llama-index

17 projects share this GitHub topic

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🧬 Members

🪢 Open source agent evals & observability: Trace, evaluate, and improve LLM applications with one open…

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each…

Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive…

[MLsys2026 Best Paper]: https://arxiv.org/abs/2506.08276. RAG on Everything with LEANN. Enjoy 97% storage…

🔍大模型应用开发实战一：RAG 技术全栈指南，在线阅读地址：https://datawhalechina.gith…

Semantic cache for LLMs. Fully integrated with LangChain and llama_index.

🧊 Open source LLM observability platform. One line of code to monitor, evaluate, and experiment. YC W23…

💬 RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. Built w/ Rasa,…

Desktop AI Assistant powered by GPT, Gemini, Claude, Grok, Ollama, DeepSeek, Perplexity, and more - chat,…

awesome-ai-sdks

A database of SDKs, frameworks, libraries, and tools for creating, monitoring, debugging and deploying…

The Continuous-Improvement Stack for Agents. Our environment data and evals power agent improvement and…

vectordb-recipes

Resource, examples & tutorials for multimodal AI, RAG and agents using vector search and LLMs

Sample to envision intelligent apps with Microsoft's Copilot stack for AI-infused product experiences.

END-TO-END-GENERATIVE-AI-PROJECTS

End to End Generative AI Industry Projects on LLM Models with Deployment_Awesome LLM Projects

Ask questions across your Markdown notes using a fully local Graph RAG engine. Built for Obsidian vaults,…

Agentic-AI-Systems

Practical system design, tools, and hands-on resources for building Gen-AI agents & agentic AI systems.

A CLI tool for threat modeling and visualizing AI agents built using popular frameworks like LangGraph,…

🔗 Related families

llm-evaluation 52 llm-observability 32 embeddings 112 vector-database 96 analytics 44 autogen 20 prompt-management 10 ycombinator 5

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.