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AgentScope: Agent-Oriented Programming for Building LLM Applications
✨ Why AgentScope?
Easy for beginners, powerful for experts.
- 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: Native support for realtime interruption and customized handling.
- More Agentic: Support agentic tools management, agentic long-term memory control 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 |
| Support reasoning model | ||
| Support streaming/non-streaming returns | ||
| tool | Support async/sync tool functions | Tool |
| 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 |
| 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 |
| Support connecting to third-party tracing platforms (e.g. Arize-Phoenix, Langfuse) | ||
| memory | Support long-term memory | Memory |
| session | Provide session/application-level automatic state management | Session |
| evaluation | Provide distributed and parallel evaluation | Evaluation |
| formatter | Support multi-agent prompt formatting with tools API | Prompt Formatter |
| Support truncation-based formatter strategy | ||
| ... |
📢 News
- [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.
- [2025-09] AgentScope Studio is open-sourced now! Check out the GitHub repo.
- [2025-08] The new tutorial of v1 is online now! Check out the tutorial 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 for detailed changes.
💬 Contact
Welcome to join our community on
| Discord | DingTalk |
|---|---|
 |
 |
📑 Table of Contents
🚀 Quickstart
💻 Installation
AgentScope requires Python 3.10 or higher.
🛠️ From source
# 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
pip install agentscope
📝 Example
👋 Hello AgentScope!
Start with a conversation between user and a ReAct agent 🤖 named "Friday"!
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.
🛠️ 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.)
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
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.
npm install -g @agentscope/studio
as_studio
📖 Documentation
- Tutorial
- Workflow
- FAQ
- Task Guides
- API
- Examples
- Game
- Workflow
- Evaluation
- Functional
⚖️ 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.
@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:
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ARGO is an open-source AI Agent platform that brings Local Manus to your desktop. With one-click model downloads, seamless closed LLM integration, and offline-first RAG knowledge bases, ARGO becomes a DeepResearch powerhouse for autonomous thinking, task planning, and 100% of your data stays locally. Support Win/Mac/Docker.