docs: integrate ReMe documentation

README.md

@@ -1,72 +1,134 @@
 # AgentScope Documentation
 
-This repository contains the official documentation for [AgentScope](https://github.com/modelscope/agentscope), a production-ready, multi-agent framework for building LLM-empowered agent applications. The docs site is built with [Mintlify](https://mintlify.com).
+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).
 
-## Overview
+## Documentation Sets
 
-AgentScope provides a simple yet efficient way to build LLM-empowered agent applications, with built-in support for:
+The two projects share one site but keep separate content and release models:
 
-- Multi-agent orchestration
-- Multiple model providers (DashScope, Gemini, OpenAI, Anthropic, Ollama)
-- Memory (short-term and long-term)
-- Tools and RAG
-- Observability and evaluation
+| 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 |
 
-## Development
+## ReMe Documentation
 
-### Prerequisites
+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.
 
-- Node.js version 19 or higher
+ReMe documentation should preserve the product principles expressed by its README:
 
-### Local Preview
+- 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 |
+| Usage | [Integration overview](reme/latest/en/integration/overview.mdx), [Services](reme/latest/en/integration/services.mdx), [Python](reme/latest/en/integration/python.mdx), [Agents](reme/latest/en/integration/agents.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 -g mint
 ```
 
-Start the local development server at the root of this repository (where `docs.json` is located):
+From the repository root, validate the site or start a preview:
 
 ```bash
+mint validate
 mint dev
 ```
 
-View your local preview at `http://localhost:3000`.
+The preview is available at `http://localhost:3000` by default.
 
 ## Repository Structure
 
-```
+```text
 .
-├── basic-concepts/       # Core AgentScope concepts (msg, agent, model, memory, tool)
-├── building-blocks/      # Advanced building blocks
-├── tutorial/             # Step-by-step tutorials
-├── out-of-box-agents/    # Pre-built agents
-├── api-reference/        # API documentation
-├── observe-and-evaluate/ # Monitoring and evaluation
-├── tune-agent/           # Agent tuning guides
-├── deploy-and-serve/     # Deployment options
-├── essentials/           # Documentation components
-├── others/               # FAQ and additional resources
-└── docs.json             # Mintlify navigation config
+├── agentscope/
+│   └── <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
 ```
 
-## Contributing
+## 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. Place `.mdx` files in the appropriate directory
-2. Update `docs.json` to include new pages in the navigation
-3. Start every page with YAML frontmatter (`title` and `description`)
-4. Follow the [writing style guidelines](CLAUDE.md) in this repo
-5. Preview changes locally before submitting a PR
+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.
 
-## Troubleshooting
+Run these checks from the repository root before submitting:
 
-- **Dev server not running**: Run `mint update` to get the latest CLI version.
-- **Page shows 404**: Ensure you are running in the folder containing `docs.json`.
+```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)

versions/1.0.21/en/basic-concepts/context-and-memory.mdx → agentscope/1.0.21/en/basic-concepts/context-and-memory.mdx

@@ -7,7 +7,7 @@ icon: 'database'
 This document introduces how AgentScope handles context and memory in agent workflows.
 
 <Info>
-For implementation details and APIs, see [Context and Memory](/versions/1.0.21/en/building-blocks/context-and-memory).
+For implementation details and APIs, see [Context and Memory](/agentscope/1.0.21/en/building-blocks/context-and-memory).
 </Info>
 
 ---

versions/1.0.21/en/basic-concepts/model.mdx → agentscope/1.0.21/en/basic-concepts/model.mdx

@@ -45,7 +45,7 @@ To support multi-agent conversations in a chatbot format, AgentScope designs a f
 Such formatters are distinguished by the suffix `ChatFormatter` (e.g., `DashScopeChatFormatter`) and `MultiAgentFormatter` (e.g., `DashScopeMultiAgentFormatter`) — the former is for two-party conversations (user + assistant), while the latter is for multi-agent conversations.
 
 <Tip>
-For detailed usage examples and a full provider reference table mapping each model class to its corresponding formatter, see [Models](/versions/1.0.21/en/building-blocks/models).
+For detailed usage examples and a full provider reference table mapping each model class to its corresponding formatter, see [Models](/agentscope/1.0.21/en/building-blocks/models).
 </Tip>
 
 

versions/1.0.21/en/building-blocks/agent.mdx → agentscope/1.0.21/en/building-blocks/agent.mdx

@@ -39,7 +39,7 @@ AgentScope provides several built-in agent types to cover different use cases. U
 | Memory compression | Automatically compress long conversation history |
 | Parallel tool calls | Execute multiple tool calls concurrently |
 | Structured output | Return typed, schema-validated responses |
-| Fine-grained MCP control | See [MCP integration](/versions/1.0.21/en/building-blocks/tool-capabilities#mcp-integration) |
+| Fine-grained MCP control | See [MCP integration](/agentscope/1.0.21/en/building-blocks/tool-capabilities#mcp-integration) |
 | Meta tool | Agent-controlled tool management |
 | Long-term memory | Self-controlled persistent memory |
 | Plan | Break complex tasks into managed subtasks and execute them systematically |

versions/1.0.21/en/building-blocks/context-and-memory.mdx → agentscope/1.0.21/en/building-blocks/context-and-memory.mdx

@@ -19,7 +19,7 @@ This mechanism supports high-level memory management in agents. For example, in
 The memory module focuses on storage and management. Algorithmic logic (for example, compression strategy) is implemented at the agent layer.
 </Note>
 
-> For conceptual background, see [Context and Memory](/versions/1.0.21/en/basic-concepts/context-and-memory).
+> For conceptual background, see [Context and Memory](/agentscope/1.0.21/en/basic-concepts/context-and-memory).
 
 In AgentScope, memory can be viewed in two layers:
 
@@ -442,10 +442,10 @@ If your backend supports all methods, it can be used in `both` mode.
 ## Further Reading
 
 <CardGroup cols={2}>
-  <Card title="Agent" icon="user-robot" href="/versions/1.0.21/en/basic-concepts/agent">
+  <Card title="Agent" icon="user-robot" href="/agentscope/1.0.21/en/basic-concepts/agent">
     Understand the agent's core methods and the ReAct paradigm.
   </Card>
-  <Card title="Tool" icon="wrench" href="/versions/1.0.21/en/basic-concepts/tool">
+  <Card title="Tool" icon="wrench" href="/agentscope/1.0.21/en/basic-concepts/tool">
     Learn how to extend agents with native functions, MCP, and skills.
   </Card>
 </CardGroup>

versions/1.0.21/en/building-blocks/models.mdx → agentscope/1.0.21/en/building-blocks/models.mdx

@@ -22,7 +22,7 @@ This document covers detailed usage examples and provider-specific references fo
 </CardGroup>
 
 <Info>
-For core concepts and design principles, see [Model](/versions/1.0.21/en/basic-concepts/model). For details on `Msg` and content blocks, see [Msg](/versions/1.0.21/en/basic-concepts/msg).
+For core concepts and design principles, see [Model](/agentscope/1.0.21/en/basic-concepts/model). For details on `Msg` and content blocks, see [Msg](/agentscope/1.0.21/en/basic-concepts/msg).
 </Info>
 
 ---
@@ -236,7 +236,7 @@ The `tool_choice` parameter controls invocation behavior:
 - `"<function_name>"`: Force a specific tool
 
 <Tip>
-Use the `Toolkit` class to auto-generate JSON schemas from Python functions with docstrings. See [Tool](/versions/1.0.21/en/basic-concepts/tool) for details.
+Use the `Toolkit` class to auto-generate JSON schemas from Python functions with docstrings. See [Tool](/agentscope/1.0.21/en/basic-concepts/tool) for details.
 </Tip>
 
 ### Provider Reference

versions/1.0.21/en/building-blocks/orchestration.mdx → agentscope/1.0.21/en/building-blocks/orchestration.mdx

@@ -388,7 +388,7 @@ await chat_room.stop()                  # Disconnect all agents
 
 ## Formatter Reference
 
-> For an introduction to Chat vs MultiAgent formatters and how to choose one, see [Model — Formatter](/versions/1.0.21/en/basic-concepts/model#chat-model).
+> For an introduction to Chat vs MultiAgent formatters and how to choose one, see [Model — Formatter](/agentscope/1.0.21/en/basic-concepts/model#chat-model).
 
 ### Formatter Table
 

versions/1.0.21/en/building-blocks/rag.mdx → agentscope/1.0.21/en/building-blocks/rag.mdx

@@ -318,10 +318,10 @@ asyncio.run(example_multimodal_rag())
 ## Further Reading
 
 <CardGroup cols={2}>
-  <Card title="Context and Memory" icon="memory" href="/versions/1.0.21/en/building-blocks/context-and-memory">
+  <Card title="Context and Memory" icon="memory" href="/agentscope/1.0.21/en/building-blocks/context-and-memory">
     Memory backends for storing and managing session messages.
   </Card>
-  <Card title="Agent" icon="user-robot" href="/versions/1.0.21/en/building-blocks/agent">
+  <Card title="Agent" icon="user-robot" href="/agentscope/1.0.21/en/building-blocks/agent">
     ReActAgent internals, tool registration, and reply lifecycle.
   </Card>
 </CardGroup>

versions/1.0.21/en/index.mdx → agentscope/1.0.21/en/index.mdx

@@ -15,11 +15,11 @@ We design for increasingly agentic LLMs. Our approach leverages the models' reas
 
 ## Start with AgentScope
 
-* [Quickstart ->](/versions/1.0.21/quickstart): Build your first chatbot agent in 5 minutes
-* [Out-of-box agents ->](/versions/1.0.21/out-of-box-agents): Try out our collection of pre-built agents for various use cases
-* [Basic concepts & building blocks ->](/versions/1.0.21/basic-concepts): Learn about the core abstractions in AgentScope and how to use them
-* [Observe & evaluate ->](/versions/1.0.21/observe-and-evaluate): Learn how to observe and evaluate your agents with built-in OTel support and evaluation tools
-* [Deploy & serve ->](/versions/1.0.21/deploy-and-serve): Deploy your agents locally, as serverless in the cloud or on your K8s cluster, with different protocols and tool sandboxes
+* [Quickstart ->](/agentscope/1.0.21/quickstart): Build your first chatbot agent in 5 minutes
+* [Out-of-box agents ->](/agentscope/1.0.21/out-of-box-agents): Try out our collection of pre-built agents for various use cases
+* [Basic concepts & building blocks ->](/agentscope/1.0.21/basic-concepts): Learn about the core abstractions in AgentScope and how to use them
+* [Observe & evaluate ->](/agentscope/1.0.21/observe-and-evaluate): Learn how to observe and evaluate your agents with built-in OTel support and evaluation tools
+* [Deploy & serve ->](/agentscope/1.0.21/deploy-and-serve): Deploy your agents locally, as serverless in the cloud or on your K8s cluster, with different protocols and tool sandboxes
 
 ---
 

versions/1.0.21/en/observe-and-evaluate/observability.mdx → agentscope/1.0.21/en/observe-and-evaluate/observability.mdx

@@ -206,7 +206,7 @@ def my_sync_fn(x: int) -> int:
 
 Token usage is tracked by the model layer and is included in trace metadata where supported. When you connect to **AgentScope Studio**, token consumption is visualized in the Studio UI so you can monitor cost and usage per run. For third-party backends, token-related attributes are exported with the LLM spans according to the OpenTelemetry semantic conventions used by AgentScope.
 
-For programmatic access to usage after a model call, use the `usage` field on the [ChatResponse](/versions/1.0.21/en/building-blocks/models) returned by the model.
+For programmatic access to usage after a model call, use the `usage` field on the [ChatResponse](/agentscope/1.0.21/en/building-blocks/models) returned by the model.
 
 ---
 
@@ -217,4 +217,4 @@ For programmatic access to usage after a model call, use the `usage` field on th
 - Use **tracing_url** to send traces to Arize-Phoenix, Langfuse, Alibaba Cloud CloudMonitor, or any OTLP endpoint.
 - Use **@trace_llm**, **@trace_reply**, **@trace_format**, **@trace_toolkit**, **@trace_embedding**, and **@trace** when implementing custom models, agents, or formatters so they appear in the same trace tree.
 
-For evaluation of agent behavior and benchmarks, see [Evaluation](/versions/1.0.21/en/observe-and-evaluate/evaluation).
+For evaluation of agent behavior and benchmarks, see [Evaluation](/agentscope/1.0.21/en/observe-and-evaluate/evaluation).

versions/1.0.21/en/others/faq.mdx → agentscope/1.0.21/en/others/faq.mdx

@@ -52,7 +52,7 @@ Create a custom model class by inheriting from `agentscope.model.ChatModelBase`
 </Accordion>
 
 <Accordion title="How do I monitor token usage in AgentScope?">
-AgentScope Studio provides visualization of token usage and tracing. See the [Observability](/versions/1.0.21/en/observe-and-evaluate/observability) section for details.
+AgentScope Studio provides visualization of token usage and tracing. See the [Observability](/agentscope/1.0.21/en/observe-and-evaluate/observability) section for details.
 </Accordion>
 
 <Accordion title="Why do I need different Formatters for different LLMs?">
@@ -77,7 +77,7 @@ The two are **complementary**—you can even fine-tune a model to better leverag
 <AccordionGroup>
 
 <Accordion title="How do I create my own agent?">
-You can use the `ReActAgent` class directly, or create a custom agent by inheriting from `AgentBase` or `ReActAgentBase`. See the [Agent](/versions/1.0.21/en/basic-concepts/agent) section for details.
+You can use the `ReActAgent` class directly, or create a custom agent by inheriting from `AgentBase` or `ReActAgentBase`. See the [Agent](/agentscope/1.0.21/en/basic-concepts/agent) section for details.
 </Accordion>
 
 <Accordion title="What's the difference between agent_base, react_agent_base, and react_agent?">
@@ -87,23 +87,23 @@ You can use the `ReActAgent` class directly, or create a custom agent by inherit
 </Accordion>
 
 <Accordion title="Does AgentScope support dynamic Pydantic models for structured output?">
-Yes. AgentScope supports **dynamic JSON Schema generation** and leverages Pydantic for validation in ReAct agents. See the [Agent](/versions/1.0.21/en/basic-concepts/agent) page for details.
+Yes. AgentScope supports **dynamic JSON Schema generation** and leverages Pydantic for validation in ReAct agents. See the [Agent](/agentscope/1.0.21/en/basic-concepts/agent) page for details.
 </Accordion>
 
 <Accordion title="Can I use Anthropic-style skills in AgentScope?">
 Yes. Place your skill definitions in a directory following AgentScope's [skill structure](https://github.com/agentscope-ai/agentscope/blob/main/examples/functionality/agent_skill/README.md), then register them via `toolkit.register_agent_skill()`.
 </Accordion>
 
 <Accordion title="How do I forward the streaming output of agents to my own frontend?">
-Use the pre-hook of the `print` function to forward printed messages. See the [Hooking Functions](/versions/1.0.21/en/building-blocks/hooking-functions) section for details.
+Use the pre-hook of the `print` function to forward printed messages. See the [Hooking Functions](/agentscope/1.0.21/en/building-blocks/hooking-functions) section for details.
 </Accordion>
 
 <Accordion title="What built-in tools does AgentScope provide?">
 AgentScope includes a set of built-in tools such as `execute_python_code`, `execute_shell_command`, and `write_text_file`. You can find the full list under the `agentscope.tool` module.
 </Accordion>
 
 <Accordion title="Is MCP (Model Control Protocol) supported?">
-Yes. AgentScope supports standard-compliant MCP for tool and service integration. See the [Tool Capabilities](/versions/1.0.21/en/building-blocks/tool-capabilities) page for details.
+Yes. AgentScope supports standard-compliant MCP for tool and service integration. See the [Tool Capabilities](/agentscope/1.0.21/en/building-blocks/tool-capabilities) page for details.
 </Accordion>
 
 </AccordionGroup>