LangChain to LlamaIndex Migration: AI-Optimized Technical Guide

Migration Decision Criteria

Migrate When:

• Document search is primary use case
• Query performance <500ms required
• Memory usage >4GB problematic
• Processing 10k+ documents regularly
• Vector store operations are bottleneck

Do NOT Migrate When:

• Heavily using LangChain agents (LlamaIndex agents are immature)
• Basic chat without document retrieval (LangChain sufficient)
• Complex multi-step reasoning workflows required
• Sophisticated memory management needed

Performance Impact Assessment

Quantified Improvements:

• Query Speed: 3-4 seconds → 400-800ms (75-85% reduction)
• Memory Usage: 8GB → 2GB baseline (75% reduction)
• System Stability: Daily restarts → weeks without restart
• Processing: 50k documents with improved throughput

Performance Degradation Areas:

• Agent capabilities: Production-ready → broken/unreliable
• Memory management: Sophisticated → basic chat history only
• Development velocity: Slower due to API instability

Critical Failure Modes

Breaking Points That Will Occur:

1. PDF Processing Silent Failures: One corrupted PDF kills entire 50k document pipeline
2. Memory Explosions: Documents >50MB crash process entirely
3. Global Settings Race Conditions: Multi-threaded apps experience random failures
4. Vector Store Connection Timeouts: Error messages provide no debugging information
5. Embedding API Rate Limits: Poor backoff handling causes 429 error cascades

Common Migration Gotchas:

• Dependency Hell: Modular package structure requires specific combinations
• Metadata Schema Changes: document_id → node_id, custom timestamps broken
• Version Instability: API changes between minor versions (0.13.3 → 0.13.4)
• Windows Path Limits: 260 character limit breaks long package names
• ARM Chip Issues: PDF libraries crash with "illegal hardware instruction"

Technical Implementation Specifications

Configuration That Actually Works:

# AVOID: Global Settings (causes race conditions)
# USE: Explicit configuration per component

# Document Loading (Production-Ready)
from llama_index.readers.pdf import PDFReader
pdf_reader = PDFReader()
documents = SimpleDirectoryReader(
    './data',
    file_extractor={".pdf": pdf_reader},
    required_exts=[".txt", ".pdf"]  # Skip problematic files
).load_data()

# Chunking (Optimized Settings)
parser = SentenceSplitter(
    chunk_size=2048,      # NOT 1000 (too small for context)
    chunk_overlap=400,    # NOT 200 (insufficient overlap)
    paragraph_separator="\n\n"  # Preserve paragraph integrity
)

# Vector Store (Avoid Global Settings)
embed_model = OpenAIEmbedding()
query_engine = index.as_query_engine(
    llm=OpenAI(),
    embed_model=embed_model,
    response_mode="compact"
)

Required Dependencies (Complete List):

pip install llama-index-core llama-index-llms-openai llama-index-embeddings-openai
pip install llama-index-vector-stores-pinecone  # Platform-specific
pip install llama-index-readers-file llama-index-readers-pdf

Resource Requirements

Time Investment Reality:

• Simple Document Q&A: 1 week minimum (not "minutes" as marketed)
• Complex Workflows with Agents: 1+ months (complete rewrite required)
• Production Migration: 6 weeks actual vs 2 weeks estimated
• Testing Phase: 1 month parallel running required

Financial Costs:

• Embedding Reprocessing: ~$0.004 per document ($180 for 50k documents)
• Vector Database: 2x bill during migration month
• Infrastructure: Higher API costs during parallel system operation

Expertise Requirements:

• Understanding of vector database schemas
• Error handling and debugging skills (poor error messages)
• Experience with async/threading issues
• Knowledge of document processing pipelines

Critical Warnings

Production Deployment Hazards:

• Memory Leaks: Still exist, require periodic service restarts
• Silent PDF Failures: Corrupt files kill entire indexing pipeline
• Async Operations: Buggy, stick to synchronous for reliability
• Error Messages: Better than LangChain but still inadequate

Compatibility Matrix:

Component	Migration Difficulty	Success Rate	Notes
Document Loading	Easy	90%	Silent PDF failures common
Text Chunking	Easy	95%	Default settings inadequate
Vector Stores	Hard	70%	Connection patterns completely different
Basic Retrieval	Medium	85%	Better performance, changed API
Agents	AVOID	20%	Requires complete rewrite, unreliable
Chat Memory	Medium	75%	Significant feature loss

Rollback Strategy Requirements

Mandatory Parallel Operation:

• Keep LangChain system running 1+ months
• Route percentage of traffic for A/B testing
• Monitor for issues that only appear under real load
• Plan for database migration downtime

Rollback Triggers:

• Agent workflows broken beyond repair
• Memory leak issues in production
• Query accuracy degradation
• System instability under load

Monitoring and Debugging

Essential Error Handling:

import traceback
try:
    response = query_engine.query(question)
except Exception as e:
    print(f"Query failed: {e}")
    print(f"Full traceback: {traceback.format_exc()}")
    # Required due to poor default error messages

Performance Monitoring Gaps:

• Limited observability tools vs LangChain/LangSmith
• No built-in metrics collection
• Manual OpenTelemetry integration required
• Basic debug logging only

Migration Success Indicators

Positive Outcomes Achieved:

• 75% reduction in query response time
• 75% reduction in memory usage
• Elimination of daily restart requirements
• Stable performance under document-heavy workloads

Acceptable Trade-offs:

• Loss of agent capabilities for improved retrieval performance
• Simplified memory management for system stability
• Reduced development velocity for production reliability

Version Management Critical Requirements

Version Pinning Strategy:

• Pin exact versions (API changes in minor releases)
• Monitor changelog religiously
• Test all upgrades in staging environment
• Budget 3-4x estimated migration time
• Plan for breaking changes between versions

This technical reference provides the operational intelligence needed for successful LangChain to LlamaIndex migration, including failure prediction, resource planning, and production deployment strategies.