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What is AgentScope 2.0?

AgentScope 2.0 is a production-ready, easy-to-use agent framework with essential abstractions that keep up with rising model capability.

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.

News

• [2026-08] INTE: Feishu (Lark) and Discord channels supported. Feishu | Discord
• [2026-08] FEAT: Channels supported — connect agents to IM platforms in agent service. Example | Docs
• [2026-08] INTE: GitHub MCP Registry and ClawHub supported as built-in hubs. Example | Docs
• [2026-08] FEAT: MCP & Skill Hub supported — browse a hub, install into your library, add to a workspace. Example | Docs
• [2026-07] INTE: Daytona-based workspace/sandbox supported. Docs
• [2026-07] INTE: K8s, OpenSandbox-based workspace/sandbox supported. Docs
• [2026-07] INTE: ReMe long-term memory supported. Example | Docs
• [2026-06] FEAT: Agentic Memory supported. Example | Docs
• [2026-06] FEAT: Distributed & Multi-Tenancy & Multi-Session RAG service supported. Docs
• [2026-06] FEAT: RAG supported. Example | Docs

More news →

Community

Welcome to join our community on

Discord	DingTalk

Quickstart

Installation

AgentScope requires Python 3.11 or higher.

From PyPI

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 modecd agentscope
uv pip install -e .

Agent

The SDK layer — compose an agent from a rich set of building blocks:

Building block	What's inside
ReAct	Reasoning-acting loop with structured output, realtime interruption & resume, and batched (sequential / concurrent) tool acting
Toolkit	Agentic tool management over Python tools, MCP servers, and skills; ships with built-in coding tools (shell, file edit, search) and task/plan tools
Model	LLM, embedding, and TTS across major providers (OpenAI, Anthropic, Gemini, DashScope, DeepSeek, Moonshot, xAI, Ollama)
Context	Automatic compaction, tool-result offload, and context injection (system prompt, RAG, memory) via built-in middleware
Event System	Unified event bus streaming reasoning, tool calls, and multimodal content (text, image, audio) to the frontend
Permission & HITL	Fine-grained control over tools and resources, confirmation, bypass mode
Middleware	Composable hooks across the loop — reply, reasoning, acting, model calling, permission checking, context compression, system prompt
Memory	Agentic memory with switchable backends (ReMe, Mem0)
Workspace / Sandbox	Isolated tool & code execution — local, Docker, Apple Container, Bubblewrap, E2B, OpenSandbox, Daytona, K8s

Start your first agent with AgentScope 2.0:

fromagentscope.agentimportAgentfromagentscope.toolimportToolkit, Bash, Grep, Glob, Read, Write, Editfromagentscope.credentialimportDashScopeCredentialfromagentscope.modelimportDashScopeChatModelfromagentscope.messageimportUserMsgfromagentscope.eventimportEventTypeimportos, asyncioasyncdefmain() ->None:
agent=Agent(
name="Friday",
system_prompt="You're a helpful assistant named Friday.",
model=DashScopeChatModel(
credential=DashScopeCredential(
api_key=os.environ["DASHSCOPE_API_KEY"]
),
model="qwen3.6-plus",
),
toolkit=Toolkit(
tools=[
Bash(),
Grep(),
Glob(),
Read(),
Write(),
Edit(),
]
),
)
asyncforevtinagent.reply_stream(UserMsg("Tony", "Hi, Friday!")):
# Handle the event stream, e.g., print the message, update UI, etc.matchevt.type:
caseEventType.REPLY_START:
...
caseEventType.MODEL_CALL_START:
...
caseEventType.TEXT_BLOCK_START:
...
caseEventType.TEXT_BLOCK_DELTA:
...
caseEventType.TEXT_BLOCK_END:
...
# Handle other event typesasyncio.run(main())

Agent Service — All You Need to Build Your App

AgentScope ships a batteries-included agent service — a FastAPI backend with a pre-built Web UI (examples/web_ui) that turns your agents into a multi-tenant, multi-session application, with rich capabilities out of the box:

Capability	What you get
Serving	Multi-tenancy, multi-session isolation, FastAPI backend, pre-built Web UI
Agent Team	Leader–worker orchestration, built-in team tools, task planning
Channels	Connect agents to IM platforms — Feishu (Lark), Discord, custom channels, message routing
RAG Service	Blob storage, index worker, multi-tenant retrieval
MCP & Skill Hub	Browse hubs (GitHub MCP Registry, ClawHub), install into your library, add to a workspace
Resource Sharing	Group- and org-level management for sharing models, MCP servers, skills, and workspaces
Persistence	SQL & NoSQL persistence of agent state and sessions
Scheduling	Scheduled tasks, agent wakeup, background task offloading

Everything above is composable, so you can assemble your own application on top of the service with minimal glue code.

Agent team — a leader agent spawns workers and coordinates them through the built-in team tools.

Task planning — the agent breaks complex work into a tracked plan and updates it as it goes.

Permission control in bypass mode — the agent runs end-to-end without pausing for tool-call confirmations.

Background task offloading — a long-running tool moves to the background; its result later wakes the agent up and the conversation resumes.

Run the following commands to start the agent service backend and the web UI:

git clone -b main https://github.com/agentscope-ai/agentscope.git
cd agentscope/examples/agent_service
# start the agent service backend
python main.py

Then open another terminal to start the web UI:

cd agentscope/examples/web_ui
# start the webui
pnpm install
pnpm dev

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

@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: