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Model Providers Overview

Swarms supports a vast array of model providers, giving you the flexibility to choose the best model for your specific use case. Whether you need high-performance inference, cost-effective solutions, or specialized capabilities, Swarms has you covered.

Supported Model Providers

Provider Description Documentation
OpenAI Industry-leading language models including GPT-4, GPT-4o, and GPT-4o-mini. Perfect for general-purpose tasks, creative writing, and complex reasoning. OpenAI Integration
Anthropic/Claude Advanced AI models known for their safety, helpfulness, and reasoning capabilities. Claude models excel at analysis, coding, and creative tasks. Claude Integration
Groq Ultra-fast inference platform offering real-time AI responses. Ideal for applications requiring low latency and high throughput. Groq Integration
Cohere Enterprise-grade language models with strong performance on business applications, text generation, and semantic search. Cohere Integration
DeepSeek Advanced reasoning models including the DeepSeek Reasoner (R1). Excellent for complex problem-solving and analytical tasks. DeepSeek Integration
Ollama Local model deployment platform allowing you to run open-source models on your own infrastructure. No API keys required. Ollama Integration
OpenRouter Unified API gateway providing access to hundreds of models from various providers through a single interface. OpenRouter Integration
XAI xAI's Grok models offering unique capabilities for research, analysis, and creative tasks with advanced reasoning abilities. XAI Integration
vLLM High-performance inference library for serving large language models with optimized memory usage and throughput. vLLM Integration
Llama4 Meta's latest open-source language models including Llama-4-Maverick and Llama-4-Scout variants with expert routing capabilities. Llama4 Integration
Azure OpenAI Enterprise-grade OpenAI models through Microsoft's cloud infrastructure with enhanced security, compliance, and enterprise features. Azure Integration

Quick Start

All model providers follow a consistent pattern in Swarms. Here's the basic template:

from swarms import Agent
import os
from dotenv import load_dotenv

load_dotenv()

# Initialize agent with your chosen model
agent = Agent(
    agent_name="Your-Agent-Name",
    model_name="gpt-4o-mini",  # Varies by provider
    system_prompt="Your system prompt here",
    agent_description="Description of what your agent does.",
)

# Run your agent
response = agent.run("Your query here")

Model Selection Guide

For High-Performance Applications

  • OpenAI GPT-4o: Best overall performance and reasoning

  • Anthropic Claude: Excellent safety and analysis capabilities

  • DeepSeek R1: Advanced reasoning and problem-solving

For Cost-Effective Solutions

  • OpenAI GPT-4o-mini: Great performance at lower cost

  • Ollama: Free local deployment

  • OpenRouter: Access to cost-effective models

For Real-Time Applications

  • Groq: Ultra-fast inference

  • vLLM: Optimized for high throughput

For Specialized Tasks

  • Llama4: Expert routing for complex workflows

  • XAI Grok: Advanced research capabilities

  • Cohere: Strong business applications

Environment Setup

Most providers require API keys. Add them to your .env file:

# OpenAI
OPENAI_API_KEY=your_openai_key

# Anthropic
ANTHROPIC_API_KEY=your_anthropic_key

# Groq
GROQ_API_KEY=your_groq_key

# Cohere
COHERE_API_KEY=your_cohere_key

# DeepSeek
DEEPSEEK_API_KEY=your_deepseek_key

# OpenRouter
OPENROUTER_API_KEY=your_openrouter_key

# XAI
XAI_API_KEY=your_xai_key

# Azure OpenAI
AZURE_API_KEY=your_azure_openai_api_key
AZURE_API_BASE=https://your-resource-name.openai.azure.com/
AZURE_API_VERSION=2024-02-15-preview

No API Key Required

Ollama and vLLM can be run locally without API keys, making them perfect for development and testing.

Advanced Features

Multi-Model Workflows

Swarms allows you to create workflows that use different models for different tasks:

from swarms import Agent, ConcurrentWorkflow

# Research agent using Claude for analysis
research_agent = Agent(
    agent_name="Research-Agent",
    model_name="claude-3-sonnet-20240229",
    system_prompt="You are a research expert."
)

# Creative agent using GPT-4o for content generation
creative_agent = Agent(
    agent_name="Creative-Agent", 
    model_name="gpt-4o",
    system_prompt="You are a creative content expert."
)

# Workflow combining both agents
workflow = ConcurrentWorkflow(
    name="Research-Creative-Workflow",
    agents=[research_agent, creative_agent]
)

Model Routing

Automatically route tasks to the most appropriate model:

from swarms import Agent, ModelRouter

# Define model preferences for different task types
model_router = ModelRouter(
    models={
        "analysis": "claude-3-sonnet-20240229",
        "creative": "gpt-4o", 
        "fast": "gpt-4o-mini",
        "local": "ollama/llama2"
    }
)

# Agent will automatically choose the best model
agent = Agent(
    agent_name="Smart-Agent",
    llm=model_router,
    system_prompt="You are a versatile assistant."
)

Getting Help

  • Documentation: Each provider has detailed documentation with examples

  • Community: Join the Swarms community for support and best practices

  • Issues: Report bugs and request features on GitHub

  • Discussions: Share your use cases and learn from others

Ready to Get Started?

Choose a model provider from the table above and follow the detailed integration guide. Each provider offers unique capabilities that can enhance your Swarms applications.