kalyanks-nlp/llm-engineer-toolkit

★ 10,361Audience · developerComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((LLM Engineer Toolkit))
    Training
      Fine-tuning libraries
      Synthetic data
    Applications
      RAG retrieval
      Agent frameworks
      Prompt engineering
    Serving
      Inference optimization
      Deployment at scale
    Quality
      Evaluation tools
      Monitoring
      Safety and security
    Structured output
      Extraction libraries
      Format enforcement

mindmap root((LLM Engineer Toolkit)) Training Fine-tuning libraries Synthetic data Applications RAG retrieval Agent frameworks Prompt engineering Serving Inference optimization Deployment at scale Quality Evaluation tools Monitoring Safety and security Structured output Extraction libraries Format enforcement

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Things people build with this

USE CASE 1

Quickly find the right open-source library for a specific LLM task such as RAG, fine-tuning, or structured output.

USE CASE 2

Use as a reference checklist when deciding which components to include in a new LLM application stack.

USE CASE 3

Discover lesser-known tools in categories like synthetic data generation, LLM safety, or prompt engineering.

Getting it running

Difficulty · easy Time to first run · 5min

In plain English

This repository is a curated directory of more than 120 open-source libraries organized by category, all related to building applications with large language models (LLMs). LLMs are the type of AI system behind tools like ChatGPT: they generate, summarize, or respond to text. The toolkit is for engineers who want to know which library to reach for at each stage of building an LLM-based product. The categories cover the full development cycle: training and fine-tuning models on custom data, building applications that use LLMs, retrieving information to augment responses (a technique called RAG), running models efficiently on hardware, serving them at scale to users, extracting structured data from text, generating synthetic training data, building autonomous AI agents, evaluating model quality, monitoring deployed systems, constructing prompts, enforcing structured output formats, and handling safety and security concerns. Each entry in the directory gives the library's name, a one-line description, and a link to its GitHub repository. The collection does not contain tutorials or code examples of its own, it is purely a reference list. The maintainer also runs a free newsletter called AIxFunda and links to related repositories covering LLM interview questions, prompt engineering techniques, and a collection of survey papers on LLMs and related research areas. The repository is maintained by Kalyan KS, who is active on LinkedIn, X (formerly Twitter), and YouTube under the same name. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1

I'm building an LLM app that needs to extract structured data from unstructured customer emails. Based on the llm-engineer-toolkit list's structured output category, which libraries should I evaluate and when would I choose each one?

Prompt 2

I want to fine-tune an open-source LLM on my company's support tickets. Using the training and fine-tuning category from the llm-engineer-toolkit, list the top 3 libraries and explain the tradeoffs between them.

Prompt 3

Help me design an LLM evaluation pipeline for a RAG chatbot using tools from the llm-engineer-toolkit evaluation category. Write a Python script that scores responses for relevance and factual accuracy.

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