Yao — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Yao. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Jun 25, 2026 • Sentiment updated: Jun 21, 2026

GitHub Statistics

Community Sentiment

Community Buzz: I want to read code by Abrash, Peyton Jones and Karpathy, not Claude's output based on a prompt from a third rater.

Pros & Cons

What People Love

Innovative solutions, User-friendly interface, HackerNews users praise the platform's potential

Common Complaints

Lack of transparency, Technical issues

Biggest Positive: Innovative solutions

Biggest Negative: Lack of transparency

Why Yao Stands Out

Yao's autonomous agent framework takes a unique approach by focusing on proactive, self-scheduling agents that work like real team members. This is made possible by its event-driven architecture, which enables agents to be triggered by email, events, and scheduled tasks. Unlike traditional AI assistants, Yao's agents can accumulate experience in private knowledge bases, allowing for continuous learning and improvement. Additionally, Yao's native MCP support and built-in GraphRAG features make it an attractive choice for developers looking to build complex, autonomous systems.

Built With

Build a proactive customer support agent that resolves issues autonomously — Yao's event-driven architecture enables agents to work like real team members, Build a multi-agent orchestration system for smart home automation — Yao's native MCP support allows for seamless integration with various devices, Build a research agent that summarizes scientific papers — Yao's built-in GraphRAG and vector search capabilities enable efficient knowledge retrieval, Build an autonomous task manager that delegates tasks to other agents — Yao's six-phase execution and multi-agent orchestration features enable dynamic collaboration, Build a personalized recommendation system that learns from user behavior — Yao's continuous learning and knowledge graph capabilities allow for accurate predictions

Getting Started

  1. Install Yao using the command `go get -u github.com/YaoApp/yao`
  2. Configure your agent by creating a `yao.yaml` file with your desired settings
  3. Initialize the Yao runtime using `yao init`
  4. Start the agent using `yao start`
  5. Try triggering an agent using an email or scheduled task to verify it works

About

✨ Build AI agents and web apps — with a single binary.

Official site: https://yaoagents.com

Category & Tags

Category: social

Tags: agent, agentic-ai, agents, ai, ai-applications, ai-dev, ai-developer-tools, ai-generated-code, ai-native, ai-native-development, aigc, api, autonomous, autonomous-agents, autonomous-robots, autonomous-systems, chatbot, cli, developer-tools, golang

Market Context

Competitive market with mixed reviews