10 Python Libraries for Building LLM Applications

🔹 1. Transformers
Core library for loading, fine-tuning, and running LLMs with ease.
👉 Learn more: https://huggingface.co/docs/transformers

🔹 2. LangChain
Connect prompts, tools, APIs, and models into powerful workflows.
👉 Learn more: https://docs.langchain.com

🔹 3. LlamaIndex
Bring your own data into LLMs for smarter, grounded responses (RAG).
👉 Learn more: https://docs.llamaindex.ai

🔹 4. vLLM
High-performance LLM serving with faster inference and better scaling.
👉 Learn more: https://docs.vllm.ai

🔹 5. Unsloth
Efficient fine-tuning with LoRA & QLoRA — even on limited hardware.
👉 Learn more: https://github.com/unslothai/unsloth

🔹 6. CrewAI
Build multi-agent systems where AI agents collaborate on tasks.
👉 Learn more: https://docs.crewai.com

🔹 7. AutoGPT
Create goal-driven autonomous agents with step-by-step execution.
👉 Learn more: https://github.com/Significant-Gravitas/AutoGPT

🔹 8. LangGraph
Design advanced, stateful workflows with branching logic.
👉 Learn more: https://docs.langchain.com/langgraph

🔹 9. DeepEval
Test and evaluate LLM outputs for accuracy and reliability.
👉 Learn more: https://github.com/confident-ai/deepeval

🔹 10. OpenAI Python SDK
Quickly integrate powerful AI features without managing infrastructure.
👉 Learn more: https://platform.openai.com/docs

❤️ Follow  for more