Model Providers

Swarms supports a wide range of LLM providers, giving you the flexibility to choose the best model for your use case. The framework provides a unified interface that works seamlessly across all supported providers.

Supported Providers

Swarms integrates with all major LLM providers through a consistent API:

OpenAI - GPT-5.4, o3
Anthropic - Claude 4.x Opus, Sonnet, Haiku
Groq - Ultra-fast inference with Llama
DeepSeek - DeepSeek models
Cohere - Command models
Ollama - Local model deployment
OpenRouter - Access to multiple providers
XAI - Grok models
Azure OpenAI - Enterprise OpenAI deployment

Configuration

Environment Setup

Configure your API keys in environment variables:

# OpenAI
OPENAI_API_KEY="sk-..."

# Anthropic
ANTHROPIC_API_KEY="sk-ant-..."

# Groq
GROQ_API_KEY="gsk_..."

# Workspace directory (optional)
WORKSPACE_DIR="agent_workspace"

Store your API keys in a .env file and use python-dotenv to load them. Never commit API keys to version control.

Usage Examples

OpenAI Models

Use OpenAI’s GPT models by specifying the model name:

from swarms import Agent

# GPT-5.4
agent = Agent(
    agent_name="GPT5-Agent",
    model_name="gpt-5.4",
    max_loops=1,
)

# o3 (Reasoning)
agent = Agent(
    agent_name="O3-Agent",
    model_name="o3",
    max_loops=1,
)

# GPT-5.4 Mini (Cost-effective)
agent = Agent(
    agent_name="GPT5-Mini-Agent",
    model_name="gpt-5.4-mini",
    max_loops=1,
)

Anthropic Claude

Claude models excel at long-form content and analysis:

from swarms import Agent

# Claude Opus (Most capable)
agent = Agent(
    agent_name="Claude-Opus-Agent",
    model_name="claude-opus-4-8",
    max_loops=1,
)

# Claude Sonnet (Balanced)
agent = Agent(
    agent_name="Claude-Sonnet-Agent",
    model_name="claude-sonnet-4-6",
    max_loops=1,
)

# Claude Haiku (Fast)
agent = Agent(
    agent_name="Claude-Haiku-Agent",
    model_name="claude-haiku-4-5",
    max_loops=1,
)

Groq (Ultra-Fast Inference)

Groq provides extremely fast inference speeds:

from swarms import Agent

# Llama 3.3 70B on Groq
agent = Agent(
    agent_name="Llama3-Groq-Agent",
    model_name="groq/llama-3.3-70b-versatile",
    max_loops=1,
)

# Llama 3.1 8B Instant on Groq (lowest latency)
agent = Agent(
    agent_name="Llama-Instant-Groq-Agent",
    model_name="groq/llama-3.1-8b-instant",
    max_loops=1,
)

DeepSeek

DeepSeek models for coding and reasoning:

from swarms import Agent

agent = Agent(
    agent_name="DeepSeek-Agent",
    model_name="deepseek/deepseek-chat",
    max_loops=1,
)

Ollama (Local Models)

Run models locally with Ollama:

from swarms import Agent

# Requires Ollama running locally
agent = Agent(
    agent_name="Llama-Local-Agent",
    model_name="ollama/llama3.2",
    max_loops=1,
)

OpenRouter (Multi-Provider Access)

Access multiple providers through OpenRouter:

from swarms import Agent

agent = Agent(
    agent_name="OpenRouter-Agent",
    model_name="openrouter/anthropic/claude-opus-4-6",
    max_loops=1,
)

Cohere

Cohere Command models:

from swarms import Agent

agent = Agent(
    agent_name="Cohere-Agent",
    model_name="cohere/command-r-plus",
    max_loops=1,
)

Model Naming Convention

Swarms uses the following naming pattern for models:

Direct provider models: "gpt-5.4", "claude-sonnet-4-6"
Provider prefix: "groq/llama-3.3-70b-versatile", "ollama/llama3.2"
OpenRouter: "openrouter/provider/model-name"

Model names are passed straight to LiteLLM, so any name LiteLLM can route works. To check a name before you build an agent, use is_model_available:

from swarms import is_model_available

print(is_model_available("claude-sonnet-4-6", include_openrouter=False))  # LiteLLM's catalogue only, no network call
print(is_model_available("openrouter/moonshotai/kimi-k3"))                # also fetches OpenRouter's live catalogue

See check_models for the full API.

Advanced Configuration

Custom Model Parameters

Configure model-specific parameters:

from swarms import Agent

agent = Agent(
    agent_name="Custom-Agent",
    model_name="claude-sonnet-4-6",
    max_loops=1,
    temperature=0.7,        # Creativity (0.0-1.0)
    context_length=8192,    # Context window size
    max_tokens=2000,        # Max output tokens
)

temperature defaults to None, which leaves it out of the request so the provider’s own default applies. max_tokens defaults to the model’s own output limit.

Dynamic Model Selection

Switch models dynamically based on task requirements:

from swarms import Agent

def get_agent_for_task(task_type: str) -> Agent:
    """Select the best model for the task type."""
    
    if task_type == "coding":
        # Use Claude for coding tasks
        return Agent(
            agent_name="Coding-Agent",
            model_name="claude-opus-4-6",
            max_loops=1,
        )
    elif task_type == "analysis":
        # Use a reasoning model for analysis
        return Agent(
            agent_name="Analysis-Agent",
            model_name="o3",
            max_loops=1,
        )
    elif task_type == "fast":
        # Use Groq for speed
        return Agent(
            agent_name="Fast-Agent",
            model_name="groq/llama-3.3-70b-versatile",
            max_loops=1,
        )
    else:
        # Default to GPT-5.4
        return Agent(
            agent_name="Default-Agent",
            model_name="gpt-5.4",
            max_loops=1,
        )

# Use the appropriate agent
agent = get_agent_for_task("coding")
result = agent.run("Write a Python function to sort a list")

Multi-Provider Workflows

Combine different providers in a single workflow:

from swarms import Agent, SequentialWorkflow

# Research with Claude (good at analysis)
researcher = Agent(
    agent_name="Researcher",
    model_name="claude-opus-4-6",
    system_prompt="Research the topic thoroughly.",
)

# Write with GPT-5.4
writer = Agent(
    agent_name="Writer",
    model_name="gpt-5.4",
    system_prompt="Write an engaging article.",
)

# Fast review with Groq
reviewer = Agent(
    agent_name="Reviewer",
    model_name="groq/llama-3.3-70b-versatile",
    system_prompt="Review and provide feedback.",
)

workflow = SequentialWorkflow(
    agents=[researcher, writer, reviewer]
)

result = workflow.run("The future of AI")

Cost Optimization

Optimize costs by using the right model for each task:

from swarms import Agent

# Use cheaper models for simple tasks
simple_agent = Agent(
    agent_name="Simple-Task-Agent",
    model_name="gpt-5.4-mini",  # Cost-effective
    max_loops=1,
)

# Use premium models only when needed
complex_agent = Agent(
    agent_name="Complex-Task-Agent",
    model_name="claude-opus-4-6",  # Most capable
    max_loops=1,
)

Best Practices

Choose the Right Model

Select models based on your specific use case - speed, cost, or capability

Monitor Usage

Track token usage per agent with agent.usage

Test Thoroughly

Test with multiple providers to find the best fit for your application

Fallback Strategy

Set fallback_models so an agent moves to another provider when one fails

Next Steps

MCP Integration

Connect to MCP servers for extended capabilities

Tools

Add custom tools to your agents

Getting Started

Core Concepts

Agent Development

Reasoning Agents

Multi-Agent Architectures

Deployment & Operations

Integrations

CLI Reference

Changelog

Model Providers

Supported Providers

Configuration

Environment Setup

Usage Examples

OpenAI Models

Anthropic Claude

Groq (Ultra-Fast Inference)

DeepSeek

Ollama (Local Models)

OpenRouter (Multi-Provider Access)

Cohere

Model Naming Convention

Advanced Configuration

Custom Model Parameters

Dynamic Model Selection

Multi-Provider Workflows

Cost Optimization

Best Practices

Choose the Right Model

Monitor Usage

Test Thoroughly

Fallback Strategy

Next Steps

MCP Integration

Tools

​Supported Providers

​Configuration

​Environment Setup

​Usage Examples

​OpenAI Models

​Anthropic Claude

​Groq (Ultra-Fast Inference)

​DeepSeek

​Ollama (Local Models)

​OpenRouter (Multi-Provider Access)

​Cohere

​Model Naming Convention

​Advanced Configuration

​Custom Model Parameters

​Dynamic Model Selection

​Multi-Provider Workflows

​Cost Optimization

​Best Practices

Choose the Right Model

Monitor Usage

Test Thoroughly

Fallback Strategy

​Next Steps

MCP Integration

Tools

Supported Providers

Configuration

Environment Setup

Usage Examples

OpenAI Models

Anthropic Claude

Groq (Ultra-Fast Inference)

DeepSeek

Ollama (Local Models)

OpenRouter (Multi-Provider Access)

Cohere

Model Naming Convention

Advanced Configuration

Custom Model Parameters

Dynamic Model Selection

Multi-Provider Workflows

Cost Optimization

Best Practices

Next Steps