AgentScope Samples

[中文README]

🎯 Kickstart Your Agent Journey! This is a repository that brings together a variety of ready-to-run Python agent examples, ranging from command-line mini-tools to full-stack deployable applications.

🌟 What is AgentScope?

AgentScope is a multi-agent framework that lets you rapidly build LLM-based intelligent applications:

Learn more in the AgentScope Documentation

• 🧠 Define agents and integrate tools
• 📡 Manage context and conversations
• 🤝 Orchestrate collaboration among multiple agents to accomplish tasks

AgentScope-Runtime is the runtime framework that enables you to deploy agents as API services:

Learn more in the AgentScope Runtime Documentation

1. 🔄 Scalable deployment management for multiple agents
2. 🛡️ Secure sandbox execution for tools

⚡ Getting Started

📌 Before running an example, please check the corresponding README.md for installation and execution instructions.

• All examples are built with Python.
• Examples are organized by functionality and usage scenario.
• Some examples use AgentScope only.
• Some examples use both AgentScope and AgentScope Runtime to implement deployable full-stack applications with frontend + backend.
• Full-stack runtime versions have folder names ending with _fullstack_runtime.

🌳 Repository Structure

├── alias/                                  # Agent to solve real-world problems
├── browser_use/
│   ├── agent_browser/                      # Pure Python browser agent
│   ├── browser_use_agent_pro/              # Advanced 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
├── tuner/                                  # Tune AgentScope applications using AgentScope Tuner
│   ├── math_agent/                         # A quick start example for tuning
│   ├── frozen_lake/                        # Teach an agent to play a game requiring multiple steps
│   ├── learn_to_ask/                       # Using LLM-as-a-judge to facilitate agent tuning
│   ├── email_search/                       # Enhance the tool use ability of your agent
│   ├── werewolves/                         # Enhance a multi-agent application
│   └── data_augment/                       # Data augmentation for tuning
├── sample_template/                        # Template for new sample contributions
└── README.md

📌 Example List

Category	Example Folder	Uses AgentScope	Use AgentScope Runtime	Description
Data Processing	data_juicer_agent/	✅	❌	Multi-agent data processing with Data-Juicer
Browser Use	browser_use/agent_browser	✅	❌	Command-line browser automation using AgentScope
	browser_use/browser_use_agent_pro	✅	❌	Advanced command-line Python browser agent using AgentScope
	browser_use/browser_use_fullstack_runtime	✅	✅	Full-stack browser automation with UI & sandbox
Deep Research	deep_research/agent_deep_research	✅	❌	Multi-agent research pipeline
	deep_research/qwen_langgraph_search_fullstack_runtime	❌	✅	Full-stack deep research app
Games	games/game_werewolves	✅	❌	Multi-agent roleplay game
Conversational Apps	conversational_agents/chatbot_fullstack_runtime	✅	✅	Chatbot application with frontend/backend
	conversational_agents/chatbot	✅	❌
	conversational_agents/multiagent_conversation	✅	❌	Multi-agent dialogue scenario
	conversational_agents/multiagent_debate	✅	❌	Agents engaging in debates
Evaluation	evaluation/ace_bench	✅	❌	Benchmarks with ACE Bench
General AI Agent	alias/	✅	✅	Agent application running in sandbox to solve diverse real-world problems
Financial Trading	evotraders/	✅	❌	Self-Evolving Multi-Agent Trading System

🌈 Featured Examples

📊 DataJuicer Agent

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

🕵🏻 Alias-Agent

Alias-Agent (short for Alias) is designed to serve as an intelligent assistant for tackle diverse and complicated real-world tasks, providing three operational modes for flexible task execution:

• Simple React: Employs vanilla reasoning-acting loops to iteratively solve problems and execute tool calls.
• Planner-Worker: Uses intelligent planning to decompose complex tasks into manageable subtasks, with dedicated worker agents handling each subtask independently.
• Built-in Agents: Leverages specialized agents tailored for specific domains, including Deep Research Agent for comprehensive analysis and Browser-use Agent for web-based interactions.

Beyond being a ready-to-use agent, we envision Alias as a foundational template that can be adapted to different scenarios.

📖 Documentation: English | 中文

📈 EvoTraders

EvoTraders is a financial trading agent framework that builds a trading system capable of continuous learning and evolution in real markets through multi-agent collaboration and memory systems. Key features include:

• Multi-Agent Collaboration: A team of specialized analysts (Fundamentals, Technical, Sentiment, Valuation) and managers collaborating like a real trading team.
• Memory Enhancement & Evolution: Agents reflect and summarize after trades using the ReMe memory framework, evolving their trading styles over time.
• Real-Time & Backtesting: Supports both real-time market data integration for live trading and backtesting modes.
• Visualized Dashboard: A comprehensive frontend to observe analysis processes, communication, and performance tracking.

📖 Documentation: English | 中文

🆘 Getting Help

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

🤝 Contributing

We welcome contributions such as:

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

See the CONTRIBUTING.md for details.

📄 License

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

Contributors ✨

Thanks goes to these wonderful people (emoji key):

Weirui Kuang 🚧 💻 👀 📖	Osier-Yi 🚧 💻 👀 📖	DavdGao 🚧	qbc 🚧	Lamont Huffman 💻 ⚠️	Daoyuan Chen 💻 💡	MeiXin Chen 💻 💡
Yilun Huang 💻 💡	ShenQianli 💻 💡	ZiTao-Li 💻 💡	Yuexiang XIE 💻 💡	Yue Cui 💻 💡 🚧 📖	Zexi Li 💻 💡	lalaliat 💻 💡
Dandan Liu 💻 💡	Tianjing Zeng 💻 💡	zhijianma 💻 💡	Jiaji 💻 💡	duoyw 💻 💡	JustinDing 💻 💡	jinliyl 💻 💡
y1y5 💻 💡	LuYi 💻 💡	Wu Yue 💻 💡	Zhiling (Bruce) Luo 💻 💡 📖	sidiluo 💻	Attan 💡 💻 📖
Add your contributions

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