agentscope

Build and run agents you can see, understand and trust.

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README.md

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δΈ­ζ–‡δΈ»ι‘΅ | Tutorial | Roadmap (Jan 2026 -) | FAQ

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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


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


<!-- BEGIN NEWS -->
- [2026-04] COMM: AgentScope 2.0 is on the way. Roadmap | Discussion
- [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
<!-- END NEWS -->

More news β†’

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"> |

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πŸ“‘ Table of Contents

- Quickstart
- Installation
- From PyPI
- From source
- Example
- Hello AgentScope!
- Voice Agent
- Realtime Voice Agent
- Human-in-the-loop
- Flexible MCP Usage
- Agentic RL
- Multi-Agent Workflows
- Documentation
- More Examples & Samples
- Functionality
- Agent
- Game
- Workflow
- Evaluation
- Tuner
- Contributing
- License
- Publications
- Contributors

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Quickstart

Installation

AgentScope requires Python 3.10 or higher.

#### From PyPI

bash
pip install agentscope

Or with uv:

bash
uv pip install agentscope

#### 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 .
or with uv:

uv pip install -e .


Example

Hello AgentScope!

Start with a conversation between user and a ReAct agent πŸ€– 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())

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 cancellation in realtime and resumed
seamlessly via robust memory preservation.

<img src="./assets/images/realtime_steering_en.gif" alt="Realtime Steering" width="60%"/>

Flexible MCP Usage

Use individual MCP tools as local callable functions to compose toolkits or wrap into a more complex tool.

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
    # ...

Agentic RL

Train your agentic application seamlessly with Reinforcement Learning integration. We also prepare multiple sample projects covering various scenarios:

| Example | Description | Model | Training Result |
|--------------------------------------------------------------------------------------------------|-------------------------------------------------------------|------------------------|-----------------------------|
| Math Agent | Tune a math-solving agent with multi-step reasoning. | Qwen3-0.6B | Accuracy: 75% β†’ 85% |
| Frozen Lake | Train an agent to navigate the Frozen Lake environment. | Qwen2.5-3B-Instruct | Success rate: 15% β†’ 86% |
| Learn to Ask | Tune agents using LLM-as-a-judge for automated feedback. | Qwen2.5-7B-Instruct | Accuracy: 47% β†’ 92% |
| Email Search | Improve tool-use capabilities without labeled ground truth. | Qwen3-4B-Instruct-2507 | Accuracy: 60% |
| Werewolf Game | Train agents for strategic multi-agent game interactions. | Qwen2.5-7B-Instruct | Werewolf win rate: 50% β†’ 80% |
| Data Augment | Generate synthetic training data to enhance tuning results. | Qwen3-0.6B | AIME-24 accuracy: 20% β†’ 60% |

Multi-Agent Workflows

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())


Documentation

- Tutorial
- FAQ
- API Docs

More Examples & Samples

Functionality

- MCP
- Anthropic Agent Skill
- Plan
- Structured Output
- RAG
- Long-Term Memory
- Session with SQLite
- Stream Printing Messages
- TTS
- Code-first Deployment
- Memory Compression

Agent

- ReAct Agent
- Voice Agent
- Deep Research Agent
- Browser-use Agent
- Meta Planner Agent
- A2A Agent
- Realtime Voice Agent

Game

- Nine-player Werewolves

Workflow

- Multi-agent Debate
- Multi-agent Conversation
- Multi-agent Concurrent
- Multi-agent Realtime Conversation

Evaluation

- ACEBench

Tuner

- Tune ReAct Agent


Contributing

We welcome contributions from the community! Please refer to our CONTRIBUTING.md for guidelines
on how to contribute.

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

- AgentScope: A Flexible yet Robust Multi-Agent Platform

text
@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>