Kye Gomez
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# Model Providers Overview
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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.
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## Supported Model Providers
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| Provider | Description | Documentation |
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|----------|-------------|---------------|
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| **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](openai_example.md) |
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| **Anthropic/Claude** | Advanced AI models known for their safety, helpfulness, and reasoning capabilities. Claude models excel at analysis, coding, and creative tasks. | [Claude Integration](claude.md) |
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| **Groq** | Ultra-fast inference platform offering real-time AI responses. Ideal for applications requiring low latency and high throughput. | [Groq Integration](groq.md) |
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| **Cohere** | Enterprise-grade language models with strong performance on business applications, text generation, and semantic search. | [Cohere Integration](cohere.md) |
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| **DeepSeek** | Advanced reasoning models including the DeepSeek Reasoner (R1). Excellent for complex problem-solving and analytical tasks. | [DeepSeek Integration](deepseek.md) |
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| **Ollama** | Local model deployment platform allowing you to run open-source models on your own infrastructure. No API keys required. | [Ollama Integration](ollama.md) |
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| **OpenRouter** | Unified API gateway providing access to hundreds of models from various providers through a single interface. | [OpenRouter Integration](openrouter.md) |
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| **XAI** | xAI's Grok models offering unique capabilities for research, analysis, and creative tasks with advanced reasoning abilities. | [XAI Integration](xai.md) |
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| **vLLM** | High-performance inference library for serving large language models with optimized memory usage and throughput. | [vLLM Integration](vllm_integration.md) |
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| **Llama4** | Meta's latest open-source language models including Llama-4-Maverick and Llama-4-Scout variants with expert routing capabilities. | [Llama4 Integration](llama4.md) |
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## Quick Start
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All model providers follow a consistent pattern in Swarms. Here's the basic template:
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```python
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from swarms import Agent
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import os
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from dotenv import load_dotenv
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load_dotenv()
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# Initialize agent with your chosen model
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agent = Agent(
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agent_name="Your-Agent-Name",
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model_name="gpt-4o-mini", # Varies by provider
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system_prompt="Your system prompt here",
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agent_description="Description of what your agent does.",
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)
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# Run your agent
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response = agent.run("Your query here")
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```
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## Model Selection Guide
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### For High-Performance Applications
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- **OpenAI GPT-4o**: Best overall performance and reasoning
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- **Anthropic Claude**: Excellent safety and analysis capabilities
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- **DeepSeek R1**: Advanced reasoning and problem-solving
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### For Cost-Effective Solutions
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- **OpenAI GPT-4o-mini**: Great performance at lower cost
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- **Ollama**: Free local deployment
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- **OpenRouter**: Access to cost-effective models
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### For Real-Time Applications
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- **Groq**: Ultra-fast inference
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- **vLLM**: Optimized for high throughput
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### For Specialized Tasks
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- **Llama4**: Expert routing for complex workflows
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- **XAI Grok**: Advanced research capabilities
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- **Cohere**: Strong business applications
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## Environment Setup
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Most providers require API keys. Add them to your `.env` file:
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```bash
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# OpenAI
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OPENAI_API_KEY=your_openai_key
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# Anthropic
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ANTHROPIC_API_KEY=your_anthropic_key
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# Groq
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GROQ_API_KEY=your_groq_key
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# Cohere
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COHERE_API_KEY=your_cohere_key
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# DeepSeek
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DEEPSEEK_API_KEY=your_deepseek_key
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# OpenRouter
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OPENROUTER_API_KEY=your_openrouter_key
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# XAI
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XAI_API_KEY=your_xai_key
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```
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!!! note "No API Key Required"
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Ollama and vLLM can be run locally without API keys, making them perfect for development and testing.
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## Advanced Features
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### Multi-Model Workflows
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Swarms allows you to create workflows that use different models for different tasks:
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```python
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from swarms import Agent, ConcurrentWorkflow
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# Research agent using Claude for analysis
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research_agent = Agent(
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agent_name="Research-Agent",
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model_name="claude-3-sonnet-20240229",
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system_prompt="You are a research expert."
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)
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# Creative agent using GPT-4o for content generation
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creative_agent = Agent(
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agent_name="Creative-Agent",
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model_name="gpt-4o",
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system_prompt="You are a creative content expert."
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)
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# Workflow combining both agents
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workflow = ConcurrentWorkflow(
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name="Research-Creative-Workflow",
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agents=[research_agent, creative_agent]
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)
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```
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### Model Routing
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Automatically route tasks to the most appropriate model:
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```python
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from swarms import Agent, ModelRouter
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# Define model preferences for different task types
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model_router = ModelRouter(
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models={
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"analysis": "claude-3-sonnet-20240229",
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"creative": "gpt-4o",
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"fast": "gpt-4o-mini",
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"local": "ollama/llama2"
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}
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)
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# Agent will automatically choose the best model
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agent = Agent(
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agent_name="Smart-Agent",
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llm=model_router,
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system_prompt="You are a versatile assistant."
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)
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```
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## Getting Help
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- **Documentation**: Each provider has detailed documentation with examples
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- **Community**: Join the Swarms community for support and best practices
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- **Issues**: Report bugs and request features on GitHub
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- **Discussions**: Share your use cases and learn from others
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!!! success "Ready to Get Started?"
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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.
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