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base on AgentScope: Agent-Oriented Programming for Building LLM Applications [**中文主页**](https://github.com/agentscope-ai/agentscope/blob/main/README_zh.md) | [**Tutorial**](https://doc.agentscope.io/) | [**Roadmap**](https://github.com/agentscope-ai/agentscope/blob/main/docs/roadmap.md) | [**FAQ**](https://doc.agentscope.io/tutorial/faq.html) <img src="https://img.alicdn.com/imgextra/i1/O1CN01nTg6w21NqT5qFKH1u_!!6000000001621-55-tps-550-550.svg" alt="AgentScope Logo" width="200" /> <h2 align="center">AgentScope: Agent-Oriented Programming for Building LLM Applications</h2> <a href="https://arxiv.org/abs/2402.14034"> <img src="https://img.shields.io/badge/cs.MA-2402.14034-B31C1C?logo=arxiv&logoColor=B31C1C" alt="arxiv" /> </a> <a href="https://pypi.org/project/agentscope/"> <img src="https://img.shields.io/badge/python-3.10+-blue?logo=python" alt="pypi" /> </a> <a href="https://pypi.org/project/agentscope/"> <img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fpypi.org%2Fpypi%2Fagentscope%2Fjson&query=%24.info.version&prefix=v&logo=pypi&label=version" alt="pypi" /> </a> <a href="https://doc.agentscope.io/"> <img src="https://img.shields.io/badge/Docs-English%7C%E4%B8%AD%E6%96%87-blue?logo=markdown" alt="docs" /> </a> <a href="https://agentscope.io/"> <img src="https://img.shields.io/badge/GUI-AgentScope_Studio-blue?logo=look&logoColor=green&color=dark-green" alt="workstation" /> </a> <a href="./LICENSE"> <img src="https://img.shields.io/badge/license-Apache--2.0-black" alt="license" /> </a> <img src="https://trendshift.io/api/badge/repositories/10079" alt="modelscope%2Fagentscope | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/> ## ✨ Why AgentScope? Easy for beginners, powerful for experts. <img src="./assets/images/agentscope_v1_0822.png" alt="AgentScope Framework" width="80%"/> - **Transparent to Developers**: Transparent is our **FIRST principle**. Prompt engineering, API invocation, agent building, workflow orchestration, all are visible and controllable for developers. No deep encapsulation or implicit magic. - **[Realtime Steering](https://doc.agentscope.io/tutorial/task_agent.html#realtime-steering)**: Native support for realtime interruption and customized handling. - **More Agentic**: Support [agentic tools management](https://doc.agentscope.io/tutorial/task_tool.html), [agentic long-term memory control](https://doc.agentscope.io/tutorial/task_long_term_memory.html) and agentic RAG, etc. - **Model Agnostic**: Programming once, run with all models. - **LEGO-style Agent Building**: All components are **modular** and **independent**. - **Multi-Agent Oriented**: Designed for **multi-agent**, **explicit** message passing and workflow orchestration, NO deep encapsulation. - **Highly Customizable**: Tools, prompt, agent, workflow, third-party libs & visualization, customization is encouraged everywhere. Quick overview of important features in **AgentScope 1.0**: | Module | Feature | Tutorial | |------------|------------------------------------------------------------------------------------|-------------------------------------------------------------------------| | model | Support async invocation | [Model](https://doc.agentscope.io/tutorial/task_model.html) | | | Support reasoning model | | | | Support streaming/non-streaming returns | | | tool | Support async/sync tool functions | [Tool](https://doc.agentscope.io/tutorial/task_tool.html) | | | Support streaming/non-streaming returns | | | | Support user interruption | | | | Support post-processing | | | | Support group-wise tools management | | | | Support agentic tools management by meta tool | | | MCP | Support streamable HTTP/SSE/StdIO transport | [MCP](https://doc.agentscope.io/tutorial/task_mcp.html) | | | Support both **stateful** and **stateless** mode MCP Client | | | | Support client- & function-level fine-grained control | | | agent | Support async execution | | | | Support parallel tool calls | | | | Support realtime steering interruption and customized handling | | | | Support automatic state management | | | | Support agent-controlled long-term memory | | | | Support agent hooks | | | tracing | Support OpenTelemetry-based tracing in LLM, tools, agent and formatter | [Tracing](https://doc.agentscope.io/tutorial/task_tracing.html) | | | Support connecting to third-party tracing platforms (e.g. Arize-Phoenix, Langfuse) | | | memory | Support long-term memory | [Memory](https://doc.agentscope.io/tutorial/task_long_term_memory.html) | | session | Provide session/application-level automatic state management | [Session](https://doc.agentscope.io/tutorial/task_state.html) | | evaluation | Provide distributed and parallel evaluation | [Evaluation](https://doc.agentscope.io/tutorial/task_eval.html) | | formatter | Support multi-agent prompt formatting with tools API | [Prompt Formatter](https://doc.agentscope.io/tutorial/task_prompt.html) | | | Support truncation-based formatter strategy | | | plan | Support ReAct-based long-term planning | [Plan](https://doc.agentscope.io/tutorial/task_plan.html) | | | Support manual plan specification | | | ... | | | ## 📢 News - **[2025-09]** A new powerful 📋**Plan** module is online now! Check out the [tutorial](https://doc.agentscope.io/tutorial/task_plan.html) for more details. - **[2025-09]** **AgentScope Runtime** is open-sourced now! Enabling effective agent deployment with sandboxed tool execution for production-ready AI applications. Check out the [GitHub repo](https://github.com/agentscope-ai/agentscope-runtime). - **[2025-09]** **AgentScope Studio** is open-sourced now! Check out the [GitHub repo](https://github.com/agentscope-ai/agentscope-studio). - **[2025-08]** The new tutorial of v1 is online now! Check out the [tutorial](https://doc.agentscope.io) for more details. - **[2025-08]** 🎉🎉 AgentScope v1 is released now! This version fully embraces the asynchronous execution, providing many new features and improvements. Check out [changelog](https://github.com/agentscope-ai/agentscope/blob/main/docs/changelog.md) for detailed changes. ## 💬 Contact Welcome to join our community on | [Discord](https://discord.gg/eYMpfnkG8h) | DingTalk | |----------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------| | <img src="https://gw.alicdn.com/imgextra/i1/O1CN01hhD1mu1Dd3BWVUvxN_!!6000000000238-2-tps-400-400.png" width="100" height="100"> | <img src="https://img.alicdn.com/imgextra/i1/O1CN01LxzZha1thpIN2cc2E_!!6000000005934-2-tps-497-477.png" width="100" height="100"> |   ## 📑 Table of Contents - [🚀 Quickstart](#-quickstart) - [💻 Installation](#-installation) - [🛠️ From source](#-from-source) - [📦 From PyPi](#-from-pypi) - [📝 Example](#-example) - [👋 Hello AgentScope!](#-hello-agentscope) - [🎯 Realtime Steering](#-realtime-steering) - [🛠️ Fine-Grained MCP Control](#-fine-grained-mcp-control) - [🧑‍🤝‍🧑 Multi-Agent Conversation](#-multi-agent-conversation) - [💻 AgentScope Studio](#-agentscope-studio) - [📖 Documentation](#-documentation) - [⚖️ License](#-license) - [📚 Publications](#-publications) - [✨ Contributors](#-contributors)  ## 🚀 Quickstart ### 💻 Installation > AgentScope requires **Python 3.10** or higher. #### 🛠️ From source ```bash # Pull the source code from GitHub git clone -b main https://github.com/agentscope-ai/agentscope.git # Install the package in editable mode cd agentscope pip install -e . ``` #### 📦 From PyPi ```bash pip install agentscope ``` ## 📝 Example ### 👋 Hello AgentScope! Start with a conversation between user and a ReAct agent 🤖 named "Friday"! ```python from agentscope.agent import ReActAgent, UserAgent from agentscope.model import DashScopeChatModel from agentscope.formatter import DashScopeChatFormatter from agentscope.memory import InMemoryMemory from agentscope.tool import Toolkit, execute_python_code, execute_shell_command import os, asyncio async def main(): toolkit = Toolkit() toolkit.register_tool_function(execute_python_code) toolkit.register_tool_function(execute_shell_command) agent = ReActAgent( name="Friday", sys_prompt="You're a helpful assistant named Friday.", model=DashScopeChatModel( model_name="qwen-max", api_key=os.environ["DASHSCOPE_API_KEY"], stream=True, ), memory=InMemoryMemory(), formatter=DashScopeChatFormatter(), toolkit=toolkit, ) user = UserAgent(name="user") msg = None while True: msg = await agent(msg) msg = await user(msg) if msg.get_text_content() == "exit": break asyncio.run(main()) ``` ### 🎯 Realtime Steering Natively support **realtime interruption** in ``ReActAgent`` with robust memory preservation, and convert interruption into an **observable event** for agent to seamlessly resume conversations. <img src="./assets/images/realtime_steering_zh.gif" alt="Realtime Steering" width="49%"/> <img src="./assets/images/realtime_steering_en.gif" alt="Realtime Steering" width="49%"/> ### 🛠️ Fine-Grained MCP Control Developers can obtain the MCP tool as a **local callable function**, and use it anywhere (e.g. call directly, pass to agent, wrap into a more complex tool, etc.) ```python from agentscope.mcp import HttpStatelessClient from agentscope.tool import Toolkit import os async def fine_grained_mcp_control(): # Initialize the MCP client client = HttpStatelessClient( name="gaode_mcp", transport="streamable_http", url=f"https://mcp.amap.com/mcp?key={os.environ['GAODE_API_KEY']}", ) # Obtain the MCP tool as a **local callable function**, and use it anywhere func = await client.get_callable_function(func_name="maps_geo") # Option 1: Call directly await func(address="Tiananmen Square", city="Beijing") # Option 2: Pass to agent as a tool toolkit = Toolkit() toolkit.register_tool_function(func) # ... # Option 3: Wrap into a more complex tool # ... ``` ### 🧑‍🤝‍🧑 Multi-Agent Conversation AgentScope provides ``MsgHub`` and pipelines to streamline multi-agent conversations, offering efficient message routing and seamless information sharing ```python from agentscope.pipeline import MsgHub, sequential_pipeline from agentscope.message import Msg import asyncio async def multi_agent_conversation(): # Create agents agent1 = ... agent2 = ... agent3 = ... agent4 = ... # Create a message hub to manage multi-agent conversation async with MsgHub( participants=[agent1, agent2, agent3], announcement=Msg("Host", "Introduce yourselves.", "assistant") ) as hub: # Speak in a sequential manner await sequential_pipeline([agent1, agent2, agent3]) # Dynamic manage the participants hub.add(agent4) hub.delete(agent3) await hub.broadcast(Msg("Host", "Goodbye!", "assistant")) asyncio.run(multi_agent_conversation()) ``` ### 💻 AgentScope Studio Use the following command to install and start AgentScope Studio, to trace and visualize your agent application. ```bash npm install -g @agentscope/studio as_studio ``` <img src="./assets/images/home.gif" width="49%" alt="home" /> <img src="./assets/images/projects.gif" width="49%" alt="projects" /> <img src="./assets/images/runtime.gif" width="49%" alt="runtime" /> <img src="./assets/images/friday.gif" width="49%" alt="friday" /> ## 📖 Documentation - Tutorial - [Installation](https://doc.agentscope.io/tutorial/quickstart_installation.html) - [Key Concepts](https://doc.agentscope.io/tutorial/quickstart_key_concept.html) - [Create Message](https://doc.agentscope.io/tutorial/quickstart_message.html) - [ReAct Agent](https://doc.agentscope.io/tutorial/quickstart_agent.html) - Workflow - [Conversation](https://doc.agentscope.io/tutorial/workflow_conversation.html) - [Multi-Agent Debate](https://doc.agentscope.io/tutorial/workflow_multiagent_debate.html) - [Concurrent Agents](https://doc.agentscope.io/tutorial/workflow_concurrent_agents.html) - [Routing](https://doc.agentscope.io/tutorial/workflow_routing.html) - [Handoffs](https://doc.agentscope.io/tutorial/workflow_handoffs.html) - FAQ - [FAQ](https://doc.agentscope.io/tutorial/faq.html) - Task Guides - [Model](https://doc.agentscope.io/tutorial/task_model.html) - [Prompt Formatter](https://doc.agentscope.io/tutorial/task_prompt.html) - [Tool](https://doc.agentscope.io/tutorial/task_tool.html) - [Memory](https://doc.agentscope.io/tutorial/task_memory.html) - [Long-Term Memory](https://doc.agentscope.io/tutorial/task_long_term_memory.html) - [Agent](https://doc.agentscope.io/tutorial/task_agent.html) - [Pipeline](https://doc.agentscope.io/tutorial/task_pipeline.html) - [State/Session Management](https://doc.agentscope.io/tutorial/task_state.html) - [Agent Hooks](https://doc.agentscope.io/tutorial/task_hook.html) - [MCP](https://doc.agentscope.io/tutorial/task_mcp.html) - [AgentScope Studio](https://doc.agentscope.io/tutorial/task_studio.html) - [Tracing](https://doc.agentscope.io/tutorial/task_tracing.html) - [Evaluation](https://doc.agentscope.io/tutorial/task_eval.html) - [Embedding](https://doc.agentscope.io/tutorial/task_embedding.html) - [Token](https://doc.agentscope.io/tutorial/task_token.html) - API - [API Docs](https://doc.agentscope.io/api/agentscope.html) - [Examples](https://github.com/agentscope-ai/agentscope/tree/main/examples) - Game - [Nine-player Werewolves](https://github.com/agentscope-ai/agentscope/tree/main/examples/game/werewolves) - Workflow - [Multi-agent Debate](https://github.com/agentscope-ai/agentscope/tree/main/examples/workflows/multiagent_debate) - [Multi-agent Conversation](https://github.com/agentscope-ai/agentscope/tree/main/examples/workflows/multiagent_conversation) - [Multi-agent Concurrent](https://github.com/agentscope-ai/agentscope/tree/main/examples/workflows/multiagent_concurrent) - Evaluation - [ACEBench](https://github.com/agentscope-ai/agentscope/tree/main/examples/evaluation/ace_bench) - Functional - [ReAct Agent](https://github.com/agentscope-ai/agentscope/tree/main/examples/react_agent) - [MCP](https://github.com/agentscope-ai/agentscope/tree/main/examples/functionality/mcp) - [Plan](https://github.com/agentscope-ai/agentscope/tree/main/examples/functionality/plan) - [Structured Output](https://github.com/agentscope-ai/agentscope/tree/main/examples/functionality/structured_output) - [Long-Term Memory](https://github.com/agentscope-ai/agentscope/tree/main/examples/functionality/long_term_memory) - [Session with SQLite](https://github.com/agentscope-ai/agentscope/tree/main/examples/functionality/session_with_sqlite) ## ⚖️ License AgentScope is released under Apache License 2.0. ## 📚 Publications If you find our work helpful for your research or application, please cite our papers. - [AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications](https://arxiv.org/abs/2508.16279) - [AgentScope: A Flexible yet Robust Multi-Agent Platform](https://arxiv.org/abs/2402.14034) ``` @article{agentscope_v1, author = { Dawei Gao, Zitao Li, Yuexiang Xie, Weirui Kuang, Liuyi Yao, Bingchen Qian, Zhijian Ma, Yue Cui, Haohao Luo, Shen Li, Lu Yi, Yi Yu, Shiqi He, Zhiling Luo, Wenmeng Zhou, Zhicheng Zhang, Xuguang He, Ziqian Chen, Weikai Liao, Farruh Isakulovich Kushnazarov, Yaliang Li, Bolin Ding, Jingren Zhou} title = {AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications}, journal = {CoRR}, volume = {abs/2508.16279}, year = {2025}, } @article{agentscope, author = { Dawei Gao, Zitao Li, Xuchen Pan, Weirui Kuang, Zhijian Ma, Bingchen Qian, Fei Wei, Wenhao Zhang, Yuexiang Xie, Daoyuan Chen, Liuyi Yao, Hongyi Peng, Zeyu Zhang, Lin Zhu, Chen Cheng, Hongzhu Shi, Yaliang Li, Bolin Ding, Jingren Zhou} title = {AgentScope: A Flexible yet Robust Multi-Agent Platform}, journal = {CoRR}, volume = {abs/2402.14034}, year = {2024}, } ``` ## ✨ Contributors All thanks to our contributors: <a href="https://github.com/agentscope-ai/agentscope/graphs/contributors"> <img src="https://contrib.rocks/image?repo=agentscope-ai/agentscope&max=999&columns=12&anon=1" /> </a> ", Assign "at most 3 tags" to the expected json: {"id":"10079","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"

AI prompts