🐍 Building LLM Applications? These 10 Python Libraries Are Essential
Using ChatGPT is easy. Building your own AI system is a different level. For that, you need the right stack.
🗂️ The 10 libraries organized by function:
Model loading and fine-tuning:
- 🤗 Transformers (HuggingFace): the foundation of the open-source LLM ecosystem
- ⚡ Unsloth: efficient fine-tuning with LoRA/QLoRA, uses much less VRAM
Orchestration and workflows:
- 🔗 LangChain: connects prompts, retrievers, APIs and model calls in one flow
- 🕸️ LangGraph: stateful workflows with branching and complex logic for agents
RAG and data:
- 📚 LlamaIndex: connects your app to documents, PDFs and knowledge bases
Serving and inference:
- 🚀 vLLM: efficient serving for open-source models, very high throughput
Agents:
- 👥 CrewAI: multi-agent system with roles, tasks and collaboration
- 🤖 AutoGPT: autonomous workflows, multi-step task planning
Evaluation:
- 📊 DeepEval: LLM pipeline testing (hallucination, relevance, faithfulness)
Hosted APIs:
- 🔑 OpenAI Python SDK: the fastest way to integrate LLMs into production
💡 Explanation in a nutshell#
Building an LLM app is like setting up a professional kitchen: you don’t just need the ingredients (the model), but also the utensils (LangChain to orchestrate), the refrigerator (LlamaIndex to store and retrieve data), the industrial oven (vLLM to serve fast), and the tasters (DeepEval to evaluate quality). Each library solves a specific part of the problem.
More information at the link 👇
Also published on LinkedIn.
