# Llama4 Model Integration

!!! info "Prerequisites"
    - Python 3.8 or higher
    - `swarms` library installed
    - Access to Llama4 model
    - Valid environment variables configured

## Quick Start

Here's a simple example of integrating Llama4 model for crypto risk analysis:

```python
from dotenv import load_dotenv
from swarms import Agent
from swarms.utils.vllm_wrapper import VLLM

load_dotenv()
model = VLLM(model_name="meta-llama/Llama-4-Maverick-17B-128E")
```

!!! tip "Environment Setup"
    Make sure to set up your environment variables properly before running the code.
    Create a `.env` file in your project root if needed.

## Detailed Implementation

### 1. Define Custom System Prompt

```python
CRYPTO_RISK_ANALYSIS_PROMPT = """
You are a cryptocurrency risk analysis expert. Your role is to:

1. Analyze market risks:
   - Volatility assessment
   - Market sentiment analysis
   - Trading volume patterns
   - Price trend evaluation

2. Evaluate technical risks:
   - Network security
   - Protocol vulnerabilities
   - Smart contract risks
   - Technical scalability

3. Consider regulatory risks:
   - Current regulations
   - Potential regulatory changes
   - Compliance requirements
   - Geographic restrictions

4. Assess fundamental risks:
   - Team background
   - Project development status
   - Competition analysis
   - Use case viability

Provide detailed, balanced analysis with both risks and potential mitigations.
Base your analysis on established crypto market principles and current market conditions.
"""
```

### 2. Initialize Agent

```python
agent = Agent(
    agent_name="Crypto-Risk-Analysis-Agent",
    agent_description="Agent for analyzing risks in cryptocurrency investments",
    system_prompt=CRYPTO_RISK_ANALYSIS_PROMPT,
    max_loops=1,
    llm=model,
)
```

## Full Code

```python
from dotenv import load_dotenv

from swarms import Agent
from swarms.utils.vllm_wrapper import VLLM

load_dotenv()

# Define custom system prompt for crypto risk analysis
CRYPTO_RISK_ANALYSIS_PROMPT = """
You are a cryptocurrency risk analysis expert. Your role is to:

1. Analyze market risks:
   - Volatility assessment
   - Market sentiment analysis
   - Trading volume patterns
   - Price trend evaluation

2. Evaluate technical risks:
   - Network security
   - Protocol vulnerabilities
   - Smart contract risks
   - Technical scalability

3. Consider regulatory risks:
   - Current regulations
   - Potential regulatory changes
   - Compliance requirements
   - Geographic restrictions

4. Assess fundamental risks:
   - Team background
   - Project development status
   - Competition analysis
   - Use case viability

Provide detailed, balanced analysis with both risks and potential mitigations.
Base your analysis on established crypto market principles and current market conditions.
"""

model = VLLM(model_name="meta-llama/Llama-4-Maverick-17B-128E")

# Initialize the agent with custom prompt
agent = Agent(
    agent_name="Crypto-Risk-Analysis-Agent",
    agent_description="Agent for analyzing risks in cryptocurrency investments",
    system_prompt=CRYPTO_RISK_ANALYSIS_PROMPT,
    max_loops=1,
    llm=model,
)

print(
    agent.run(
        "Conduct a risk analysis of the top cryptocurrencies. Think for 2 loops internally"
    )
)
```

!!! warning "Resource Usage"
    The Llama4 model requires significant computational resources. Ensure your system meets the minimum requirements.

## FAQ

??? question "What is the purpose of max_loops parameter?"
    The `max_loops` parameter determines how many times the agent will iterate through its thinking process. In this example, it's set to 1 for a single pass analysis.

??? question "Can I use a different model?"
    Yes, you can replace the VLLM wrapper with other compatible models. Just ensure you update the model initialization accordingly.

??? question "How do I customize the system prompt?"
    You can modify the `CRYPTO_RISK_ANALYSIS_PROMPT` string to match your specific use case while maintaining the structured format.

!!! note "Best Practices"
    - Always handle API errors gracefully
    - Monitor model performance and resource usage
    - Keep your prompts clear and specific
    - Test thoroughly before production deployment

!!! example "Sample Usage"
    ```python
    response = agent.run(
        "Conduct a risk analysis of the top cryptocurrencies. Think for 2 loops internally"
    )
    print(response)
    ```