AgentScope — AI Agent Framework: Live Stats & TrendScore

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

GitHub data synced: Apr 30, 2026 • Sentiment updated: Apr 19, 2026

GitHub Statistics

Community Sentiment

Community Buzz: AgentScope makes you think in agents. That's a ...

Pros & Cons

What People Love

Innovative agent-based approach, OpenClaw challenge

Common Complaints

Crash issues, Invalid YAML issues

Biggest Positive: Innovative Agents

Biggest Negative: Crash Issues

Why AgentScope Stands Out

AgentScope stands out from alternative agent frameworks with its focus on multi-agent and multi-modal capabilities, allowing for more complex and realistic interactions. Its production-ready architecture and built-in support for finetuning enable developers to quickly deploy and serve their agents. Additionally, AgentScope's approach to leveraging the models' reasoning and tool use abilities rather than constraining them with strict prompts and opinionated orchestrations sets it apart from other frameworks. By providing a simple and extensible way to build and run agents, AgentScope solves the problem of complexity and inflexibility in agent development.

Built With

Build a multi-agent chatbot that handles customer inquiries — AgentScope's extensible architecture and built-in support for MCP and A2A enable seamless integration of multiple agents, Build a real-time voice agent that assists with daily tasks — AgentScope's realtime voice agent support and message hub facilitate flexible multi-agent orchestration, Build a research agent that analyzes large datasets — AgentScope's tools and skills enable efficient data processing and analysis, Build a personalized recommender system using large language models — AgentScope's finetuning capabilities and support for human-in-the-loop steering allow for tailored recommendations, Build an autonomous agent that learns from its environment — AgentScope's agentic RL via Trinity-RFT library and ReMe for enhanced long-term memory enable agents to learn and adapt

Getting Started

  1. pip install agentscope
  2. import agentscope and initialize the ReAct agent with agentscope.init()
  3. Configure the agent's tools and skills using the agentscope.config module
  4. Define the agent's behavior using the agentscope.behavior module
  5. Try running the realtime voice agent example to verify it works

About

Build and run agents you can see, understand and trust.

Official site: https://docs.agentscope.io/

Category & Tags

Category: multi-agent

Tags: agent, chatbot, large-language-models, llm, llm-agent, mcp, multi-agent, multi-modal, react-agent

Market Context

Competitive AI market