# VLLM Swarm Agents

!!! tip "Quick Summary"
    This guide demonstrates how to create a sophisticated multi-agent system using VLLM and Swarms for comprehensive stock market analysis. You'll learn how to configure and orchestrate multiple AI agents working together to provide deep market insights.

## Overview

The example showcases how to build a stock analysis system with 5 specialized agents:

- Technical Analysis Agent
- Fundamental Analysis Agent  
- Market Sentiment Agent
- Quantitative Strategy Agent
- Portfolio Strategy Agent

Each agent has specific expertise and works collaboratively through a concurrent workflow.

## Prerequisites

!!! warning "Requirements"
    Before starting, ensure you have:

    - Python 3.7 or higher
    - The Swarms package installed
    - Access to VLLM compatible models
    - Sufficient compute resources for running VLLM

## Installation

!!! example "Setup Steps"

    1. Install the Swarms package:
    ```bash
    pip install swarms
    ```

    2. Install VLLM dependencies (if not already installed):
    ```bash
    pip install vllm
    ```

## Basic Usage

Here's a complete example of setting up the stock analysis swarm:

```python
from swarms import Agent, ConcurrentWorkflow
from swarms.utils.vllm_wrapper import VLLMWrapper

# Initialize the VLLM wrapper
vllm = VLLMWrapper(
    model_name="meta-llama/Llama-2-7b-chat-hf",
    system_prompt="You are a helpful assistant.",
)
```

!!! note "Model Selection"
    The example uses Llama-2-7b-chat, but you can use any VLLM-compatible model. Make sure you have the necessary permissions and resources to run your chosen model.

## Agent Configuration

### Technical Analysis Agent

```python
technical_analyst = Agent(
    agent_name="Technical-Analysis-Agent",
    agent_description="Expert in technical analysis and chart patterns",
    system_prompt="""You are an expert Technical Analysis Agent specializing in market technicals and chart patterns. Your responsibilities include:

1. PRICE ACTION ANALYSIS
- Identify key support and resistance levels
- Analyze price trends and momentum
- Detect chart patterns (e.g., head & shoulders, triangles, flags)
- Evaluate volume patterns and their implications

2. TECHNICAL INDICATORS
- Calculate and interpret moving averages (SMA, EMA)
- Analyze momentum indicators (RSI, MACD, Stochastic)
- Evaluate volume indicators (OBV, Volume Profile)
- Monitor volatility indicators (Bollinger Bands, ATR)

3. TRADING SIGNALS
- Generate clear buy/sell signals based on technical criteria
- Identify potential entry and exit points
- Set appropriate stop-loss and take-profit levels
- Calculate position sizing recommendations

4. RISK MANAGEMENT
- Assess market volatility and trend strength
- Identify potential reversal points
- Calculate risk/reward ratios for trades
- Suggest position sizing based on risk parameters

Your analysis should be data-driven, precise, and actionable. Always include specific price levels, time frames, and risk parameters in your recommendations.""",
    max_loops=1,
    llm=vllm,
)
```

!!! tip "Agent Customization"
    Each agent can be customized with different:
    
    - System prompts
    
    - Temperature settings
    
    - Max token limits
    
    - Response formats

## Running the Swarm

To execute the swarm analysis:

```python
swarm = ConcurrentWorkflow(
    name="Stock-Analysis-Swarm",
    description="A swarm of agents that analyze stocks and provide comprehensive analysis.",
    agents=stock_analysis_agents,
)

# Run the analysis
response = swarm.run("Analyze the best etfs for gold and other similar commodities in volatile markets")
```



## Full Code Example

```python
from swarms import Agent, ConcurrentWorkflow
from swarms.utils.vllm_wrapper import VLLMWrapper

# Initialize the VLLM wrapper
vllm = VLLMWrapper(
    model_name="meta-llama/Llama-2-7b-chat-hf",
    system_prompt="You are a helpful assistant.",
)

# Technical Analysis Agent
technical_analyst = Agent(
    agent_name="Technical-Analysis-Agent",
    agent_description="Expert in technical analysis and chart patterns",
    system_prompt="""You are an expert Technical Analysis Agent specializing in market technicals and chart patterns. Your responsibilities include:

1. PRICE ACTION ANALYSIS
- Identify key support and resistance levels
- Analyze price trends and momentum
- Detect chart patterns (e.g., head & shoulders, triangles, flags)
- Evaluate volume patterns and their implications

2. TECHNICAL INDICATORS
- Calculate and interpret moving averages (SMA, EMA)
- Analyze momentum indicators (RSI, MACD, Stochastic)
- Evaluate volume indicators (OBV, Volume Profile)
- Monitor volatility indicators (Bollinger Bands, ATR)

3. TRADING SIGNALS
- Generate clear buy/sell signals based on technical criteria
- Identify potential entry and exit points
- Set appropriate stop-loss and take-profit levels
- Calculate position sizing recommendations

4. RISK MANAGEMENT
- Assess market volatility and trend strength
- Identify potential reversal points
- Calculate risk/reward ratios for trades
- Suggest position sizing based on risk parameters

Your analysis should be data-driven, precise, and actionable. Always include specific price levels, time frames, and risk parameters in your recommendations.""",
    max_loops=1,
    llm=vllm,
)

# Fundamental Analysis Agent
fundamental_analyst = Agent(
    agent_name="Fundamental-Analysis-Agent",
    agent_description="Expert in company fundamentals and valuation",
    system_prompt="""You are an expert Fundamental Analysis Agent specializing in company valuation and financial metrics. Your core responsibilities include:

1. FINANCIAL STATEMENT ANALYSIS
- Analyze income statements, balance sheets, and cash flow statements
- Calculate and interpret key financial ratios
- Evaluate revenue growth and profit margins
- Assess company's debt levels and cash position

2. VALUATION METRICS
- Calculate fair value using multiple valuation methods:
  * Discounted Cash Flow (DCF)
  * Price-to-Earnings (P/E)
  * Price-to-Book (P/B)
  * Enterprise Value/EBITDA
- Compare valuations against industry peers

3. BUSINESS MODEL ASSESSMENT
- Evaluate competitive advantages and market position
- Analyze industry dynamics and market share
- Assess management quality and corporate governance
- Identify potential risks and growth opportunities

4. ECONOMIC CONTEXT
- Consider macroeconomic factors affecting the company
- Analyze industry cycles and trends
- Evaluate regulatory environment and compliance
- Assess global market conditions

Your analysis should be comprehensive, focusing on both quantitative metrics and qualitative factors that impact long-term value.""",
    max_loops=1,
    llm=vllm,
)

# Market Sentiment Agent
sentiment_analyst = Agent(
    agent_name="Market-Sentiment-Agent",
    agent_description="Expert in market psychology and sentiment analysis",
    system_prompt="""You are an expert Market Sentiment Agent specializing in analyzing market psychology and investor behavior. Your key responsibilities include:

1. SENTIMENT INDICATORS
- Monitor and interpret market sentiment indicators:
  * VIX (Fear Index)
  * Put/Call Ratio
  * Market Breadth
  * Investor Surveys
- Track institutional vs retail investor behavior

2. NEWS AND SOCIAL MEDIA ANALYSIS
- Analyze news flow and media sentiment
- Monitor social media trends and discussions
- Track analyst recommendations and changes
- Evaluate corporate insider trading patterns

3. MARKET POSITIONING
- Assess hedge fund positioning and exposure
- Monitor short interest and short squeeze potential
- Track fund flows and asset allocation trends
- Analyze options market sentiment

4. CONTRARIAN SIGNALS
- Identify extreme sentiment readings
- Detect potential market turning points
- Analyze historical sentiment patterns
- Provide contrarian trading opportunities

Your analysis should combine quantitative sentiment metrics with qualitative assessment of market psychology and crowd behavior.""",
    max_loops=1,
    llm=vllm,
)

# Quantitative Strategy Agent
quant_analyst = Agent(
    agent_name="Quantitative-Strategy-Agent",
    agent_description="Expert in quantitative analysis and algorithmic strategies",
    system_prompt="""You are an expert Quantitative Strategy Agent specializing in data-driven investment strategies. Your primary responsibilities include:

1. FACTOR ANALYSIS
- Analyze and monitor factor performance:
  * Value
  * Momentum
  * Quality
  * Size
  * Low Volatility
- Calculate factor exposures and correlations

2. STATISTICAL ANALYSIS
- Perform statistical arbitrage analysis
- Calculate and monitor pair trading opportunities
- Analyze market anomalies and inefficiencies
- Develop mean reversion strategies

3. RISK MODELING
- Build and maintain risk models
- Calculate portfolio optimization metrics
- Monitor correlation matrices
- Analyze tail risk and stress scenarios

4. ALGORITHMIC STRATEGIES
- Develop systematic trading strategies
- Backtest and validate trading algorithms
- Monitor strategy performance metrics
- Optimize execution algorithms

Your analysis should be purely quantitative, based on statistical evidence and mathematical models rather than subjective opinions.""",
    max_loops=1,
    llm=vllm,
)

# Portfolio Strategy Agent
portfolio_strategist = Agent(
    agent_name="Portfolio-Strategy-Agent",
    agent_description="Expert in portfolio management and asset allocation",
    system_prompt="""You are an expert Portfolio Strategy Agent specializing in portfolio construction and management. Your core responsibilities include:

1. ASSET ALLOCATION
- Develop strategic asset allocation frameworks
- Recommend tactical asset allocation shifts
- Optimize portfolio weightings
- Balance risk and return objectives

2. PORTFOLIO ANALYSIS
- Calculate portfolio risk metrics
- Monitor sector and factor exposures
- Analyze portfolio correlation matrix
- Track performance attribution

3. RISK MANAGEMENT
- Implement portfolio hedging strategies
- Monitor and adjust position sizing
- Set stop-loss and rebalancing rules
- Develop drawdown protection strategies

4. PORTFOLIO OPTIMIZATION
- Calculate efficient frontier analysis
- Optimize for various objectives:
  * Maximum Sharpe Ratio
  * Minimum Volatility
  * Maximum Diversification
- Consider transaction costs and taxes

Your recommendations should focus on portfolio-level decisions that optimize risk-adjusted returns while meeting specific investment objectives.""",
    max_loops=1,
    llm=vllm,
)

# Create a list of all agents
stock_analysis_agents = [
    technical_analyst,
    fundamental_analyst,
    sentiment_analyst,
    quant_analyst,
    portfolio_strategist
]

swarm = ConcurrentWorkflow(
    name="Stock-Analysis-Swarm",
    description="A swarm of agents that analyze stocks and provide a comprehensive analysis of the current trends and opportunities.",
    agents=stock_analysis_agents,
)

swarm.run("Analyze the best etfs for gold and other similiar commodities in volatile markets")
```

## Best Practices

!!! success "Optimization Tips"
    1. **Agent Design**
        - Keep system prompts focused and specific
        
        - Use clear role definitions
        
        - Include error handling guidelines
    
    2. **Resource Management**
        
        - Monitor memory usage with large models
        
        - Implement proper cleanup procedures
        
        - Use batching for multiple queries
    
    3. **Output Handling**
        
        - Implement proper logging
        
        - Format outputs consistently
        
        - Include error checking

## Common Issues and Solutions

!!! warning "Troubleshooting"
    Common issues you might encounter:

    1. **Memory Issues**
        
        - *Problem*: VLLM consuming too much memory
        
        - *Solution*: Adjust batch sizes and model parameters
    
    2. **Agent Coordination**
        
        - *Problem*: Agents providing conflicting information
        
        - *Solution*: Implement consensus mechanisms or priority rules
    
    3. **Performance**
        
        - *Problem*: Slow response times
        
        - *Solution*: Use proper batching and optimize model loading

## FAQ

??? question "Can I use different models for different agents?"
    Yes, you can initialize multiple VLLM wrappers with different models for each agent. However, be mindful of memory usage.

??? question "How many agents can run concurrently?"
    The number depends on your hardware resources. Start with 3-5 agents and scale based on performance.

??? question "Can I customize agent communication patterns?"
    Yes, you can modify the ConcurrentWorkflow class or create custom workflows for specific communication patterns.

## Advanced Configuration

!!! example "Extended Settings"
    ```python
    vllm = VLLMWrapper(
        model_name="meta-llama/Llama-2-7b-chat-hf",
        system_prompt="You are a helpful assistant.",
        temperature=0.7,
        max_tokens=2048,
        top_p=0.95,
    )
    ```

## Contributing

!!! info "Get Involved"
    We welcome contributions! Here's how you can help:

    1. Report bugs and issues
    2. Submit feature requests
    3. Contribute to documentation
    4. Share example use cases

## Resources

!!! abstract "Additional Reading"
    - [VLLM Documentation](https://docs.vllm.ai/en/latest/)
    