AI Agent Skills

The Context Layer for unstructured data: typed, versioned datasets over S3, GCS, Azure

Coordinate multi-agent teams for software and product work

A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.

50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.

Essential skills for autonomous agents

🏛️ 三省六部制 · OpenClaw Multi-Agent Orchestration System — 9 specialized AI agents with real-time dashboard, model config, and full audit trails

Web dashboard for Hermes Agent — multi-platform AI chat, session management, scheduled jobs, usage analytics

🐬DeepChat - A smart assistant that connects powerful AI to your personal world

agentUniverse is a LLM multi-agent framework that allows developers to easily build multi-agent applications.

RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for L…

Universal memory layer for AI Agents

Build, deploy, and orchestrate AI agents. Sim is the central intelligence layer for your AI workforce.

Label Studio is a multi-type data labeling and annotation tool with standardized output format

Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.

AnyCrawl 🚀: A Node.js/TypeScript crawler that turns websites into LLM-ready data and extracts structured SERP results from Google/Bing/Baidu/etc. Native multi-threading…

The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!