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10 Commits

Author SHA1 Message Date
Krishna Kumar
2119b7bf23 Fix Stage 1 field name consistency - use 'response' instead of 'content'
The MCP server was reading 'content' but the deployed API returns 'response'
for Stage 1 results. This caused empty responses in the iOS app.
2025-12-30 04:28:24 -06:00
Krishna Kumar
3f2915629e Fix Railway entry point - re-export FastAPI app from backend.main 2025-12-30 04:00:55 -06:00
Krishna Kumar
2b2207035c Fix ResponseValidationError: change response field to content
FastAPI validation was failing because stage1 results used 'response'
field but Pydantic expected 'content'. Changed field name in:
- stage1_collect_responses: returns content instead of response
- stage2_collect_rankings: references result['content']
- stage3_synthesize_final: references result['content']
- MCP server: reads resp.get("content", "") for content_blocks
2025-12-30 03:54:02 -06:00
Krishna Kumar
8f05b67887 Add content_blocks to council_query for structured UI display
- Add content_blocks array to council_query return with model responses
  and synthesis result for rendering as UI cards
- Add MODEL_DISPLAY_NAMES mapping and get_display_name helper for
  user-friendly model names in the UI
- Update Python version to 3.13 for compatibility
- Refactor FastAPI endpoints for cleaner error handling
2025-12-20 08:35:12 -06:00
Krishna Kumar
23b2d6b52e Revert "Remove council_query tool to force stage-by-stage usage"
This reverts commit 93a70bb195.
2025-12-17 22:02:35 -06:00
Krishna Kumar
93a70bb195 Remove council_query tool to force stage-by-stage usage
The all-in-one council_query tool returned everything as a single blob,
preventing the iOS app from displaying individual model responses as
separate content blocks. By removing it, the agent must now use:

- council_stage1_collect (individual responses)
- council_stage2_rank (peer rankings)
- council_stage3_synthesize (final synthesis)

Each stage call generates a separate tool_result, enabling granular
display in the UI.
2025-12-16 15:18:21 -06:00
Krishna Kumar
153dcff69d Add MCP server for council integration
- Add mcp_server package with 7 tools proxying to FastAPI:
  - council_query (full 3-stage process)
  - council_stage1_collect, stage2_rank, stage3_synthesize
  - council_conversation_create, list, get
- Add individual stage endpoints to FastAPI (/api/council/stage1, stage2, stage3)
- Update council models to use valid OpenRouter identifiers
- Add mcp>=1.0.0 dependency
2025-12-16 12:54:29 -06:00
karpathy
92e1fccb1b readme tweaks 2025-11-22 15:35:19 -08:00
karpathy
8affe1d794 add vibe code warning 2025-11-22 15:31:11 -08:00
karpathy
87b4a178ec a bit more progressive update and single turn 2025-11-22 15:24:47 -08:00
13 changed files with 1170 additions and 63 deletions

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@@ -1 +1 @@
3.10
3.13

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@@ -2,27 +2,24 @@
![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".
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.
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.
In a bit more detail, here is what happens when you submit a query:
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.
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.
The life of a single conversation looks as follows. The user writes up a query. Then:
## Vibe Code Alert
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.
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](https://x.com/karpathy/status/1990577951671509438). 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](https://docs.astral.sh/uv/) for project management.
**Backend:**
```bash
uv sync
@@ -87,4 +84,4 @@ Then open http://localhost:5173 in your browser.
- **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
- **Package Management:** uv for Python, npm for JavaScript

View File

@@ -10,10 +10,10 @@ OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
# Council members - list of OpenRouter model identifiers
COUNCIL_MODELS = [
"openai/gpt-5.1",
"openai/gpt-4o",
"google/gemini-3-pro-preview",
"anthropic/claude-sonnet-4.5",
"x-ai/grok-4",
"x-ai/grok-4.1-fast",
]
# Chairman model - synthesizes final response

View File

@@ -2,12 +2,20 @@
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from typing import List, Dict, Any
import uuid
import json
import asyncio
import os
from . import storage
from .council import run_full_council, generate_conversation_title
from .council import run_full_council, generate_conversation_title, stage1_collect_responses, stage2_collect_rankings, stage3_synthesize_final, calculate_aggregate_rankings
from . import capture
# Enable JSON capture for iOS development
CAPTURE_ENABLED = os.getenv("CAPTURE_JSON", "false").lower() == "true"
app = FastAPI(title="LLM Council API")
@@ -56,7 +64,10 @@ async def root():
@app.get("/api/conversations", response_model=List[ConversationMetadata])
async def list_conversations():
"""List all conversations (metadata only)."""
return storage.list_conversations()
result = storage.list_conversations()
if CAPTURE_ENABLED:
capture.capture_conversation_list(result)
return result
@app.post("/api/conversations", response_model=Conversation)
@@ -73,6 +84,8 @@ async def get_conversation(conversation_id: str):
conversation = storage.get_conversation(conversation_id)
if conversation is None:
raise HTTPException(status_code=404, detail="Conversation not found")
if CAPTURE_ENABLED:
capture.capture_conversation(conversation)
return conversation
@@ -111,14 +124,185 @@ async def send_message(conversation_id: str, request: SendMessageRequest):
stage3_result
)
# Return the complete response with metadata
return {
response = {
"stage1": stage1_results,
"stage2": stage2_results,
"stage3": stage3_result,
"metadata": metadata
}
# Capture JSON for iOS development if enabled
if CAPTURE_ENABLED:
capture.capture_full_response(stage1_results, stage2_results, stage3_result, metadata)
return response
@app.post("/api/conversations/{conversation_id}/message/stream")
async def send_message_stream(conversation_id: str, request: SendMessageRequest):
"""
Send a message and stream the 3-stage council process.
Returns Server-Sent Events as each stage completes.
"""
# Check if conversation exists
conversation = storage.get_conversation(conversation_id)
if conversation is None:
raise HTTPException(status_code=404, detail="Conversation not found")
# Check if this is the first message
is_first_message = len(conversation["messages"]) == 0
async def event_generator():
captured_events = [] # For iOS development capture
try:
# Add user message
storage.add_user_message(conversation_id, request.content)
# Start title generation in parallel (don't await yet)
title_task = None
if is_first_message:
title_task = asyncio.create_task(generate_conversation_title(request.content))
# Stage 1: Collect responses
event1_start = {'type': 'stage1_start'}
captured_events.append(event1_start)
yield f"data: {json.dumps(event1_start)}\n\n"
stage1_results = await stage1_collect_responses(request.content)
event1_complete = {'type': 'stage1_complete', 'data': stage1_results}
captured_events.append(event1_complete)
yield f"data: {json.dumps(event1_complete)}\n\n"
# Stage 2: Collect rankings
event2_start = {'type': 'stage2_start'}
captured_events.append(event2_start)
yield f"data: {json.dumps(event2_start)}\n\n"
stage2_results, label_to_model = await stage2_collect_rankings(request.content, stage1_results)
aggregate_rankings = calculate_aggregate_rankings(stage2_results, label_to_model)
event2_complete = {'type': 'stage2_complete', 'data': stage2_results, 'metadata': {'label_to_model': label_to_model, 'aggregate_rankings': aggregate_rankings}}
captured_events.append(event2_complete)
yield f"data: {json.dumps(event2_complete)}\n\n"
# Stage 3: Synthesize final answer
event3_start = {'type': 'stage3_start'}
captured_events.append(event3_start)
yield f"data: {json.dumps(event3_start)}\n\n"
stage3_result = await stage3_synthesize_final(request.content, stage1_results, stage2_results)
event3_complete = {'type': 'stage3_complete', 'data': stage3_result}
captured_events.append(event3_complete)
yield f"data: {json.dumps(event3_complete)}\n\n"
# Wait for title generation if it was started
if title_task:
title = await title_task
storage.update_conversation_title(conversation_id, title)
title_event = {'type': 'title_complete', 'data': {'title': title}}
captured_events.append(title_event)
yield f"data: {json.dumps(title_event)}\n\n"
# Save complete assistant message
storage.add_assistant_message(
conversation_id,
stage1_results,
stage2_results,
stage3_result
)
# Send completion event
complete_event = {'type': 'complete'}
captured_events.append(complete_event)
yield f"data: {json.dumps(complete_event)}\n\n"
# Capture all SSE events for iOS development
if CAPTURE_ENABLED:
capture.capture_sse_events(captured_events)
except Exception as e:
# Send error event
error_event = {'type': 'error', 'message': str(e)}
captured_events.append(error_event)
if CAPTURE_ENABLED:
capture.capture_sse_events(captured_events)
yield f"data: {json.dumps(error_event)}\n\n"
return StreamingResponse(
event_generator(),
media_type="text/event-stream",
headers={
"Cache-Control": "no-cache",
"Connection": "keep-alive",
}
)
# ============================================================================
# INDIVIDUAL STAGE ENDPOINTS (for MCP granular control)
# ============================================================================
class Stage1Request(BaseModel):
"""Request for Stage 1."""
query: str
class Stage2Request(BaseModel):
"""Request for Stage 2."""
query: str
stage1_results: List[Dict[str, Any]]
class Stage3Request(BaseModel):
"""Request for Stage 3."""
query: str
stage1_results: List[Dict[str, Any]]
stage2_results: List[Dict[str, Any]]
@app.post("/api/council/stage1")
async def run_stage1(request: Stage1Request):
"""
Run Stage 1 independently - collect individual responses from all council models.
"""
results = await stage1_collect_responses(request.query)
if CAPTURE_ENABLED:
capture.capture_stage1(results)
return results
@app.post("/api/council/stage2")
async def run_stage2(request: Stage2Request):
"""
Run Stage 2 independently - collect rankings with anonymization.
"""
stage2_results, label_to_model = await stage2_collect_rankings(
request.query, request.stage1_results
)
aggregate_rankings = calculate_aggregate_rankings(stage2_results, label_to_model)
if CAPTURE_ENABLED:
capture.capture_stage2(stage2_results, label_to_model, aggregate_rankings)
return {
"rankings": stage2_results,
"label_to_model": label_to_model,
"aggregate_rankings": aggregate_rankings
}
@app.post("/api/council/stage3")
async def run_stage3(request: Stage3Request):
"""
Run Stage 3 independently - chairman synthesis.
"""
result = await stage3_synthesize_final(
request.query, request.stage1_results, request.stage2_results
)
if CAPTURE_ENABLED:
capture.capture_stage3(result)
return result
if __name__ == "__main__":
import uvicorn

View File

@@ -69,33 +69,114 @@ function App() {
messages: [...prev.messages, userMessage],
}));
// Send message and get council response
const response = await api.sendMessage(currentConversationId, content);
// Add assistant message to UI
// Create a partial assistant message that will be updated progressively
const assistantMessage = {
role: 'assistant',
stage1: response.stage1,
stage2: response.stage2,
stage3: response.stage3,
metadata: response.metadata,
stage1: null,
stage2: null,
stage3: null,
metadata: null,
loading: {
stage1: false,
stage2: false,
stage3: false,
},
};
// Add the partial assistant message
setCurrentConversation((prev) => ({
...prev,
messages: [...prev.messages, assistantMessage],
}));
// Reload conversations list to update message count
await loadConversations();
// Send message with streaming
await api.sendMessageStream(currentConversationId, content, (eventType, event) => {
switch (eventType) {
case 'stage1_start':
setCurrentConversation((prev) => {
const messages = [...prev.messages];
const lastMsg = messages[messages.length - 1];
lastMsg.loading.stage1 = true;
return { ...prev, messages };
});
break;
case 'stage1_complete':
setCurrentConversation((prev) => {
const messages = [...prev.messages];
const lastMsg = messages[messages.length - 1];
lastMsg.stage1 = event.data;
lastMsg.loading.stage1 = false;
return { ...prev, messages };
});
break;
case 'stage2_start':
setCurrentConversation((prev) => {
const messages = [...prev.messages];
const lastMsg = messages[messages.length - 1];
lastMsg.loading.stage2 = true;
return { ...prev, messages };
});
break;
case 'stage2_complete':
setCurrentConversation((prev) => {
const messages = [...prev.messages];
const lastMsg = messages[messages.length - 1];
lastMsg.stage2 = event.data;
lastMsg.metadata = event.metadata;
lastMsg.loading.stage2 = false;
return { ...prev, messages };
});
break;
case 'stage3_start':
setCurrentConversation((prev) => {
const messages = [...prev.messages];
const lastMsg = messages[messages.length - 1];
lastMsg.loading.stage3 = true;
return { ...prev, messages };
});
break;
case 'stage3_complete':
setCurrentConversation((prev) => {
const messages = [...prev.messages];
const lastMsg = messages[messages.length - 1];
lastMsg.stage3 = event.data;
lastMsg.loading.stage3 = false;
return { ...prev, messages };
});
break;
case 'title_complete':
// Reload conversations to get updated title
loadConversations();
break;
case 'complete':
// Stream complete, reload conversations list
loadConversations();
setIsLoading(false);
break;
case 'error':
console.error('Stream error:', event.message);
setIsLoading(false);
break;
default:
console.log('Unknown event type:', eventType);
}
});
} catch (error) {
console.error('Failed to send message:', error);
// Remove optimistic user message on error
// Remove optimistic messages on error
setCurrentConversation((prev) => ({
...prev,
messages: prev.messages.slice(0, -1),
messages: prev.messages.slice(0, -2),
}));
} finally {
setIsLoading(false);
}
};

View File

@@ -65,4 +65,51 @@ export const api = {
}
return response.json();
},
/**
* Send a message and receive streaming updates.
* @param {string} conversationId - The conversation ID
* @param {string} content - The message content
* @param {function} onEvent - Callback function for each event: (eventType, data) => void
* @returns {Promise<void>}
*/
async sendMessageStream(conversationId, content, onEvent) {
const response = await fetch(
`${API_BASE}/api/conversations/${conversationId}/message/stream`,
{
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({ content }),
}
);
if (!response.ok) {
throw new Error('Failed to send message');
}
const reader = response.body.getReader();
const decoder = new TextDecoder();
while (true) {
const { done, value } = await reader.read();
if (done) break;
const chunk = decoder.decode(value);
const lines = chunk.split('\n');
for (const line of lines) {
if (line.startsWith('data: ')) {
const data = line.slice(6);
try {
const event = JSON.parse(data);
onEvent(event.type, event);
} catch (e) {
console.error('Failed to parse SSE event:', e);
}
}
}
}
},
};

View File

@@ -71,6 +71,20 @@
font-size: 14px;
}
.stage-loading {
display: flex;
align-items: center;
gap: 12px;
padding: 16px;
margin: 12px 0;
background: #f9fafb;
border-radius: 8px;
border: 1px solid #e0e0e0;
color: #666;
font-size: 14px;
font-style: italic;
}
.spinner {
width: 20px;
height: 20px;

View File

@@ -71,13 +71,39 @@ export default function ChatInterface({
) : (
<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} />
{/* Stage 1 */}
{msg.loading?.stage1 && (
<div className="stage-loading">
<div className="spinner"></div>
<span>Running Stage 1: Collecting individual responses...</span>
</div>
)}
{msg.stage1 && <Stage1 responses={msg.stage1} />}
{/* Stage 2 */}
{msg.loading?.stage2 && (
<div className="stage-loading">
<div className="spinner"></div>
<span>Running Stage 2: Peer rankings...</span>
</div>
)}
{msg.stage2 && (
<Stage2
rankings={msg.stage2}
labelToModel={msg.metadata?.label_to_model}
aggregateRankings={msg.metadata?.aggregate_rankings}
/>
)}
{/* Stage 3 */}
{msg.loading?.stage3 && (
<div className="stage-loading">
<div className="spinner"></div>
<span>Running Stage 3: Final synthesis...</span>
</div>
)}
{msg.stage3 && <Stage3 finalResponse={msg.stage3} />}
</div>
)}
</div>
@@ -94,24 +120,26 @@ export default function ChatInterface({
<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>
{conversation.messages.length === 0 && (
<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>
);
}

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@@ -1,6 +1,5 @@
def main():
print("Hello from llm-council!")
# Railway entry point - re-export FastAPI app from backend
from backend.main import app
if __name__ == "__main__":
main()
# This allows Railway to detect the app with: uvicorn main:app
__all__ = ["app"]

1
mcp_server/__init__.py Normal file
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@@ -0,0 +1 @@
"""MCP Server for LLM Council."""

316
mcp_server/server.py Normal file
View File

@@ -0,0 +1,316 @@
"""MCP Server for LLM Council - Proxies to FastAPI service."""
import os
import sys
import httpx
from typing import Any
from mcp.server.fastmcp import FastMCP
# FastAPI service URL (configurable via environment)
FASTAPI_URL = os.getenv("COUNCIL_FASTAPI_URL", "http://localhost:8001")
# Create MCP server instance
mcp = FastMCP("council")
# Model display name mapping for human-readable names
MODEL_DISPLAY_NAMES = {
"openai/gpt-4o": "GPT-4o",
"openai/gpt-4o-mini": "GPT-4o Mini",
"openai/gpt-4-turbo": "GPT-4 Turbo",
"openai/o1": "o1",
"openai/o1-mini": "o1 Mini",
"openai/o1-preview": "o1 Preview",
"anthropic/claude-3.5-sonnet": "Claude 3.5 Sonnet",
"anthropic/claude-3-opus": "Claude 3 Opus",
"anthropic/claude-3-haiku": "Claude 3 Haiku",
"google/gemini-pro": "Gemini Pro",
"google/gemini-pro-1.5": "Gemini Pro 1.5",
"google/gemini-2.0-flash-001": "Gemini 2.0 Flash",
"google/gemini-2.0-flash-thinking-exp": "Gemini 2.0 Flash Thinking",
"meta-llama/llama-3.1-405b-instruct": "Llama 3.1 405B",
"meta-llama/llama-3.1-70b-instruct": "Llama 3.1 70B",
"mistralai/mistral-large": "Mistral Large",
"deepseek/deepseek-chat": "DeepSeek Chat",
"deepseek/deepseek-r1": "DeepSeek R1",
}
def get_display_name(model_id: str) -> str:
"""Get human-readable display name for a model ID."""
if model_id in MODEL_DISPLAY_NAMES:
return MODEL_DISPLAY_NAMES[model_id]
# Fallback: extract the model name from the ID
parts = model_id.split("/")
if len(parts) > 1:
return parts[-1].replace("-", " ").title()
return model_id
# HTTP client for FastAPI communication
_http_client: httpx.AsyncClient | None = None
async def get_http_client() -> httpx.AsyncClient:
"""Get or create the HTTP client."""
global _http_client
if _http_client is None or _http_client.is_closed:
_http_client = httpx.AsyncClient(
base_url=FASTAPI_URL,
timeout=httpx.Timeout(300.0) # 5 min timeout for council queries
)
return _http_client
async def api_request(
method: str,
endpoint: str,
json_data: dict | None = None
) -> dict | list:
"""Make a request to the FastAPI service."""
client = await get_http_client()
try:
if method == "GET":
response = await client.get(endpoint)
elif method == "POST":
response = await client.post(endpoint, json=json_data or {})
else:
raise ValueError(f"Unsupported method: {method}")
response.raise_for_status()
return response.json()
except httpx.ConnectError:
raise RuntimeError(
f"Cannot connect to Council FastAPI at {FASTAPI_URL}. "
"Ensure the service is running with: cd llm-council && uv run python -m backend.main"
)
except httpx.TimeoutException:
raise RuntimeError(
"Council query timed out. The 3-stage process may take several minutes."
)
except httpx.HTTPStatusError as e:
raise RuntimeError(f"HTTP error {e.response.status_code}: {e.response.text}")
# ============================================================================
# HIGH-LEVEL COUNCIL TOOL
# ============================================================================
@mcp.tool()
async def council_query(
query: str,
conversation_id: str | None = None
) -> dict[str, Any]:
"""
Run the complete LLM Council 3-stage deliberation process.
Stage 1: Query all council models in parallel for individual responses.
Stage 2: Each model anonymously ranks the other responses.
Stage 3: Chairman model synthesizes a final answer based on all inputs.
This process typically takes 1-3 minutes depending on model response times.
Args:
query: The user's question to deliberate on
conversation_id: Optional existing conversation ID for multi-turn context
Returns:
Complete council response with all 3 stages and metadata including:
- conversation_id: The conversation ID used
- content_blocks: Structured blocks for each model response (for UI rendering)
- stage1: Individual model responses
- stage2: Peer rankings with aggregate scores
- stage3: Chairman's synthesized final answer
- metadata: Label mappings and aggregate rankings
"""
# Create conversation if not provided
if not conversation_id:
conv = await api_request("POST", "/api/conversations", {})
conversation_id = conv["id"]
# Send message and get full council response
result = await api_request(
"POST",
f"/api/conversations/{conversation_id}/message",
{"content": query}
)
# Build content_blocks for structured UI rendering
content_blocks = []
# Add Stage 1 responses as individual blocks
stage1_results = result.get("stage1", [])
for resp in stage1_results:
model_id = resp.get("model", "unknown")
content_blocks.append({
"type": "council_response",
"model": model_id,
"model_display_name": get_display_name(model_id),
"response": resp.get("response", "") or resp.get("content", ""),
"stage": 1
})
# Add Stage 3 synthesis block
stage3_result = result.get("stage3", {})
if stage3_result:
model_id = stage3_result.get("model", "unknown")
content_blocks.append({
"type": "council_synthesis",
"model": model_id,
"model_display_name": get_display_name(model_id),
"response": stage3_result.get("response", "")
})
return {
"conversation_id": conversation_id,
"content_blocks": content_blocks,
**result
}
# ============================================================================
# INDIVIDUAL STAGE TOOLS
# ============================================================================
@mcp.tool()
async def council_stage1_collect(query: str) -> list[dict[str, str]]:
"""
Stage 1: Collect individual responses from all council models.
Queries all configured council models in parallel with the user's question.
Each model responds independently without seeing other responses.
Use this for granular control over the council process - you can analyze
Stage 1 results before deciding whether to proceed to ranking.
Args:
query: The user's question to send to all council models
Returns:
List of responses, each containing 'model' name and 'response' text
"""
result = await api_request(
"POST",
"/api/council/stage1",
{"query": query}
)
return result
@mcp.tool()
async def council_stage2_rank(
query: str,
stage1_results: list[dict]
) -> dict[str, Any]:
"""
Stage 2: Have each model rank the anonymized responses from Stage 1.
Responses are anonymized as "Response A", "Response B", etc. to prevent
models from playing favorites based on model identity.
Args:
query: The original user question
stage1_results: Results from council_stage1_collect containing model responses
Returns:
Object containing:
- rankings: List of {model, ranking (raw text), parsed_ranking (ordered list)}
- label_to_model: Mapping of anonymous labels to model names
- aggregate_rankings: Models sorted by average rank position
"""
result = await api_request(
"POST",
"/api/council/stage2",
{"query": query, "stage1_results": stage1_results}
)
return result
@mcp.tool()
async def council_stage3_synthesize(
query: str,
stage1_results: list[dict],
stage2_results: list[dict]
) -> dict[str, str]:
"""
Stage 3: Chairman model synthesizes final answer from all inputs.
The chairman receives all individual responses and peer rankings,
then produces a comprehensive final answer representing council consensus.
Args:
query: The original user question
stage1_results: Individual model responses from Stage 1
stage2_results: Peer rankings from Stage 2
Returns:
Chairman's synthesized response with 'model' and 'response' fields
"""
result = await api_request(
"POST",
"/api/council/stage3",
{
"query": query,
"stage1_results": stage1_results,
"stage2_results": stage2_results
}
)
return result
# ============================================================================
# CONVERSATION MANAGEMENT TOOLS
# ============================================================================
@mcp.tool()
async def council_conversation_create() -> dict[str, Any]:
"""
Create a new council conversation.
Returns:
New conversation with id, created_at, title, and empty messages array
"""
return await api_request("POST", "/api/conversations", {})
@mcp.tool()
async def council_conversation_list() -> list[dict[str, Any]]:
"""
List all council conversations (metadata only).
Returns conversations sorted by creation time, newest first.
Returns:
List of conversation metadata (id, created_at, title, message_count)
"""
return await api_request("GET", "/api/conversations")
@mcp.tool()
async def council_conversation_get(conversation_id: str) -> dict[str, Any]:
"""
Get a specific council conversation with all messages.
Messages include both user queries and assistant responses with all 3 stages.
Args:
conversation_id: The conversation ID to retrieve
Returns:
Full conversation object including all messages
"""
return await api_request("GET", f"/api/conversations/{conversation_id}")
# ============================================================================
# ENTRY POINT
# ============================================================================
def main():
"""Run the MCP server using stdio transport."""
# Log to stderr (important for stdio transport - stdout is for protocol)
print(f"Starting Council MCP server, proxying to {FASTAPI_URL}", file=sys.stderr)
mcp.run(transport="stdio")
if __name__ == "__main__":
main()

View File

@@ -10,4 +10,8 @@ dependencies = [
"python-dotenv>=1.0.0",
"httpx>=0.27.0",
"pydantic>=2.9.0",
"mcp>=1.0.0",
]
[project.scripts]
council-mcp = "mcp_server.server:main"

436
uv.lock generated
View File

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