of response labels in ranked order
+    """
+    import re
+
+    # Look for "FINAL RANKING:" section
+    if "FINAL RANKING:" in ranking_text:
+        # Extract everything after "FINAL RANKING:"
+        parts = ranking_text.split("FINAL RANKING:")
+        if len(parts) >= 2:
+            ranking_section = parts[1]
+            # Try to extract numbered list format (e.g., "1. Response A")
+            # This pattern looks for: number, period, optional space, "Response X"
+            numbered_matches = re.findall(r'\d+\.\s*Response [A-Z]', ranking_section)
+            if numbered_matches:
+                # Extract just the "Response X" part
+                return [re.search(r'Response [A-Z]', m).group() for m in numbered_matches]
+
+            # Fallback: Extract all "Response X" patterns in order
+            matches = re.findall(r'Response [A-Z]', ranking_section)
+            return matches
+
+    # Fallback: try to find any "Response X" patterns in order
+    matches = re.findall(r'Response [A-Z]', ranking_text)
+    return matches
+
+
+def calculate_aggregate_rankings(
+    stage2_results: List[Dict[str, Any]],
+    label_to_model: Dict[str, str]
+) -> List[Dict[str, Any]]:
+    """
+    Calculate aggregate rankings across all models.
+
+    Args:
+        stage2_results: Rankings from each model
+        label_to_model: Mapping from anonymous labels to model names
+
+    Returns:
+        List of dicts with model name and average rank, sorted best to worst
+    """
+    from collections import defaultdict
+
+    # Track positions for each model
+    model_positions = defaultdict(list)
+
+    for ranking in stage2_results:
+        ranking_text = ranking['ranking']
+
+        # Parse the ranking from the structured format
+        parsed_ranking = parse_ranking_from_text(ranking_text)
+
+        for position, label in enumerate(parsed_ranking, start=1):
+            if label in label_to_model:
+                model_name = label_to_model[label]
+                model_positions[model_name].append(position)
+
+    # Calculate average position for each model
+    aggregate = []
+    for model, positions in model_positions.items():
+        if positions:
+            avg_rank = sum(positions) / len(positions)
+            aggregate.append({
+                "model": model,
+                "average_rank": round(avg_rank, 2),
+                "rankings_count": len(positions)
+            })
+
+    # Sort by average rank (lower is better)
+    aggregate.sort(key=lambda x: x['average_rank'])
+
+    return aggregate
+
+
+async def run_full_council(user_query: str) -> Tuple[List, List, Dict, Dict]:
+    """
+    Run the complete 3-stage council process.
+
+    Args:
+        user_query: The user's question
+
+    Returns:
+        Tuple of (stage1_results, stage2_results, stage3_result, metadata)
+    """
+    # Stage 1: Collect individual responses
+    stage1_results = await stage1_collect_responses(user_query)
+
+    # If no models responded successfully, return error
+    if not stage1_results:
+        return [], [], {
+            "model": "error",
+            "response": "All models failed to respond. Please try again."
+        }, {}
+
+    # Stage 2: Collect rankings
+    stage2_results, label_to_model = await stage2_collect_rankings(user_query, stage1_results)
+
+    # Calculate aggregate rankings
+    aggregate_rankings = calculate_aggregate_rankings(stage2_results, label_to_model)
+
+    # Stage 3: Synthesize final answer
+    stage3_result = await stage3_synthesize_final(
+        user_query,
+        stage1_results,
+        stage2_results
+    )
+
+    # Prepare metadata
+    metadata = {
+        "label_to_model": label_to_model,
+        "aggregate_rankings": aggregate_rankings
+    }
+
+    return stage1_results, stage2_results, stage3_result, metadata
--- a/backend/main.py
+++ b/backend/main.py
@@ -0,0 +1,115 @@
+"""FastAPI backend for LLM Council."""
+
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import List, Dict, Any
+import uuid
+
+from . import storage
+from .council import run_full_council
+
+app = FastAPI(title="LLM Council API")
+
+# Enable CORS for local development
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["http://localhost:5173", "http://localhost:3000"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+class CreateConversationRequest(BaseModel):
+    """Request to create a new conversation."""
+    pass
+
+
+class SendMessageRequest(BaseModel):
+    """Request to send a message in a conversation."""
+    content: str
+
+
+class ConversationMetadata(BaseModel):
+    """Conversation metadata for list view."""
+    id: str
+    created_at: str
+    message_count: int
+
+
+class Conversation(BaseModel):
+    """Full conversation with all messages."""
+    id: str
+    created_at: str
+    messages: List[Dict[str, Any]]
+
+
+@app.get("/")
+async def root():
+    """Health check endpoint."""
+    return {"status": "ok", "service": "LLM Council API"}
+
+
+@app.get("/api/conversations", response_model=List[ConversationMetadata])
+async def list_conversations():
+    """List all conversations (metadata only)."""
+    return storage.list_conversations()
+
+
+@app.post("/api/conversations", response_model=Conversation)
+async def create_conversation(request: CreateConversationRequest):
+    """Create a new conversation."""
+    conversation_id = str(uuid.uuid4())
+    conversation = storage.create_conversation(conversation_id)
+    return conversation
+
+
+@app.get("/api/conversations/{conversation_id}", response_model=Conversation)
+async def get_conversation(conversation_id: str):
+    """Get a specific conversation with all its messages."""
+    conversation = storage.get_conversation(conversation_id)
+    if conversation is None:
+        raise HTTPException(status_code=404, detail="Conversation not found")
+    return conversation
+
+
+@app.post("/api/conversations/{conversation_id}/message")
+async def send_message(conversation_id: str, request: SendMessageRequest):
+    """
+    Send a message and run the 3-stage council process.
+    Returns the complete response with all stages.
+    """
+    # Check if conversation exists
+    conversation = storage.get_conversation(conversation_id)
+    if conversation is None:
+        raise HTTPException(status_code=404, detail="Conversation not found")
+
+    # Add user message
+    storage.add_user_message(conversation_id, request.content)
+
+    # Run the 3-stage council process
+    stage1_results, stage2_results, stage3_result, metadata = await run_full_council(
+        request.content
+    )
+
+    # Add assistant message with all stages
+    storage.add_assistant_message(
+        conversation_id,
+        stage1_results,
+        stage2_results,
+        stage3_result
+    )
+
+    # Return the complete response with metadata
+    return {
+        "stage1": stage1_results,
+        "stage2": stage2_results,
+        "stage3": stage3_result,
+        "metadata": metadata
+    }
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8001)
--- a/backend/openrouter.py
+++ b/backend/openrouter.py
@@ -0,0 +1,79 @@
+"""OpenRouter API client for making LLM requests."""
+
+import httpx
+from typing import List, Dict, Any, Optional
+from .config import OPENROUTER_API_KEY, OPENROUTER_API_URL
+
+
+async def query_model(
+    model: str,
+    messages: List[Dict[str, str]],
+    timeout: float = 120.0
+) -> Optional[Dict[str, Any]]:
+    """
+    Query a single model via OpenRouter API.
+
+    Args:
+        model: OpenRouter model identifier (e.g., "openai/gpt-4o")
+        messages: List of message dicts with 'role' and 'content'
+        timeout: Request timeout in seconds
+
+    Returns:
+        Response dict with 'content' and optional 'reasoning_details', or None if failed
+    """
+    headers = {
+        "Authorization": f"Bearer {OPENROUTER_API_KEY}",
+        "Content-Type": "application/json",
+    }
+
+    payload = {
+        "model": model,
+        "messages": messages,
+    }
+
+    try:
+        async with httpx.AsyncClient(timeout=timeout) as client:
+            response = await client.post(
+                OPENROUTER_API_URL,
+                headers=headers,
+                json=payload
+            )
+            response.raise_for_status()
+
+            data = response.json()
+            message = data['choices'][0]['message']
+
+            return {
+                'content': message.get('content'),
+                'reasoning_details': message.get('reasoning_details')
+            }
+
+    except Exception as e:
+        print(f"Error querying model {model}: {e}")
+        return None
+
+
+async def query_models_parallel(
+    models: List[str],
+    messages: List[Dict[str, str]]
+) -> Dict[str, Optional[Dict[str, Any]]]:
+    """
+    Query multiple models in parallel.
+
+    Args:
+        models: List of OpenRouter model identifiers
+        messages: List of message dicts to send to each model
+
+    Returns:
+        Dict mapping model identifier to response dict (or None if failed)
+    """
+    import asyncio
+
+    # Create tasks for all models
+    tasks = [query_model(model, messages) for model in models]
+
+    # Wait for all to complete
+    responses = await asyncio.gather(*tasks)
+
+    # Map models to their responses
+    return {model: response for model, response in zip(models, responses)}
--- a/backend/storage.py
+++ b/backend/storage.py
@@ -0,0 +1,154 @@
+"""JSON-based storage for conversations."""
+
+import json
+import os
+from datetime import datetime
+from typing import List, Dict, Any, Optional
+from pathlib import Path
+from .config import DATA_DIR
+
+
+def ensure_data_dir():
+    """Ensure the data directory exists."""
+    Path(DATA_DIR).mkdir(parents=True, exist_ok=True)
+
+
+def get_conversation_path(conversation_id: str) -> str:
+    """Get the file path for a conversation."""
+    return os.path.join(DATA_DIR, f"{conversation_id}.json")
+
+
+def create_conversation(conversation_id: str) -> Dict[str, Any]:
+    """
+    Create a new conversation.
+
+    Args:
+        conversation_id: Unique identifier for the conversation
+
+    Returns:
+        New conversation dict
+    """
+    ensure_data_dir()
+
+    conversation = {
+        "id": conversation_id,
+        "created_at": datetime.utcnow().isoformat(),
+        "messages": []
+    }
+
+    # Save to file
+    path = get_conversation_path(conversation_id)
+    with open(path, 'w') as f:
+        json.dump(conversation, f, indent=2)
+
+    return conversation
+
+
+def get_conversation(conversation_id: str) -> Optional[Dict[str, Any]]:
+    """
+    Load a conversation from storage.
+
+    Args:
+        conversation_id: Unique identifier for the conversation
+
+    Returns:
+        Conversation dict or None if not found
+    """
+    path = get_conversation_path(conversation_id)
+
+    if not os.path.exists(path):
+        return None
+
+    with open(path, 'r') as f:
+        return json.load(f)
+
+
+def save_conversation(conversation: Dict[str, Any]):
+    """
+    Save a conversation to storage.
+
+    Args:
+        conversation: Conversation dict to save
+    """
+    ensure_data_dir()
+
+    path = get_conversation_path(conversation['id'])
+    with open(path, 'w') as f:
+        json.dump(conversation, f, indent=2)
+
+
+def list_conversations() -> List[Dict[str, Any]]:
+    """
+    List all conversations (metadata only).
+
+    Returns:
+        List of conversation metadata dicts
+    """
+    ensure_data_dir()
+
+    conversations = []
+    for filename in os.listdir(DATA_DIR):
+        if filename.endswith('.json'):
+            path = os.path.join(DATA_DIR, filename)
+            with open(path, 'r') as f:
+                data = json.load(f)
+                # Return metadata only
+                conversations.append({
+                    "id": data["id"],
+                    "created_at": data["created_at"],
+                    "message_count": len(data["messages"])
+                })
+
+    # Sort by creation time, newest first
+    conversations.sort(key=lambda x: x["created_at"], reverse=True)
+
+    return conversations
+
+
+def add_user_message(conversation_id: str, content: str):
+    """
+    Add a user message to a conversation.
+
+    Args:
+        conversation_id: Conversation identifier
+        content: User message content
+    """
+    conversation = get_conversation(conversation_id)
+    if conversation is None:
+        raise ValueError(f"Conversation {conversation_id} not found")
+
+    conversation["messages"].append({
+        "role": "user",
+        "content": content
+    })
+
+    save_conversation(conversation)
+
+
+def add_assistant_message(
+    conversation_id: str,
+    stage1: List[Dict[str, Any]],
+    stage2: List[Dict[str, Any]],
+    stage3: Dict[str, Any]
+):
+    """
+    Add an assistant message with all 3 stages to a conversation.
+
+    Args:
+        conversation_id: Conversation identifier
+        stage1: List of individual model responses
+        stage2: List of model rankings
+        stage3: Final synthesized response
+    """
+    conversation = get_conversation(conversation_id)
+    if conversation is None:
+        raise ValueError(f"Conversation {conversation_id} not found")
+
+    conversation["messages"].append({
+        "role": "assistant",
+        "stage1": stage1,
+        "stage2": stage2,
+        "stage3": stage3
+    })
+
+    save_conversation(conversation)
--- a/frontend/.gitignore
+++ b/frontend/.gitignore
@@ -0,0 +1,24 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
--- a/frontend/README.md
+++ b/frontend/README.md
@@ -0,0 +1,16 @@
+# React + Vite
+
+This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
+
+Currently, two official plugins are available:
+
+- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh
+- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
+
+## React Compiler
+
+The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation).
+
+## Expanding the ESLint configuration
+
+If you are developing a production application, we recommend using TypeScript with type-aware lint rules enabled. Check out the [TS template](https://github.com/vitejs/vite/tree/main/packages/create-vite/template-react-ts) for information on how to integrate TypeScript and [`typescript-eslint`](https://typescript-eslint.io) in your project.
--- a/frontend/eslint.config.js
+++ b/frontend/eslint.config.js
@@ -0,0 +1,29 @@
+import js from '@eslint/js'
+import globals from 'globals'
+import reactHooks from 'eslint-plugin-react-hooks'
+import reactRefresh from 'eslint-plugin-react-refresh'
+import { defineConfig, globalIgnores } from 'eslint/config'
+
+export default defineConfig([
+  globalIgnores(['dist']),
+  {
+    files: ['**/*.{js,jsx}'],
+    extends: [
+      js.configs.recommended,
+      reactHooks.configs.flat.recommended,
+      reactRefresh.configs.vite,
+    ],
+    languageOptions: {
+      ecmaVersion: 2020,
+      globals: globals.browser,
+      parserOptions: {
+        ecmaVersion: 'latest',
+        ecmaFeatures: { jsx: true },
+        sourceType: 'module',
+      },
+    },
+    rules: {
+      'no-unused-vars': ['error', { varsIgnorePattern: '^[A-Z_]' }],
+    },
+  },
+])
--- a/frontend/index.html
+++ b/frontend/index.html
@@ -0,0 +1,13 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>frontend</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.jsx"></script>
+  </body>
+</html>
--- a/frontend/package-lock.json
+++ b/frontend/package-lock.json
--- a/frontend/package.json
+++ b/frontend/package.json
@@ -0,0 +1,28 @@
+{
+  "name": "frontend",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "lint": "eslint .",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "react": "^19.2.0",
+    "react-dom": "^19.2.0",
+    "react-markdown": "^10.1.0"
+  },
+  "devDependencies": {
+    "@eslint/js": "^9.39.1",
+    "@types/react": "^19.2.5",
+    "@types/react-dom": "^19.2.3",
+    "@vitejs/plugin-react": "^5.1.1",
+    "eslint": "^9.39.1",
+    "eslint-plugin-react-hooks": "^7.0.1",
+    "eslint-plugin-react-refresh": "^0.4.24",
+    "globals": "^16.5.0",
+    "vite": "^7.2.4"
+  }
+}
--- a/frontend/public/vite.svg
+++ b/frontend/public/vite.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>
--- a/frontend/src/App.css
+++ b/frontend/src/App.css
@@ -0,0 +1,15 @@
+* {
+  box-sizing: border-box;
+}
+
+.app {
+  display: flex;
+  height: 100vh;
+  width: 100vw;
+  overflow: hidden;
+  background: #ffffff;
+  color: #333;
+  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen',
+    'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue',
+    sans-serif;
+}
--- a/frontend/src/App.jsx
+++ b/frontend/src/App.jsx
@@ -0,0 +1,120 @@
+import { useState, useEffect } from 'react';
+import Sidebar from './components/Sidebar';
+import ChatInterface from './components/ChatInterface';
+import { api } from './api';
+import './App.css';
+
+function App() {
+  const [conversations, setConversations] = useState([]);
+  const [currentConversationId, setCurrentConversationId] = useState(null);
+  const [currentConversation, setCurrentConversation] = useState(null);
+  const [isLoading, setIsLoading] = useState(false);
+
+  // Load conversations on mount
+  useEffect(() => {
+    loadConversations();
+  }, []);
+
+  // Load conversation details when selected
+  useEffect(() => {
+    if (currentConversationId) {
+      loadConversation(currentConversationId);
+    }
+  }, [currentConversationId]);
+
+  const loadConversations = async () => {
+    try {
+      const convs = await api.listConversations();
+      setConversations(convs);
+    } catch (error) {
+      console.error('Failed to load conversations:', error);
+    }
+  };
+
+  const loadConversation = async (id) => {
+    try {
+      const conv = await api.getConversation(id);
+      setCurrentConversation(conv);
+    } catch (error) {
+      console.error('Failed to load conversation:', error);
+    }
+  };
+
+  const handleNewConversation = async () => {
+    try {
+      const newConv = await api.createConversation();
+      setConversations([
+        { id: newConv.id, created_at: newConv.created_at, message_count: 0 },
+        ...conversations,
+      ]);
+      setCurrentConversationId(newConv.id);
+    } catch (error) {
+      console.error('Failed to create conversation:', error);
+    }
+  };
+
+  const handleSelectConversation = (id) => {
+    setCurrentConversationId(id);
+  };
+
+  const handleSendMessage = async (content) => {
+    if (!currentConversationId) return;
+
+    setIsLoading(true);
+    try {
+      // Optimistically add user message to UI
+      const userMessage = { role: 'user', content };
+      setCurrentConversation((prev) => ({
+        ...prev,
+        messages: [...prev.messages, userMessage],
+      }));
+
+      // Send message and get council response
+      const response = await api.sendMessage(currentConversationId, content);
+
+      // Add assistant message to UI
+      const assistantMessage = {
+        role: 'assistant',
+        stage1: response.stage1,
+        stage2: response.stage2,
+        stage3: response.stage3,
+        metadata: response.metadata,
+      };
+
+      setCurrentConversation((prev) => ({
+        ...prev,
+        messages: [...prev.messages, assistantMessage],
+      }));
+
+      // Reload conversations list to update message count
+      await loadConversations();
+    } catch (error) {
+      console.error('Failed to send message:', error);
+      // Remove optimistic user message on error
+      setCurrentConversation((prev) => ({
+        ...prev,
+        messages: prev.messages.slice(0, -1),
+      }));
+    } finally {
+      setIsLoading(false);
+    }
+  };
+
+  return (
+    <div className="app">
+      <Sidebar
+        conversations={conversations}
+        currentConversationId={currentConversationId}
+        onSelectConversation={handleSelectConversation}
+        onNewConversation={handleNewConversation}
+      />
+      <ChatInterface
+        conversation={currentConversation}
+        onSendMessage={handleSendMessage}
+        isLoading={isLoading}
+      />
+    </div>
+  );
+}
+
+export default App;
--- a/frontend/src/api.js
+++ b/frontend/src/api.js
@@ -0,0 +1,68 @@
+/**
+ * API client for the LLM Council backend.
+ */
+
+const API_BASE = 'http://localhost:8001';
+
+export const api = {
+  /**
+   * List all conversations.
+   */
+  async listConversations() {
+    const response = await fetch(`${API_BASE}/api/conversations`);
+    if (!response.ok) {
+      throw new Error('Failed to list conversations');
+    }
+    return response.json();
+  },
+
+  /**
+   * Create a new conversation.
+   */
+  async createConversation() {
+    const response = await fetch(`${API_BASE}/api/conversations`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify({}),
+    });
+    if (!response.ok) {
+      throw new Error('Failed to create conversation');
+    }
+    return response.json();
+  },
+
+  /**
+   * Get a specific conversation.
+   */
+  async getConversation(conversationId) {
+    const response = await fetch(
+      `${API_BASE}/api/conversations/${conversationId}`
+    );
+    if (!response.ok) {
+      throw new Error('Failed to get conversation');
+    }
+    return response.json();
+  },
+
+  /**
+   * Send a message in a conversation.
+   */
+  async sendMessage(conversationId, content) {
+    const response = await fetch(
+      `${API_BASE}/api/conversations/${conversationId}/message`,
+      {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+        body: JSON.stringify({ content }),
+      }
+    );
+    if (!response.ok) {
+      throw new Error('Failed to send message');
+    }
+    return response.json();
+  },
+};
--- a/frontend/src/assets/react.svg
+++ b/frontend/src/assets/react.svg
@@ -0,0 +1 @@
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--- a/frontend/src/components/ChatInterface.css
+++ b/frontend/src/components/ChatInterface.css
@@ -0,0 +1,149 @@
+.chat-interface {
+  flex: 1;
+  display: flex;
+  flex-direction: column;
+  height: 100vh;
+  background: #ffffff;
+}
+
+.messages-container {
+  flex: 1;
+  overflow-y: auto;
+  padding: 24px;
+}
+
+.empty-state {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  height: 100%;
+  color: #666;
+  text-align: center;
+}
+
+.empty-state h2 {
+  margin: 0 0 8px 0;
+  font-size: 24px;
+  color: #333;
+}
+
+.empty-state p {
+  margin: 0;
+  font-size: 16px;
+}
+
+.message-group {
+  margin-bottom: 32px;
+}
+
+.user-message,
+.assistant-message {
+  margin-bottom: 16px;
+}
+
+.message-label {
+  font-size: 12px;
+  font-weight: 600;
+  color: #666;
+  margin-bottom: 8px;
+  text-transform: uppercase;
+  letter-spacing: 0.5px;
+}
+
+.user-message .message-content {
+  background: #f0f7ff;
+  padding: 16px;
+  border-radius: 8px;
+  border: 1px solid #d0e7ff;
+  color: #333;
+  line-height: 1.6;
+  max-width: 80%;
+  white-space: pre-wrap;
+}
+
+.loading-indicator {
+  display: flex;
+  align-items: center;
+  gap: 12px;
+  padding: 16px;
+  color: #666;
+  font-size: 14px;
+}
+
+.spinner {
+  width: 20px;
+  height: 20px;
+  border: 2px solid #e0e0e0;
+  border-top-color: #4a90e2;
+  border-radius: 50%;
+  animation: spin 0.8s linear infinite;
+}
+
+@keyframes spin {
+  to {
+    transform: rotate(360deg);
+  }
+}
+
+.input-form {
+  display: flex;
+  align-items: flex-end;
+  gap: 12px;
+  padding: 24px;
+  border-top: 1px solid #e0e0e0;
+  background: #fafafa;
+}
+
+.message-input {
+  flex: 1;
+  padding: 14px;
+  background: #ffffff;
+  border: 1px solid #d0d0d0;
+  border-radius: 8px;
+  color: #333;
+  font-size: 15px;
+  font-family: inherit;
+  line-height: 1.5;
+  outline: none;
+  resize: vertical;
+  min-height: 80px;
+  max-height: 300px;
+}
+
+.message-input:focus {
+  border-color: #4a90e2;
+  box-shadow: 0 0 0 3px rgba(74, 144, 226, 0.1);
+}
+
+.message-input:disabled {
+  opacity: 0.5;
+  cursor: not-allowed;
+  background: #f5f5f5;
+}
+
+.send-button {
+  padding: 14px 28px;
+  background: #4a90e2;
+  border: 1px solid #4a90e2;
+  border-radius: 8px;
+  color: #fff;
+  font-size: 15px;
+  font-weight: 600;
+  cursor: pointer;
+  transition: background 0.2s;
+  white-space: nowrap;
+  align-self: flex-end;
+}
+
+.send-button:hover:not(:disabled) {
+  background: #357abd;
+  border-color: #357abd;
+}
+
+.send-button:disabled {
+  opacity: 0.5;
+  cursor: not-allowed;
+  background: #ccc;
+  border-color: #ccc;
+}
--- a/frontend/src/components/ChatInterface.jsx
+++ b/frontend/src/components/ChatInterface.jsx
@@ -0,0 +1,117 @@
+import { useState, useEffect, useRef } from 'react';
+import ReactMarkdown from 'react-markdown';
+import Stage1 from './Stage1';
+import Stage2 from './Stage2';
+import Stage3 from './Stage3';
+import './ChatInterface.css';
+
+export default function ChatInterface({
+  conversation,
+  onSendMessage,
+  isLoading,
+}) {
+  const [input, setInput] = useState('');
+  const messagesEndRef = useRef(null);
+
+  const scrollToBottom = () => {
+    messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' });
+  };
+
+  useEffect(() => {
+    scrollToBottom();
+  }, [conversation]);
+
+  const handleSubmit = (e) => {
+    e.preventDefault();
+    if (input.trim() && !isLoading) {
+      onSendMessage(input);
+      setInput('');
+    }
+  };
+
+  const handleKeyDown = (e) => {
+    // Submit on Enter (without Shift)
+    if (e.key === 'Enter' && !e.shiftKey) {
+      e.preventDefault();
+      handleSubmit(e);
+    }
+  };
+
+  if (!conversation) {
+    return (
+      <div className="chat-interface">
+        <div className="empty-state">
+          <h2>Welcome to LLM Council</h2>
+          <p>Create a new conversation to get started</p>
+        </div>
+      </div>
+    );
+  }
+
+  return (
+    <div className="chat-interface">
+      <div className="messages-container">
+        {conversation.messages.length === 0 ? (
+          <div className="empty-state">
+            <h2>Start a conversation</h2>
+            <p>Ask a question to consult the LLM Council</p>
+          </div>
+        ) : (
+          conversation.messages.map((msg, index) => (
+            <div key={index} className="message-group">
+              {msg.role === 'user' ? (
+                <div className="user-message">
+                  <div className="message-label">You</div>
+                  <div className="message-content">
+                    <div className="markdown-content">
+                      <ReactMarkdown>{msg.content}</ReactMarkdown>
+                    </div>
+                  </div>
+                </div>
+              ) : (
+                <div className="assistant-message">
+                  <div className="message-label">LLM Council</div>
+                  <Stage1 responses={msg.stage1} />
+                  <Stage2
+                    rankings={msg.stage2}
+                    labelToModel={msg.metadata?.label_to_model}
+                    aggregateRankings={msg.metadata?.aggregate_rankings}
+                  />
+                  <Stage3 finalResponse={msg.stage3} />
+                </div>
+              )}
+            </div>
+          ))
+        )}
+
+        {isLoading && (
+          <div className="loading-indicator">
+            <div className="spinner"></div>
+            <span>Consulting the council...</span>
+          </div>
+        )}
+
+        <div ref={messagesEndRef} />
+      </div>
+
+      <form className="input-form" onSubmit={handleSubmit}>
+        <textarea
+          className="message-input"
+          placeholder="Ask your question... (Shift+Enter for new line, Enter to send)"
+          value={input}
+          onChange={(e) => setInput(e.target.value)}
+          onKeyDown={handleKeyDown}
+          disabled={isLoading}
+          rows={3}
+        />
+        <button
+          type="submit"
+          className="send-button"
+          disabled={!input.trim() || isLoading}
+        >
+          Send
+        </button>
+      </form>
+    </div>
+  );
+}
--- a/frontend/src/components/Sidebar.css
+++ b/frontend/src/components/Sidebar.css
@@ -0,0 +1,78 @@
+.sidebar {
+  width: 260px;
+  background: #f8f8f8;
+  border-right: 1px solid #e0e0e0;
+  display: flex;
+  flex-direction: column;
+  height: 100vh;
+}
+
+.sidebar-header {
+  padding: 16px;
+  border-bottom: 1px solid #e0e0e0;
+}
+
+.sidebar-header h1 {
+  font-size: 18px;
+  margin: 0 0 12px 0;
+  color: #333;
+}
+
+.new-conversation-btn {
+  width: 100%;
+  padding: 10px;
+  background: #4a90e2;
+  border: 1px solid #4a90e2;
+  border-radius: 6px;
+  color: #fff;
+  cursor: pointer;
+  font-size: 14px;
+  transition: background 0.2s;
+  font-weight: 500;
+}
+
+.new-conversation-btn:hover {
+  background: #357abd;
+  border-color: #357abd;
+}
+
+.conversation-list {
+  flex: 1;
+  overflow-y: auto;
+  padding: 8px;
+}
+
+.no-conversations {
+  padding: 16px;
+  text-align: center;
+  color: #999;
+  font-size: 14px;
+}
+
+.conversation-item {
+  padding: 12px;
+  margin-bottom: 4px;
+  border-radius: 6px;
+  cursor: pointer;
+  transition: background 0.2s;
+}
+
+.conversation-item:hover {
+  background: #f0f0f0;
+}
+
+.conversation-item.active {
+  background: #e8f0fe;
+  border: 1px solid #4a90e2;
+}
+
+.conversation-title {
+  color: #333;
+  font-size: 14px;
+  margin-bottom: 4px;
+}
+
+.conversation-meta {
+  color: #999;
+  font-size: 12px;
+}
--- a/frontend/src/components/Sidebar.jsx
+++ b/frontend/src/components/Sidebar.jsx
@@ -0,0 +1,43 @@
+import { useState, useEffect } from 'react';
+import './Sidebar.css';
+
+export default function Sidebar({
+  conversations,
+  currentConversationId,
+  onSelectConversation,
+  onNewConversation,
+}) {
+  return (
+    <div className="sidebar">
+      <div className="sidebar-header">
+        <h1>LLM Council</h1>
+        <button className="new-conversation-btn" onClick={onNewConversation}>
+          + New Conversation
+        </button>
+      </div>
+
+      <div className="conversation-list">
+        {conversations.length === 0 ? (
+          <div className="no-conversations">No conversations yet</div>
+        ) : (
+          conversations.map((conv) => (
+            <div
+              key={conv.id}
+              className={`conversation-item ${
+                conv.id === currentConversationId ? 'active' : ''
+              }`}
+              onClick={() => onSelectConversation(conv.id)}
+            >
+              <div className="conversation-title">
+                Conversation {conv.id.slice(0, 8)}...
+              </div>
+              <div className="conversation-meta">
+                {conv.message_count} messages
+              </div>
+            </div>
+          ))
+        )}
+      </div>
+    </div>
+  );
+}
--- a/frontend/src/components/Stage1.css
+++ b/frontend/src/components/Stage1.css
@@ -0,0 +1,65 @@
+.stage {
+  margin: 24px 0;
+  padding: 20px;
+  background: #fafafa;
+  border-radius: 8px;
+  border: 1px solid #e0e0e0;
+}
+
+.stage-title {
+  margin: 0 0 16px 0;
+  color: #333;
+  font-size: 16px;
+  font-weight: 600;
+}
+
+.tabs {
+  display: flex;
+  gap: 8px;
+  margin-bottom: 16px;
+  flex-wrap: wrap;
+}
+
+.tab {
+  padding: 8px 16px;
+  background: #ffffff;
+  border: 1px solid #d0d0d0;
+  border-radius: 6px 6px 0 0;
+  color: #666;
+  cursor: pointer;
+  font-size: 14px;
+  transition: all 0.2s;
+}
+
+.tab:hover {
+  background: #f0f0f0;
+  color: #333;
+  border-color: #4a90e2;
+}
+
+.tab.active {
+  background: #ffffff;
+  color: #4a90e2;
+  border-color: #4a90e2;
+  border-bottom-color: #ffffff;
+  font-weight: 600;
+}
+
+.tab-content {
+  background: #ffffff;
+  padding: 16px;
+  border-radius: 6px;
+  border: 1px solid #e0e0e0;
+}
+
+.model-name {
+  color: #888;
+  font-size: 12px;
+  margin-bottom: 12px;
+  font-family: monospace;
+}
+
+.response-text {
+  color: #333;
+  line-height: 1.6;
+}
--- a/frontend/src/components/Stage1.jsx
+++ b/frontend/src/components/Stage1.jsx
@@ -0,0 +1,36 @@
+import { useState } from 'react';
+import ReactMarkdown from 'react-markdown';
+import './Stage1.css';
+
+export default function Stage1({ responses }) {
+  const [activeTab, setActiveTab] = useState(0);
+
+  if (!responses || responses.length === 0) {
+    return null;
+  }
+
+  return (
+    <div className="stage stage1">
+      <h3 className="stage-title">Stage 1: Individual Responses</h3>
+
+      <div className="tabs">
+        {responses.map((resp, index) => (
+          <button
+            key={index}
+            className={`tab ${activeTab === index ? 'active' : ''}`}
+            onClick={() => setActiveTab(index)}
+          >
+            {resp.model.split('/')[1] || resp.model}
+          </button>
+        ))}
+      </div>
+
+      <div className="tab-content">
+        <div className="model-name">{responses[activeTab].model}</div>
+        <div className="response-text markdown-content">
+          <ReactMarkdown>{responses[activeTab].response}</ReactMarkdown>
+        </div>
+      </div>
+    </div>
+  );
+}
--- a/frontend/src/components/Stage2.css
+++ b/frontend/src/components/Stage2.css
@@ -0,0 +1,153 @@
+.stage2 {
+  background: #fafafa;
+}
+
+.stage2 h4 {
+  margin: 20px 0 8px 0;
+  color: #333;
+  font-size: 14px;
+  font-weight: 600;
+}
+
+.stage2 h4:first-of-type {
+  margin-top: 0;
+}
+
+.stage-description {
+  margin: 0 0 12px 0;
+  color: #666;
+  font-size: 13px;
+  line-height: 1.5;
+}
+
+.aggregate-rankings {
+  background: #f0f7ff;
+  padding: 16px;
+  border-radius: 8px;
+  margin-bottom: 20px;
+  border: 2px solid #d0e7ff;
+}
+
+.aggregate-rankings h4 {
+  margin: 0 0 12px 0;
+  color: #2a7ae2;
+  font-size: 15px;
+}
+
+.aggregate-list {
+  display: flex;
+  flex-direction: column;
+  gap: 8px;
+}
+
+.aggregate-item {
+  display: flex;
+  align-items: center;
+  gap: 12px;
+  padding: 10px;
+  background: #ffffff;
+  border-radius: 6px;
+  border: 1px solid #d0e7ff;
+}
+
+.rank-position {
+  color: #2a7ae2;
+  font-weight: 700;
+  font-size: 16px;
+  min-width: 35px;
+}
+
+.rank-model {
+  flex: 1;
+  color: #333;
+  font-family: monospace;
+  font-size: 14px;
+  font-weight: 500;
+}
+
+.rank-score {
+  color: #666;
+  font-size: 13px;
+  font-family: monospace;
+}
+
+.stage2 .tabs {
+  display: flex;
+  gap: 8px;
+  margin-bottom: 16px;
+  flex-wrap: wrap;
+}
+
+.stage2 .tab {
+  padding: 8px 16px;
+  background: #ffffff;
+  border: 1px solid #d0d0d0;
+  border-radius: 6px 6px 0 0;
+  color: #666;
+  cursor: pointer;
+  font-size: 14px;
+  transition: all 0.2s;
+}
+
+.stage2 .tab:hover {
+  background: #f0f0f0;
+  color: #333;
+  border-color: #4a90e2;
+}
+
+.stage2 .tab.active {
+  background: #ffffff;
+  color: #4a90e2;
+  border-color: #4a90e2;
+  border-bottom-color: #ffffff;
+  font-weight: 600;
+}
+
+.stage2 .tab-content {
+  background: #ffffff;
+  padding: 16px;
+  border-radius: 6px;
+  border: 1px solid #e0e0e0;
+  margin-bottom: 20px;
+}
+
+.ranking-model {
+  color: #888;
+  font-size: 12px;
+  font-family: monospace;
+  margin-bottom: 12px;
+}
+
+.ranking-content {
+  color: #333;
+  line-height: 1.6;
+  font-size: 14px;
+}
+
+.parsed-ranking {
+  margin-top: 16px;
+  padding-top: 16px;
+  border-top: 2px solid #e0e0e0;
+}
+
+.parsed-ranking strong {
+  color: #2a7ae2;
+  font-size: 13px;
+}
+
+.parsed-ranking ol {
+  margin: 8px 0 0 0;
+  padding-left: 24px;
+  color: #333;
+}
+
+.parsed-ranking li {
+  margin: 4px 0;
+  font-family: monospace;
+  font-size: 13px;
+}
+
+.rank-count {
+  color: #999;
+  font-size: 12px;
+}
--- a/frontend/src/components/Stage2.jsx
+++ b/frontend/src/components/Stage2.jsx
@@ -0,0 +1,99 @@
+import { useState } from 'react';
+import ReactMarkdown from 'react-markdown';
+import './Stage2.css';
+
+function deAnonymizeText(text, labelToModel) {
+  if (!labelToModel) return text;
+
+  let result = text;
+  // Replace each "Response X" with the actual model name
+  Object.entries(labelToModel).forEach(([label, model]) => {
+    const modelShortName = model.split('/')[1] || model;
+    result = result.replace(new RegExp(label, 'g'), `**${modelShortName}**`);
+  });
+  return result;
+}
+
+export default function Stage2({ rankings, labelToModel, aggregateRankings }) {
+  const [activeTab, setActiveTab] = useState(0);
+
+  if (!rankings || rankings.length === 0) {
+    return null;
+  }
+
+  return (
+    <div className="stage stage2">
+      <h3 className="stage-title">Stage 2: Peer Rankings</h3>
+
+      <h4>Raw Evaluations</h4>
+      <p className="stage-description">
+        Each model evaluated all responses (anonymized as Response A, B, C, etc.) and provided rankings.
+        Below, model names are shown in <strong>bold</strong> for readability, but the original evaluation used anonymous labels.
+      </p>
+
+      <div className="tabs">
+        {rankings.map((rank, index) => (
+          <button
+            key={index}
+            className={`tab ${activeTab === index ? 'active' : ''}`}
+            onClick={() => setActiveTab(index)}
+          >
+            {rank.model.split('/')[1] || rank.model}
+          </button>
+        ))}
+      </div>
+
+      <div className="tab-content">
+        <div className="ranking-model">
+          {rankings[activeTab].model}
+        </div>
+        <div className="ranking-content markdown-content">
+          <ReactMarkdown>
+            {deAnonymizeText(rankings[activeTab].ranking, labelToModel)}
+          </ReactMarkdown>
+        </div>
+
+        {rankings[activeTab].parsed_ranking &&
+         rankings[activeTab].parsed_ranking.length > 0 && (
+          <div className="parsed-ranking">
+            <strong>Extracted Ranking:</strong>
+            <ol>
+              {rankings[activeTab].parsed_ranking.map((label, i) => (
+                <li key={i}>
+                  {labelToModel && labelToModel[label]
+                    ? labelToModel[label].split('/')[1] || labelToModel[label]
+                    : label}
+                </li>
+              ))}
+            </ol>
+          </div>
+        )}
+      </div>
+
+      {aggregateRankings && aggregateRankings.length > 0 && (
+        <div className="aggregate-rankings">
+          <h4>Aggregate Rankings (Street Cred)</h4>
+          <p className="stage-description">
+            Combined results across all peer evaluations (lower score is better):
+          </p>
+          <div className="aggregate-list">
+            {aggregateRankings.map((agg, index) => (
+              <div key={index} className="aggregate-item">
+                <span className="rank-position">#{index + 1}</span>
+                <span className="rank-model">
+                  {agg.model.split('/')[1] || agg.model}
+                </span>
+                <span className="rank-score">
+                  Avg: {agg.average_rank.toFixed(2)}
+                </span>
+                <span className="rank-count">
+                  ({agg.rankings_count} votes)
+                </span>
+              </div>
+            ))}
+          </div>
+        </div>
+      )}
+    </div>
+  );
+}
--- a/frontend/src/components/Stage3.css
+++ b/frontend/src/components/Stage3.css
@@ -0,0 +1,25 @@
+.stage3 {
+  background: #f0fff0;
+  border-color: #c8e6c8;
+}
+
+.final-response {
+  background: #ffffff;
+  padding: 20px;
+  border-radius: 6px;
+  border: 1px solid #c8e6c8;
+}
+
+.chairman-label {
+  color: #2d8a2d;
+  font-size: 12px;
+  font-family: monospace;
+  margin-bottom: 12px;
+  font-weight: 600;
+}
+
+.final-text {
+  color: #333;
+  line-height: 1.7;
+  font-size: 15px;
+}
--- a/frontend/src/components/Stage3.jsx
+++ b/frontend/src/components/Stage3.jsx
@@ -0,0 +1,22 @@
+import ReactMarkdown from 'react-markdown';
+import './Stage3.css';
+
+export default function Stage3({ finalResponse }) {
+  if (!finalResponse) {
+    return null;
+  }
+
+  return (
+    <div className="stage stage3">
+      <h3 className="stage-title">Stage 3: Final Council Answer</h3>
+      <div className="final-response">
+        <div className="chairman-label">
+          Chairman: {finalResponse.model.split('/')[1] || finalResponse.model}
+        </div>
+        <div className="final-text markdown-content">
+          <ReactMarkdown>{finalResponse.response}</ReactMarkdown>
+        </div>
+      </div>
+    </div>
+  );
+}
--- a/frontend/src/index.css
+++ b/frontend/src/index.css
@@ -0,0 +1,98 @@
+:root {
+  font-family: system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', sans-serif;
+  line-height: 1.5;
+  font-weight: 400;
+
+  font-synthesis: none;
+  text-rendering: optimizeLegibility;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+
+* {
+  margin: 0;
+  padding: 0;
+  box-sizing: border-box;
+}
+
+body {
+  margin: 0;
+  min-width: 320px;
+  min-height: 100vh;
+  background: #f5f5f5;
+}
+
+#root {
+  height: 100vh;
+  width: 100vw;
+  overflow: hidden;
+}
+
+/* Global markdown styling */
+.markdown-content {
+  padding: 12px;
+}
+
+.markdown-content p {
+  margin: 0 0 12px 0;
+}
+
+.markdown-content p:last-child {
+  margin-bottom: 0;
+}
+
+.markdown-content h1,
+.markdown-content h2,
+.markdown-content h3,
+.markdown-content h4,
+.markdown-content h5,
+.markdown-content h6 {
+  margin: 16px 0 8px 0;
+}
+
+.markdown-content h1:first-child,
+.markdown-content h2:first-child,
+.markdown-content h3:first-child,
+.markdown-content h4:first-child,
+.markdown-content h5:first-child,
+.markdown-content h6:first-child {
+  margin-top: 0;
+}
+
+.markdown-content ul,
+.markdown-content ol {
+  margin: 0 0 12px 0;
+  padding-left: 24px;
+}
+
+.markdown-content li {
+  margin: 4px 0;
+}
+
+.markdown-content pre {
+  background: #f5f5f5;
+  padding: 12px;
+  border-radius: 4px;
+  overflow-x: auto;
+  margin: 0 0 12px 0;
+}
+
+.markdown-content code {
+  background: #f5f5f5;
+  padding: 2px 6px;
+  border-radius: 3px;
+  font-family: monospace;
+  font-size: 0.9em;
+}
+
+.markdown-content pre code {
+  background: none;
+  padding: 0;
+}
+
+.markdown-content blockquote {
+  margin: 0 0 12px 0;
+  padding-left: 16px;
+  border-left: 4px solid #ddd;
+  color: #666;
+}
--- a/frontend/src/main.jsx
+++ b/frontend/src/main.jsx
@@ -0,0 +1,10 @@
+import { StrictMode } from 'react'
+import { createRoot } from 'react-dom/client'
+import './index.css'
+import App from './App.jsx'
+
+createRoot(document.getElementById('root')).render(
+  <StrictMode>
+    <App />
+  </StrictMode>,
+)
--- a/frontend/vite.config.js
+++ b/frontend/vite.config.js
@@ -0,0 +1,7 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+
+// https://vite.dev/config/
+export default defineConfig({
+  plugins: [react()],
+})
--- a/header.jpg
+++ b/header.jpg
--- a/main.py
+++ b/main.py
@@ -0,0 +1,6 @@
+def main():
+    print("Hello from llm-council!")
+
+
+if __name__ == "__main__":
+    main()
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -0,0 +1,13 @@
+[project]
+name = "llm-council"
+version = "0.1.0"
+description = "Your LLM Council"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "fastapi>=0.115.0",
+    "uvicorn[standard]>=0.32.0",
+    "python-dotenv>=1.0.0",
+    "httpx>=0.27.0",
+    "pydantic>=2.9.0",
+]
--- a/start.sh
+++ b/start.sh
@@ -0,0 +1,31 @@
+#!/bin/bash
+
+# LLM Council - Start script
+
+echo "Starting LLM Council..."
+echo ""
+
+# Start backend
+echo "Starting backend on http://localhost:8001..."
+uv run python -m backend.main &
+BACKEND_PID=$!
+
+# Wait a bit for backend to start
+sleep 2
+
+# Start frontend
+echo "Starting frontend on http://localhost:5173..."
+cd frontend
+npm run dev &
+FRONTEND_PID=$!
+
+echo ""
+echo "✓ LLM Council is running!"
+echo "  Backend:  http://localhost:8001"
+echo "  Frontend: http://localhost:5173"
+echo ""
+echo "Press Ctrl+C to stop both servers"
+
+# Wait for Ctrl+C
+trap "kill $BACKEND_PID $FRONTEND_PID 2>/dev/null; exit" SIGINT SIGTERM
+wait
--- a/uv.lock
+++ b/uv.lock
@@ -0,0 +1,683 @@
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