AgentScope Sample Agents

[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.

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📖 About AgentScope & AgentScope Runtime

AgentScope

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

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.

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✨ Getting Started

• 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.

Install Requirements

• AgentScope Documentation
• AgentScope Runtime Documentation

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🌳 Repository Structure

├── 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
│
├── functionality/
│   ├── long_term_memory_mem0/              # Long-term memory integration
│   ├── mcp/                                # Memory/Context Protocol demo
│   ├── plan/                               # Plan with ReAct Agent
│   ├── rag/                                # RAG in AgentScope
│   ├── session_with_sqlite/                # Persistent conversation with SQLite
│   ├── stream_printing_messages/           # Streaming and printing messages
│   ├── structured_output/                  # Structured output parsing and validation
│   ├── multiagent_concurrent/              # Concurrent multi-agent task execution
│   └── meta_planner_agent/                  # Planning agent with tool orchestration
│
└── README.md

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📌 Example List

Category	Example Folder	Uses AgentScope	Use AgentScope Runtime	Description
Browser Use	browser_use/agent_browser	✅	❌	Command-line browser automation 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
Functionality Demos	functionality/long_term_memory_mem0	✅	❌	Long-term memory with mem0 support
	functionality/mcp	✅	❌	Memory/Context Protocol demo
	functionality/session_with_sqlite	✅	❌	Persistent context with SQLite
	functionality/structured_output	✅	❌	Structured data extraction and validation
	functionality/multiagent_concurrent	✅	❌	Concurrent task execution by multiple agents
	functionality/meta_planner_agent	✅	❌	Planning agent with tool orchestration
	functionality/plan	✅	❌	Task planning with ReAct agent
	functionality/rag	✅	❌	Retrieval-Augmented Generation (RAG) integration
	functionality/stream_printing_messages	✅	❌	Real-time message streaming and printing

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ℹ️ 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

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🤝 Contributing

We welcome contributions such as:

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

See the Contributing for details.

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📄 License

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

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🔗 Resources

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

Contributors ✨

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!