KalyanKS-NLP/llm-engineer-toolkit

Project overview

The rapid expansion of the LLM tooling ecosystem makes discovery and comparison increasingly difficult; this project addresses that gap with a broad, structured catalog covering 14 lifecycle domains.

Project type

RAG · Model Development · Model Runtime

Deployment

Refer to project documentation

License

Apache-2.0

Best for

• AI engineers and developers who need a structured starting point for discovering LLM libraries across specific lifecycle domains.

Key capabilities

• Provides a curated index of 120+ LLM libraries organized into categories such as Training, Inference, RAG, Agents, Evaluation, and Monitoring.
• Offers quick links to easily navigate through various LLM engineering categories such as Application Development, Serving, and Data Extraction.

Limitations and risks

• The project is a curated static list of resources; it does not provide a runtime environment or deployable service, so teams needing execution, hosting, or orchestration should look elsewhere.

Evidence and sources

• GitHub project description: A curated list of 120+ LLM libraries category wise.
• GitHub repository metadata: ai-engineer
• README: This repository contains a curated list of 120+ LLM libraries category wise.
• README: | [🚀 LLM Training](#llm-training-and-fine-tuning) | [🧱 LLM Application Development](#llm-application-development) | [🩸LLM RAG](#llm-rag) | | [🟩 LLM Inference](#llm-inference)| [🚧…
• README: unsloth | Fine-tune LLMs faster with less memory.

Reviewed at: 2026-07-18T02:27:31Z