feat(filters): release v1.3.0 for async context compression

- Add native i18n support across 9 languages
- Implement non-blocking frontend log emission for zero TTFB delay
- Add token_usage_status_threshold to intelligently control status notifications
- Automatically detect and skip compression for copilot_sdk models
- Set debug_mode default to false for a quieter production environment
- Update documentation and remove legacy bilingual code

.agent/workflows/plugin-development.md

@@ -12,11 +12,11 @@ Reference: `.github/copilot-instructions.md`
 
 ### Bilingual Requirement
 
-Every plugin **MUST** have bilingual versions for both code and documentation:
+Every plugin **MUST** have a single internationalized code file and bilingual documentation:
 
-- **Code**:
-  - English: `plugins/{type}/{name}/{name}.py`
-  - Chinese: `plugins/{type}/{name}/{name_cn}.py` (or `中文名.py`)
+- **Code (i18n)**:
+  - `plugins/{type}/{name}/{name}.py`
+  - The single `.py` file must implement internal i18n (e.g., using `navigator.language` or backend headers) to support multiple languages natively, rather than splitting into separate files.
 - **README**:
   - English: `plugins/{type}/{name}/README.md`
   - Chinese: `plugins/{type}/{name}/README_CN.md`
@@ -81,14 +81,13 @@ Reference: `.github/workflows/release.yml`
 - **Release Information Compliance**: When a release is requested, the agent must generate a standard release summary (English commit title + bilingual bullet points) as defined in Section 3 & 5.
 - **Default Action (Prepare Only)**: When performing a version bump or update, the agent should update all files locally but **STOP** before committing. Present the changes and the **proposed Release/Commit Message** to the user and wait for explicit confirmation to commit/push.
 - **Consistency**: When bumping, update version in **ALL** locations:
-  1. English Code (`.py`)
-  2. Chinese Code (`.py`)
-  3. English README (`README.md`)
-  4. Chinese README (`README_CN.md`)
-  5. Docs Index (`docs/.../index.md`)
-  6. Docs Index CN (`docs/.../index.zh.md`)
-  7. Docs Detail (`docs/.../{name}.md`)
-  8. Docs Detail CN (`docs/.../{name}.zh.md`)
+  1. Code (`.py`)
+  2. English README (`README.md`)
+  3. Chinese README (`README_CN.md`)
+  4. Docs Index (`docs/.../index.md`)
+  5. Docs Index CN (`docs/.../index.zh.md`)
+  6. Docs Detail (`docs/.../{name}.md`)
+  7. Docs Detail CN (`docs/.../{name}.zh.md`)
 
 ### Automated Release Process
 
@@ -120,7 +119,7 @@ When the user confirms a release, the agent **MUST** follow these content standa
    - Before committing, present a "Release Draft" containing:
      - **Title**: e.g., `Release v0.1.1: [Plugin Name] - [Brief Summary]`
      - **Changelog**: English-only list of commits since the last release, including hashes (e.g., `896de02 docs(config): reorder antigravity model alias example`).
-     - **Verification Status**: Confirm all 8+ files have been updated and synced.
+     - **Verification Status**: Confirm all 7+ files have been updated and synced.
 3. **Internal Documentation**: Ensure "What's New" sections in READMEs and `docs/` match exactly the changes being released.
 
 ### Pull Request Check
@@ -134,7 +133,7 @@ When the user confirms a release, the agent **MUST** follow these content standa
 
 Before committing:
 
-- [ ] Code is bilingual and functional?
+- [ ] Code is internal i18n supported (`.py`) and fully functional?
 - [ ] Docstrings have updated version?
 - [ ] READMEs are updated and bilingual?
 - [ ] **Key Capabilities** in READMEs still cover all legacy core features + new features?

.github/copilot-instructions.md

@@ -8,27 +8,26 @@ This document defines the standard conventions and best practices for OpenWebUI
 
 ## 🏗️ 项目结构与命名 (Project Structure & Naming)
 
-### 1. 双语版本要求 (Bilingual Version Requirements)
+### 1. 语言与代码规范 (Language & Code Requirements)
 
 #### 插件代码 (Plugin Code)
 
-每个插件必须提供两个版本：
+每个插件**必须**采用单文件国际化 (i18n) 设计。严禁为不同语言创建独立的源代码文件（如 `_cn.py`）。
 
-1. **英文版本**: `plugin_name.py` - 英文界面、提示词和注释
-2. **中文版本**: `plugin_name_cn.py` - 中文界面、提示词和注释
+1.  **单代码文件**: `plugins/{type}/{name}/{name}.py`
+2.  **内置 i18n**: 必须在代码中根据前端传来的用户语言（如 `__user__` 中的 `language` 或通过 `get_user_language` 脚本读取）动态切换界面显示、提示词和状态日志。
 
-示例：
+示例目录结构：
 ```
 plugins/actions/export_to_docx/
-├── export_to_word.py      # English version
-├── export_to_word_cn.py    # Chinese version
-├── README.md               # English documentation
-└── README_CN.md            # Chinese documentation
+├── export_to_word.py      # 单个代码文件，内置多语言支持
+├── README.md               # 英文文档 (English documentation)
+└── README_CN.md            # 中文文档
 ```
 
 #### 文档 (Documentation)
 
-每个插件目录必须包含双语 README 文件：
+尽管代码是合一的，但为了市场展示和 SEO，每个插件目录仍**必须**包含双语 README 文件：
 
 - `README.md` - English documentation
 - `README_CN.md` - 中文文档
@@ -58,12 +57,10 @@ plugins/actions/export_to_docx/
 plugins/
 ├── actions/           # Action 插件 (用户触发的功能)
 │   ├── my_action/
-│   │   ├── my_action.py          # English version
-│   │   ├── 我的动作.py            # Chinese version
+│   │   ├── my_action.py          # 单文件，内置 i18n
 │   │   ├── README.md              # English documentation
 │   │   └── README_CN.md           # Chinese documentation
-│   ├── ACTION_PLUGIN_TEMPLATE.py      # English template
-│   ├── ACTION_PLUGIN_TEMPLATE_CN.py   # Chinese template
+│   ├── ACTION_PLUGIN_TEMPLATE.py      # 通用 i18n 模板
 │   └── README.md
 ├── filters/           # Filter 插件 (输入处理)
 │   └── ...
@@ -474,7 +471,7 @@ async def get_user_language(self):
 
 #### 适用场景与引导 (Usage Guidelines)
 
-- **语言适配**: 动态获取界面语言 (`ru-RU`, `zh-CN`) 自动切换输出语言。
+- **语言适配**: 动态获取界面语言 (`ru-RU`, `zh-CN`) 自动切换输出语言和 UI 翻译。这对于单文件 i18n 插件至关重要。
 - **时区处理**: 获取 `Intl.DateTimeFormat().resolvedOptions().timeZone` 处理时间。
 - **客户端存储**: 读取 `localStorage` 中的用户偏好设置。
 - **硬件能力**: 获取 `navigator.clipboard` 或 `navigator.geolocation` (需授权)。
@@ -932,8 +929,7 @@ Filter 实例是**单例 (Singleton)**。
 
 ### 1. ✅ 开发检查清单 (Development Checklist)
 
-- [ ] 创建英文版插件代码 (`plugin_name.py`)
-- [ ] 创建中文版插件代码 (`plugin_name_cn.py`)
+- [ ] 代码实现了内置 i18n 逻辑 (`.py`)
 - [ ] 编写英文 README (`README.md`)
 - [ ] 编写中文 README (`README_CN.md`)
 - [ ] 包含标准化文档字符串
@@ -941,7 +937,7 @@ Filter 实例是**单例 (Singleton)**。
 - [ ] 使用 Lucide 图标
 - [ ] 实现 Valves 配置
 - [ ] 使用 logging 而非 print
-- [ ] 测试双语界面
+- [ ] 测试 i18n 界面适配
 - [ ] **一致性检查**: 确保文档、代码、README 同步
 - [ ] **README 结构**: 
     - **Key Capabilities** (英文) / **核心功能** (中文): 必须包含所有核心功能
@@ -988,13 +984,14 @@ Filter 实例是**单例 (Singleton)**。
 2. **变更列表 (Bilingual Changes)**: 
    - 英文: Clear descriptions of technical/functional changes.
    - 中文: 清晰描述用户可见的功能改进或修复。
-3. **核查状态 (Verification)**: 确认版本号已在相关 8+ 处位置同步更新。
+3. **核查状态 (Verification)**: 确认版本号已在相关 7+ 处位置同步更新（1 个代码文件 + 2 个 README + 4 个 Docs 文件）。
 
 ### 4. 🤖 Git 提交与推送规范 (Git Operations & Push Rules)
 
 - **核心原则**: 默认仅进行**本地文件准备**（更新代码、READMEs、Docs、版本号），**严禁**在未获用户明确许可的情况下自动执行 `git commit` 或 `git push`。
 - **允许 (需确认)**: 只有在用户明确表示“发布”、“Commit it”、“Release”或“提交”后，才允许直接推送到 `main` 分支或创建 PR。
 - **功能分支**: 推荐在进行大规模重构或实验性功能开发时，创建功能分支 (`feature/xxx`) 进行隔离。
+- **PR 提交**: 必须使用 GitHub CLI (`gh`) 创建 Pull Request。示例：`gh pr create --title "feat: ..." --body "..."`。
 
 ### 5. 🤝 贡献者认可规范 (Contributor Recognition)
 
@@ -1004,8 +1001,7 @@ Filter 实例是**单例 (Singleton)**。
 
 ## 📚 参考资源 (Reference Resources)
 
-- [Action 插件模板 (英文)](plugins/actions/ACTION_PLUGIN_TEMPLATE.py)
-- [Action 插件模板 (中文)](plugins/actions/ACTION_PLUGIN_TEMPLATE_CN.py)
+- [Action 插件模板](plugins/actions/ACTION_PLUGIN_TEMPLATE.py)
 - [插件开发指南](plugins/actions/PLUGIN_DEVELOPMENT_GUIDE.md)
 - [Lucide Icons](https://lucide.dev/icons/)
 - [OpenWebUI 文档](https://docs.openwebui.com/)

.gitignore

@@ -139,3 +139,4 @@ logs/
 
 # OpenWebUI specific
 # Add any specific ignores for OpenWebUI plugins if needed
+.git-worktrees/

docs/plugins/actions/index.md

@@ -23,7 +23,7 @@ Actions are interactive plugins that:
 
     Intelligently analyzes text content and generates interactive mind maps with beautiful visualizations.
 
-    **Version:** 0.9.2
+    **Version:** 1.0.0
 
     [:octicons-arrow-right-24: Documentation](smart-mind-map.md)
 

docs/plugins/actions/index.zh.md

@@ -23,7 +23,7 @@ Actions 是交互式插件，能够：
 
     智能分析文本并生成交互式、精美的思维导图。
 
-    **版本：** 0.8.0
+    **版本：** 1.0.0
 
     [:octicons-arrow-right-24: 查看文档](smart-mind-map.md)
 

docs/plugins/actions/smart-mind-map.md

@@ -1,7 +1,7 @@
 # Smart Mind Map
 
 <span class="category-badge action">Action</span>
-<span class="version-badge">v0.9.2</span>
+<span class="version-badge">v1.0.0</span>
 
 Intelligently analyzes text content and generates interactive mind maps for better visualization and understanding.
 
@@ -17,7 +17,8 @@ The Smart Mind Map plugin transforms text content into beautiful, interactive mi
 - :material-gesture-swipe: **Rich Controls**: Zoom, reset view, expand level selector (All/2/3) and fullscreen
 - :material-palette: **Theme Aware**: Auto-detects OpenWebUI light/dark theme with manual toggle
 - :material-download: **One-Click Export**: Download high-res PNG, copy SVG, or copy Markdown source
-- :material-translate: **Multi-language**: Matches output language to the input text
+- :material-translate: **i18n Embedded**: One code file smartly detects frontend languages and translates the output.
+- :material-arrow-all: **Auto-Sizing & Direct Embed**: Seamlessly scales to display massive canvas inline (requires setting toggle).
 
 ---
 
@@ -50,6 +51,7 @@ The Smart Mind Map plugin transforms text content into beautiful, interactive mi
 | `MIN_TEXT_LENGTH` | integer | `100` | Minimum characters required before analysis runs |
 | `CLEAR_PREVIOUS_HTML` | boolean | `false` | Clear previous plugin HTML instead of merging |
 | `MESSAGE_COUNT` | integer | `1` | Number of recent messages to include (1–5) |
+| `ENABLE_DIRECT_EMBED_MODE` | boolean | `false` | Enable inline full-width UI for OpenWebUI 0.8.0+ |
 
 ---
 

docs/plugins/actions/smart-mind-map.zh.md

@@ -1,7 +1,7 @@
 # Smart Mind Map（智能思维导图）
 
 <span class="category-badge action">Action</span>
-<span class="version-badge">v0.9.2</span>
+<span class="version-badge">v1.0.0</span>
 
 智能分析文本内容，生成交互式思维导图，帮助你更直观地理解信息结构。
 
@@ -17,7 +17,8 @@ Smart Mind Map 会将文本转换成漂亮的交互式思维导图。插件会
 - :material-gesture-swipe: **丰富控制**：缩放/重置、展开层级（全部/2/3 级）与全屏
 - :material-palette: **主题感知**：自动检测 OpenWebUI 亮/暗色主题并支持手动切换
 - :material-download: **一键导出**：下载高分辨率 PNG、复制 SVG 或 Markdown
-- :material-translate: **多语言**：输出语言与输入文本一致
+- :material-translate: **内置 i18n 语言识别**：单个文件自动检测控制台前端语言，无需繁杂的各种语言包版本。
+- :material-arrow-all: **直出全屏版体验 (需配置开启)**：新版直出渲染抛开沙盒限制，纵情铺满屏幕，享受原生的图表体验。
 
 ---
 
@@ -50,6 +51,7 @@ Smart Mind Map 会将文本转换成漂亮的交互式思维导图。插件会
 | `MIN_TEXT_LENGTH` | integer | `100` | 开始分析所需的最少字符数 |
 | `CLEAR_PREVIOUS_HTML` | boolean | `false` | 生成新导图时是否清除之前的插件 HTML |
 | `MESSAGE_COUNT` | integer | `1` | 用于生成的最近消息数量（1–5） |
+| `ENABLE_DIRECT_EMBED_MODE` | boolean | `false` | 是否开启沉浸式直出模式 (需要 Open WebUI 0.8.0+ ) |
 
 ---
 

docs/plugins/filters/async-context-compression.md

@@ -1,137 +1,81 @@
-# Async Context Compression
+# Async Context Compression Filter
 
-<span class="category-badge filter">Filter</span>
-<span class="version-badge">v1.2.2</span>
+**Author:** [Fu-Jie](https://github.com/Fu-Jie/openwebui-extensions) | **Version:** 1.3.0 | **Project:** [OpenWebUI Extensions](https://github.com/Fu-Jie/openwebui-extensions) | **License:** MIT
 
-Reduces token consumption in long conversations through intelligent summarization while maintaining conversational coherence.
+This filter reduces token consumption in long conversations through intelligent summarization and message compression while keeping conversations coherent.
+
+## What's new in 1.3.0
+
+- **Internationalization (i18n)**: Complete localization of user-facing messages across 9 languages (English, Chinese, Japanese, Korean, French, German, Spanish, Italian).
+- **Smart Status Display**: Added `token_usage_status_threshold` valve (default 80%) to intelligently control when token usage status is shown.
+- **Improved Performance**: Frontend language detection and logging are optimized to be completely non-blocking, maintaining lightning-fast TTFB.
+- **Copilot SDK Integration**: Automatically detects and skips compression for copilot_sdk based models to prevent conflicts.
+- **Configuration**: `debug_mode` is now set to `false` by default for a quieter production experience.
 
 ---
 
-## Overview
+## Core Features
 
-The Async Context Compression filter helps manage token usage in long conversations by:
-
-- Intelligently summarizing older messages
-- Preserving important context
-- Reducing API costs
-- Maintaining conversation coherence
-
-This is especially useful for:
-
-- Long-running conversations
-- Complex multi-turn discussions
-- Cost optimization
-- Token limit management
-
-## Features
-
-- :material-arrow-collapse-vertical: **Smart Compression**: AI-powered context summarization
-- :material-clock-fast: **Async Processing**: Non-blocking background compression
-- :material-memory: **Context Preservation**: Keeps important information
-- :material-currency-usd-off: **Cost Reduction**: Minimize token usage
-- :material-console: **Frontend Debugging**: Debug logs in browser console
-- :material-alert-circle-check: **Enhanced Error Reporting**: Clear error status notifications
-- :material-check-all: **Open WebUI v0.7.x Compatibility**: Dynamic DB session handling
-- :material-account-convert: **Improved Compatibility**: Summary role changed to `assistant`
-- :material-shield-check: **Enhanced Stability**: Resolved race conditions in state management
-- :material-ruler: **Preflight Context Check**: Validates context fit before sending
-- :material-format-align-justify: **Structure-Aware Trimming**: Preserves document structure
-- :material-content-cut: **Native Tool Output Trimming**: Trims verbose tool outputs (Note: Non-native tool outputs are not fully injected into context)
-- :material-chart-bar: **Detailed Token Logging**: Granular token breakdown
-- :material-account-search: **Smart Model Matching**: Inherit config from base models
-- :material-image-off: **Multimodal Support**: Images are preserved but tokens are **NOT** calculated
+- ✅ **Full i18n Support**: Native localization across 9 languages.
+- ✅ Automatic compression triggered by token thresholds.
+- ✅ Asynchronous summarization that does not block chat responses.
+- ✅ Persistent storage via Open WebUI's shared database connection (PostgreSQL, SQLite, etc.).
+- ✅ Flexible retention policy to keep the first and last N messages.
+- ✅ Smart injection of historical summaries back into the context.
+- ✅ Structure-aware trimming that preserves document structure (headers, intro, conclusion).
+- ✅ Native tool output trimming for cleaner context when using function calling.
+- ✅ Real-time context usage monitoring with warning notifications (>90%).
+- ✅ Detailed token logging for precise debugging and optimization.
+- ✅ **Smart Model Matching**: Automatically inherits configuration from base models for custom presets.
+- ⚠ **Multimodal Support**: Images are preserved but their tokens are **NOT** calculated. Please adjust thresholds accordingly.
 
 ---
 
-## Installation
+## Installation & Configuration
 
-1. Download the plugin file: [`async_context_compression.py`](https://github.com/Fu-Jie/openwebui-extensions/tree/main/plugins/filters/async-context-compression)
-2. Upload to OpenWebUI: **Admin Panel** → **Settings** → **Functions**
-3. Configure compression settings
-4. Enable the filter
+### 1) Database (automatic)
+
+- Uses Open WebUI's shared database connection; no extra configuration needed.
+- The `chat_summary` table is created on first run.
+
+### 2) Filter order
+
+- Recommended order: pre-filters (<10) → this filter (10) → post-filters (>10).
 
 ---
 
-## How It Works
+## Configuration Parameters
 
-```mermaid
-graph TD
-    A[Incoming Messages] --> B{Token Count > Threshold?}
-    B -->|No| C[Pass Through]
-    B -->|Yes| D[Summarize Older Messages]
-    D --> E[Preserve Recent Messages]
-    E --> F[Combine Summary + Recent]
-    F --> G[Send to LLM]
-```
+| Parameter                      | Default  | Description                                                                                                                                                           |
+| :----------------------------- | :------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `priority`                     | `10`     | Execution order; lower runs earlier.                                                                                                                                  |
+| `compression_threshold_tokens` | `64000`  | Trigger asynchronous summary when total tokens exceed this value. Set to 50%-70% of your model's context window.                                                      |
+| `max_context_tokens`           | `128000` | Hard cap for context; older messages (except protected ones) are dropped if exceeded.                                                                                 |
+| `keep_first`                   | `1`      | Always keep the first N messages (protects system prompts).                                                                                                           |
+| `keep_last`                    | `6`      | Always keep the last N messages to preserve recent context.                                                                                                           |
+| `summary_model`                | `None`   | Model for summaries. Strongly recommended to set a fast, economical model (e.g., `gemini-2.5-flash`, `deepseek-v3`). Falls back to the current chat model when empty. |
+| `summary_model_max_context`    | `0`      | Max context tokens for the summary model. If 0, falls back to `model_thresholds` or global `max_context_tokens`.                                                      |
+| `max_summary_tokens`           | `16384`  | Maximum tokens for the generated summary.                                                                                                                             |
+| `summary_temperature`          | `0.3`    | Randomness for summary generation. Lower is more deterministic.                                                                                                       |
+| `model_thresholds`             | `{}`     | Per-model overrides for `compression_threshold_tokens` and `max_context_tokens` (useful for mixed models).                                                            |
+| `enable_tool_output_trimming`  | `false`  | When enabled and `function_calling: "native"` is active, trims verbose tool outputs to extract only the final answer.                                                 |
+| `debug_mode`                   | `false`  | Log verbose debug info. Set to `false` in production.                                                                                                                 |
+| `show_debug_log`               | `false`  | Print debug logs to browser console (F12). Useful for frontend debugging.                                                                                             |
+| `show_token_usage_status`      | `true`   | Show token usage status notification in the chat interface.                                                                                                           |
+| `token_usage_status_threshold` | `80`     | The minimum usage percentage (0-100) required to show a context usage status notification.                                                                            |
 
 ---
 
-## Configuration
+## ⭐ Support
 
-| Option | Type | Default | Description |
-|--------|------|---------|-------------|
-| `compression_threshold_tokens` | integer | `64000` | Trigger compression above this token count |
-| `max_context_tokens` | integer | `128000` | Hard limit for context |
-| `keep_first` | integer | `1` | Always keep the first N messages |
-| `keep_last` | integer | `6` | Always keep the last N messages |
-| `summary_model` | string | `None` | Model to use for summarization |
-| `summary_model_max_context` | integer | `0` | Max context tokens for summary model |
-| `max_summary_tokens` | integer | `16384` | Maximum tokens for the summary |
-| `enable_tool_output_trimming` | boolean | `false` | Enable trimming of large tool outputs |
+If this plugin has been useful, a star on [OpenWebUI Extensions](https://github.com/Fu-Jie/openwebui-extensions) is a big motivation for me. Thank you for the support.
 
----
+## Troubleshooting ❓
 
-## Example
+- **Initial system prompt is lost**: Keep `keep_first` greater than 0 to protect the initial message.
+- **Compression effect is weak**: Raise `compression_threshold_tokens` or lower `keep_first` / `keep_last` to allow more aggressive compression.
+- **Submit an Issue**: If you encounter any problems, please submit an issue on GitHub: [OpenWebUI Extensions Issues](https://github.com/Fu-Jie/openwebui-extensions/issues)
 
-### Before Compression
+## Changelog
 
-```
-[Message 1] User: Tell me about Python...
-[Message 2] AI: Python is a programming language...
-[Message 3] User: What about its history?
-[Message 4] AI: Python was created by Guido...
-[Message 5] User: And its features?
-[Message 6] AI: Python has many features...
-... (many more messages)
-[Message 20] User: Current question
-```
-
-### After Compression
-
-```
-[Summary] Previous conversation covered Python basics,
-history, features, and common use cases...
-
-[Message 18] User: Recent question about decorators
-[Message 19] AI: Decorators in Python are...
-[Message 20] User: Current question
-```
-
----
-
-## Requirements
-
-!!! note "Prerequisites"
-    - OpenWebUI v0.3.0 or later
-    - Access to an LLM for summarization
-
-!!! tip "Best Practices"
-    - Set appropriate token thresholds based on your model's context window
-    - Preserve more recent messages for technical discussions
-    - Test compression settings in non-critical conversations first
-
----
-
-## Troubleshooting
-
-??? question "Compression not triggering?"
-    Check if the token count exceeds your configured threshold. Enable debug logging for more details.
-
-??? question "Important context being lost?"
-    Increase the `preserve_recent` setting or lower the compression ratio.
-
----
-
-## Source Code
-
-[:fontawesome-brands-github: View on GitHub](https://github.com/Fu-Jie/openwebui-extensions/tree/main/plugins/filters/async-context-compression){ .md-button }
+See the full history on GitHub: [OpenWebUI Extensions](https://github.com/Fu-Jie/openwebui-extensions)

docs/plugins/filters/async-context-compression.zh.md

@@ -1,137 +1,119 @@
-# Async Context Compression（异步上下文压缩）
+# 异步上下文压缩过滤器
 
-<span class="category-badge filter">Filter</span>
-<span class="version-badge">v1.2.2</span>
+**作者:** [Fu-Jie](https://github.com/Fu-Jie/openwebui-extensions) | **版本:** 1.3.0 | **项目:** [OpenWebUI Extensions](https://github.com/Fu-Jie/openwebui-extensions) | **许可证:** MIT
 
-通过智能摘要减少长对话的 token 消耗，同时保持对话连贯。
+> **重要提示**：为了确保所有过滤器的可维护性和易用性，每个过滤器都应附带清晰、完整的文档，以确保其功能、配置和使用方法得到充分说明。
+
+本过滤器通过智能摘要和消息压缩技术，在保持对话连贯性的同时，显著降低长对话的 Token 消耗。
+
+## 1.3.0 版本更新
+
+- **国际化 (i18n) 支持**: 完成了所有用户可见消息的本地化，现已原生支持 9 种语言（含中、英、日、韩及欧洲主要语言）。
+- **智能状态显示**: 新增 `token_usage_status_threshold` 阀门（默认 80%），可以智能控制何时显示 Token 用量状态，减少不必要的打扰。
+- **性能大幅优化**: 对前端语言检测和日志处理流程进行了非阻塞重构，完全不影响首字节响应时间（TTFB），保持毫秒级极速推流。
+- **Copilot SDK 兼容**: 自动检测并跳过基于 `copilot_sdk` 模型的上下文压缩，避免冲突。
+- **配置项调整**: 为了提供更安静的生产环境体验，`debug_mode` 现已默认设置为 `false`。
 
 ---
 
-## 概览
+## 核心特性
 
-Async Context Compression 过滤器通过以下方式帮助管理长对话的 token 使用：
+- ✅ **全方位国际化**: 原生支持 9 种界面语言。
+- ✅ **自动压缩**: 基于 Token 阈值自动触发上下文压缩。
+- ✅ **异步摘要**: 后台生成摘要，不阻塞当前对话响应。
+- ✅ **持久化存储**: 复用 Open WebUI 共享数据库连接，自动支持 PostgreSQL/SQLite 等。
+- ✅ **灵活保留策略**: 可配置保留对话头部和尾部消息，确保关键信息连贯。
+- ✅ **智能注入**: 将历史摘要智能注入到新上下文中。
+- ✅ **结构感知裁剪**: 智能折叠过长消息，保留文档骨架（标题、首尾）。
+- ✅ **原生工具输出裁剪**: 支持裁剪冗长的工具调用输出。
+- ✅ **实时监控**: 实时监控上下文使用情况，超过 90% 发出警告。
+- ✅ **详细日志**: 提供精确的 Token 统计日志，便于调试。
+- ✅ **智能模型匹配**: 自定义模型自动继承基础模型的阈值配置。
+- ⚠ **多模态支持**: 图片内容会被保留，但其 Token **不参与计算**。请相应调整阈值。
 
-- 智能总结较早的消息
-- 保留关键信息
-- 降低 API 成本
-- 保持对话一致性
-
-特别适用于：
-
-- 长时间会话
-- 多轮复杂讨论
-- 成本优化
-- 上下文长度控制
-
-## 功能特性
-
-- :material-arrow-collapse-vertical: **智能压缩**：AI 驱动的上下文摘要
-- :material-clock-fast: **异步处理**：后台非阻塞压缩
-- :material-memory: **保留上下文**：尽量保留重要信息
-- :material-currency-usd-off: **降低成本**：减少 token 使用
-- :material-console: **前端调试**：支持浏览器控制台日志
-- :material-alert-circle-check: **增强错误报告**：清晰的错误状态通知
-- :material-check-all: **Open WebUI v0.7.x 兼容性**：动态数据库会话处理
-- :material-account-convert: **兼容性提升**：摘要角色改为 `assistant`
-- :material-shield-check: **稳定性增强**：解决状态管理竞态条件
-- :material-ruler: **预检上下文检查**：发送前验证上下文是否超限
-- :material-format-align-justify: **结构感知裁剪**：保留文档结构的智能裁剪
-- :material-content-cut: **原生工具输出裁剪**：自动裁剪冗长的工具输出（注意：非原生工具调用输出不会完整注入上下文）
-- :material-chart-bar: **详细 Token 日志**：提供细粒度的 Token 统计
-- :material-account-search: **智能模型匹配**：自定义模型自动继承基础模型配置
-- :material-image-off: **多模态支持**：图片内容保留但 Token **不参与计算**
+详细的工作原理和流程请参考 [工作流程指南](WORKFLOW_GUIDE_CN.md)。
 
 ---
 
-## 安装
+## 安装与配置
 
-1. 下载插件文件：[`async_context_compression.py`](https://github.com/Fu-Jie/openwebui-extensions/tree/main/plugins/filters/async-context-compression)
-2. 上传到 OpenWebUI：**Admin Panel** → **Settings** → **Functions**
-3. 配置压缩参数
-4. 启用过滤器
+### 1. 数据库（自动）
+
+- 自动使用 Open WebUI 的共享数据库连接，**无需额外配置**。
+- 首次运行自动创建 `chat_summary` 表。
+
+### 2. 过滤器顺序
+
+- 建议顺序：前置过滤器（<10）→ 本过滤器（10）→ 后置过滤器（>10）。
 
 ---
 
-## 工作原理
+## 配置参数
 
-```mermaid
-graph TD
-    A[Incoming Messages] --> B{Token Count > Threshold?}
-    B -->|No| C[Pass Through]
-    B -->|Yes| D[Summarize Older Messages]
-    D --> E[Preserve Recent Messages]
-    E --> F[Combine Summary + Recent]
-    F --> G[Send to LLM]
+您可以在过滤器的设置中调整以下参数：
+
+### 核心参数
+
+| 参数                           | 默认值   | 描述                                                                                  |
+| :----------------------------- | :------- | :------------------------------------------------------------------------------------ |
+| `priority`                     | `10`     | 过滤器执行顺序，数值越小越先执行。                                                    |
+| `compression_threshold_tokens` | `64000`  | **重要**: 当上下文总 Token 超过此值时后台生成摘要，建议设为模型上下文窗口的 50%-70%。 |
+| `max_context_tokens`           | `128000` | **重要**: 上下文硬上限，超过即移除最早消息（保留受保护消息）。                        |
+| `keep_first`                   | `1`      | 始终保留对话开始的 N 条消息，保护系统提示或环境变量。                                 |
+| `keep_last`                    | `6`      | 始终保留对话末尾的 N 条消息，确保最近上下文连贯。                                     |
+
+### 摘要生成配置
+
+| 参数                  | 默认值  | 描述                                                                                                                                        |
+| :-------------------- | :------ | :------------------------------------------------------------------------------------------------------------------------------------------ |
+| `summary_model`       | `None`  | 用于生成摘要的模型 ID。**强烈建议**配置快速、经济、上下文窗口大的模型（如 `gemini-2.5-flash`、`deepseek-v3`）。留空则尝试复用当前对话模型。 |
+| `summary_model_max_context` | `0`     | 摘要模型的最大上下文 Token 数。如果为 0，则回退到 `model_thresholds` 或全局 `max_context_tokens`。                                          |
+| `max_summary_tokens`  | `16384` | 生成摘要时允许的最大 Token 数。                                                                                                             |
+| `summary_temperature` | `0.1`   | 控制摘要生成的随机性，较低的值结果更稳定。                                                                                                  |
+
+### 高级配置
+
+#### `model_thresholds` (模型特定阈值)
+
+这是一个字典配置，可为特定模型 ID 覆盖全局 `compression_threshold_tokens` 与 `max_context_tokens`，适用于混合不同上下文窗口的模型。
+
+**默认包含 GPT-4、Claude 3.5、Gemini 1.5/2.0、Qwen 2.5/3、DeepSeek V3 等推荐阈值。**
+
+**配置示例：**
+
+```json
+{
+  "gpt-4": {
+    "compression_threshold_tokens": 8000,
+    "max_context_tokens": 32000
+  },
+  "gemini-2.5-flash": {
+    "compression_threshold_tokens": 734000,
+    "max_context_tokens": 1048576
+  }
+}
 ```
 
----
-
-## 配置项
-
-| 选项 | 类型 | 默认值 | 说明 |
-|--------|------|---------|-------------|
-| `compression_threshold_tokens` | integer | `64000` | 超过该 token 数触发压缩 |
-| `max_context_tokens` | integer | `128000` | 上下文硬性上限 |
-| `keep_first` | integer | `1` | 始终保留的前 N 条消息 |
-| `keep_last` | integer | `6` | 始终保留的后 N 条消息 |
-| `summary_model` | string | `None` | 用于摘要的模型 |
-| `summary_model_max_context` | integer | `0` | 摘要模型的最大上下文 Token 数 |
-| `max_summary_tokens` | integer | `16384` | 摘要的最大 token 数 |
-| `enable_tool_output_trimming` | boolean | `false` | 启用长工具输出裁剪 |
+| 参数                           | 默认值   | 描述                                                                                                                                    |
+| :----------------------------- | :------- | :-------------------------------------------------------------------------------------------------------------------------------------- |
+| `enable_tool_output_trimming`  | `false`  | 启用时，若 `function_calling: "native"` 激活，将裁剪冗长的工具输出以仅提取最终答案。                                                        |
+| `debug_mode`                   | `false`   | 是否在 Open WebUI 的控制台日志中打印详细的调试信息。生产环境默认且建议设为 `false`。 |
+| `show_debug_log`               | `false`  | 是否在浏览器控制台 (F12) 打印调试日志。便于前端调试。                                                                   |
+| `show_token_usage_status`      | `true`   | 是否在对话结束时显示 Token 使用情况的状态通知。                                                                         |
+| `token_usage_status_threshold` | `80`     | 触发显示上下文用量状态通知的最低百分比阈值 (0-100)。                                                                    |
 
 ---
 
-## 示例
+## ⭐ 支持
 
-### 压缩前
+如果这个插件对你有帮助，欢迎到 [OpenWebUI Extensions](https://github.com/Fu-Jie/openwebui-extensions) 点个 Star，这将是我持续改进的动力，感谢支持。
 
-```
-[Message 1] User: Tell me about Python...
-[Message 2] AI: Python is a programming language...
-[Message 3] User: What about its history?
-[Message 4] AI: Python was created by Guido...
-[Message 5] User: And its features?
-[Message 6] AI: Python has many features...
-... (many more messages)
-[Message 20] User: Current question
-```
+## 故障排除 (Troubleshooting) ❓
 
-### 压缩后
+- **初始系统提示丢失**：将 `keep_first` 设置为大于 0。
+- **压缩效果不明显**：提高 `compression_threshold_tokens`，或降低 `keep_first` / `keep_last` 以增强压缩力度。
+- **提交 Issue**: 如果遇到任何问题，请在 GitHub 上提交 Issue：[OpenWebUI Extensions Issues](https://github.com/Fu-Jie/openwebui-extensions/issues)
 
-```
-[Summary] Previous conversation covered Python basics,
-history, features, and common use cases...
+## 更新日志
 
-[Message 18] User: Recent question about decorators
-[Message 19] AI: Decorators in Python are...
-[Message 20] User: Current question
-```
-
----
-
-## 运行要求
-
-!!! note "前置条件"
-    - OpenWebUI v0.3.0 及以上
-    - 需要可用的 LLM 用于摘要
-
-!!! tip "最佳实践"
-    - 根据模型上下文窗口设置合适的 token 阈值
-    - 技术讨论可适当提高 `preserve_recent`
-    - 先在非关键对话中测试压缩效果
-
----
-
-## 常见问题
-
-??? question "没有触发压缩？"
-    检查 token 数是否超过配置的阈值，并开启调试日志了解细节。
-
-??? question "重要上下文丢失？"
-    提高 `preserve_recent` 或降低压缩比例。
-
----
-
-## 源码
-
-[:fontawesome-brands-github: 在 GitHub 查看](https://github.com/Fu-Jie/openwebui-extensions/tree/main/plugins/filters/async-context-compression){ .md-button }
+完整历史请查看 GitHub 项目： [OpenWebUI Extensions](https://github.com/Fu-Jie/openwebui-extensions)

docs/plugins/filters/index.md

@@ -22,7 +22,7 @@ Filters act as middleware in the message pipeline:
 
     Reduces token consumption in long conversations through intelligent summarization while maintaining coherence.
 
-    **Version:** 1.2.2
+    **Version:** 1.3.0
 
     [:octicons-arrow-right-24: Documentation](async-context-compression.md)
 

docs/plugins/filters/index.zh.md

@@ -22,7 +22,7 @@ Filter 充当消息管线中的中间件：
 
     通过智能总结减少长对话的 token 消耗，同时保持连贯性。
 
-    **版本：** 1.2.2
+    **版本：** 1.3.0
 
     [:octicons-arrow-right-24: 查看文档](async-context-compression.md)
 

plugins/actions/smart-mind-map/README.md

@@ -2,21 +2,26 @@
 
 Smart Mind Map is a powerful OpenWebUI action plugin that intelligently analyzes long-form text content and automatically generates interactive mind maps, helping users structure and visualize knowledge.
 
-**Author:** [Fu-Jie](https://github.com/Fu-Jie/openwebui-extensions) | **Version:** 0.9.2 | **Project:** [OpenWebUI Extensions](https://github.com/Fu-Jie/openwebui-extensions) | **License:** MIT
+**Author:** [Fu-Jie](https://github.com/Fu-Jie) | **Version:** 1.0.0 | **Project:** [OpenWebUI Extensions](https://github.com/Fu-Jie/openwebui-extensions) | **License:** MIT
 
-## What's New in v0.9.2
+## What's New in v1.0.0
 
-**Language Rule Alignment**
+### Direct Embed & UI Refinements
 
-- **Input Language First**: Mind map output now strictly matches the input text language.
-- **Consistent Behavior**: Matches the infographic language rule for predictable multilingual output.
+- **Native Multi-language UI (i18n)**: The plugin interface (buttons, settings, status) now automatically adapts to your browser's language setting for a seamless global experience.
+- **Direct Embed Mode**: Introduced a native-like inline display mode for Open WebUI 0.8.0+, enabling a seamless full-width canvas.
+- **Adaptive Auto-Sizing**: Mind map now dynamically scales its height and perfectly refits to the window to eliminate scrollbar artifacts.
+- **Subdued & Compact UI**: Completely redesigned the header tooling bar to a slender, single-line configuration to maximize visual rendering space.
+- **Configurable Experience**: Added `ENABLE_DIRECT_EMBED_MODE` valve to explicitly toggle the new inline rendering behavior.
 
 ## Key Features 🔑
 
 - ✅ **Intelligent Text Analysis**: Automatically identifies core themes, key concepts, and hierarchical structures.
+- ✅ **Native Multi-language UI**: Automatic interface translation (i18n) based on system language for a native feel.
 - ✅ **Interactive Visualization**: Generates beautiful interactive mind maps based on Markmap.js.
+- ✅ **Direct Embed Mode**: (Optional) For Open WebUI 0.8.0+, render natively inline to fill entire UI width.
 - ✅ **High-Resolution PNG Export**: Export mind maps as high-quality PNG images (9x scale).
-- ✅ **Complete Control Panel**: Zoom controls, expand level selection, and fullscreen mode.
+- ✅ **Complete Control Panel**: Zoom controls, expand level selection, and fullscreen mode within a compact toolbar.
 - ✅ **Theme Switching**: Manual theme toggle button with automatic theme detection.
 - ✅ **Image Output Mode**: Generate static SVG images embedded directly in Markdown for cleaner history.
 
@@ -37,6 +42,7 @@ Smart Mind Map is a powerful OpenWebUI action plugin that intelligently analyzes
 | `CLEAR_PREVIOUS_HTML` | `false` | Whether to clear previous plugin-generated HTML content. |
 | `MESSAGE_COUNT` | `1` | Number of recent messages to use for generation (1-5). |
 | `OUTPUT_MODE` | `html` | Output mode: `html` (interactive) or `image` (static). |
+| `ENABLE_DIRECT_EMBED_MODE` | `false` | Enable Direct Embed Mode (Open WebUI 0.8.0+ native layout) instead of Legacy Mode. |
 
 ## ⭐ Support
 

plugins/actions/smart-mind-map/README_CN.md

@@ -2,21 +2,26 @@
 
 思维导图是一个强大的 OpenWebUI 动作插件，能够智能分析长篇文本内容，自动生成交互式思维导图，帮助用户结构化和可视化知识。
 
-**作者:** [Fu-Jie](https://github.com/Fu-Jie/openwebui-extensions) | **版本:** 0.9.2 | **项目:** [OpenWebUI Extensions](https://github.com/Fu-Jie/openwebui-extensions) | **许可证:** MIT
+**作者:** [Fu-Jie](https://github.com/Fu-Jie) | **版本:** 1.0.0 | **项目:** [OpenWebUI Extensions](https://github.com/Fu-Jie/openwebui-extensions) | **许可证:** MIT
 
-## v0.9.2 更新亮点
+## v1.0.0 最新更新
 
-**语言规则对齐**
+### 嵌入式直出与 UI 细节全线重构
 
-- **输入语言优先**：导图输出严格与输入文本语言一致。
-- **一致性提升**：与信息图语言规则保持一致，多语言输出更可预期。
+- **原生多语言界面 (Native i18n)**：插件界面（按钮、设置说明、状态提示）现在会根据您浏览器的语言设置自动适配系统语言。
+- **原生态嵌入模式 (Direct Embed)**：针对 Open WebUI 0.8.0+ 的前端架构支持了纯正的内容内联（Inline）直出模式，不再受气泡和 Markdown 隔离，真正撑满屏幕宽度。
+- **自动响应边界 (Auto-Sizing)**：突破以前高度僵死的问题。思维导图现在可以根据您的当前屏幕大小弹性伸缩（动态 `clamp()` 高度），彻底消灭丑陋的局部滚动条与白边。
+- **极简专业 UI (Compact UI)**：推倒重做了头部的菜单栏，统一使用了一套干净、单行的极简全透明微拟物 Toolbar 设计，为导图画布省下极大的垂直空间。
+- **模式配置自由**：为了照顾阅读流连贯的习惯，新增了 `ENABLE_DIRECT_EMBED_MODE` 配置开关。您必须在设置中显式开启才能体验宽广内联全屏模式。
 
 ## 核心特性 🔑
 
 - ✅ **智能文本分析**：自动识别文本的核心主题、关键概念和层次结构。
+- ✅ **原生多语言界面**：根据系统语言自动切换界面语言 (i18n)，提供原生交互体验。
 - ✅ **交互式可视化**：基于 Markmap.js 生成美观的交互式思维导图。
+- ✅ **直出全景内嵌 (Direct Embed)**：(可选开关) 对于 Open WebUI 0.8.0+，直接填补整个前端宽度，去除气泡剥离感。
 - ✅ **高分辨率 PNG 导出**：导出高质量的 PNG 图片（9 倍分辨率）。
-- ✅ **完整控制面板**：缩放控制、展开层级选择、全屏模式。
+- ✅ **完整控制面板**：极简清爽的单行大屏缩放控制、展开层级选择、全局全屏等核心操作。
 - ✅ **主题切换**：手动主题切换按钮与自动主题检测。
 - ✅ **图片输出模式**：生成静态 SVG 图片直接嵌入 Markdown，聊天记录更简洁。
 
@@ -37,6 +42,7 @@
 | `CLEAR_PREVIOUS_HTML` | `false` | 在生成新的思维导图时，是否清除之前的 HTML 内容。 |
 | `MESSAGE_COUNT` | `1` | 用于生成思维导图的最近消息数量（1-5）。 |
 | `OUTPUT_MODE` | `html` | 输出模式：`html`（交互式）或 `image`（静态图片）。 |
+| `ENABLE_DIRECT_EMBED_MODE` | `false` | 是否开启沉浸式直出嵌入模式（需要 Open WebUI v0.8.0+ 环境）。如果保持 `false` 将会维持旧版的对话流 Markdown 渲染模式。 |
 
 ## ⭐ 支持
 

plugins/filters/async-context-compression/README.md

@@ -1,18 +1,22 @@
 # Async Context Compression Filter
 
-**Author:** [Fu-Jie](https://github.com/Fu-Jie/openwebui-extensions) | **Version:** 1.2.2 | **Project:** [OpenWebUI Extensions](https://github.com/Fu-Jie/openwebui-extensions) | **License:** MIT
+**Author:** [Fu-Jie](https://github.com/Fu-Jie/openwebui-extensions) | **Version:** 1.3.0 | **Project:** [OpenWebUI Extensions](https://github.com/Fu-Jie/openwebui-extensions) | **License:** MIT
 
 This filter reduces token consumption in long conversations through intelligent summarization and message compression while keeping conversations coherent.
 
-## What's new in 1.2.2
+## What's new in 1.3.0
 
-- **Critical Fix**: Resolved `TypeError: 'str' object is not callable` caused by variable name conflict in logging function.
-- **Compatibility**: Enhanced `params` handling to support Pydantic objects, improving compatibility with different OpenWebUI versions.
+- **Internationalization (i18n)**: Complete localization of user-facing messages across 9 languages (English, Chinese, Japanese, Korean, French, German, Spanish, Italian).
+- **Smart Status Display**: Added `token_usage_status_threshold` valve (default 80%) to intelligently control when token usage status is shown.
+- **Improved Performance**: Frontend language detection and logging are optimized to be completely non-blocking, maintaining lightning-fast TTFB.
+- **Copilot SDK Integration**: Automatically detects and skips compression for copilot_sdk based models to prevent conflicts.
+- **Configuration**: `debug_mode` is now set to `false` by default for a quieter production experience.
 
 ---
 
 ## Core Features
 
+- ✅ **Full i18n Support**: Native localization across 9 languages.
 - ✅ Automatic compression triggered by token thresholds.
 - ✅ Asynchronous summarization that does not block chat responses.
 - ✅ Persistent storage via Open WebUI's shared database connection (PostgreSQL, SQLite, etc.).
@@ -55,8 +59,10 @@ This filter reduces token consumption in long conversations through intelligent
 | `summary_temperature`          | `0.3`    | Randomness for summary generation. Lower is more deterministic.                                                                                                       |
 | `model_thresholds`             | `{}`     | Per-model overrides for `compression_threshold_tokens` and `max_context_tokens` (useful for mixed models).                                                            |
 | `enable_tool_output_trimming`  | `false`  | When enabled and `function_calling: "native"` is active, trims verbose tool outputs to extract only the final answer.                                                 |
-| `debug_mode`                   | `true`   | Log verbose debug info. Set to `false` in production.                                                                                                                 |
+| `debug_mode`                   | `false`  | Log verbose debug info. Set to `false` in production.                                                                                                                 |
 | `show_debug_log`               | `false`  | Print debug logs to browser console (F12). Useful for frontend debugging.                                                                                             |
+| `show_token_usage_status`      | `true`   | Show token usage status notification in the chat interface.                                                                                                           |
+| `token_usage_status_threshold` | `80`     | The minimum usage percentage (0-100) required to show a context usage status notification.                                                                            |
 
 ---
 

plugins/filters/async-context-compression/README_CN.md

@@ -1,20 +1,24 @@
 # 异步上下文压缩过滤器
 
-**作者:** [Fu-Jie](https://github.com/Fu-Jie/openwebui-extensions) | **版本:** 1.2.2 | **项目:** [OpenWebUI Extensions](https://github.com/Fu-Jie/openwebui-extensions) | **许可证:** MIT
+**作者:** [Fu-Jie](https://github.com/Fu-Jie/openwebui-extensions) | **版本:** 1.3.0 | **项目:** [OpenWebUI Extensions](https://github.com/Fu-Jie/openwebui-extensions) | **许可证:** MIT
 
 > **重要提示**：为了确保所有过滤器的可维护性和易用性，每个过滤器都应附带清晰、完整的文档，以确保其功能、配置和使用方法得到充分说明。
 
 本过滤器通过智能摘要和消息压缩技术，在保持对话连贯性的同时，显著降低长对话的 Token 消耗。
 
-## 1.2.2 版本更新
+## 1.3.0 版本更新
 
-- **严重错误修复**: 解决了因日志函数变量名冲突导致的 `TypeError: 'str' object is not callable` 错误。
-- **兼容性增强**: 改进了 `params` 处理逻辑以支持 Pydantic 对象，提高了对不同 OpenWebUI 版本的兼容性。
+- **国际化 (i18n) 支持**: 完成了所有用户可见消息的本地化，现已原生支持 9 种语言（含中、英、日、韩及欧洲主要语言）。
+- **智能状态显示**: 新增 `token_usage_status_threshold` 阀门（默认 80%），可以智能控制何时显示 Token 用量状态，减少不必要的打扰。
+- **性能大幅优化**: 对前端语言检测和日志处理流程进行了非阻塞重构，完全不影响首字节响应时间（TTFB），保持毫秒级极速推流。
+- **Copilot SDK 兼容**: 自动检测并跳过基于 `copilot_sdk` 模型的上下文压缩，避免冲突。
+- **配置项调整**: 为了提供更安静的生产环境体验，`debug_mode` 现已默认设置为 `false`。
 
 ---
 
 ## 核心特性
 
+- ✅ **全方位国际化**: 原生支持 9 种界面语言。
 - ✅ **自动压缩**: 基于 Token 阈值自动触发上下文压缩。
 - ✅ **异步摘要**: 后台生成摘要，不阻塞当前对话响应。
 - ✅ **持久化存储**: 复用 Open WebUI 共享数据库连接，自动支持 PostgreSQL/SQLite 等。
@@ -93,9 +97,10 @@
 | 参数                           | 默认值   | 描述                                                                                                                                    |
 | :----------------------------- | :------- | :-------------------------------------------------------------------------------------------------------------------------------------- |
 | `enable_tool_output_trimming`  | `false`  | 启用时，若 `function_calling: "native"` 激活，将裁剪冗长的工具输出以仅提取最终答案。                                                        |
-| `debug_mode`                   | `true`   | 是否在 Open WebUI 的控制台日志中打印详细的调试信息（如 Token 计数、压缩进度、数据库操作等）。生产环境建议设为 `false`。 |
+| `debug_mode`                   | `false`   | 是否在 Open WebUI 的控制台日志中打印详细的调试信息。生产环境默认且建议设为 `false`。 |
 | `show_debug_log`               | `false`  | 是否在浏览器控制台 (F12) 打印调试日志。便于前端调试。                                                                   |
 | `show_token_usage_status`      | `true`   | 是否在对话结束时显示 Token 使用情况的状态通知。                                                                         |
+| `token_usage_status_threshold` | `80`     | 触发显示上下文用量状态通知的最低百分比阈值 (0-100)。                                                                    |
 
 ---
 

plugins/filters/async-context-compression/async_context_compression.py

@@ -5,17 +5,17 @@ author: Fu-Jie
 author_url: https://github.com/Fu-Jie/openwebui-extensions
 funding_url: https://github.com/open-webui
 description: Reduces token consumption in long conversations while maintaining coherence through intelligent summarization and message compression.
-version: 1.2.2
+version: 1.3.0
 openwebui_id: b1655bc8-6de9-4cad-8cb5-a6f7829a02ce
 license: MIT
 
 ═══════════════════════════════════════════════════════════════════════════════
-📌 What's new in 1.2.1
+📌 What's new in 1.3.0
 ═══════════════════════════════════════════════════════════════════════════════
 
-  ✅ Smart Configuration: Automatically detects base model settings for custom models and adds `summary_model_max_context` for independent summary limits.
-  ✅ Performance & Refactoring: Optimized threshold parsing with caching and removed redundant code for better efficiency.
-  ✅ Bug Fixes & Modernization: Fixed `datetime` deprecation warnings and corrected type annotations.
+  ✅ Smart Status Display: Added `token_usage_status_threshold` valve (default 80%) to control when token usage status is shown, reducing unnecessary notifications.
+  ✅ Copilot SDK Integration: Automatically detects and skips compression for copilot_sdk based models to prevent conflicts.
+  ✅ Improved User Experience: Status messages now only appear when token usage exceeds the configured threshold, keeping the interface cleaner.
 
 ═══════════════════════════════════════════════════════════════════════════════
 📌 Overview
@@ -150,7 +150,7 @@ summary_temperature
   Description: Controls the randomness of the summary generation. Lower values produce more deterministic output.
 
 debug_mode
-  Default: true
+  Default: false
   Description: Prints detailed debug information to the log. Recommended to set to `false` in production.
 
 show_debug_log
@@ -268,6 +268,7 @@ import hashlib
 import time
 import contextlib
 import logging
+from functools import lru_cache
 
 # Setup logger
 logger = logging.getLogger(__name__)
@@ -391,6 +392,130 @@ class ChatSummary(owui_Base):
     )
 
 
+TRANSLATIONS = {
+    "en-US": {
+        "status_context_usage": "Context Usage (Estimated): {tokens} / {max_tokens} Tokens ({ratio}%)",
+        "status_high_usage": " | ⚠️ High Usage",
+        "status_loaded_summary": "Loaded historical summary (Hidden {count} historical messages)",
+        "status_context_summary_updated": "Context Summary Updated: {tokens} / {max_tokens} Tokens ({ratio}%)",
+        "status_generating_summary": "Generating context summary in background...",
+        "status_summary_error": "Summary Error: {error}",
+        "summary_prompt_prefix": "【Previous Summary: The following is a summary of the historical conversation, provided for context only. Do not reply to the summary content itself; answer the subsequent latest questions directly.】\n\n",
+        "summary_prompt_suffix": "\n\n---\nBelow is the recent conversation:",
+        "tool_trimmed": "... [Tool outputs trimmed]\n{content}",
+        "content_collapsed": "\n... [Content collapsed] ...\n",
+    },
+    "zh-CN": {
+        "status_context_usage": "上下文用量 (预估): {tokens} / {max_tokens} Tokens ({ratio}%)",
+        "status_high_usage": " | ⚠️ 用量较高",
+        "status_loaded_summary": "已加载历史总结 (隐藏了 {count} 条历史消息)",
+        "status_context_summary_updated": "上下文总结已更新: {tokens} / {max_tokens} Tokens ({ratio}%)",
+        "status_generating_summary": "正在后台生成上下文总结...",
+        "status_summary_error": "总结生成错误: {error}",
+        "summary_prompt_prefix": "【前情提要：以下是历史对话的总结，仅供上下文参考。请不要回复总结内容本身，直接回答之后最新的问题。】\n\n",
+        "summary_prompt_suffix": "\n\n---\n以下是最近的对话：",
+        "tool_trimmed": "... [工具输出已裁剪]\n{content}",
+        "content_collapsed": "\n... [内容已折叠] ...\n",
+    },
+    "zh-HK": {
+        "status_context_usage": "上下文用量 (預估): {tokens} / {max_tokens} Tokens ({ratio}%)",
+        "status_high_usage": " | ⚠️ 用量較高",
+        "status_loaded_summary": "已載入歷史總結 (隱藏了 {count} 條歷史訊息)",
+        "status_context_summary_updated": "上下文總結已更新: {tokens} / {max_tokens} Tokens ({ratio}%)",
+        "status_generating_summary": "正在後台生成上下文總結...",
+        "status_summary_error": "總結生成錯誤: {error}",
+        "summary_prompt_prefix": "【前情提要：以下是歷史對話的總結，僅供上下文參考。請不要回覆總結內容本身，直接回答之後最新的問題。】\n\n",
+        "summary_prompt_suffix": "\n\n---\n以下是最近的對話：",
+        "tool_trimmed": "... [工具輸出已裁剪]\n{content}",
+        "content_collapsed": "\n... [內容已折疊] ...\n",
+    },
+    "zh-TW": {
+        "status_context_usage": "上下文用量 (預估): {tokens} / {max_tokens} Tokens ({ratio}%)",
+        "status_high_usage": " | ⚠️ 用量較高",
+        "status_loaded_summary": "已載入歷史總結 (隱藏了 {count} 條歷史訊息)",
+        "status_context_summary_updated": "上下文總結已更新: {tokens} / {max_tokens} Tokens ({ratio}%)",
+        "status_generating_summary": "正在後台生成上下文總結...",
+        "status_summary_error": "總結生成錯誤: {error}",
+        "summary_prompt_prefix": "【前情提要：以下是歷史對話的總結，僅供上下文参考。請不要回覆總結內容本身，直接回答之後最新的問題。】\n\n",
+        "summary_prompt_suffix": "\n\n---\n以下是最近的對話：",
+        "tool_trimmed": "... [工具輸出已裁剪]\n{content}",
+        "content_collapsed": "\n... [內容已折疊] ...\n",
+    },
+    "ja-JP": {
+        "status_context_usage": "コンテキスト使用量 (推定): {tokens} / {max_tokens} トークン ({ratio}%)",
+        "status_high_usage": " | ⚠️ 使用量高",
+        "status_loaded_summary": "履歴の要約を読み込みました ({count} 件の履歴メッセージを非表示)",
+        "status_context_summary_updated": "コンテキストの要約が更新されました: {tokens} / {max_tokens} トークン ({ratio}%)",
+        "status_generating_summary": "バックグラウンドでコンテキスト要約を生成しています...",
+        "status_summary_error": "要約エラー: {error}",
+        "summary_prompt_prefix": "【これまでのあらすじ：以下は過去の会話の要約であり、コンテキストの参考としてのみ提供されます。要約の内容自体には返答せず、その後の最新の質問に直接答えてください。】\n\n",
+        "summary_prompt_suffix": "\n\n---\n以下は最近の会話です：",
+        "tool_trimmed": "... [ツールの出力をトリミングしました]\n{content}",
+        "content_collapsed": "\n... [コンテンツが折りたたまれました] ...\n",
+    },
+    "ko-KR": {
+        "status_context_usage": "컨텍스트 사용량 (예상): {tokens} / {max_tokens} 토큰 ({ratio}%)",
+        "status_high_usage": " | ⚠️ 사용량 높음",
+        "status_loaded_summary": "이전 요약 불러옴 ({count}개의 이전 메시지 숨김)",
+        "status_context_summary_updated": "컨텍스트 요약 업데이트됨: {tokens} / {max_tokens} 토큰 ({ratio}%)",
+        "status_generating_summary": "백그라운드에서 컨텍스트 요약 생성 중...",
+        "status_summary_error": "요약 오류: {error}",
+        "summary_prompt_prefix": "【이전 요약: 다음은 이전 대화의 요약이며 문맥 참고용으로만 제공됩니다. 요약 내용 자체에 답하지 말고 последу의 최신 질문에 직접 답하세요.】\n\n",
+        "summary_prompt_suffix": "\n\n---\n다음은 최근 대화입니다:",
+        "tool_trimmed": "... [도구 출력 잘림]\n{content}",
+        "content_collapsed": "\n... [내용 접힘] ...\n",
+    },
+    "fr-FR": {
+        "status_context_usage": "Utilisation du contexte (estimée) : {tokens} / {max_tokens} jetons ({ratio}%)",
+        "status_high_usage": " | ⚠️ Utilisation élevée",
+        "status_loaded_summary": "Résumé historique chargé ({count} messages d'historique masqués)",
+        "status_context_summary_updated": "Résumé du contexte mis à jour : {tokens} / {max_tokens} jetons ({ratio}%)",
+        "status_generating_summary": "Génération du résumé du contexte en arrière-plan...",
+        "status_summary_error": "Erreur de résumé : {error}",
+        "summary_prompt_prefix": "【Résumé précédent : Ce qui suit est un résumé de la conversation historique, fourni uniquement pour le contexte. Ne répondez pas au contenu du résumé lui-même ; répondez directement aux dernières questions.】\n\n",
+        "summary_prompt_suffix": "\n\n---\nVoici la conversation récente :",
+        "tool_trimmed": "... [Sorties d'outils coupées]\n{content}",
+        "content_collapsed": "\n... [Contenu réduit] ...\n",
+    },
+    "de-DE": {
+        "status_context_usage": "Kontextnutzung (geschätzt): {tokens} / {max_tokens} Tokens ({ratio}%)",
+        "status_high_usage": " | ⚠️ Hohe Nutzung",
+        "status_loaded_summary": "Historische Zusammenfassung geladen ({count} historische Nachrichten ausgeblendet)",
+        "status_context_summary_updated": "Kontextzusammenfassung aktualisiert: {tokens} / {max_tokens} Tokens ({ratio}%)",
+        "status_generating_summary": "Kontextzusammenfassung wird im Hintergrund generiert...",
+        "status_summary_error": "Zusammenfassungsfehler: {error}",
+        "summary_prompt_prefix": "【Vorherige Zusammenfassung: Das Folgende ist eine Zusammenfassung der historischen Konversation, die nur als Kontext dient. Antworten Sie nicht auf den Inhalt der Zusammenfassung selbst, sondern direkt auf die nachfolgenden neuesten Fragen.】\n\n",
+        "summary_prompt_suffix": "\n\n---\nHier ist die jüngste Konversation:",
+        "tool_trimmed": "... [Werkzeugausgaben gekürzt]\n{content}",
+        "content_collapsed": "\n... [Inhalt ausgeblendet] ...\n",
+    },
+    "es-ES": {
+        "status_context_usage": "Uso del contexto (estimado): {tokens} / {max_tokens} Tokens ({ratio}%)",
+        "status_high_usage": " | ⚠️ Uso elevado",
+        "status_loaded_summary": "Resumen histórico cargado ({count} mensajes históricos ocultos)",
+        "status_context_summary_updated": "Resumen del contexto actualizado: {tokens} / {max_tokens} Tokens ({ratio}%)",
+        "status_generating_summary": "Generando resumen del contexto en segundo plano...",
+        "status_summary_error": "Error de resumen: {error}",
+        "summary_prompt_prefix": "【Resumen anterior: El siguiente es un resumen de la conversación histórica, proporcionado solo como contexto. No responda al contenido del resumen en sí; responda directamente a las preguntas más recientes.】\n\n",
+        "summary_prompt_suffix": "\n\n---\nA continuación se muestra la conversación reciente:",
+        "tool_trimmed": "... [Salidas de herramientas recortadas]\n{content}",
+        "content_collapsed": "\n... [Contenido contraído] ...\n",
+    },
+    "it-IT": {
+        "status_context_usage": "Utilizzo contesto (stimato): {tokens} / {max_tokens} Token ({ratio}%)",
+        "status_high_usage": " | ⚠️ Utilizzo elevato",
+        "status_loaded_summary": "Riepilogo storico caricato ({count} messaggi storici nascosti)",
+        "status_context_summary_updated": "Riepilogo contesto aggiornato: {tokens} / {max_tokens} Token ({ratio}%)",
+        "status_generating_summary": "Generazione riepilogo contesto in background...",
+        "status_summary_error": "Errore riepilogo: {error}",
+        "summary_prompt_prefix": "【Riepilogo precedente: Il seguente è un riepilogo della conversazione storica, fornito solo per contesto. Non rispondere al contenuto del riepilogo stesso; rispondi direttamente alle domande più recenti.】\n\n",
+        "summary_prompt_suffix": "\n\n---\nDi seguito è riportata la conversazione recente:",
+        "tool_trimmed": "... [Output degli strumenti tagliati]\n{content}",
+        "content_collapsed": "\n... [Contenuto compresso] ...\n",
+    },
+}
+
+
 # Global cache for tiktoken encoding
 TIKTOKEN_ENCODING = None
 if tiktoken:
@@ -400,6 +525,26 @@ if tiktoken:
         logger.error(f"[Init] Failed to load tiktoken encoding: {e}")
 
 
+@lru_cache(maxsize=1024)
+def _get_cached_tokens(text: str) -> int:
+    """Calculates tokens with LRU caching for exact string matches."""
+    if not text:
+        return 0
+    if TIKTOKEN_ENCODING:
+        try:
+            # tiktoken logic is relatively fast, but caching it based on exact string match
+            # turns O(N) encoding time to O(1) dictionary lookup for historical messages.
+            return len(TIKTOKEN_ENCODING.encode(text))
+        except Exception as e:
+            logger.warning(
+                f"[Token Count] tiktoken error: {e}, falling back to character estimation"
+            )
+            pass
+
+    # Fallback strategy: Rough estimation (1 token ≈ 4 chars)
+    return len(text) // 4
+
+
 class Filter:
     def __init__(self):
         self.valves = self.Valves()
@@ -409,8 +554,105 @@ class Filter:
             sessionmaker(bind=self._db_engine) if self._db_engine else None
         )
         self._model_thresholds_cache: Optional[Dict[str, Any]] = None
+
+        # Fallback mapping for variants not in TRANSLATIONS keys
+        self.fallback_map = {
+            "es-AR": "es-ES",
+            "es-MX": "es-ES",
+            "fr-CA": "fr-FR",
+            "en-CA": "en-US",
+            "en-GB": "en-US",
+            "en-AU": "en-US",
+            "de-AT": "de-DE",
+        }
+
         self._init_database()
 
+    def _resolve_language(self, lang: str) -> str:
+        """Resolve the best matching language code from the TRANSLATIONS dict."""
+        target_lang = lang
+
+        # 1. Direct match
+        if target_lang in TRANSLATIONS:
+            return target_lang
+
+        # 2. Variant fallback (explicit mapping)
+        if target_lang in self.fallback_map:
+            target_lang = self.fallback_map[target_lang]
+            if target_lang in TRANSLATIONS:
+                return target_lang
+
+        # 3. Base language fallback (e.g. fr-BE -> fr-FR)
+        if "-" in lang:
+            base_lang = lang.split("-")[0]
+            for supported_lang in TRANSLATIONS:
+                if supported_lang.startswith(base_lang + "-"):
+                    return supported_lang
+
+        # 4. Final Fallback to en-US
+        return "en-US"
+
+    def _get_translation(self, lang: str, key: str, **kwargs) -> str:
+        """Get translated string for the given language and key."""
+        target_lang = self._resolve_language(lang)
+        lang_dict = TRANSLATIONS.get(target_lang, TRANSLATIONS["en-US"])
+        text = lang_dict.get(key, TRANSLATIONS["en-US"].get(key, key))
+        if kwargs:
+            try:
+                text = text.format(**kwargs)
+            except Exception as e:
+                logger.warning(f"Translation formatting failed for {key}: {e}")
+        return text
+
+    async def _get_user_context(
+        self,
+        __user__: Optional[Dict[str, Any]],
+        __event_call__: Optional[Callable[[Any], Awaitable[None]]] = None,
+    ) -> Dict[str, str]:
+        """Extract basic user context with safe fallbacks."""
+        if isinstance(__user__, (list, tuple)):
+            user_data = __user__[0] if __user__ else {}
+        elif isinstance(__user__, dict):
+            user_data = __user__
+        else:
+            user_data = {}
+
+        user_id = user_data.get("id", "unknown_user")
+        user_name = user_data.get("name", "User")
+        user_language = user_data.get("language", "en-US")
+
+        if __event_call__:
+            try:
+                js_code = """
+                    return (
+                        document.documentElement.lang ||
+                        localStorage.getItem('locale') || 
+                        localStorage.getItem('language') || 
+                        navigator.language || 
+                        'en-US'
+                    );
+                """
+                frontend_lang = await asyncio.wait_for(
+                    __event_call__({"type": "execute", "data": {"code": js_code}}),
+                    timeout=1.0,
+                )
+                if frontend_lang and isinstance(frontend_lang, str):
+                    user_language = frontend_lang
+            except asyncio.TimeoutError:
+                logger.warning(
+                    "Failed to retrieve frontend language: Timeout (using fallback)"
+                )
+            except Exception as e:
+                logger.warning(
+                    f"Failed to retrieve frontend language: {type(e).__name__}: {e}"
+                )
+
+        return {
+            "user_id": user_id,
+            "user_name": user_name,
+            "user_language": user_language,
+        }
+
     def _parse_model_thresholds(self) -> Dict[str, Any]:
         """Parse model_thresholds string into a dictionary.
 
@@ -574,7 +816,7 @@ class Filter:
             description="The temperature for summary generation.",
         )
         debug_mode: bool = Field(
-            default=True, description="Enable detailed logging for debugging."
+            default=False, description="Enable detailed logging for debugging."
         )
         show_debug_log: bool = Field(
             default=False, description="Show debug logs in the frontend console"
@@ -582,6 +824,12 @@ class Filter:
         show_token_usage_status: bool = Field(
             default=True, description="Show token usage status notification"
         )
+        token_usage_status_threshold: int = Field(
+            default=80,
+            ge=0,
+            le=100,
+            description="Only show token usage status when usage exceeds this percentage (0-100). Set to 0 to always show.",
+        )
         enable_tool_output_trimming: bool = Field(
             default=False,
             description="Enable trimming of large tool outputs (only works with native function calling).",
@@ -654,20 +902,7 @@ class Filter:
 
     def _count_tokens(self, text: str) -> int:
         """Counts the number of tokens in the text."""
-        if not text:
-            return 0
-
-        if TIKTOKEN_ENCODING:
-            try:
-                return len(TIKTOKEN_ENCODING.encode(text))
-            except Exception as e:
-                if self.valves.debug_mode:
-                    logger.warning(
-                        f"[Token Count] tiktoken error: {e}, falling back to character estimation"
-                    )
-
-        # Fallback strategy: Rough estimation (1 token ≈ 4 chars)
-        return len(text) // 4
+        return _get_cached_tokens(text)
 
     def _calculate_messages_tokens(self, messages: List[Dict]) -> int:
         """Calculates the total tokens for a list of messages."""
@@ -693,6 +928,20 @@ class Filter:
 
         return total_tokens
 
+    def _estimate_messages_tokens(self, messages: List[Dict]) -> int:
+        """Fast estimation of tokens based on character count (1/4 ratio)."""
+        total_chars = 0
+        for msg in messages:
+            content = msg.get("content", "")
+            if isinstance(content, list):
+                for part in content:
+                    if isinstance(part, dict) and part.get("type") == "text":
+                        total_chars += len(part.get("text", ""))
+            else:
+                total_chars += len(str(content))
+
+        return total_chars // 4
+
     def _get_model_thresholds(self, model_id: str) -> Dict[str, int]:
         """Gets threshold configuration for a specific model.
 
@@ -830,12 +1079,14 @@ class Filter:
                 }})();
             """
 
-            await __event_call__(
+            asyncio.create_task(
+                __event_call__(
                     {
                         "type": "execute",
                         "data": {"code": js_code},
                     }
                 )
+            )
         except Exception as e:
             logger.error(f"Error emitting debug log: {e}")
 
@@ -876,17 +1127,55 @@ class Filter:
                 js_code = f"""
                     console.log("%c[Compression] {safe_message}", "{css}");
                 """
-                # Add timeout to prevent blocking if frontend connection is broken
-                await asyncio.wait_for(
-                    event_call({"type": "execute", "data": {"code": js_code}}),
-                    timeout=2.0,
-                )
-            except asyncio.TimeoutError:
-                logger.warning(
-                    f"Failed to emit log to frontend: Timeout (connection may be broken)"
+                asyncio.create_task(
+                    event_call({"type": "execute", "data": {"code": js_code}})
                 )
             except Exception as e:
-                logger.error(f"Failed to emit log to frontend: {type(e).__name__}: {e}")
+                logger.error(
+                    f"Failed to process log to frontend: {type(e).__name__}: {e}"
+                )
+
+    def _should_show_status(self, usage_ratio: float) -> bool:
+        """
+        Check if token usage status should be shown based on threshold.
+
+        Args:
+            usage_ratio: Current usage ratio (0.0 to 1.0)
+
+        Returns:
+            True if status should be shown, False otherwise
+        """
+        if not self.valves.show_token_usage_status:
+            return False
+
+        # If threshold is 0, always show
+        if self.valves.token_usage_status_threshold == 0:
+            return True
+
+        # Check if usage exceeds threshold
+        threshold_ratio = self.valves.token_usage_status_threshold / 100.0
+        return usage_ratio >= threshold_ratio
+
+    def _should_skip_compression(
+        self, body: dict, __model__: Optional[dict] = None
+    ) -> bool:
+        """
+        Check if compression should be skipped.
+        Returns True if:
+        1. The base model includes 'copilot_sdk'
+        """
+        # Check if base model includes copilot_sdk
+        if __model__:
+            base_model_id = __model__.get("base_model_id", "")
+            if "copilot_sdk" in base_model_id.lower():
+                return True
+
+        # Also check model in body
+        model_id = body.get("model", "")
+        if "copilot_sdk" in model_id.lower():
+            return True
+
+        return False
 
     async def inlet(
         self,
@@ -903,6 +1192,19 @@ class Filter:
         Compression Strategy: Only responsible for injecting existing summaries, no Token calculation.
         """
 
+        # Check if compression should be skipped (e.g., for copilot_sdk)
+        if self._should_skip_compression(body, __model__):
+            if self.valves.debug_mode:
+                logger.info(
+                    "[Inlet] Skipping compression: copilot_sdk detected in base model"
+                )
+            if self.valves.show_debug_log and __event_call__:
+                await self._log(
+                    "[Inlet] ⏭️ Skipping compression: copilot_sdk detected",
+                    event_call=__event_call__,
+                )
+            return body
+
         messages = body.get("messages", [])
 
         # --- Native Tool Output Trimming (Opt-in, only for native function calling) ---
@@ -966,8 +1268,14 @@ class Filter:
                                 final_answer = content[last_match_end:].strip()
 
                                 if final_answer:
-                                    msg["content"] = (
-                                        f"... [Tool outputs trimmed]\n{final_answer}"
+                                    msg["content"] = self._get_translation(
+                                        (
+                                            __user__.get("language", "en-US")
+                                            if __user__
+                                            else "en-US"
+                                        ),
+                                        "tool_trimmed",
+                                        content=final_answer,
                                     )
                                     trimmed_count += 1
                             else:
@@ -980,8 +1288,14 @@ class Filter:
                                 if len(parts) > 1:
                                     final_answer = parts[-1].strip()
                                     if final_answer:
-                                        msg["content"] = (
-                                            f"... [Tool outputs trimmed]\n{final_answer}"
+                                        msg["content"] = self._get_translation(
+                                            (
+                                                __user__.get("language", "en-US")
+                                                if __user__
+                                                else "en-US"
+                                            ),
+                                            "tool_trimmed",
+                                            content=final_answer,
                                         )
                                         trimmed_count += 1
 
@@ -1173,6 +1487,10 @@ class Filter:
         # Target is to compress up to the (total - keep_last) message
         target_compressed_count = max(0, len(messages) - self.valves.keep_last)
 
+        # Get user context for i18n
+        user_ctx = await self._get_user_context(__user__, __event_call__)
+        lang = user_ctx["user_language"]
+
         await self._log(
             f"[Inlet] Recorded target compression progress: {target_compressed_count}",
             event_call=__event_call__,
@@ -1207,10 +1525,9 @@ class Filter:
 
             # 2. Summary message (Inserted as Assistant message)
             summary_content = (
-                f"【Previous Summary: The following is a summary of the historical conversation, provided for context only. Do not reply to the summary content itself; answer the subsequent latest questions directly.】\n\n"
-                f"{summary_record.summary}\n\n"
-                f"---\n"
-                f"Below is the recent conversation:"
+                self._get_translation(lang, "summary_prompt_prefix")
+                + f"{summary_record.summary}"
+                + self._get_translation(lang, "summary_prompt_suffix")
             )
             summary_msg = {"role": "assistant", "content": summary_content}
 
@@ -1249,14 +1566,25 @@ class Filter:
                 "max_context_tokens", self.valves.max_context_tokens
             )
 
-            # Calculate total tokens
+            # --- Fast Estimation Check ---
+            estimated_tokens = self._estimate_messages_tokens(calc_messages)
+
+            # Since this is a hard limit check, only skip precise calculation if we are far below it (margin of 15%)
+            if estimated_tokens < max_context_tokens * 0.85:
+                total_tokens = estimated_tokens
+                await self._log(
+                    f"[Inlet] 🔎 Fast Preflight Check (Est): {total_tokens}t / {max_context_tokens}t (Well within limit)",
+                    event_call=__event_call__,
+                )
+            else:
+                # Calculate exact total tokens via tiktoken
                 total_tokens = await asyncio.to_thread(
                     self._calculate_messages_tokens, calc_messages
                 )
 
                 # Preflight Check Log
                 await self._log(
-                f"[Inlet] 🔎 Preflight Check: {total_tokens}t / {max_context_tokens}t ({(total_tokens/max_context_tokens*100):.1f}%)",
+                    f"[Inlet] 🔎 Precise Preflight Check: {total_tokens}t / {max_context_tokens}t ({(total_tokens/max_context_tokens*100):.1f}%)",
                     event_call=__event_call__,
                 )
 
@@ -1325,7 +1653,9 @@ class Filter:
                                 first_line_found = True
                                 # Add placeholder if there's more content coming
                                 if idx < last_line_idx:
-                                    kept_lines.append("\n... [Content collapsed] ...\n")
+                                    kept_lines.append(
+                                        self._get_translation(lang, "content_collapsed")
+                                    )
                                 continue
 
                             # Keep last non-empty line
@@ -1347,6 +1677,11 @@ class Filter:
                             target_msg["metadata"]["is_trimmed"] = True
 
                             # Calculate token reduction
+                            # Use current token strategy
+                            if total_tokens == estimated_tokens:
+                                old_tokens = len(content) // 4
+                                new_tokens = len(target_msg["content"]) // 4
+                            else:
                                 old_tokens = self._count_tokens(content)
                                 new_tokens = self._count_tokens(target_msg["content"])
                             diff = old_tokens - new_tokens
@@ -1362,7 +1697,12 @@ class Filter:
                     # Strategy 2: Fallback - Drop Oldest Message Entirely (FIFO)
                     # (User requested to remove progressive trimming for other cases)
                     dropped = tail_messages.pop(0)
-                    dropped_tokens = self._count_tokens(str(dropped.get("content", "")))
+                    if total_tokens == estimated_tokens:
+                        dropped_tokens = len(str(dropped.get("content", ""))) // 4
+                    else:
+                        dropped_tokens = self._count_tokens(
+                            str(dropped.get("content", ""))
+                        )
                     total_tokens -= dropped_tokens
 
                     if self.valves.show_debug_log and __event_call__:
@@ -1382,6 +1722,16 @@ class Filter:
             final_messages = candidate_messages
 
             # Calculate detailed token stats for logging
+            if total_tokens == estimated_tokens:
+                system_tokens = (
+                    len(system_prompt_msg.get("content", "")) // 4
+                    if system_prompt_msg
+                    else 0
+                )
+                head_tokens = self._estimate_messages_tokens(head_messages)
+                summary_tokens = len(summary_content) // 4
+                tail_tokens = self._estimate_messages_tokens(tail_messages)
+            else:
                 system_tokens = (
                     self._count_tokens(system_prompt_msg.get("content", ""))
                     if system_prompt_msg
@@ -1408,11 +1758,17 @@ class Filter:
             # Prepare status message (Context Usage format)
             if max_context_tokens > 0:
                 usage_ratio = total_section_tokens / max_context_tokens
-                status_msg = f"Context Usage (Estimated): {total_section_tokens} / {max_context_tokens} Tokens ({usage_ratio*100:.1f}%)"
+                # Only show status if threshold is met
+                if self._should_show_status(usage_ratio):
+                    status_msg = self._get_translation(
+                        lang,
+                        "status_context_usage",
+                        tokens=total_section_tokens,
+                        max_tokens=max_context_tokens,
+                        ratio=f"{usage_ratio*100:.1f}",
+                    )
                     if usage_ratio > 0.9:
-                    status_msg += " | ⚠️ High Usage"
-            else:
-                status_msg = f"Loaded historical summary (Hidden {compressed_count} historical messages)"
+                        status_msg += self._get_translation(lang, "status_high_usage")
 
                     if __event_emitter__:
                         await __event_emitter__(
@@ -1424,6 +1780,21 @@ class Filter:
                                 },
                             }
                         )
+            else:
+                # For the case where max_context_tokens is 0, show summary info without threshold check
+                if self.valves.show_token_usage_status and __event_emitter__:
+                    status_msg = self._get_translation(
+                        lang, "status_loaded_summary", count=compressed_count
+                    )
+                    await __event_emitter__(
+                        {
+                            "type": "status",
+                            "data": {
+                                "description": status_msg,
+                                "done": True,
+                            },
+                        }
+                    )
 
             # Emit debug log to frontend (Keep the structured log as well)
             await self._emit_debug_log(
@@ -1454,6 +1825,17 @@ class Filter:
                 "max_context_tokens", self.valves.max_context_tokens
             )
 
+            # --- Fast Estimation Check ---
+            estimated_tokens = self._estimate_messages_tokens(calc_messages)
+
+            # Only skip precise calculation if we are clearly below the limit
+            if estimated_tokens < max_context_tokens * 0.85:
+                total_tokens = estimated_tokens
+                await self._log(
+                    f"[Inlet] 🔎 Fast limit check (Est): {total_tokens}t / {max_context_tokens}t",
+                    event_call=__event_call__,
+                )
+            else:
                 total_tokens = await asyncio.to_thread(
                     self._calculate_messages_tokens, calc_messages
                 )
@@ -1476,7 +1858,12 @@ class Filter:
                     > start_trim_index + 1  # Keep at least 1 message after keep_first
                 ):
                     dropped = final_messages.pop(start_trim_index)
-                    dropped_tokens = self._count_tokens(str(dropped.get("content", "")))
+                    if total_tokens == estimated_tokens:
+                        dropped_tokens = len(str(dropped.get("content", ""))) // 4
+                    else:
+                        dropped_tokens = self._count_tokens(
+                            str(dropped.get("content", ""))
+                        )
                     total_tokens -= dropped_tokens
 
                 await self._log(
@@ -1485,14 +1872,21 @@ class Filter:
                 )
 
             # Send status notification (Context Usage format)
-            if __event_emitter__:
-                status_msg = f"Context Usage (Estimated): {total_tokens} / {max_context_tokens} Tokens"
             if max_context_tokens > 0:
                 usage_ratio = total_tokens / max_context_tokens
-                    status_msg += f" ({usage_ratio*100:.1f}%)"
+                # Only show status if threshold is met
+                if self._should_show_status(usage_ratio):
+                    status_msg = self._get_translation(
+                        lang,
+                        "status_context_usage",
+                        tokens=total_tokens,
+                        max_tokens=max_context_tokens,
+                        ratio=f"{usage_ratio*100:.1f}",
+                    )
                     if usage_ratio > 0.9:
-                        status_msg += " | ⚠️ High Usage"
+                        status_msg += self._get_translation(lang, "status_high_usage")
 
+                    if __event_emitter__:
                         await __event_emitter__(
                             {
                                 "type": "status",
@@ -1517,6 +1911,7 @@ class Filter:
         body: dict,
         __user__: Optional[dict] = None,
         __metadata__: dict = None,
+        __model__: dict = None,
         __event_emitter__: Callable[[Any], Awaitable[None]] = None,
         __event_call__: Callable[[Any], Awaitable[None]] = None,
     ) -> dict:
@@ -1524,6 +1919,23 @@ class Filter:
         Executed after the LLM response is complete.
         Calculates Token count in the background and triggers summary generation (does not block current response, does not affect content output).
         """
+        # Check if compression should be skipped (e.g., for copilot_sdk)
+        if self._should_skip_compression(body, __model__):
+            if self.valves.debug_mode:
+                logger.info(
+                    "[Outlet] Skipping compression: copilot_sdk detected in base model"
+                )
+            if self.valves.show_debug_log and __event_call__:
+                await self._log(
+                    "[Outlet] ⏭️ Skipping compression: copilot_sdk detected",
+                    event_call=__event_call__,
+                )
+            return body
+
+        # Get user context for i18n
+        user_ctx = await self._get_user_context(__user__, __event_call__)
+        lang = user_ctx["user_language"]
+
         chat_ctx = self._get_chat_context(body, __metadata__)
         chat_id = chat_ctx["chat_id"]
         if not chat_id:
@@ -1547,6 +1959,7 @@ class Filter:
                 body,
                 __user__,
                 target_compressed_count,
+                lang,
                 __event_emitter__,
                 __event_call__,
             )
@@ -1561,6 +1974,7 @@ class Filter:
         body: dict,
         user_data: Optional[dict],
         target_compressed_count: Optional[int],
+        lang: str = "en-US",
         __event_emitter__: Callable[[Any], Awaitable[None]] = None,
         __event_call__: Callable[[Any], Awaitable[None]] = None,
     ):
@@ -1595,27 +2009,48 @@ class Filter:
                 event_call=__event_call__,
             )
 
-            # Calculate Token count in a background thread
+            # --- Fast Estimation Check ---
+            estimated_tokens = self._estimate_messages_tokens(messages)
+
+            # For triggering summary generation, we need to be more precise if we are in the grey zone
+            # Margin is 15% (skip tiktoken if estimated is < 85% of threshold)
+            # Note: We still use tiktoken if we exceed threshold, because we want an accurate usage status report
+            if estimated_tokens < compression_threshold_tokens * 0.85:
+                current_tokens = estimated_tokens
+                await self._log(
+                    f"[🔍 Background Calculation] Fast estimate ({current_tokens}) is well below threshold ({compression_threshold_tokens}). Skipping tiktoken.",
+                    event_call=__event_call__,
+                )
+            else:
+                # Calculate Token count precisely in a background thread
                 current_tokens = await asyncio.to_thread(
                     self._calculate_messages_tokens, messages
                 )
-
                 await self._log(
-                f"[🔍 Background Calculation] Token count: {current_tokens}",
+                    f"[🔍 Background Calculation] Precise token count: {current_tokens}",
                     event_call=__event_call__,
                 )
 
             # Send status notification (Context Usage format)
-            if __event_emitter__ and self.valves.show_token_usage_status:
+            if __event_emitter__:
                 max_context_tokens = thresholds.get(
                     "max_context_tokens", self.valves.max_context_tokens
                 )
-                status_msg = f"Context Usage (Estimated): {current_tokens} / {max_context_tokens} Tokens"
                 if max_context_tokens > 0:
                     usage_ratio = current_tokens / max_context_tokens
-                    status_msg += f" ({usage_ratio*100:.1f}%)"
+                    # Only show status if threshold is met
+                    if self._should_show_status(usage_ratio):
+                        status_msg = self._get_translation(
+                            lang,
+                            "status_context_usage",
+                            tokens=current_tokens,
+                            max_tokens=max_context_tokens,
+                            ratio=f"{usage_ratio*100:.1f}",
+                        )
                         if usage_ratio > 0.9:
-                        status_msg += " | ⚠️ High Usage"
+                            status_msg += self._get_translation(
+                                lang, "status_high_usage"
+                            )
 
                         await __event_emitter__(
                             {
@@ -1642,6 +2077,7 @@ class Filter:
                     body,
                     user_data,
                     target_compressed_count,
+                    lang,
                     __event_emitter__,
                     __event_call__,
                 )
@@ -1672,6 +2108,7 @@ class Filter:
         body: dict,
         user_data: Optional[dict],
         target_compressed_count: Optional[int],
+        lang: str = "en-US",
         __event_emitter__: Callable[[Any], Awaitable[None]] = None,
         __event_call__: Callable[[Any], Awaitable[None]] = None,
     ):
@@ -1811,7 +2248,9 @@ class Filter:
                     {
                         "type": "status",
                         "data": {
-                            "description": "Generating context summary in background...",
+                            "description": self._get_translation(
+                                lang, "status_generating_summary"
+                            ),
                             "done": False,
                         },
                     }
@@ -1849,7 +2288,11 @@ class Filter:
                     {
                         "type": "status",
                         "data": {
-                            "description": f"Context summary updated (Compressed {len(middle_messages)} messages)",
+                            "description": self._get_translation(
+                                lang,
+                                "status_loaded_summary",
+                                count=len(middle_messages),
+                            ),
                             "done": True,
                         },
                     }
@@ -1910,10 +2353,9 @@ class Filter:
 
                     # Summary
                     summary_content = (
-                        f"【System Prompt: The following is a summary of the historical conversation, provided for context only. Do not reply to the summary content itself; answer the subsequent latest questions directly.】\n\n"
-                        f"{new_summary}\n\n"
-                        f"---\n"
-                        f"Below is the recent conversation:"
+                        self._get_translation(lang, "summary_prompt_prefix")
+                        + f"{new_summary}"
+                        + self._get_translation(lang, "summary_prompt_suffix")
                     )
                     summary_msg = {"role": "assistant", "content": summary_content}
 
@@ -1943,13 +2385,22 @@ class Filter:
                     max_context_tokens = thresholds.get(
                         "max_context_tokens", self.valves.max_context_tokens
                     )
-                    # 6. Emit Status
-                    status_msg = f"Context Summary Updated: {token_count} / {max_context_tokens} Tokens"
+                    # 6. Emit Status (only if threshold is met)
                     if max_context_tokens > 0:
-                        ratio = (token_count / max_context_tokens) * 100
-                        status_msg += f" ({ratio:.1f}%)"
-                        if ratio > 90.0:
-                            status_msg += " | ⚠️ High Usage"
+                        usage_ratio = token_count / max_context_tokens
+                        # Only show status if threshold is met
+                        if self._should_show_status(usage_ratio):
+                            status_msg = self._get_translation(
+                                lang,
+                                "status_context_summary_updated",
+                                tokens=token_count,
+                                max_tokens=max_context_tokens,
+                                ratio=f"{usage_ratio*100:.1f}",
+                            )
+                            if usage_ratio > 0.9:
+                                status_msg += self._get_translation(
+                                    lang, "status_high_usage"
+                                )
 
                             await __event_emitter__(
                                 {
@@ -1979,7 +2430,9 @@ class Filter:
                     {
                         "type": "status",
                         "data": {
-                            "description": f"Summary Error: {str(e)[:100]}...",
+                            "description": self._get_translation(
+                                lang, "status_summary_error", error=str(e)[:100]
+                            ),
                             "done": True,
                         },
                     }