awesome-llm-apps: 100+ Runnable AI Agent & RAG Application Examples
A curated GitHub repository collecting over 100 deployable AI agent and RAG (Retrieval-Augmented Generation) applications built with LLMs. The collection is designed for practical use — clone, customize, and ship. With 110,915 total stars and 202 added today, it reflects strong community interest in applied LLM tooling.
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Expert Support Case Study: Bolstering a RAG App with LLM-as-a-Judge
Hugging Face published a case study describing how Digital Green used an LLM-as-a-Judge approach to evaluate and improve a retrieval-augmented generation (RAG) application. The post covers the methodology for using LLMs to score and validate RAG outputs, providing a practical deployment pattern for quality assurance in production AI systems. It serves as a concrete example of enterprise-grade evaluation pipelines built on top of RAG architectures.
12-Factor Agents: Principles for Production-Grade LLM-Powered Software
The '12-factor-agents' repository proposes a set of design principles for building production-ready LLM-powered software, analogous to the classic 12-factor app methodology for cloud-native applications. The project has accumulated over 21,000 GitHub stars with 733 added in a single day, indicating strong community traction. It is implemented in TypeScript and focuses on practical patterns for deploying AI agents reliably in production environments.
earendil-works/pi: AI Agent Toolkit with Coding Agent CLI, Unified LLM API, and Multi-UI Libraries
The earendil-works/pi repository is an open-source TypeScript toolkit providing a coding agent CLI, unified LLM API abstraction, TUI and web UI libraries, a Slack bot integration, and vLLM pod support. It has accumulated 53,875 GitHub stars with 444 new stars today, indicating significant community traction. The project spans multiple components of the agent-tool ecosystem including inference backends and developer-facing interfaces.
Open-source LLMs as LangChain Agents
This Hugging Face blog post explores using open-source LLMs as agents within the LangChain framework. It examines the capability of various open-weight models to perform tool use, reasoning, and multi-step task execution in agentic settings. The post likely benchmarks or compares several models on agent-relevant tasks, providing practical guidance for deploying open-source alternatives to proprietary models in agent pipelines.
LiteLLM AI gateway trending: 50K stars, unified interface for 100+ LLM APIs
LiteLLM is a Python SDK and proxy server providing a unified OpenAI-compatible interface to 100+ LLM APIs including Bedrock, Azure, OpenAI, VertexAI, Anthropic, and others. It includes cost tracking, guardrails, load balancing, and logging. The project is trending on GitHub with ~50K total stars and 141 new stars today, signaling continued strong adoption as an AI gateway layer.
GLM-5.1 Open-Weights Model Targets Long-Running Agentic Tasks; Andrew Ng on Coding Agent Acceleration by Software Domain
Z.ai released GLM-5.1, an open-weights mixture-of-experts LLM (754B total / 40B active parameters) designed for sustained agentic coding tasks lasting up to eight hours, featuring iterative planning-execution-evaluation loops with thousands of tool calls. The model claims top open-weights performance on Artificial Analysis Intelligence Index and SWE-Bench Pro, available under MIT license via HuggingFace. The accompanying editorial by Andrew Ng offers a tiered framework for how much coding agents accelerate different software work categories—frontend most, then backend, infrastructure, and research least—with practical implications for team organization. A secondary item references data-center opposition and LLM helpfulness failure modes.
TradingAgents: Multi-Agent LLM Financial Trading Framework
TradingAgents is an open-source Python framework by TauricResearch that applies multi-agent LLM architectures to financial trading tasks. The repository has accumulated 81,650 GitHub stars with 284 added today, indicating strong community traction. It represents a concrete deployment pattern for agentic AI systems in quantitative finance.
vLLM: High-Throughput LLM Inference and Serving Engine Trending on GitHub
vLLM is an open-source Python library providing high-throughput and memory-efficient inference and serving for large language models. The project has accumulated over 80,500 GitHub stars with 98 new stars today, indicating continued strong community interest. It is a widely adopted inference backend in the AI/ML ecosystem, supporting PagedAttention and various optimization techniques for LLM deployment.