TradingAgents/docs/MIGRATION_GUIDE.md

Migration Guide: AI Provider Agnostic Update

Overview

This project has been updated to support multiple AI/LLM providers instead of being locked to OpenAI. You can now use OpenAI, Ollama (local), Anthropic, Google, Groq, and others.

What Changed

1. New LLM Factory Module

File: tradingagents/llm_factory.py

A new factory pattern implementation that creates LLM instances for any supported provider. This module:

• Provides a unified interface for all providers
• Handles provider-specific initialization
• Includes helpful error messages for missing dependencies
• Supports: OpenAI, Ollama, Anthropic, Google, Azure, Groq, Together AI, HuggingFace, OpenRouter

2. Updated Configuration

File: tradingagents/default_config.py

Enhanced configuration with:

• llm_provider: Specify which provider to use
• temperature: Control model randomness
• llm_kwargs: Pass additional provider-specific parameters
• Example configurations for all providers (commented)

3. Refactored Graph Initialization

File: tradingagents/graph/trading_graph.py

• Removed hardcoded provider checks (OpenAI, Anthropic, Google)
• Now uses the LLM factory for provider-agnostic initialization
• Simplified code with automatic provider handling

4. Type Annotation Updates

Files:

• tradingagents/graph/setup.py

• tradingagents/graph/signal_processing.py

• tradingagents/graph/reflection.py

• Removed specific type hints (e.g., ChatOpenAI)

• Now accept any LangChain-compatible LLM

• Maintains full functionality while being provider-agnostic

5. Updated Dependencies

File: requirements.txt

• Organized dependencies by purpose
• Added comments for optional provider packages
• Included langchain-core and langchain-community
• Documented which packages are needed for each provider

6. Environment Variables

File: .env.example

• Added examples for all supported providers
• Documented which API keys are needed for each
• Included Ollama configuration (no API key needed)

New Files

Documentation

1. docs/LLM_PROVIDER_GUIDE.md

   • Comprehensive guide for all supported providers
   • Setup instructions for each provider
   • Model recommendations
   • Troubleshooting tips
   • Environment variable setup

2. docs/MULTI_PROVIDER_SUPPORT.md

   • Quick reference for switching providers
   • Code examples for each provider
   • Installation notes
   • Environment setup

Examples

3. examples/llm_provider_configs.py
   • Pre-configured settings for all providers
   • Ready-to-use configuration dictionaries
   • Usage examples

Migration Steps

For Existing Users (Currently Using OpenAI)

No changes required! The default configuration still uses OpenAI. Your existing code will work as-is.

To Switch to a Different Provider

Option 1: Using Ollama (Free, Local)

from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG

config = DEFAULT_CONFIG.copy()
config["llm_provider"] = "ollama"
config["deep_think_llm"] = "llama3:70b"
config["quick_think_llm"] = "llama3:8b"
config["backend_url"] = "http://localhost:11434"

ta = TradingAgentsGraph(config=config)

Setup:

1. Install Ollama: https://ollama.ai
2. Pull models: ollama pull llama3
3. Install langchain-community: pip install langchain-community

Option 2: Using Anthropic Claude

config = DEFAULT_CONFIG.copy()
config["llm_provider"] = "anthropic"
config["deep_think_llm"] = "claude-3-opus-20240229"
config["quick_think_llm"] = "claude-3-haiku-20240307"

ta = TradingAgentsGraph(config=config)

Setup:

1. Get API key from https://console.anthropic.com/
2. Set environment: export ANTHROPIC_API_KEY=your-key
3. Install: pip install langchain-anthropic

Option 3: Using Google Gemini

config = DEFAULT_CONFIG.copy()
config["llm_provider"] = "google"
config["deep_think_llm"] = "gemini-1.5-pro"
config["quick_think_llm"] = "gemini-1.5-flash"

ta = TradingAgentsGraph(config=config)

Setup:

1. Get API key from https://makersuite.google.com/app/apikey
2. Set environment: export GOOGLE_API_KEY=your-key
3. Install: pip install langchain-google-genai (already in requirements.txt)

Benefits

1. Cost Savings

• Use free local models with Ollama (Llama 3, Mistral, etc.)
• Choose cheaper providers like Groq for specific tasks
• Mix and match: expensive models for complex tasks, cheap for simple ones

2. Privacy

• Run models locally with Ollama
• No data sent to external APIs
• Full control over your data

3. Performance

• Use Groq for ultra-fast inference
• Choose the best model for each task
• Experiment with different providers

4. Flexibility

• Not locked to a single vendor
• Easy to switch providers
• Test multiple providers simultaneously

5. Future-Proof

• Easy to add new providers
• Stay up-to-date with latest models
• Adapt to changing AI landscape

Breaking Changes

None! This update is fully backward compatible. Existing code using OpenAI will continue to work without modifications.

Testing

To test different providers:

from tradingagents.llm_factory import LLMFactory

# Test provider creation
openai_llm = LLMFactory.create_llm(
    provider="openai",
    model="gpt-4o-mini",
    temperature=0.7
)

ollama_llm = LLMFactory.create_llm(
    provider="ollama",
    model="llama3",
    base_url="http://localhost:11434",
    temperature=0.7
)

# Verify it works
response = openai_llm.invoke("Hello, how are you?")
print(response.content)

Troubleshooting

Import Errors

If you get ImportError for a provider:

# For Ollama
pip install langchain-community

# For Groq
pip install langchain-groq

# For Together AI
pip install langchain-together

API Key Not Found

Make sure environment variables are set:

# Check
echo $OPENAI_API_KEY

# Set
export OPENAI_API_KEY=your-key

# Or add to .env file
echo "OPENAI_API_KEY=your-key" >> .env

Ollama Connection Failed

1. Make sure Ollama is running: ollama serve
2. Check if model is available: ollama list
3. Pull model if needed: ollama pull llama3
4. Verify endpoint: default is http://localhost:11434

Support

For detailed provider setup and configuration:

• See docs/LLM_PROVIDER_GUIDE.md
• See docs/MULTI_PROVIDER_SUPPORT.md
• Check example configs in examples/llm_provider_configs.py

Future Enhancements

Potential future additions:

• Support for more providers (Cohere, AI21, etc.)
• Automatic provider fallback
• Cost tracking per provider
• Performance benchmarking
• Provider-specific optimizations