LLM & RAG Library Comparison & Selection Guide

Date: October 9, 2025
Purpose: Detailed comparison of open-source libraries for LLM & RAG enhancements

📊 Library Comparison Matrix

1. Multi-LLM Abstraction Layers

Library	Pros	Cons	Recommendation	Priority
LangChain (Current)	- Already integrated - Extensive ecosystem - Good documentation - Active community	- Can be verbose - Some abstraction overhead	✅ Keep as primary	🔴 High
LiteLLM	- Unified API (OpenAI format) - 100+ models - Simple migration - Load balancing built-in	- Less feature-rich than LangChain - Newer project	✅ Add for routing	🟡 Medium
Haystack	- Production-ready - Great for pipelines - Strong retrieval focus	- Different paradigm - Migration effort high	❌ Skip (LangChain sufficient)	⚫ Low
LlamaIndex	- Excellent for RAG - Data connectors - Good indexing	- Overlap with LangChain - Another abstraction layer	❌ Skip (too similar)	⚫ Low

Decision: Keep LangChain, add LiteLLM for provider routing

2. Advanced Reranking

Library	Pros	Cons	Recommendation	Priority
RAGatouille	- Production ColBERT wrapper - Easy to use - Pre-trained models - 10-20% quality boost	- Larger model size - Slower than cross-encoder	✅ High priority	🔴 High
ColBERT v2	- State-of-the-art - Late interaction - Best quality	- Complex setup - Training required	✅ Via RAGatouille	🔴 High
Cohere Rerank API	- Cloud-based - No infrastructure - High quality	- External dependency - Cost per request	✅ Add as option	🟢 Low
Cross-Encoder (Current)	- Fast - Good baseline - Already integrated	- Lower quality than ColBERT	✅ Keep as fallback	🔴 High

Decision: Add RAGatouille (ColBERT), keep cross-encoder as fallback, optionally add Cohere

3. Prompt Engineering & Optimization

Library	Pros	Cons	Recommendation	Priority
DSPy	- Automatic optimization - Declarative syntax - Research-backed - Stanford project	- Learning curve - Newer paradigm - Limited examples	✅ High priority	🟡 Medium
LangChain Hub	- Prompt versioning - Community prompts - Easy integration	- Simple features - Manual optimization	✅ Add for versioning	🟢 Low
PromptTools	- Testing framework - A/B testing built-in - Good for experiments	- Limited features - Manual work	✅ Add for testing	🟢 Low
Guidance (Microsoft)	- Structured generation - Template language - Type safety	- Different approach - Less flexible	⚠️ Evaluate later	⚫ Low
LMQL	- Query language for LLMs - Constraints - Research project	- Niche use case - Small community	❌ Skip for now	⚫ Low

Decision: Start with DSPy for optimization, add LangChain Hub for versioning

4. Prompt Compression

Library	Pros	Cons	Recommendation	Priority
LLMLingua	- 2-3x compression - 40-60% cost savings - >90% quality maintained - Microsoft Research	- Adds latency (compression time) - Python only	✅ Immediate add	🔴 High
LongLLMLingua	- Better for long docs - Same benefits as LLMLingua	- Same cons - Slightly newer	✅ Use for long docs	🟡 Medium
AutoCompressor	- Context compression - Learned compression	- More complex setup - Training needed	⚠️ Evaluate later	⚫ Low

Decision: Add LLMLingua immediately for cost savings

5. Semantic Caching

Library	Pros	Cons	Recommendation	Priority
GPTCache	- Production-ready - Multiple backends - Semantic similarity - 90%+ cost reduction	- Additional infrastructure - Cache management needed	✅ High priority	🟡 Medium
Momento	- Serverless cache - No infrastructure - LLM-focused	- Vendor lock-in - Cost per request	⚠️ Consider for cloud	⚫ Low
Redis + Embeddings (Current)	- Already have Redis - Full control - No new dependencies	- Manual implementation - More code to maintain	✅ Keep as baseline	🔴 High

Decision: Integrate GPTCache with existing Redis, benchmark against current implementation

6. Vector Databases

Database	Pros	Cons	Current Status	Recommendation
OpenSearch	- Already integrated - Production-ready - Hybrid search - Analytics features	- Heavier than specialized DBs - More expensive	✅ Implemented	✅ Keep as primary
MongoDB Atlas	- Already integrated - Transactional + vector - Single database	- Newer vector search - Limited features vs OpenSearch	✅ Implemented	✅ Keep for MongoDB users
Qdrant	- Fast - Easy to use - Good filtering	- Already implemented - Less mature than OpenSearch	✅ Implemented	✅ Keep as option
Milvus	- Scalable - High performance - Open source	- Complex setup - Overkill for most	❌ Not needed	⚫ Low priority
Weaviate	- RAG-focused - Good features - GraphQL API	- Already have 5 stores - Not needed	❌ Skip	⚫ Low
Chroma	- Simple - Good for prototyping	- Not production-scale - Limited features	❌ Skip	⚫ Low
Pinecone	- Serverless - No infrastructure	- Vendor lock-in - Cost per request - Already have better options	❌ Skip	⚫ Low

Decision: Keep current vector stores (OpenSearch, MongoDB, Qdrant, Azure, Vertex)

7. Graph Databases (for Graph RAG)

Database	Pros	Cons	Recommendation	Priority
Neo4j	- Industry standard - Excellent tooling - Cypher query language - LangChain integration	- Separate infrastructure - Learning curve	✅ Add for graph RAG	🔵 Future
ArangoDB	- Multi-model (graph + doc) - Good performance	- Smaller community - Less mature	⚠️ Consider as alternative	⚫ Low
AWS Neptune	- Managed service - Gremlin/SPARQL	- Vendor lock-in - Cost	⚠️ Cloud option	⚫ Low
NetworkX	- Python native - Good for algorithms - Free	- Not a database - In-memory only - Not scalable	✅ Use for analysis	🟢 Low

Decision: Add Neo4j when implementing Graph RAG (Phase 4)

8. Evaluation Frameworks

Framework	Pros	Cons	Current Status	Recommendation
RAGAS	- Already integrated - Comprehensive metrics - Research-backed	- Some metrics slow - LangChain dependency	✅ Implemented	✅ Keep
TruLens	- Observability focus - Real-time evaluation - Good dashboards	- Another tool to maintain - Overlap with RAGAS	⚠️ Consider adding	🟢 Low
Phoenix (Arize)	- Production monitoring - Drift detection - Good for ML Ops	- External service - Cost	⚠️ Evaluate	⚫ Low
LangSmith (Current)	- Already integrated - Excellent tracing - Dataset management	- Paid service - LangChain-focused	✅ Implemented	✅ Keep

Decision: Keep RAGAS + LangSmith, optionally add TruLens for additional observability

9. Document Processing

Library	Pros	Cons	Current Status	Recommendation
Unstructured	- Already integrated - Many file types - Good extraction	- Can be slow - Large dependency	✅ Implemented	✅ Keep
LlamaParse	- Excellent for PDFs - Layout preservation - LlamaIndex team	- External API - Cost per document	⚠️ Consider for PDFs	🟢 Low
PyPDF	- Already integrated - Fast - Free	- Basic extraction - No layout	✅ Implemented	✅ Keep
Docling (IBM)	- Research-grade - Layout analysis - Table extraction	- Newer - Heavy dependencies	⚠️ Evaluate	⚫ Low

Decision: Keep current stack (Unstructured + PyPDF + python-docx)

10. Multimodal Processing

Library	Pros	Cons	Recommendation	Priority
OpenAI CLIP	- State-of-the-art - Pre-trained - Easy to use	- Large model - GPU recommended	✅ Add for vision	🔵 Future
OpenAI Whisper	- Best audio transcription - Open source - Many languages	- GPU recommended - Slow on CPU	✅ Add for audio	🔵 Future
LLaVA	- Vision-language model - Open source - Good quality	- Large model - Self-hosting needed	⚠️ Consider	⚫ Low
ImageBind (Meta)	- Multimodal embeddings - 6 modalities - Research-backed	- Large model - Complex setup	⚠️ Evaluate	⚫ Low

Decision: Add CLIP + Whisper when implementing multimodal (Phase 4)

11. Fine-Tuning & Training

Library	Pros	Cons	Recommendation	Priority
OpenAI Fine-tuning API	- Managed service - No infrastructure - GPT models	- Cost - Less control - Data privacy	✅ Add for GPT	🔵 Future
Hugging Face PEFT	- Parameter-efficient - LoRA, QLoRA - Open source	- Requires GPU - More complex	✅ Add for local	🔵 Future
Sentence Transformers	- Already have - Easy fine-tuning - Good for embeddings	- Limited to sentence transformers	✅ Implemented	✅ Keep
Axolotl	- Training framework - Many models - Good configs	- Complex setup - GPU cluster needed	⚠️ Overkill	⚫ Low

Decision: Use OpenAI API for GPT fine-tuning, PEFT for local models (Phase 5)

🎯 Recommended Library Stack (Final)

Immediate Additions (Priority 1-2)

# Add to requirements.txt

# Multi-LLM Support
langchain-anthropic>=0.1.0
langchain-google-genai>=1.0.0
litellm>=1.30.0

# Prompt Compression (High ROI)
llmlingua>=0.2.0

# Enhanced Caching
gptcache>=0.1.43

# Semantic Routing
semantic-router>=0.0.23

Expected Impact:

Cost: -50% (compression + caching)
Latency: -30% (caching + routing)
Flexibility: Multiple LLM providers

Near-Term Additions (Priority 3-4)

# Advanced Reranking
ragatouille>=0.0.8
colbert-ai>=0.2.0
cohere>=4.0.0  # Optional

# Prompt Engineering
dspy-ai>=2.4.0
langchainhub>=0.1.0
prompttools>=0.2.0

Expected Impact:

Quality: +15-20% (ColBERT + DSPy)
Maintainability: Better prompt management

Future Additions (Priority 5+)

# Graph RAG
neo4j>=5.14.0
langchain-neo4j>=0.1.0
networkx>=3.2.0

# Multimodal
openai-whisper>=20231117

# Fine-Tuning
peft>=0.7.0
bitsandbytes>=0.41.0
accelerate>=0.25.0

# Optional Evaluation
trulens-eval>=0.20.0

Expected Impact:

Capabilities: Graph RAG, multimodal support
Quality: Fine-tuned models for domain

📈 ROI Analysis

High ROI (Immediate)

Library	Implementation Effort	Expected Impact	ROI
LLMLingua	🟢 Low (1-2 weeks)	💰 Cost -40-60%	⭐⭐⭐⭐⭐
GPTCache	🟢 Low (1-2 weeks)	💰 Cost -30-40% ⚡ Latency -40%	⭐⭐⭐⭐⭐
Multi-LLM	🟡 Medium (2-3 weeks)	💰 Cost -20-30% 🔄 Flexibility	⭐⭐⭐⭐
Semantic Router	🟢 Low (1 week)	⚡ Latency -20%	⭐⭐⭐⭐

Medium ROI (Near-Term)

Library	Implementation Effort	Expected Impact	ROI
RAGatouille	🟡 Medium (2-3 weeks)	📊 Quality +15-20%	⭐⭐⭐⭐
DSPy	🔴 High (3-4 weeks)	📊 Quality +15-25%	⭐⭐⭐
LangChain Hub	🟢 Low (1 week)	🔧 Maintainability	⭐⭐⭐

Long-Term ROI (Future)

Library	Implementation Effort	Expected Impact	ROI
Neo4j	🔴 High (4-6 weeks)	🆕 New capabilities	⭐⭐⭐
CLIP + Whisper	🔴 High (4-6 weeks)	🆕 Multimodal support	⭐⭐⭐
PEFT	🔴 High (6-8 weeks)	📊 Quality +10-15% 💰 Cost -30% (long-term)	⭐⭐⭐

🔍 Detailed Library Analysis

1. LLMLingua (Prompt Compression)

What it does: Compresses prompts while maintaining quality

Technical Details:

Uses small LM to identify important tokens
Achieves 2-3x compression
90% quality preservation
Supports long documents (LongLLMLingua)

Integration Points:

packages/rag/retrievers.py - Compress retrieved contexts
packages/conversational/dialogue_manager.py - Compress chat history
packages/agents/graphs.py - Compress prompts before LLM

Example:

from llmlingua import PromptCompressor

compressor = PromptCompressor()

# Original: 2000 tokens, $0.02
original_prompt = "..." 

# Compressed: 800 tokens, $0.008
compressed = compressor.compress_prompt(
    original_prompt,
    target_token=800
)

Benchmarks:

Cost reduction: 40-60%
Quality drop: <5%
Latency increase: +50-100ms (compression time)
Net benefit: Massive cost savings

2. RAGatouille (ColBERT)

What it does: State-of-the-art neural reranking

Technical Details:

Late interaction mechanism
Token-level similarity
Pre-trained on MS MARCO
Fine-tunable

Integration Points:

packages/rag/rerankers.py - Add ColBERTReranker
Multi-stage: cross-encoder (fast) → ColBERT (accurate)

Example:

from ragatouille import RAGPretrainedModel

reranker = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")

results = reranker.rerank(
    query="What are the side effects?",
    documents=retrieved_docs,
    k=5
)

Benchmarks:

Quality improvement: 10-20% (NDCG@5)
Latency: ~200ms per query
Model size: ~500MB
Net benefit: Significant quality boost

3. DSPy (Prompt Optimization)

What it does: Automatic prompt engineering with LLMs

Technical Details:

Declarative programming for LLMs
Auto-optimizes prompts via examples
Supports few-shot, chain-of-thought
Modular components

Integration Points:

packages/prompts/ - New package
Optimize retrieval prompts
Optimize generation prompts
A/B test optimized vs manual

Example:

import dspy

class RAGSystem(dspy.Module):
    def __init__(self):
        self.retrieve = dspy.Retrieve(k=5)
        self.generate = dspy.ChainOfThought("context, question -> answer")
    
    def forward(self, question):
        context = self.retrieve(question).passages
        return self.generate(context=context, question=question)

# Optimize
optimizer = dspy.BootstrapFewShot(metric=answer_quality)
optimized_rag = optimizer.compile(RAGSystem(), trainset=examples)

Benchmarks:

Quality improvement: 15-25% (with optimization)
Setup time: 3-4 weeks
Optimization time: Hours per prompt
Net benefit: Systematic prompt improvement

4. GPTCache (Semantic Cache)

What it does: Cache LLM responses with semantic similarity

Technical Details:

Multiple similarity functions
Multiple cache backends (Redis, SQLite, etc.)
Configurable TTL, eviction
Pre/post-processors

Integration Points:

packages/caching/semantic.py - Enhance existing cache
Replace or complement current implementation

Example:

from gptcache import cache
from gptcache.adapter import openai

cache.init()
cache.set_openai_key()

# Cached automatically
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "What is RAG?"}]
)

# Cache hit on similar query
response2 = openai.ChatCompletion.create(
    model="gpt-4", 
    messages=[{"role": "user", "content": "Explain RAG to me"}]
)  # \<50ms, $0.00

Benchmarks:

Cache hit rate: 30-50% (depends on traffic)
Latency: <50ms on hit
Cost savings: 90%+ on cached queries
Net benefit: Huge cost and latency savings

5. LiteLLM (Multi-Provider Routing)

What it does: Unified API for 100+ LLM providers

Technical Details:

OpenAI-compatible API
Built-in load balancing
Automatic retries
Cost tracking

Integration Points:

packages/agents/graphs.py - Replace ChatOpenAI
config/settings.py - Multi-provider config

Example:

from litellm import completion

# OpenAI
response = completion(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello"}]
)

# Anthropic (same API)
response = completion(
    model="claude-3-opus-20240229",
    messages=[{"role": "user", "content": "Hello"}]
)

# Automatic routing
response = completion(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello"}],
    fallbacks=["claude-3-opus", "gpt-3.5-turbo"]
)

Benchmarks:

Setup time: 1-2 weeks
Latency overhead: <10ms
Cost optimization: 20-30% (via routing)
Net benefit: Flexibility and reliability

✅ Implementation Checklist

Phase 1: Quick Wins (Weeks 1-4)

Week 1: Multi-LLM Support

Install langchain-anthropic, langchain-google-genai, litellm
Extend config/settings.py with provider configs
Create packages/llm/provider_factory.py
Update packages/agents/graphs.py to support providers
Test Anthropic Claude integration
Test Google Gemini integration
Update cost tracking for new providers
Document provider selection strategy

Week 2: Prompt Compression

Install llmlingua
Create packages/rag/prompt_compression.py
Integrate with retrieval pipeline
Test compression ratios (0.3, 0.5, 0.7)
Measure quality impact (RAGAS metrics)
A/B test compressed vs original
Deploy to staging
Monitor production metrics

Week 3: Enhanced Caching

Week 4: Query Routing

Install semantic-router
Create packages/rag/query_router.py
Implement query complexity classifier
Add routing logic (simple→cache, complex→full retrieval)
Test routing accuracy
Add routing metrics
Deploy to production
Document routing strategies

Phase 2: Advanced Retrieval (Weeks 5-8)

Week 5-6: ColBERT Reranking

Week 7: Integration & Optimization

Week 8: Testing & Validation

Phase 3: Prompt Engineering (Weeks 9-12)

Week 9-10: DSPy Integration

Week 11: Prompt Management

Week 12: Production Deployment

📚 Resources & References

Official Documentation

LangChain: https://python.langchain.com/docs/
DSPy: https://github.com/stanfordnlp/dspy
RAGatouille: https://github.com/bclavie/RAGatouille
LLMLingua: https://github.com/microsoft/LLMLingua
GPTCache: https://github.com/zilliztech/GPTCache
LiteLLM: https://docs.litellm.ai/

Research Papers

ColBERT: "ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT"
DSPy: "DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines"
LLMLingua: "LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models"
RAGAS: "RAGAS: Automated Evaluation of Retrieval Augmented Generation"

Community Resources

LangChain Discord
DSPy GitHub Discussions
RAGatouille GitHub Issues

Document Version: 1.0
Last Updated: October 9, 2025
Status: ✅ Ready for Reference

📊 Library Comparison Matrix​

1. Multi-LLM Abstraction Layers​

2. Advanced Reranking​

3. Prompt Engineering & Optimization​

4. Prompt Compression​

5. Semantic Caching​

6. Vector Databases​

7. Graph Databases (for Graph RAG)​

8. Evaluation Frameworks​

9. Document Processing​

10. Multimodal Processing​

11. Fine-Tuning & Training​

🎯 Recommended Library Stack (Final)​

Immediate Additions (Priority 1-2)​

Near-Term Additions (Priority 3-4)​

Future Additions (Priority 5+)​

📈 ROI Analysis​

High ROI (Immediate)​

Medium ROI (Near-Term)​

Long-Term ROI (Future)​

🔍 Detailed Library Analysis​

1. LLMLingua (Prompt Compression)​

2. RAGatouille (ColBERT)​

3. DSPy (Prompt Optimization)​

4. GPTCache (Semantic Cache)​

5. LiteLLM (Multi-Provider Routing)​

✅ Implementation Checklist​

Phase 1: Quick Wins (Weeks 1-4)​

Phase 2: Advanced Retrieval (Weeks 5-8)​

Phase 3: Prompt Engineering (Weeks 9-12)​

📚 Resources & References​

Official Documentation​

Research Papers​

Community Resources​

📊 Library Comparison Matrix

1. Multi-LLM Abstraction Layers

2. Advanced Reranking

3. Prompt Engineering & Optimization

4. Prompt Compression

5. Semantic Caching

6. Vector Databases

7. Graph Databases (for Graph RAG)

8. Evaluation Frameworks

9. Document Processing

10. Multimodal Processing

11. Fine-Tuning & Training

🎯 Recommended Library Stack (Final)

Immediate Additions (Priority 1-2)

Near-Term Additions (Priority 3-4)

Future Additions (Priority 5+)

📈 ROI Analysis

High ROI (Immediate)

Medium ROI (Near-Term)

Long-Term ROI (Future)

🔍 Detailed Library Analysis

1. LLMLingua (Prompt Compression)

2. RAGatouille (ColBERT)

3. DSPy (Prompt Optimization)

4. GPTCache (Semantic Cache)

5. LiteLLM (Multi-Provider Routing)

✅ Implementation Checklist

Phase 1: Quick Wins (Weeks 1-4)

Phase 2: Advanced Retrieval (Weeks 5-8)

Phase 3: Prompt Engineering (Weeks 9-12)

📚 Resources & References

Official Documentation

Research Papers

Community Resources