agentscope README

The project's README from github.com/agentscope-ai/agentscope. As one markdown file: README.md.

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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.

agentscope

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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 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.

Agent team
Agent team — a leader agent spawns workers and coordinates them through the built-in team tools.
Task planning
Task planning — the agent breaks complex work into a tracked plan and updates it as it goes.
Permission control in bypass mode
Permission control in bypass mode — the agent runs end-to-end without pausing for tool-call confirmations.
Background task offloading
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.

@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:

agentscope (agentscope-ai/agentscope)

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33.4K ·GitHub
Agentscope
What is this?AgentScope 2.0 is a production-ready, easy-to-use Python framework for building and running LLM agents. It leans on the model's own reasoning and tool use instead of boxing it in with strict prompts and opinionated orchestration.
Is it a framework I code against, or a finished product I can just run?Both layers: an SDK for composing agents from building blocks (ReAct loop, toolkit, memory, middleware), plus a batteries-included agent service — a FastAPI backend with a pre-built Web UI that turns your agents into a multi-tenant, multi-session app.
How do I get my first agent running?Install from PyPI or from source — Python 3.11 or higher is required. You can then start your first agent in the console, a terminal mode for testing and debugging agents.
Which LLM providers can I plug in — and can I run models locally?The Model block covers LLM, embedding and TTS across OpenAI, Anthropic, Gemini, DashScope, DeepSeek, Moonshot, Volcengine, xAI and Ollama, so you can run fully local models through Ollama without any paid API.
Is everything in it stable, or are some parts still experimental?The framework is positioned as production-ready, but its newest addition — the realtime voice agent — is explicitly labeled experimental in the release notes.
What can agents actually do beyond chat?A lot out of the box: the Toolkit manages Python tools, MCP servers and skills, and ships built-in coding tools (shell, file editing, search) plus task and plan tools. Hubs like the GitHub MCP Registry and ClawHub let you browse and install MCP servers and skills.
Can several agents work together as a team?Yes — a leader agent spawns workers and coordinates them through built-in team tools, breaking complex work into a tracked plan. Pipelines also run multiple agents behind one event stream, and A2A support lets an agent chat with any remote A2A-compliant agent.
Can I connect my agent to Discord or other chat apps?Yes — the agent service includes Channels for connecting agents to IM platforms: Feishu (Lark), Discord and DingTalk are supported, plus custom channels with message routing.
Where does tool and code execution run — is it sandboxed?You pick the workspace: local, Docker, Apple Container, Bubblewrap, E2B, OpenSandbox, Daytona or Kubernetes. Permissions are fine-grained, with tool confirmations, a bypass mode and human-in-the-loop control.
What are similar projects?ruflo, hive, turnstone, brigade, open-multi-agent, Raven, LangBot, opensquilla
Which topics is it in?a2a-protocol, voice-agent, llm-agent, sandboxing, tool-calling, voice-assistant, human-in-the-loop, discord, fastapi, tts, agent-framework, memory
Creator
agentscope-ai
Size
65.1 MB
Stars
33.4K
Updated
CommitOct 15Releasev2.0.10Oct 12
Age
2y
Commits · 90 Days