agentscope

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中文主页 | Documentation | Roadmap

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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.

<img src="https://img.alicdn.com/imgextra/i3/O1CN01k5XbA5dLCIB4S3xd_!!6000000003894-2-tps-2193-1564.png" alt="agentscope" width="100%"/>

News

<!-- BEGIN NEWS -->

• [2026-09] FEAT: Model routing supported — use ModelRouterMiddleware to select an appropriate chat model for each reply.
• [2026-09] INTE: Jev classifier model supported through the TypeSafe SDK via JevClassifierModel.
• [2026-09] FEAT: agentscope-skill now supports AgentScope v2.
• [2026-09] INTE: Support DashScope, OpenAI, Gemini and xAI realtime APIs in RealtimeAgent. Example | Docs
• [2026-09] FEAT Experimental: Realtime voice agent supported. Example | Docs
• [2026-09] FEAT: A2A protocol supported — chat with any remote A2A agent via A2AAgent. Example | Docs
• [2026-08] FEAT: Pipeline supported — run multiple agents by a fixed logic behind one event stream. Example | Docs
• [2026-08] INTE: DingTalk channel supported. Docs
• [2026-08] FEAT: Console supported — test and debug agents in the terminal. Example | Docs
• [2026-08] INTE: Feishu (Lark) and Discord channels supported. Feishu | Discord

<!-- END NEWS -->

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"> |

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 mode
cd 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, Volcengine, 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 in console:

from agentscope.agent import Agent
from agentscope.console import launch_console
from agentscope.tool import Toolkit, Bash, Grep, Glob, Read, Write, Edit
from agentscope.credential import DashScopeCredential
from agentscope.model import DashScopeChatModel

import os, asyncio


async def main() -> 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(),
            ]
        ),
    )

    # Chat with the agent in the terminal — streamed output, tool-call
    # confirmation and Ctrl+C interruption are all handled for you
    await launch_console(agent)

asyncio.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.

<table> <tr> <td align="center"> <img src="assets/images/team.gif" alt="Agent team" width="100%"/> <br/> _{<b>Agent team</b> — a leader agent spawns workers and coordinates them through the built-in team tools.} </td> </tr> <tr> <td align="center"> <img src="assets/images/task.gif" alt="Task planning" width="100%"/> <br/>