Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.

The PHP Agentic Framework to build production-ready AI driven applications. Connect components (LLMs, vector DBs, memory) to agents that can interact with your data. With its modular architecture it's best suited for building RAG, multi-agent workflows, or business process automations.

Hands-on LangGraph course repo for building production-grade LLM agents with Agentic RAG, ReAct, and reflection workflows.

Tutorials and exercises are done in the course 'Building RAG Agents with LLM'

A tool that converts scientific PDFs into plain text for your LLM-related needs, such as building RAGs or agents for academic knowledge. It was developed in collaboration with the LlamaIndex team.

A developer-first, modern Python (3.13+) framework for building intelligent applications. IntelliBricks simplifies LLM interactions with robust tools like structured outputs, agent customization, RAG integration, and seamless API conversion using FastAPI or Litestar.

도서 Building AI Agents with LLMs, RAG, and Knowledge Graphs, First Edition의 한글판의 깃허브 레포지토리입니다.

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This example repository illustrates the usage of LLMs with Quarkus by using the quarkus-langchain4j extension to build integrations with ChatGPT or Hugging Face. The code dives into simple conversations, retrieval augmented generation (RAG) and building agents.

Dynamic Agent is a Rust-based framework for building Retrieval-Augmented Generation (RAG) AI agents with multi-LLM and multi-vector store support, featuring WebSocket communication, caching, and flexible configuration.

Code and resources for the course named Building RAG Agents with LLMs by NVIDIA

# Modern AI Toolkit AI experiments using free LLMs (Ollama, Groq). Features Colab notebooks, Streamlit/FastAPI apps, RAG, AI agents, and document processing. A playground for building AI apps with zero-cost tools. Includes setup guides.

Cours pptx and notebooks

Generative-AI-with-Langchain-and-Huggingface explores cutting-edge generative AI concepts. Topics include LangChain basics, ChromaDB, conversational memory, vector databases, document Q&A with RAG, text summarization (refine chains, YT/video summarization), building LLMs, search engines, and advanced tools/agents.

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Learn Artificial Intelligence by Building LLM apps and enhancing them with RAG, LoRA and AI Agents.

Building production ready LLM's with prompt Engineering, finetuning, RAG and Agents. understanding different techniques, frameworks(LangChain, LLamaIndex) that are involved.

Multi-Agentic RAG-based AI Research Scientist building intelligent systems that combine multi-agent architectures with retrieval-augmented generation. Focused on LLMs, vector databases, reasoning agents, and scalable AI pipelines to deliver accurate, context-aware solutions for complex real-world problems.

Hands‑on AI Agent Security Evaluation — Explore and simulate 15 advanced LLM attack techniques (prompt injection, RAG poisoning, multi‑agent compromise, etc.) with interactive Jupyter tutorials. Includes adversarial testing methods, vulnerability analysis, and defense strategies for building secure AI systems.

Building RAG Agents with LLMs

This course teaches practical deployment of retrieval-based LLMs. Learn to compose predictable LLM systems, design dialog and document reasoning, leverage embeddings for retrieval and guardrailing, and implement RAG agents for question answering. Topics include LLM interfaces, LangChain, embeddings, and vector stores.

This project was to build a Retrieval-Augmented Generation (RAG) pipeline using NVIDIA's NIM (NeMo Inference Microservices) and LangChain, with a Streamlit UI for interaction. The project processes documents (PDFs), creates vector embeddings, and allows natural language querying over the content using powerful LLM capabilities.

Building RAG Agents with LLMs - Nvidia Self Paced Course

A primer for building agentic RAG applications with LLMs.

Material from the NVIDIA DLI course "Building RAG Agents with LLMs"

Learning materials for the course Building RAG Agents with LLMs organized by NVIDIA.

Go framework for building agentic LLM pipelines with RAG support

Practical AI System Architecture: Building Intelligent Systems with LLMs, RAG, and Agent Frameworks

Hands-on 2-day course for building LLM agents with RAG, memory, tools, and multi-agent architectures.

Code and projects from the Udemy course "Mastering Generative AI and LLMs". Focus on Agentic AI, RAG, QLoRA, and building autonomous agents with LLMs using frameworks like HuggingFace, LangChain, and Gradio.