## 1. Project Overview & Quickstart (agentscope-ai/agentscope)

## File: README.md

[**中文主页**](https://github.com/agentscope-ai/agentscope/blob/main/README_zh.md) | [**Documentation**](https://docs.agentscope.io/) | [**Roadmap**](https://github.com/orgs/agentscope-ai/projects/2/views/1)







## 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] `FEAT`:** Console supported — test and debug agents in the terminal. [Example](https://github.com/agentscope-ai/agentscope/tree/main/examples/console) | [Docs](https://docs.agentscope.io/latest/en/building-blocks/console)
- **[2026-08] `INTE`:** Feishu (Lark) and Discord channels supported. [Feishu](https://docs.agentscope.io/latest/en/deploy/channel/feishu) | [Discord](https://docs.agentscope.io/latest/en/deploy/channel/discord)
- **[2026-08] `FEAT`:** Channels supported — connect agents to IM platforms in agent service. [Example](https://github.com/agentscope-ai/agentscope/tree/main/examples/agent_service) | [Docs](https://docs.agentscope.io/latest/en/deploy/channel/overview)
- **[2026-08] `INTE`:** GitHub MCP Registry and ClawHub supported as built-in hubs. [Example](https://github.com/agentscope-ai/agentscope/tree/main/examples/agent_service) | [Docs](https://docs.agentscope.io/latest/en/deploy/hub)
- **[2026-08] `FEAT`:** MCP & Skill Hub supported — browse a hub, install into your library, add to a workspace. [Example](https://github.com/agentscope-ai/agentscope/tree/main/examples/agent_service) | [Docs](https://docs.agentscope.io/latest/en/deploy/hub)
- **[2026-07] `INTE`:** Daytona-based workspace/sandbox supported. [Docs](https://docs.agentscope.io/latest/en/building-blocks/workspace)
- **[2026-07] `INTE`:** K8s, OpenSandbox-based workspace/sandbox supported. [Docs](https://docs.agentscope.io/latest/en/building-blocks/workspace)
- **[2026-07] `INTE`:** ReMe long-term memory supported. [Example](https://github.com/agentscope-ai/agentscope/tree/main/examples/long_term_memory/reme) | [Docs](https://docs.agentscope.io/latest/en/building-blocks/long-term-memory)
- **[2026-06] `FEAT`:** Agentic Memory supported. [Example](https://github.com/agentscope-ai/agentscope/tree/main/examples/long_term_memory/agentic_memory) | [Docs](https://docs.agentscope.io/latest/en/building-blocks/long-term-memory)
- **[2026-06] `FEAT`:** Distributed & Multi-Tenancy & Multi-Session RAG service supported. [Docs](https://docs.agentscope.io/latest/en/deploy/agent-team)


[More news →](./docs/NEWS.md)

## Community

Welcome to join our community on

| [Discord](https://discord.gg/eYMpfnkG8h)                                                                                         | DingTalk                                                                  |
|----------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------|
|  |  |

## Quickstart

### Installation

> AgentScope requires **Python 3.11** or higher.

#### From PyPI

```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

uv pip install -e .
```

## Agent

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

| Building block | What's inside |
|---|---|
| [**ReAct**](https://docs.agentscope.io/latest/en/building-blocks/agent/overview) | Reasoning-acting loop with structured output, realtime interruption & resume, and batched (sequential / concurrent) tool acting |
| [**Toolkit**](https://docs.agentscope.io/latest/en/building-blocks/tool/overview) | 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**](https://docs.agentscope.io/latest/en/building-blocks/model/overview) | LLM, embedding, and TTS across major providers (OpenAI, Anthropic, Gemini, DashScope, DeepSeek, Moonshot, xAI, Ollama) |
| [**Context**](https://docs.agentscope.io/latest/en/building-blocks/context/overview) | Automatic compaction, tool-result offload, and context injection (system prompt, RAG, memory) via built-in middleware |
| [**Event System**](https://docs.agentscope.io/latest/en/building-blocks/message-and-event) | Unified event bus streaming reasoning, tool calls, and multimodal content (text, image, audio) to the frontend |
| [**Permission & HITL**](https://docs.agentscope.io/latest/en/building-blocks/permission-system/overview) | Fine-grained control over tools and resources, confirmation, bypass mode |
| [**Middleware**](https://docs.agentscope.io/latest/en/building-blocks/middleware) | Composable hooks across the loop — reply, reasoning, acting, model calling, permission checking, context compression, system prompt |
| [**Memory**](https://docs.agentscope.io/latest/en/building-blocks/long-term-memory) | Agentic memory with switchable backends (ReMe, Mem0) |
| [**Workspace / Sandbox**](https://docs.agentscope.io/latest/en/building-blocks/workspace/overview) | Isolated tool & code execution — local, Docker, Apple Container, Bubblewrap, E2B, OpenSandbox, Daytona, K8s |

Start your first agent with AgentScope 2.0 in console:

```python
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**](https://docs.agentscope.io/latest/en/deploy/agent-service) | Multi-tenancy, multi-session isolation, FastAPI backend, pre-built Web UI |
| [**Agent Team**](https://docs.agentscope.io/latest/en/deploy/agent-team) | Leader–worker orchestration, built-in team tools, task planning |
| [**Channels**](https://docs.agentscope.io/latest/en/deploy/channel/overview) | Connect agents to IM platforms — Feishu (Lark), Discord, custom channels, message routing |
| [**RAG Service**](https://docs.agentscope.io/latest/en/deploy/rag) | Blob storage, index worker, multi-tenant retrieval |
| [**MCP & Skill Hub**](https://docs.agentscope.io/latest/en/deploy/hub/overview) | Browse hubs (GitHub MCP Registry, ClawHub), install into your library, add to a workspace |
| [**Resource Sharing**](https://docs.agentscope.io/latest/en/deploy/sharing) | Group- and org-level management for sharing models, MCP servers, skills, and workspaces |
| [**Persistence**](https://docs.agentscope.io/latest/en/deploy/agent-service#storage-backend) | SQL & NoSQL persistence of agent state and sessions |
| [**Scheduling**](https://docs.agentscope.io/latest/en/deploy/agent-service) | 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:

```bash
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:

```bash
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](./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](https://arxiv.org/abs/2508.16279)

- [AgentScope: A Flexible yet Robust Multi-Agent Platform](https://arxiv.org/abs/2402.14034)

```
@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">
  
</a>

---

## File: docs/NEWS.md

- **[2026-08] `FEAT`:** Console supported — test and debug agents in the terminal. [Example](https://github.com/agentscope-ai/agentscope/tree/main/examples/console) | [Docs](https://docs.agentscope.io/latest/en/building-blocks/console)
- **[2026-08] `INTE`:** Feishu (Lark) and Discord channels supported. [Feishu](https://docs.agentscope.io/latest/en/deploy/channel/feishu) | [Discord](https://docs.agentscope.io/latest/en/deploy/channel/discord)
- **[2026-08] `FEAT`:** Channels supported — connect agents to IM platforms in agent service. [Example](https://github.com/agentscope-ai/agentscope/tree/main/examples/agent_service) | [Docs](https://docs.agentscope.io/latest/en/deploy/channel/overview)
- **[2026-08] `INTE`:** GitHub MCP Registry and ClawHub supported as built-in hubs. [Example](https://github.com/agentscope-ai/agentscope/tree/main/examples/agent_service) | [Docs](https://docs.agentscope.io/latest/en/deploy/hub)
- **[2026-08] `FEAT`:** MCP & Skill Hub supported — browse a hub, install into your library, add to a workspace. [Example](https://github.com/agentscope-ai/agentscope/tree/main/examples/agent_service) | [Docs](https://docs.agentscope.io/latest/en/deploy/hub)
- **[2026-07] `INTE`:** Daytona-based workspace/sandbox supported. [Docs](https://docs.agentscope.io/latest/en/building-blocks/workspace)
- **[2026-07] `INTE`:** K8s, OpenSandbox-based workspace/sandbox supported. [Docs](https://docs.agentscope.io/latest/en/building-blocks/workspace)
- **[2026-07] `INTE`:** ReMe long-term memory supported. [Example](https://github.com/agentscope-ai/agentscope/tree/main/examples/long_term_memory/reme) | [Docs](https://docs.agentscope.io/latest/en/building-blocks/long-term-memory)
- **[2026-06] `FEAT`:** Agentic Memory supported. [Example](https://github.com/agentscope-ai/agentscope/tree/main/examples/long_term_memory/agentic_memory) | [Docs](https://docs.agentscope.io/latest/en/building-blocks/long-term-memory)
- **[2026-06] `FEAT`:** Distributed & Multi-Tenancy & Multi-Session RAG service supported. [Docs](https://docs.agentscope.io/latest/en/deploy/agent-team)
- **[2026-06] `FEAT`:** RAG supported. [Example](https://github.com/agentscope-ai/agentscope/tree/main/examples/rag) | [Docs](https://docs.agentscope.io/latest/en/building-blocks/rag)
- **[2026-06] `INTE`:** Mem0 supported. [Example](https://github.com/agentscope-ai/agentscope/tree/main/examples/long_term_memory) | [Docs](https://docs.agentscope.io/latest/en)
- **[2026-06] `FEAT`:** Agent Team supported. [Example](https://github.com/agentscope-ai/agentscope/tree/main/examples/agent_service) | [Docs](https://docs.agentscope.io/latest/en/deploy/agent-team)
- **[2026-05] `RELS`:** AgentScope 2.0 released! [Docs](https://docs.agentscope.io/)

---

## File: docs/NEWS_zh.md

- **[2026-08] `功能`:** 支持 Console —— 在终端中测试与调试智能体。[样例](https://github.com/agentscope-ai/agentscope/tree/main/examples/console) | [文档](https://docs.agentscope.io/latest/zh/building-blocks/console)
- **[2026-08] `集成`:** 支持飞书（Lark）与 Discord 频道。[飞书](https://docs.agentscope.io/latest/zh/deploy/channel/feishu) | [Discord](https://docs.agentscope.io/latest/zh/deploy/channel/discord)
- **[2026-08] `功能`:** 支持消息频道 —— 将智能体接入即时通讯平台。[样例](https://github.com/agentscope-ai/agentscope/tree/main/examples/agent_service) | [文档](https://docs.agentscope.io/latest/zh/deploy/channel/overview)
- **[2026-08] `集成`:** 内置集成 GitHub MCP Registry 与 ClawHub。[样例](https://github.com/agentscope-ai/agentscope/tree/main/examples/agent_service) | [文档](https://docs.agentscope.io/latest/zh/deploy/hub)
- **[2026-08] `功能`:** 支持 MCP & Skill Hub —— 浏览 hub、安装到个人库、再添加到工作区。[样例](https://github.com/agentscope-ai/agentscope/tree/main/examples/agent_service) | [文档](https://docs.agentscope.io/latest/zh/deploy/hub)
- **[2026-07] `集成`:** 集成 Daytona 工作区/沙箱实现。 [文档](https://docs.agentscope.io/latest/en/building-blocks/workspace)
- **[2026-07] `集成`:** 集成 K8s，OpenSandbox 工作区/沙箱实现。 [文档](https://docs.agentscope.io/latest/en/building-blocks/workspace)
- **[2026-07] `集成`:** 集成 ReMe 长期记忆。 [样例](https://github.com/agentscope-ai/agentscope/tree/main/examples/long_term_memory/reme) | [文档](https://docs.agentscope.io/latest/zh/building-blocks/long-term-memory)
- **[2026-06] `功能`:** 支持 Agentic Memory。 [样例](https://github.com/agentscope-ai/agentscope/tree/main/examples/long_term_memory/agentic_memory) | [文档](https://docs.agentscope.io/latest/zh/building-blocks/long-term-memory)
- **[2026-06] `功能`:** 支持分布式 & 多租户 & 多会话 RAG 服务。 [文档](https://docs.agentscope.io/latest/en/deploy/agent-team)
- **[2026-06] `功能`:** 支持多模态 RAG。 [样例](https://github.com/agentscope-ai/agentscope/tree/main/examples/rag) | [文档](https://docs.agentscope.io/latest/en/building-blocks/rag)
- **[2026-06] `集成`:** 集成 Mem0 长期记忆。 [Example](https://github.com/agentscope-ai/agentscope/tree/main/examples/long_term_memory) | [Docs](https://docs.agentscope.io/latest/zh)
- **[2026-06] `功能`:** 支持 Agent Team。[样例](https://github.com/agentscope-ai/agentscope/tree/main/examples/agent_service) | [文档](https://docs.agentscope.io/latest/zh/deploy/agent-team)
- **[2026-05] `发布`:** AgentScope 2.0 已发布！[文档](https://docs.agentscope.io/)

---

## File: docs/roadmap.md

# Roadmap

## Long-term Goals

Offering **agent-oriented programming (AOP)** as a new programming paradigm to organize the design and implementation of next-generation LLM-empowered applications.

## Current Focus (January 2026 - )

### 🎙️ Voice Agent

**Voice agents** are a domain we are highly focused on, and AgentScope will continue to invest in this direction.

AgentScope aims to build **production-ready** voice agents rather than demonstration prototypes. This means our voice agents will:

- Support **production-grade** deployment, including seamless frontend integration
- Support **tool invocation**, not just voice conversations
- Support **multi-agent** voice interactions

#### Development Roadmap

Our development strategy for voice agents consists of **three progressive milestones**:

1. **TTS Models** → 2. **Multimodal Models** → 3. **Real-time Multimodal Models**

---

#### Phase 1: TTS (Text-to-Speech) Models

- **Build TTS model base class infrastructure**
  - Design and implement a unified TTS model base class
  - Establish standardized interfaces for TTS model integration

- **Horizontal API expansion**
  - Support mainstream TTS APIs (e.g., OpenAI TTS, Google TTS, Azure TTS, ElevenLabs, etc.)
  - Ensure consistent behavior across different TTS providers

---

#### Phase 2: Multimodal Models (Non-Realtime)

- **Enable ReAct agents with multimodal support**
  - Integrate multimodal models (e.g., qwen3-omni, gpt-audio) into existing ReAct agent framework
  - Support audio input/output in non-realtime mode

- **Advanced multimodal agent capabilities**
  - Enable tool invocation within multimodal conversations
  - Support multi-agent workflows with multimodal communication

---

#### Phase 3: Real-time Multimodal Models


- **Beyond request-response**: Explore streaming, interrupt handling, and concurrent multimodal processing
- **New programming paradigms**: Design agent programming models specifically tailored for real-time interactions
- **Production readiness**: Ensure low-latency performance, stability, and scalability for production deployment

### 🛠️ Agent Skill

Provide **production-ready** agent skill integration solutions.

### 🌐 Ecosystem Expansion

- **A2UI (Agent-to-UI)**: Enable seamless agent-to-user interface interactions
- **A2A (Agent-to-Agent)**: Enhance agent-to-agent communication capabilities

### 🚀 Agentic RL

- Support using [Tinker](https://tinker-docs.thinkingmachines.ai/) backend to tune agent applications on devices without GPU.
- Support tuning agent applications based on their run history.
- Integrate with AgentScope Runtime to provide better environment abstraction.
- Add more tutorials and examples on how to build complex judge functions with the help of evaluation module.
- Add more tutorials and examples on data selection and augmentation.

### 📈 Code Quality

Continuous refinement and improvement of code quality and maintainability.

# Completed Milestones

### AgentScope V1.0.0 Roadmap

We are deeply grateful for the continuous support from the open-source community that has witnessed AgentScope's
growth. Throughout our journey, we have maintained **developer-centric transparency** as our core principle,
which will continue to guide our future development.

As the AI agent ecosystem rapidly evolves, we recognize the need to adapt AgentScope to meet emerging trends and
requirements. We are excited to announce the upcoming release of AgentScope v1.0.0, which marks a significant shift
towards deployment-focused and secondary development direction. This new version will provide comprehensive support for agent developers
with enhanced deployment capabilities and practical features. Specifically, the update will include:

- ✨New Features
  - 🛠️ Tool/MCP
    - Support both sync/async tool functions
    - Support streaming tool function
    - Support parallel execution of tool functions
    - Provide more flexible support for the MCP server

  - 💾 Memory
    - Enhance the existing short-term memory
    - Support long-term memory

  - 🤖 Agent
    - Provide powerful ReAct-based out-of-the-box agents

- 👨‍💻 Development
  - Provide enhanced AgentScope Studio with visual components for developing, tracing and debugging
  - Provide a built-in copilot for developing/drafting AgentScope applications

- 🔍 Evaluation
  - Provide built-in benchmarking and evaluation toolkit for agents
  - Support result visualization

- 🏗️ Deployment
  - Support asynchronous agent execution
  - Support session/state management
  - Provide sandbox for tool execution

Stay tuned for our detailed release notes and beta version, which will be available soon. Follow our GitHub
repository and official channels for the latest updates. We look forward to your valuable feedback and continued
support in shaping the future of AgentScope.

## 2. Official Technical Reference & Guides (agentscope-ai/docs)

## File: README.md

# AgentScope Documentation

This repository is the source for the unified
[AgentScope documentation site](https://docs.agentscope.io). It publishes English and
Chinese documentation for [AgentScope](https://github.com/agentscope-ai/agentscope) and
[ReMe](https://github.com/agentscope-ai/ReMe) with
[Mintlify](https://mintlify.com).

## Documentation Sets

The two projects share one site but keep separate content and release models:

| Project | Documentation scope | Live documentation | Update model |
|---|---|---|---|
| AgentScope | Building, operating, and deploying agent applications | [AgentScope docs](https://docs.agentscope.io/latest/en/index) | Immutable version directories |
| ReMe | File-native agent memory, workflows, integrations, and stable contracts | [ReMe docs](https://docs.agentscope.io/reme/latest/en/overview) | One continuously updated `latest` version |

## ReMe Documentation

The ReMe repository [README](https://github.com/agentscope-ai/ReMe#readme) is the concise
project entry point. This repository owns the complete public documentation: concepts,
verified workflows, integrations, reference material, troubleshooting, and contribution
guidance.

ReMe documentation should preserve the product principles expressed by its README:

- memory files are user-owned source data;
- indexes, graphs, and caches are rebuildable derived state;
- conversations and resources become correctable, traceable Markdown memory;
- Python, AgentScope, MCP, CLI, HTTP, plugins, and Skills are integration boundaries over
  the same file-native memory model.

The current ReMe content is organized by user goal:

| Area | Pages | Purpose |
|---|---|---|
| Get Started | [Overview](reme/latest/en/overview.mdx), [Quick Start](reme/latest/en/quickstart.mdx), [Concepts](reme/latest/en/concepts.mdx) | Explain the product, complete the first memory loop, and establish its principles |
| Integration | [Integration overview](reme/latest/en/integration/overview.mdx), [Deployment](reme/latest/en/integration/deployment.mdx), [Python SDK](reme/latest/en/integration/python-sdk.mdx), [Plugins](reme/latest/en/integration/plugins.mdx) | Choose and implement the correct runtime boundary |
| Resources | [Reference](reme/latest/en/reference.mdx), [FAQ](reme/latest/en/faq.mdx), [Contribution](reme/latest/en/contribution.mdx) | Find stable contracts, solve common problems, and contribute changes |

Chinese pages mirror the same structure under `reme/latest/zh/`. Update both languages
together and keep their intent, headings, examples, and links aligned.

## Local Development

Use an active Node.js LTS release (Node 20 or 22).

Install the [Mintlify CLI](https://www.npmjs.com/package/mint):

```bash
npm i mint
```

From the repository root, validate the site or start a preview:

```bash
mint validate
mint dev
```

The preview is available at `http://localhost:3000` by default.

## Repository Structure

```text
.
├── versions/
│   └── <version>/{en,zh}/   # Versioned AgentScope documentation
├── reme/
│   └── latest/{en,zh}/      # Current ReMe documentation
├── images/                  # Shared static assets
├── scripts/                 # Documentation maintenance scripts
├── docs.json                # Mintlify navigation and redirects
└── CLAUDE.md                # Writing and review guidelines
```

## Content Boundaries

Keep public documentation focused on user goals and stable behavior:

- Verify ReMe behavior against current code, public schemas, tests, CLI help, and
  `reme/config/default.yaml`.
- Explain the stable contract and link to authoritative source instead of duplicating
  implementation details.
- Keep concepts focused on product intent; place detailed development mechanics in source
  code, tests, and repository-local guidance.
- Lead with the outcome, use direct language, and introduce every table, image, diagram,
  and code block.
- Start each MDX page with `title` and `description` frontmatter.

See [CLAUDE.md](CLAUDE.md) for the complete writing and component guidelines.

## Version Management

AgentScope documentation is immutable within each published version directory. For a new
AgentScope release:

1. Copy the latest relevant version into a new project version directory.
2. Update every version-specific internal link in the copied pages.
3. Add the version under the matching project tab for both languages in `docs.json`.
4. Point the relevant `latest` or `stable` redirect at the new version.
5. Run `mint validate` before submitting the change.

AgentScope versions can be created with `scripts/create-version.sh`.

ReMe does not keep historical release directories. Update `reme/latest/{en,zh}/`
directly, keep canonical internal links under `/reme/latest/...`, and leave its
`docs.json` version label as `latest`. The `/reme/stable/...` alias and obsolete
numeric-version URLs redirect to the current pages for compatibility.

## Contribution Workflow

When adding or updating documentation:

1. Verify the behavior in the corresponding project source.
2. Update the correct project, version, and language pages.
3. For ReMe, make the equivalent English and Chinese changes in `reme/latest/`.
4. Update `docs.json` when navigation, versions, or compatibility redirects change.
5. Check page links and run `mint validate` before submitting a pull request.

Run these checks from the repository root before submitting:

```bash
git diff --check
mint validate
```

## Resources

- [AgentScope GitHub](https://github.com/agentscope-ai/agentscope)
- [ReMe GitHub](https://github.com/agentscope-ai/ReMe)
- [ReMe English documentation](https://docs.agentscope.io/reme/latest/en/overview)
- [ReMe 中文文档](https://docs.agentscope.io/reme/latest/zh/overview)
- [Mintlify Documentation](https://mintlify.com/docs)

---

## File: reme/README.md

# ReMe documentation

ReMe documentation is part of the unified Mintlify site in this repository.

- `latest/` contains the current `en/` and `zh/` MDX pages and is updated in place.
- ReMe does not maintain numbered or historical documentation directories.
- Shared ReMe media lives in `images/reme/`.
- Navigation is defined by the `ReMe` tab for each language in `docs.json`.
- Canonical internal links use `/reme/latest/...`; compatibility redirects preserve old URLs.

Keep the documentation focused on product philosophy, verified user workflows, and stable contracts. Implementation details belong in the ReMe source, schemas, tests, and runtime help.