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Migration Guide

Step-by-step guide for migrating from existing AI frameworks to OpenDXA

Overview

This guide helps teams migrate from existing AI development frameworks to OpenDXA, providing practical steps, code examples, and best practices for a smooth transition.

Migration Strategies

  • Build new features with OpenDXA alongside existing system
  • Gradually migrate components as they prove stable
  • Minimize risk while demonstrating value

Direct Migration Approach

  • Replace existing components directly with OpenDXA equivalents
  • Faster adoption but higher risk
  • Best for greenfield projects or major refactoring

Hybrid Integration Approach

  • Use OpenDXA for specific capabilities while maintaining existing infrastructure
  • Gradual adoption with clear integration boundaries
  • Ideal for large, complex systems

Framework-Specific Migration

From LangChain

Before (LangChain): 

from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

llm = OpenAI(temperature=0.7)
prompt = PromptTemplate(
 input_variables=["query"],
 template="Analyze this query: {query}"
)
chain = LLMChain(llm=llm, prompt=prompt)
result = chain.run("What are the trends?")

After (OpenDXA): 

# Configure LLM resource
llm = create_llm_resource(
 provider="openai",
 model="gpt-4",
 temperature=0.7
)

# Simple reasoning with built-in transparency
result = reason("Analyze this query: What are the trends?")
log("Analysis complete", level="INFO")

From LlamaIndex

Before (LlamaIndex): 

from llama_index import GPTVectorStoreIndex, Document
from llama_index.query_engine import RetrieverQueryEngine

documents = [Document(text="...")]
index = GPTVectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What is the summary?")

After (OpenDXA): 

# Create knowledge resource
kb = create_kb_resource(documents=document_list)

# Query with automatic context management
summary = kb.query("What is the summary?")
insights = reason(f"Provide insights based on: {summary}")

From Custom AI Solutions

Assessment Steps: 1. Identify core AI workflows in existing system 2. Map existing patterns to OpenDXA capabilities 3. Create parallel implementations for testing 4. Gradually migrate production workloads

Common Migration Patterns: - Prompt Management → Dana reasoning functions - State Management → Built-in context management - Error Handling → Transparent execution and logging - Integration Points → Resource-based architecture

📋 Migration Checklist

Pre-Migration Assessment

  • Audit existing AI workflows and dependencies
  • Identify critical integration points
  • Assess team readiness and training needs
  • Plan rollback strategies for each component

Migration Execution

  • Set up OpenDXA development environment
  • Create parallel implementations of key workflows
  • Implement comprehensive testing strategies
  • Monitor performance and reliability metrics

Post-Migration Validation

  • Verify functional equivalence with existing system
  • Validate performance characteristics
  • Confirm integration stability
  • Document lessons learned and optimizations

Migration Tools and Utilities

Code Analysis Tools

  • Dependency Scanner: Identify framework-specific dependencies
  • Pattern Matcher: Find common patterns suitable for OpenDXA migration
  • Complexity Analyzer: Assess migration complexity for each component

Migration Assistants

  • Code Generator: Generate OpenDXA equivalents for common patterns
  • Test Generator: Create test suites for migrated components
  • Performance Profiler: Compare before/after performance metrics

🚨 Common Migration Challenges

Technical Challenges

  • State Management: Mapping existing state to OpenDXA context system
  • Integration Points: Adapting existing API integrations
  • Performance: Ensuring equivalent or better performance
  • Testing: Validating migrated functionality

Organizational Challenges

  • Team Training: Getting team up to speed on OpenDXA
  • Change Management: Managing resistance to new technology
  • Timeline Pressure: Balancing speed with thorough migration
  • Risk Management: Minimizing impact on production systems

Solutions and Best Practices

  • Start with non-critical components for learning
  • Maintain comprehensive testing throughout migration
  • Implement feature flags for gradual rollout
  • Establish clear rollback procedures

📈 Success Metrics

Technical Metrics

  • Development Velocity: Time to implement new features
  • Debug Time: Time to identify and fix issues
  • Reliability: System uptime and error rates
  • Performance: Response times and resource usage

Business Metrics

  • Team Productivity: Developer efficiency improvements
  • Maintenance Cost: Reduced ongoing maintenance effort
  • Time to Market: Faster feature delivery
  • Quality: Reduced bug rates and customer issues

🤝 Support and Resources

Migration Support

  • Documentation: Comprehensive migration guides and examples
  • Community: Active community support and knowledge sharing
  • Professional Services: Expert migration assistance available
  • Training: Workshops and training programs for teams

Additional Resources

Need migration assistance? Contact our Professional Services team or join our Community Forum for support.