Groq-Powered Multi-Agent Debate Analysis

This project now includes a powerful stock analysis system using Groq LLMs with LangChain for multi-agent debates. The system provides fast, cost-effective analysis through intelligent agent debates.

🚀 Features

• Multi-Agent Debate System: Bull vs Bear researchers debate investment thesis
• Risk Management Consensus: 3-way debate between conservative, neutral, and aggressive risk managers
• Fast Inference: Uses Groq's Llama 3.1 models (70B for deep thinking, 8B for quick thinking)
• Memory-Based Learning: Learns from past trading decisions using ChromaDB
• Flexible Data Sources: Integrates with yfinance, Alpha Vantage, and more

🏗️ Architecture

``` ┌─────────────────────────────────────────────────────────────┐ │ Stock Analysis Request │ └────────────────────┬────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Market & News Analysts │ │ (Gather data: technical, fundamental, news, social) │ └────────────────────┬────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Investment Debate │ │ ┌──────────────┐ ┌──────────────┐ │ │ │ Bull │ ←───→ │ Bear │ │ │ │ Researcher │ │ Researcher │ │ │ └──────────────┘ └──────────────┘ │ │ │ │ │ │ └────────┬───────────────┘ │ │ ▼ │ │ ┌────────────────┐ │ │ │ Research │ (Judge: BUY/SELL/HOLD) │ │ │ Manager │ │ │ └────────┬───────┘ │ └──────────────────┼──────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Trader Decision │ │ (Action: BUY/SELL/HOLD) │ └────────────────────┬────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ Risk Management Debate │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │Conservative│ │ Neutral │ │Aggressive│ │ │ │ Debator │ │ Debator │ │ Debator │ │ │ └──────────┘ └──────────┘ └──────────┘ │ │ │ │ │ │ │ └────────────┼─────────────┘ │ │ ▼ │ │ ┌──────────────────┐ │ │ │ Risk Manager │ (Judge final risk level) │ │ └──────────────────┘ │ └─────────────────────────────────────────────────────────────┘ ```

📦 Installation

Create virtual environment

python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

Install dependencies (includes langchain-groq)

pip install -r requirements.txt ```

🚦 Running the Service

Start the Debate-Analyst API Server

```bash cd agents/debate-analyst source venv/bin/activate python api_server.py ```

The server will start on http://localhost:8001

Start the Next.js Application

```bash

From project root

npm run dev ```

The Next.js app will start on http://localhost:3000

📡 API Usage

Using the Next.js API Endpoint

The /api/groq-debate endpoint provides a comprehensive multi-agent debate analysis interface.

Base URL: https://autoinvestment.broker/api or http://localhost:3000/api

Get System Information

```bash

Get API information about available agents and models

curl https://autoinvestment.broker/api/groq-debate

Response:

{ "system": "Multi-Agent Debate Analysis", "version": "2.4", "agents": [ "Market Analyst", "News Analyst", "Fundamentals Analyst", "Bull Researcher", "Bear Researcher", "Research Manager", "Trader", "Risk Managers", "Portfolio Manager", "External Consultant" ], "supported_providers": ["groq", "openai", "anthropic"] } ```

Example 1: Basic Analysis (Groq Default)

```bash curl -X POST https://autoinvestment.broker/api/groq-debate
-H "Content-Type: application/json"
-d '{ "symbol": "AAPL" }' ```

Example 2: Extended Debate Analysis

```bash curl -X POST https://autoinvestment.broker/api/groq-debate
-H "Content-Type: application/json"
-d '{ "symbol": "TSLA", "date": "2024-12-19", "max_debate_rounds": 3, "llm_provider": "groq" }' ```

Example 3: Custom Models with Anthropic

```bash curl -X POST https://autoinvestment.broker/api/groq-debate
-H "Content-Type: application/json"
-d '{ "symbol": "NVDA", "llm_provider": "anthropic", "deep_think_llm": "claude-3-5-sonnet-20241022", "quick_think_llm": "claude-3-5-haiku-20241022", "max_debate_rounds": 2 }' ```

Example 4: OpenAI Models

```bash curl -X POST https://autoinvestment.broker/api/groq-debate
-H "Content-Type: application/json"
-d '{ "symbol": "MSFT", "llm_provider": "openai", "deep_think_llm": "gpt-4o", "quick_think_llm": "gpt-4o-mini" }' ```

Response Format

```json { "success": true, "symbol": "AAPL", "date": "2024-12-19", "analysis": { "bull_arguments": [ "Strong fundamentals with consistent 15% YoY revenue growth", "Leading market position in premium smartphone segment with 50%+ profit share", "Services revenue growing 20% annually with high margins" ], "bear_arguments": [ "P/E ratio of 28 above historical average of 22", "iPhone revenue growth slowing in key markets", "Increasing regulatory pressure in EU and China markets" ], "final_decision": "BUY", "confidence_level": "Medium-High", "reasoning": "The bull case is stronger based on fundamental analysis and growth trajectory despite valuation concerns", "risk_assessment": "Medium risk with controlled position sizing recommended", "thesis_compliance": { "financial_health": "9/12", "growth_score": "4/6", "pe_ratio": 28, "adr_status": "NYSE: AAPL", "analyst_coverage": 45, "compliance_percentage": 45 } }, "debate_history": [ { "agent": "bull_researcher", "message": "THESIS COMPLIANCE:\n✓ Financial Health: 9/12\n✓ Growth Score: 4/6\n...", "timestamp": "2024-12-19T10:00:00Z" }, { "agent": "bear_researcher", "message": "BEAR CASE SUMMARY:\nP/E of 28 exceeds historical average...", "timestamp": "2024-12-19T10:01:30Z" } ], "metadata": { "llm_provider": "groq", "deep_think_model": "llama-3.3-70b-versatile", "quick_think_model": "llama-3.1-8b-instant", "debate_rounds": 2, "total_tokens": 15420, "execution_time_ms": 8500 } } ```

Using JavaScript/TypeScript

``` interface GroqDebateRequest { symbol: string date?: string max_debate_rounds?: number }

async function analyzeStock(symbol: string) { const response = await fetch('/api/groq-debate', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ symbol, date: new Date().toISOString().split('T')[0], max_debate_rounds: 2 }) })

const result = await response.json()

if (result.success) { console.log('Bull Arguments:', result.analysis.bull_arguments) console.log('Bear Arguments:', result.analysis.bear_arguments) console.log('Final Decision:', result.analysis.final_decision) console.log('Reasoning:', result.analysis.reasoning) }

return result }

// Usage analyzeStock('AAPL').then(result => { console.log('Analysis complete:', result) }) ```

Direct API Server Usage

You can also call the debate-analyst service directly:

```bash

Direct call to debate-analyst

curl -X POST http://localhost:8001/analyze
-H "Content-Type: application/json"
-d '{ "symbol": "AAPL", "date": "2024-12-11" }' ```

🧠 How It Works