Are you building applications powered by Large Language Models (LLMs)? This comprehensive guide brings together over 120 specialized libraries that every LLM engineer should know about – organized by category for easy reference.
Introduction
The landscape of LLM tools has exploded in recent years, making it challenging to know which libraries to use for specific tasks. This curated toolkit organizes essential libraries by function, helping you quickly find the right tools for your project.
Whether you’re training custom models, building RAG systems, creating AI agents, or deploying production applications, this guide will point you to the most effective libraries for each task.
LLM Training and Fine-Tuning Tools
Fine-tuning allows you to customize pre-trained models for specific tasks. Here’s a comparison of the most popular fine-tuning libraries:
| Library | Key Features | Best For | GitHub Stars |
|---|---|---|---|
| Unsloth | 2x faster training, 50% less memory usage | Low-resource fine-tuning | 7.6k+ |
| PEFT | Parameter-efficient methods (LoRA, QLoRA) | Production fine-tuning | 12k+ |
| TRL | RLHF support, DPO implementation | Alignment tuning | 8.5k+ |
| Axolotl | All-in-one fine-tuning CLI | Quick experimentation | 4.8k+ |
| LlamaFactory | Easy UI, supports numerous models | User-friendly tuning | 11k+ |
When to use what:
- For efficient, low-resource tuning: Unsloth or PEFT
- For RLHF-based alignment: TRL
- For beginner-friendly interfaces: LlamaFactory or Axolotl
Application Development Frameworks
Building applications with LLMs requires robust frameworks to handle context, memory, and tool integration.
Framework Comparison
| Framework | Strengths | Limitations | Best For |
|---|---|---|---|
| LangChain | Extensive ecosystem, active community | Can be complex for simple use cases | Production applications |
| LlamaIndex | Specialized for RAG, data connectors | Less focused on general workflows | Data-heavy applications |
| Haystack | Modular pipeline design, document focus | Steeper learning curve | Enterprise search |
| Griptape | Structured workflows, memory management | Newer, smaller community | Agent applications |
Multi-API Access Tools
Libraries like LiteLLM and AI Gateway let you use a single interface to access multiple LLM providers, making it easy to switch between models or implement fallbacks.
UI Components
| Library | Best For | Notes |
|---|---|---|
| Streamlit | Quick prototyping | Fastest time-to-demo |
| Gradio | Interactive interfaces | Great for model showcasing |
| Chainlit | Chat applications | Built for LLM conversations |
RAG Libraries
Retrieval Augmented Generation (RAG) enhances LLM responses with relevant data from external sources.
| Library | Specialization | When to Use |
|---|---|---|
| FastGraph RAG | Graph-based retrieval | For complex knowledge relationships |
| Chonkie | Optimized chunking | When document segmentation is critical |
| RAGChecker | RAG evaluation | For debugging retrieval quality |
| Rerankers | Result refinement | To improve relevance of retrieved context |
My top recommendations for RAG:
- Start with LlamaIndex for its out-of-box RAG capabilities
- Add Rerankers to improve result quality
- Use RAGChecker to evaluate and diagnose issues
Inference and Serving Solutions
Deploying LLMs efficiently requires specialized inference engines:
| Library | Key Advantage | Best For |
|---|---|---|
| vLLM | Continuous batching, PagedAttention | High-throughput production |
| TensorRT-LLM | NVIDIA optimization | Enterprise GPU deployment |
| LightLLM | Lightweight design | Resource-constrained environments |
| LLM Compressor | Model quantization | Reducing model size |
For serving, consider LitServe which adds batching, streaming, and GPU autoscaling to FastAPI.
Data Management Tools
Data Extraction
| Library | Best For | Features |
|---|---|---|
| Crawl4AI | Web scraping | LLM-friendly format |
| Docling | Document parsing | Multi-format support |
| Llama Parse | Advanced PDF extraction | Table and layout understanding |
| MegaParse | Universal parsing | Handles all document types |
Data Generation
For synthetic data generation, DataDreamer provides comprehensive workflows, while fabricator specializes in LLM-based dataset creation.
Agent Frameworks
LLM agents can autonomously solve complex tasks through reasoning and tool use.
Agent Framework Comparison
| Framework | Special Features | Use Case |
|---|---|---|
| CrewAI | Role-based agent teams | Multi-agent collaboration |
| LangGraph | Structured reasoning flows | Complex decision processes |
| AutoGen | Multi-agent conversation | Agent conversation systems |
| Pydantic AI | Production-grade validation | Enterprise applications |
| AgentOps | Agent monitoring | Operational visibility |
Agent Memory Solutions:
Evaluation and Monitoring
Evaluation Libraries
| Library | Focus Area | Key Features |
|---|---|---|
| Ragas | RAG evaluation | Context relevance, answer correctness |
| DeepEval | LLM evaluation | Comprehensive metrics suite |
| Evals | Benchmark registry | Standard performance tests |
| Giskard | ML/LLM testing | Vulnerability detection |
Monitoring Solutions
For production monitoring, consider:
- Helicone – One-line integration for comprehensive LLM observability
- LangSmith – Detailed tracing for LangChain applications
- Phoenix – Open-source AI observability platform
Prompt Engineering and Structured Output
Prompt Engineering
| Library | Key Capability | When to Use |
|---|---|---|
| LLMLingua | Prompt compression | For long contexts |
| DSPy | Programmatic prompting | Complex reasoning chains |
| Promptify | NLP task prompts | Specialized NLP workflows |
Structured Output
Getting reliable structured data from LLMs:
| Library | Approach | Strengths |
|---|---|---|
| Instructor | Pydantic integration | Clean, validated outputs |
| Guidance | Constrained generation | Control over format |
| Outlines | Grammar-based generation | Guaranteed valid outputs |
| LMQL | Query language | Precise output control |
Safety and Security
Protecting your LLM applications:
| Library | Protection Type | Key Features |
|---|---|---|
| LLM Guard | Input/output scanning | Content moderation, PII detection |
| Guardrails | Output validation | Schema enforcement, safety rails |
| NeMo Guardrails | Conversational safety | Topical boundaries, harmful content |
| JailbreakEval | Security testing | Vulnerability assessment |
My Personal Experience with Key Libraries
As an LLM engineer who has built several production systems, here are my personal insights on some key libraries:
LangChain vs LlamaIndex: I’ve found LangChain to be more versatile for general applications, while LlamaIndex excels specifically at RAG. For complex projects, I often use both – LlamaIndex for document retrieval and LangChain for the overall application structure.
vLLM for Deployment: After testing multiple inference engines, vLLM consistently provides the best throughput for high-traffic applications. Its continuous batching can handle 5-10x more requests than basic deployment methods.
Instructor for Structured Output: This has been a game-changer for ensuring clean, validated outputs from LLMs. The Pydantic integration makes it seamless to use in Python applications.
CrewAI for Multi-Agent Systems: When building systems with multiple specialized agents, CrewAI’s role-based approach has proven more intuitive than alternatives, especially for business stakeholders to understand.
Frequently Asked Questions
Q: I’m just getting started with LLMs. Which libraries should I learn first?
A: Start with a framework like LangChain or LlamaIndex, then add specialized libraries as needed. For beginners, I recommend this learning path:
- Basic LLM interaction: LangChain or LlamaIndex
- Simple UI: Streamlit or Gradio
- RAG implementation: Add vector stores and rerankers
- Evaluation: Ragas for testing your RAG system
Q: What’s the best way to optimize costs when working with commercial LLM APIs?
A: Implement these libraries:
- LiteLLM for model routing and fallbacks
- LLMLingua for prompt compression
- RouteLLM to direct simpler queries to cheaper models
- GPTCache to cache common responses
Q: How do I choose between open-source and commercial LLMs?
A: Consider these factors:
- Performance requirements (commercial models often perform better)
- Budget constraints
- Data privacy concerns
- Specialization needs (domain-specific capabilities)
Using a wrapper like LiteLLM allows you to easily switch between providers or implement a hybrid approach.
Remember that the LLM ecosystem is rapidly evolving, with new libraries emerging regularly. Stay updated by following key GitHub repositories and joining communities like Hugging Face, LangChain, and LlamaIndex.
