中文主页 | Tutorial | Roadmap (Jan 2026 -) | FAQ

What is AgentScope?

AgentScope is a production-ready, easy-to-use agent framework with essential abstractions that work with rising model capability and built-in support for finetuning.

We design for increasingly agentic LLMs. Our approach leverages the models' reasoning and tool use abilities rather than constraining them with strict prompts and opinionated orchestrations.

Why use AgentScope?

Simple: start building your agents in 5 minutes with built-in ReAct agent, tools, skills, human-in-the-loop steering, memory, planning, realtime voice, evaluation and model finetuning
Extensible: large number of ecosystem integrations for tools, memory and observability; built-in support for MCP and A2A; message hub for flexible multi-agent orchestration and workflows
Production-ready: deploy and serve your agents locally, as serverless in the cloud, or on your K8s cluster with built-in OTel support

<img src="./assets/images/agentscope.png" width="90%" /> The AgentScope Ecosystem

News

[2026-03] RELS: We recently developed and open sourced an AI assistant named CoPaw (Co Personal Agent Workstation), built upon AgentScope, AgentScope-Runtime, and Reme.
[2026-02] FEAT: Realtime Voice Agent support. Example | Multi-Agent Realtime Example | Tutorial
[2026-01] COMM: Biweekly Meetings launched to share ecosystem updates and development plans - join us! Details & Schedule
[2026-01] FEAT: Database support & memory compression in memory module. Example | Tutorial
[2025-12] INTG: A2A (Agent-to-Agent) protocol support. Example | Tutorial
[2025-12] FEAT: TTS (Text-to-Speech) support. Example | Tutorial
[2025-11] INTG: Anthropic Agent Skill support. Example | Tutorial
[2025-11] RELS: Alias-Agent for diverse real-world tasks and Data-Juicer Agent for data processing open-sourced. Alias-Agent | Data-Juicer Agent
[2025-11] INTG: Agentic RL via Trinity-RFT library. Example | Trinity-RFT
[2025-11] INTG: ReMe for enhanced long-term memory. Example
[2025-11] RELS: agentscope-samples repository launched and agentscope-runtime upgraded with Docker/K8s deployment and VNC-powered GUI sandboxes. Samples | Runtime

More news →

Community

Welcome to join our community on

| Discord | DingTalk | |----------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------| | <img src="https://gw.alicdn.com/imgextra/i1/O1CN01hhD1mu1Dd3BWVUvxN_!!6000000000238-2-tps-400-400.png" width="100" height="100"> | <img src="./assets/images/dingtalk_qr_code.png" width="100" height="100"> |

📑 Table of Contents

Quickstart
- Installation
  - From PyPI
  - From source
Example
Documentation
More Examples & Samples
- Functionality
- Agent
- Game
- Workflow
- Evaluation
- Tuner
Contributing
License
Publications
Contributors

Quickstart

Installation

AgentScope requires Python 3.10 or higher.

From PyPI

pip install agentscope

Or with uv:

uv pip install agentscope

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 .
# or with uv:
# uv pip install -e .

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())

Voice Agent

Create a voice-enabled ReAct agent that can understand and respond with speech, even playing a multi-agent werewolf game with voice interactions.

https://github.com/user-attachments/assets/c5f05254-aff6-4375-90df-85e8da95d5da

Realtime Voice Agent

Build a realtime voice agent with web interface that can interact with users via voice input and output.

Realtime chatbot | Realtime Multi-Agent Example

https://github.com/user-attachments/assets/1b7b114b-e995-4586-9b3f-d3bb9fcd2558

Human-in-the-loop

Support realtime interruption in ReActAgent: conversation can be interrupted via

Agentscope

Install / Use

README