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Model Integration in Agents

About Model Integration

Agents supports multiple model providers through LiteLLM integration, allowing you to easily switch between different language models. This document outlines the available providers and how to use them with agents.

Important Note on Model Names

Required Format

When specifying a model in an agent, you must use the format provider/model_name. For example: 

"openai/gpt-4"
"anthropic/claude-3-opus-latest"
"cohere/command-r-plus"
This format ensures the agent knows which provider to use for the specified model.

Available Model Providers

OpenAI

OpenAI Models
  • Provider name: openai
  • Available Models:
    • gpt-4
    • gpt-3.5-turbo
    • gpt-4-turbo-preview

Anthropic

Anthropic Models
  • Provider name: anthropic
  • Available Models:
    • Claude 3 Opus:
      • claude-3-opus-latest
      • claude-3-opus-20240229
    • Claude 3 Sonnet:
      • claude-3-sonnet-20240229
      • claude-3-5-sonnet-latest
      • claude-3-5-sonnet-20240620
      • claude-3-7-sonnet-latest
      • claude-3-7-sonnet-20250219
      • claude-3-5-sonnet-20241022
    • Claude 3 Haiku:
      • claude-3-haiku-20240307
      • claude-3-5-haiku-20241022
      • claude-3-5-haiku-latest
    • Legacy Models:
      • claude-2
      • claude-2.1
      • claude-instant-1
      • claude-instant-1.2

Cohere

Cohere Models
  • Provider name: cohere
  • Available Models:
    • Command:
      • command
      • command-r
      • command-r-08-2024
      • command-r7b-12-2024
    • Command Light:
      • command-light
    • Command R Plus:
      • command-r-plus
      • command-r-plus-08-2024

Google

Google Models
  • Provider name: google
  • Available Models:
    • gemini-pro
    • gemini-pro-vision

Mistral

Mistral Models
  • Provider name: mistral
  • Available Models:
    • mistral-tiny
    • mistral-small
    • mistral-medium

Using Different Models In Your Agents

To use a different model with your Swarms agent, specify the model name in the model_name parameter when initializing the Agent, using the provider/model_name format:

from swarms import Agent

# Using OpenAI's GPT-4
agent = Agent(
    agent_name="Research-Agent",
    model_name="openai/gpt-4o",  # Note the provider/model_name format
    # ... other parameters
)

# Using Anthropic's Claude
agent = Agent(
    agent_name="Analysis-Agent",
    model_name="anthropic/claude-3-sonnet-20240229",  # Note the provider/model_name format
    # ... other parameters
)

# Using Cohere's Command
agent = Agent(
    agent_name="Text-Agent",
    model_name="cohere/command-r-plus",  # Note the provider/model_name format
    # ... other parameters
)

Model Configuration

When using different models, you can configure various parameters:

agent = Agent(
    agent_name="Custom-Agent",
    model_name="openai/gpt-4",
    temperature=0.7,  # Controls randomness (0.0 to 1.0)
    max_tokens=2000,  # Maximum tokens in response
    top_p=0.9,       # Nucleus sampling parameter
    frequency_penalty=0.0,  # Reduces repetition
    presence_penalty=0.0,   # Encourages new topics
    # ... other parameters
)

Best Practices

Model Selection

Choosing the Right Model

  • Choose models based on your specific use case
  • Consider cost, performance, and feature requirements
  • Test different models for your specific task

Error Handling

Error Management

  • Implement proper error handling for model-specific errors
  • Handle rate limits and API quotas appropriately

Cost Management

Cost Considerations

  • Monitor token usage and costs
  • Use appropriate model sizes for your needs

Example Use Cases

1. Complex Analysis (GPT-4)

agent = Agent(
    agent_name="Analysis-Agent",
    model_name="openai/gpt-4",  # Note the provider/model_name format
    temperature=0.3,  # Lower temperature for more focused responses
    max_tokens=4000
)

2. Creative Tasks (Claude)

agent = Agent(
    agent_name="Creative-Agent",
    model_name="anthropic/claude-3-sonnet-20240229",  # Note the provider/model_name format
    temperature=0.8,  # Higher temperature for more creative responses
    max_tokens=2000
)

3. Vision Tasks (Gemini)

agent = Agent(
    agent_name="Vision-Agent",
    model_name="google/gemini-pro-vision",  # Note the provider/model_name format
    temperature=0.4,
    max_tokens=1000
)

Troubleshooting

Common Issues

If you encounter issues with specific models:

  1. Verify your API keys are correctly set
  2. Check model availability in your region
  3. Ensure you have sufficient quota/credits
  4. Verify the model name is correct and supported

Additional Resources