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TradingAgents/docs/LLM_PROVIDER_GUIDE.md

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LLM Provider Configuration Guide

This project now supports multiple AI/LLM providers through a unified interface. You can easily switch between providers by modifying the configuration.

Supported Providers

The following providers are currently supported:

  1. OpenAI - GPT-4, GPT-4o, GPT-3.5-turbo, etc.
  2. Ollama - Local models (Llama 3, Mistral, etc.)
  3. Anthropic - Claude models (Opus, Sonnet, Haiku)
  4. Google - Gemini models
  5. Azure OpenAI - Microsoft's Azure-hosted OpenAI models
  6. OpenRouter - Access to multiple models through one API
  7. Groq - Fast inference for open-source models
  8. Together AI - Open-source models
  9. HuggingFace - Models from HuggingFace Hub

Configuration

Basic Configuration

Edit tradingagents/default_config.py or pass a custom config dictionary:

config = {
    "llm_provider": "openai",  # Provider name
    "deep_think_llm": "gpt-4o",  # Model for complex reasoning
    "quick_think_llm": "gpt-4o-mini",  # Model for quick tasks
    "backend_url": "https://api.openai.com/v1",  # API endpoint (optional)
    "temperature": 0.7,  # Sampling temperature
    "llm_kwargs": {},  # Additional provider-specific parameters
}

Provider-Specific Examples

OpenAI (Default)

config = {
    "llm_provider": "openai",
    "deep_think_llm": "gpt-4o",
    "quick_think_llm": "gpt-4o-mini",
    "backend_url": "https://api.openai.com/v1",
    "temperature": 0.7,
}

Required Environment Variables:

export OPENAI_API_KEY=sk-your-api-key-here

Required Packages:

pip install langchain-openai

Ollama (Local Models)

config = {
    "llm_provider": "ollama",
    "deep_think_llm": "llama3:70b",  # Or llama3, mistral, etc.
    "quick_think_llm": "llama3:8b",
    "backend_url": "http://localhost:11434",  # Default Ollama endpoint
    "temperature": 0.7,
}

Setup:

  1. Install Ollama from https://ollama.ai
  2. Pull models: ollama pull llama3
  3. Verify: ollama list

Required Environment Variables:

  • None (uses local Ollama instance)

Required Packages:

pip install langchain-community

Anthropic (Claude)

config = {
    "llm_provider": "anthropic",
    "deep_think_llm": "claude-3-opus-20240229",
    "quick_think_llm": "claude-3-haiku-20240307",
    "temperature": 0.7,
}

Required Environment Variables:

export ANTHROPIC_API_KEY=sk-ant-your-api-key-here

Required Packages:

pip install langchain-anthropic

Google (Gemini)

config = {
    "llm_provider": "google",
    "deep_think_llm": "gemini-1.5-pro",
    "quick_think_llm": "gemini-1.5-flash",
    "temperature": 0.7,
}

Required Environment Variables:

export GOOGLE_API_KEY=your-google-api-key-here

Required Packages:

pip install langchain-google-genai

OpenRouter (Multi-Provider)

config = {
    "llm_provider": "openrouter",
    "deep_think_llm": "anthropic/claude-3-opus",
    "quick_think_llm": "anthropic/claude-3-haiku",
    "backend_url": "https://openrouter.ai/api/v1",
    "temperature": 0.7,
}

Required Environment Variables:

export OPENAI_API_KEY=sk-or-your-openrouter-key

Required Packages:

pip install langchain-openai

Groq (Fast Inference)

config = {
    "llm_provider": "groq",
    "deep_think_llm": "mixtral-8x7b-32768",
    "quick_think_llm": "llama3-8b-8192",
    "temperature": 0.7,
}

Required Environment Variables:

export GROQ_API_KEY=gsk-your-groq-api-key

Required Packages:

pip install langchain-groq

Azure OpenAI

config = {
    "llm_provider": "azure",
    "deep_think_llm": "gpt-4-deployment-name",
    "quick_think_llm": "gpt-35-turbo-deployment-name",
    "backend_url": "https://your-resource.openai.azure.com/",
    "temperature": 0.7,
    "llm_kwargs": {
        "api_version": "2024-02-01",
    }
}

Required Environment Variables:

export AZURE_OPENAI_API_KEY=your-azure-key
export AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/

Required Packages:

pip install langchain-openai

Together AI

config = {
    "llm_provider": "together",
    "deep_think_llm": "meta-llama/Llama-3-70b-chat-hf",
    "quick_think_llm": "meta-llama/Llama-3-8b-chat-hf",
    "temperature": 0.7,
}

Required Environment Variables:

export TOGETHER_API_KEY=your-together-api-key

Required Packages:

pip install langchain-together

Usage in Code

Using Default Configuration

from tradingagents.graph.trading_graph import TradingAgentsGraph

# Uses default config from tradingagents/default_config.py
graph = TradingAgentsGraph()

Using Custom Configuration

from tradingagents.graph.trading_graph import TradingAgentsGraph

# Custom config for Ollama
custom_config = {
    "llm_provider": "ollama",
    "deep_think_llm": "llama3:70b",
    "quick_think_llm": "llama3:8b",
    "backend_url": "http://localhost:11434",
    "temperature": 0.7,
    # ... other config options
}

graph = TradingAgentsGraph(config=custom_config)

Programmatically Creating LLM Instances

from tradingagents.llm_factory import LLMFactory, get_llm_instance

# Method 1: Direct factory usage
llm = LLMFactory.create_llm(
    provider="ollama",
    model="llama3",
    base_url="http://localhost:11434",
    temperature=0.7
)

# Method 2: Using config dictionary
config = {
    "llm_provider": "anthropic",
    "quick_think_llm": "claude-3-haiku-20240307",
    "temperature": 0.7,
}
llm = get_llm_instance(config, model_type="quick_think")

Advanced Configuration

Additional LLM Parameters

You can pass additional provider-specific parameters via llm_kwargs:

config = {
    "llm_provider": "openai",
    "deep_think_llm": "gpt-4o",
    "quick_think_llm": "gpt-4o-mini",
    "temperature": 0.7,
    "llm_kwargs": {
        "max_tokens": 4096,
        "top_p": 0.9,
        "frequency_penalty": 0.0,
        "presence_penalty": 0.0,
    }
}

Model Recommendations by Use Case

Best for Cost Efficiency

  • Deep Think: Ollama Llama 3 70B (local, free)
  • Quick Think: Ollama Llama 3 8B (local, free)

Best for Quality

  • Deep Think: GPT-4o or Claude 3 Opus
  • Quick Think: GPT-4o-mini or Claude 3 Haiku

Best for Speed

  • Deep Think: Groq Mixtral 8x7B
  • Quick Think: Groq Llama 3 8B

Best for Privacy

  • Deep Think: Ollama Llama 3 70B (local)
  • Quick Think: Ollama Llama 3 8B (local)

Troubleshooting

Import Errors

If you get import errors for a specific provider:

pip install langchain-[provider]

For example:

pip install langchain-anthropic  # For Anthropic
pip install langchain-community  # For Ollama
pip install langchain-groq       # For Groq

API Key Issues

Make sure your environment variables are set correctly:

# Check if set
echo $OPENAI_API_KEY

# Set temporarily
export OPENAI_API_KEY=your-key

# Set permanently (add to ~/.bashrc or ~/.zshrc)
echo 'export OPENAI_API_KEY=your-key' >> ~/.bashrc

Ollama Connection Issues

If Ollama fails to connect:

  1. Check if Ollama is running: ollama list
  2. Verify the endpoint: Default is http://localhost:11434
  3. Try pulling the model: ollama pull llama3

Model Not Found

Make sure you're using the correct model identifier for each provider:

  • OpenAI: gpt-4o, gpt-4o-mini, gpt-3.5-turbo
  • Anthropic: claude-3-opus-20240229, claude-3-sonnet-20240229, claude-3-haiku-20240307
  • Google: gemini-1.5-pro, gemini-1.5-flash
  • Ollama: llama3, llama3:70b, mistral, etc.

Migration from OpenAI-Only Version

If you're upgrading from an older version that only supported OpenAI:

  1. The default configuration still uses OpenAI, so existing code will work
  2. To switch providers, update your config: 
    config["llm_provider"] = "ollama"  # or "anthropic", "google", etc.
config["deep_think_llm"] = "llama3:70b"
config["quick_think_llm"] = "llama3:8b"
config["backend_url"] = "http://localhost:11434"
  3. Install required packages for your chosen provider
  4. Set appropriate environment variables

Contributing

To add support for a new provider:

  1. Edit tradingagents/llm_factory.py
  2. Add a new _create_[provider]_llm() method
  3. Update the create_llm() method to handle the new provider
  4. Update this documentation
  5. Submit a pull request