[中文README]

Welcome to the AgentScope Sample Agents repository! 🎯 This repository provides ready-to-use Python sample agents built on top of:

• AgentScope
• AgentScope Runtime

The examples cover a wide range of use cases — from lightweight command-line agents to full-stack deployable applications with both backend and frontend.

AgentScope is a multi-agent framework designed to provide a simple and efficient way to build LLM-powered agent applications. It offers abstractions for defining agents, integrating tools, managing conversations, and orchestrating multi-agent workflows.

AgentScope Runtime is a comprehensive runtime framework that addresses two key challenges in deploying and operating agents:

1. Effective Agent Deployment – Scalable deployment and management of agents across environments.
2. Sandboxed Tool Execution – Secure, isolated execution of tools and external actions.

It includes agent deployment and secure sandboxed tool execution, and can be used with AgentScope or other agent frameworks.

• All samples are Python-based.
• Samples are organized by functional use case.
• Some samples use only AgentScope (pure Python agents).
• Others use both AgentScope and AgentScope Runtime to implement full-stack deployable applications with frontend + backend.
• Full-stack runtime versions have folder names ending with: _fullstack_runtime

📌 Before running any example, check its README.md for installation and execution instructions.

• AgentScope Documentation
• AgentScope Runtime Documentation

├── alias/                                  # Agent to solve real-world problems
├── browser_use/
│   ├── agent_browser/                      # Pure Python browser agent
│   └── browser_use_fullstack_runtime/      # Full-stack runtime version with frontend/backend
│
├── deep_research/
│   ├── agent_deep_research/                # Pure Python multi-agent research
│   └── qwen_langgraph_search_fullstack_runtime/    # Full-stack runtime-enabled research app
│
├── games/
│   └── game_werewolves/                    # Role-based social deduction game
│
├── conversational_agents/
│   ├── chatbot/                            # Chatbot application
│   ├── chatbot_fullstack_runtime/          # Runtime-powered chatbot with UI
│   ├── multiagent_conversation/            # Multi-agent dialogue scenario
│   └── multiagent_debate/                  # Agents engaging in debates
│
├── evaluation/
│   └── ace_bench/                          # Benchmarks and evaluation tools
│
├── data_juicer_agent/                      # Data processing multi-agent system
├── sample_template/                        # Template for new sample contributions
└── README.md

A powerful multi-agent data processing system that leverages Data-Juicer's 200+ operators for intelligent data processing:

• Intelligent Query: Find suitable operators from 200+ data processing operators
• Automated Pipeline: Generate Data-Juicer YAML configurations from natural language
• Custom Development: Create domain-specific operators with AI assistance
• Multiple Retrieval Modes: LLM-based and vector-based operator matching
• MCP Integration: Native Model Context Protocol support

📖 Documentation: English | 中文

If you:

• Need installation help
• Encounter issues
• Want to understand how a sample works

Please:

1. Read the sample-specific README.md.
2. File a GitHub Issue.
3. Join the community discussions:

Discord	DingTalk

We welcome contributions such as:

• Bug reports
• New feature requests
• Documentation improvements
• Code contributions

See the Contributing for details.

This project is licensed under the Apache 2.0 License – see the LICENSE file for details.

• AgentScope Documentation
• AgentScope Runtime Documentation
• AgentScope GitHub Repository
• AgentScope Runtime GitHub Repository

Thanks goes to these wonderful people (emoji key):

Weirui Kuang 🚧 💻 👀 📖	Osier-Yi 🚧 💻 👀 📖	DavdGao 🚧	qbc 🚧	Lamont Huffman 💻 ⚠️
Add your contributions

This project follows the all-contributors specification. Contributions of any kind welcome!