Agent

DeerFlow 2.0 is a ground-up rewrite of an open-source super agent framework designed to orchestrate sub-agents, memory, and sandboxes. It utilizes extensible skills and various LLM providers to perform complex tasks through a flexible, modular architecture. The platform supports multiple deployment modes, including Docker and local development, to facilitate efficient research and automation workflows.

This project builds an executable thinking framework rather than a simple collection of quotes, based on Zhang Xuefeng's books, interviews, and decision records. It provides users with in-depth analysis and advice by distilling core mental models, decision heuristics, and communication DNA. Users can install it into Claude Code to perform professional selection and career planning analysis from Zhang Xuefeng's perspective.

awesome-openclaw-tutorial is a comprehensive guide covering OpenClaw installation, configuration, and practical use cases. The tutorial is proofread based on the stable OpenClaw v2026.4.11 version, providing a detailed path from zero-based entry to advanced automation workflows. It contains over 70 practical scenarios designed to help users improve personal productivity through AI.

Awesome-finance-skills is a plug-and-play collection of financial skills designed to empower Large Language Models with real-time news analysis, stock data processing, and market forecasting capabilities. The project supports various mainstream Agent frameworks, allowing AI to possess professional financial analysis and research report generation functions through simple installation. Users can leverage its built-in logic visualization and sentiment analysis tools to rapidly enhance the decision-support level of AI agents in the financial sector.

WeKnora is an intelligent knowledge management and Q&A framework that utilizes LLMs to provide enterprise-grade document understanding and semantic retrieval. The platform offers both a RAG-based Quick Q&A mode for fast queries and a ReACT Agent engine for complex, multi-source reasoning tasks. It features a highly modular architecture that supports various document formats, multiple LLM providers, and seamless integration with popular IM channels for private or local deployment.

Hermes Agent is a self-improving AI assistant that builds skills from experience and maintains a deepening model of user interactions across sessions. It supports a wide range of LLM providers and can be deployed on diverse infrastructure, including local machines, VPS, or serverless environments. The platform features a robust messaging gateway for cross-platform communication and includes built-in tools for scheduled automation and parallel task delegation.

Claude Code Game Studios transforms a standard AI coding session into a structured game development environment by organizing 49 specialized agents into a professional studio hierarchy. The system provides 72 slash commands and automated validation hooks to manage workflows ranging from initial brainstorming to final release. Users maintain full control over the project while benefiting from expert-level guidance, quality gates, and enforced coding standards.

EvoCUA is a high-performance open-source multimodal model designed for end-to-end computer automation across various desktop applications. It currently holds the top position on the OSWorld leaderboard and demonstrates superior cross-OS generalization capabilities. Additionally, the model is recognized for its robust safety profile, exhibiting the lowest rate of unintended behaviors among leading computer-use agents.

colleague.skill allows users to create AI personas based on the work habits and personalities of their colleagues by processing various communication data sources. The system utilizes a two-part architecture that combines professional work capabilities with nuanced behavioral traits to simulate realistic interactions. Users can easily manage, update, and roll back these AI skills to maintain accurate representations of their team members' expertise.

This repository provides a comprehensive educational framework for building the infrastructure, or harness, required to support intelligent AI agents. It emphasizes that while agency is derived from trained models, the harness is essential for providing the tools, context, and environment necessary for effective operation. Through twelve progressive sessions, developers learn to implement key mechanisms like tool dispatching, task management, and subagent coordination.