fix: bound idle evaluation model streams

.dockerignore

@@ -1,24 +0,0 @@
-.git
-.gitignore
-.env
-.env.*
-!.env.example
-.venv
-**/__pycache__
-**/*.py[cod]
-.pytest_cache
-.ruff_cache
-.mypy_cache
-.coverage
-htmlcov
-.runtime
-.logs
-node_modules
-.svelte-kit
-build
-dist
-*.egg-info
-*.sqlite3
-*.db
-tests
-docs

.env.deploy.example

@@ -0,0 +1,11 @@
+# 本文件是部署配置示例；真实配置写入 .env.deploy，且不要提交到 git。
+
+export OPENAI_API_KEY=""
+export OPENAI_BASE_URL="http://model.mify.ai.srv/v1"
+export OPENAI_MODEL="xiaomi/mimo-v2-flash"
+
+export CLAW_BACKEND_HOST="127.0.0.1"
+export CLAW_BACKEND_PORT="8765"
+export CLAW_FRONTEND_HOST="0.0.0.0"
+export CLAW_FRONTEND_PORT="3000"
+export CLAW_API_URL="http://127.0.0.1:8765"

.env.example

@@ -1,45 +0,0 @@
-# Public origin
-WEBUI_URL=http://localhost:3000
-CORS_ALLOW_ORIGIN=http://localhost:3000
-
-# Generate all values below; never reuse the example strings.
-WEBUI_SECRET_KEY=
-WEBUI_ADMIN_EMAIL=admin@example.invalid
-WEBUI_ADMIN_PASSWORD=
-OPENWEBUI_FORWARD_JWT_SECRET=
-INTERNAL_PROVIDER_KEY=
-INTERNAL_GATEWAY_KEY=
-
-# The provider key is server-side only. Store it only in a protected .env.
-MODEL_API_KEY=
-MODEL_API_BASE_URL=https://api.k1412.top
-
-# Extreme tier provider. Keep the key only in the protected server-side .env.
-DEEPSEEK_API_KEY=
-DEEPSEEK_API_BASE_URL=https://api.deepseek.com
-
-# Storage
-POSTGRES_USER=agent
-POSTGRES_PASSWORD=
-POSTGRES_DB=agent
-DATABASE_URL=postgresql+asyncpg://agent:replace-me@postgres:5432/agent
-REDIS_URL=redis://redis:6379/0
-
-# Workspace execution
-EXECUTION_PROVIDER=local-docker
-WORKSPACE_IMAGE=k1412-agent-workspace:dev
-WORKSPACE_NETWORK_ENABLED=true
-WORKSPACE_MEMORY_LIMIT=2g
-WORKSPACE_CPU_LIMIT=2
-WORKSPACE_PIDS_LIMIT=512
-WORKSPACE_DOWNLOAD_MAX_BYTES=134217728
-
-# Future remote execution node (only used by EXECUTION_PROVIDER=ssh-docker)
-WORKSPACE_SSH_HOST=
-WORKSPACE_SSH_CONFIG_DIR=
-
-# Branding
-# Open WebUI's license permits removing its user-facing branding only for
-# deployments with no more than 50 end users in a rolling 30-day period, unless
-# you have written permission or an enterprise license.
-K1412_REMOVE_UPSTREAM_BRANDING=false

.gitattributes

@@ -1 +0,0 @@
-*.patch -whitespace

.gitignore

@@ -1,24 +1,50 @@
 __pycache__/
 *.py[cod]
+.DS_Store
 .pytest_cache/
-.ruff_cache/
 .mypy_cache/
+.ruff_cache/
 .venv/
-*.egg-info/
+venv/
 build/
 dist/
+*.egg-info/
 
+archive/
+.omx/
+.clawd-agents/
+
+# Local agent/runtime artifacts
+.claude/
+.claude.json
+.port_sessions/
+scratchpad/
+tasks/
+router_session_parquet/
+.logs/
+
+# Environment files
 .env
 .env.*
-!.env.example
+skills/*/.local/
 
-.coverage
+# Local benchmark outputs
-htmlcov/
+benchmark_artifacts/
-.logs/
+jobs/
-.runtime/
+output_terminal/
+tb2*.json
+humaneval_results.json
 
-node_modules/
-.svelte-kit/
 
-*.sqlite3
+test_cases
-*.db
+e-commerce
+benchmarks/data/*.jsonl
+benchmarks/data/manifest.json
+claude-code-sourcemap-main
+router_session_parquet
+
+# Locally managed annotation pages served by /annotations
+annotation-pages/
+
+标签定义
+.codex-tmp

LICENSE

@@ -1,189 +0,0 @@
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README.md

@@ -1,150 +1,815 @@
-# K1412 Agent
+# ZK Data Agent
 
-K1412 Agent is a multi-user web coding Agent. Every user request enters the
+ZK Data Agent 是基于 `claw-code-agent` 改造的团队通用 Agent 工作台。
-same independently evolvable Agent loop; its scheduler, context policy, memory,
-tools, evidence rules, and child Agents are owned by this repository.
 
-The production instance is available at <https://agent.k1412.top>.
+它不是一个单点数据工具，也不是只会聊天的 Web UI。这个项目的核心目标是把“通用 Agent 能力”稳定下来：让 Agent 能理解任务、选择 Skill、调用 Tools、读写文件、执行 Python、保留会话、展示工具链路，并把团队反复使用的工作经验沉淀成可维护的能力包。
 
-## Architecture
+数据开发、线上挖掘、ELK 查询、数据工场 SQL 查询，都是已经通过 Skill 和 Tools 落地的子能力。
+
+## 这个项目解决什么
+
+团队日常有很多任务不只是“问模型一句话”：
+
+- 要读文件、查日志、跑 SQL、分析线上数据。
+- 要生成中间结果、落盘文件、继续追问和修正。
+- 要把某类任务的经验固化下来，下一次让 Agent 按同样的方法做。
+- 要让用户看到 Agent 做了什么、调用了什么工具、文件生成在哪里。
+- 要让多人共用一套平台，而不是每个人本地各跑一份零散脚本。
+
+ZK Data Agent 做的是这层通用底座。业务能力通过 Skill 和 Tools 逐步沉淀。
+
+## 核心设计
+
+### Agent Loop
+
+Agent 每轮对话不是一次性生成文本，而是一个循环：
 
 ```text
-browser
+用户输入
-  |
+  -> 组装系统提示词、Skill 提示词、会话上下文、工具定义
-  v
+  -> 模型决定直接回复或返回 tool_calls
-Open WebUI (auth, RBAC, chat history, UI)
+  -> 后端执行对应 tool handler
-  |
+  -> 工具结果写入会话
-  v
+  -> 下一轮模型继续判断
-Agent Runtime (model gateway + Agent loop)
+  -> 直到输出最终回复、等待用户 review 或被取消
-  |
-  v
-Workspace Gateway (identity, policy, audit)
-  |
-  v
-one Docker workspace per Open WebUI user on a dedicated execution host
 ```
 
-Open WebUI is pinned and lightly patched. Its picker exposes only four model
+这个循环让 Agent 可以边观察、边执行、边修正，而不是只能一次性回答。
-names and a separate read-only thinking status. Provider URLs, provider model
-IDs, API keys, tool server settings, system prompts, and runtime parameters
-remain server-side.
 
-Model identity and thinking control are intentionally separate:
+### Skill
 
-| UI model | Provider model | Thinking | Reasoning effort |
+Skill 是“经验层”。它用 `SKILL.md` 描述某类任务应该怎样做、什么时候需要用户确认、可以调用哪些工具、产物应该放在哪里。
-| --- | --- | --- | --- |
-| Luna | `ChatGPT-5.6:Luna` | On, no effort levels | — |
-| Terra | `ChatGPT-5.6:Terra` | On, no effort levels | — |
-| Sol | `ChatGPT-5.6:Sol` | On, no effort levels | — |
-| DeepSeek V4 Pro | `deepseek-v4-pro` | On | Extreme (`max`) |
 
-The three Ollama models advertise `thinking`, but they are Qwen-family models
+项目级 Skill 统一放在：
-with a boolean thinking switch rather than GPT-OSS-style low/medium/high
-effort levels. Luna, Terra, and Sol are different models, not three reasoning
-settings for one model.
 
-## Local development
+```text
+skills/<skill-name>/SKILL.md
+```
 
-1. Copy `.env.example` to `.env` and fill in the secret values. Never commit
+Skill 适合承载：
-   `.env`. Generate new values with `./scripts/init-secrets.sh`.
+
-2. Build the user workspace image:
+- 工作流程
+- 业务边界
+- review 门禁
+- 工具调用经验
+- 输入输出格式约定
+- 常见错误和注意事项
+
+Skill 不应该写成大段不可执行代码。稳定、强格式、可复用的能力应该下沉到 Tool。
+
+### Tools
+
+Tools 是“执行层”。工具负责稳定地做事情，例如读写文件、执行 Python、查询 parquet、转换 records、导出 JSONL。
+
+主要位置：
+
+```text
+src/agent_tools.py
+src/agent_tool_specs/
+src/data_agent_records.py
+src/data_agent_inputs.py
+src/data_agent_router_sessions.py
+```
+
+工具适合承载：
+
+- 强格式转换
+- 数据校验
+- 路径归一
+- 线上数据读取
+- 账号级 Python 执行环境
+- 需要审计和约束的外部调用
+
+原则是：不要长期让 Agent 手写易错脚本来完成稳定流程。能工具化的，尽量工具化。
+
+### 会话工作区
+
+每个用户、每个会话都有独立目录：
+
+```text
+.port_sessions/accounts/<account_id>/sessions/<session_id>/
+  input/       用户输入文件
+  scratchpad/  临时脚本、中间分析、草稿
+  output/      最终产物
+  session.json 会话历史
+```
+
+会话工作区是这个项目和普通聊天机器人很不一样的地方。Agent 的产物不是散落在项目根目录，而是跟随当前会话保存，方便回看、下载和继续追问。
+
+### Review 门禁
+
+一些任务不能让 Agent 一步到位，例如：
+
+- 数据生成目标还不清楚。
+- 标签边界存在歧义。
+- 线上候选需要人工抽样确认。
+- 输出格式会影响训练、评测或对外交付。
+
+这类流程要通过 Skill 和 Tool 实现 review 门禁。目标、计划、候选样本、最终导出都应该分阶段展示给用户确认。
+
+### 可观测性
+
+Web UI 会展示：
+
+- 当前模型和上下文状态
+- Skill 列表和启用状态
+- 工具调用过程
+- 总结版思考阶段
+- 历史会话
+- 当前会话输入/输出文件
+- 后台运行和刷新恢复状态
+
+目标是让用户知道 Agent 正在做什么，而不是只看到一个黑盒回复。
+
+## 和 Claude Code 的区别
+
+这个项目继承了 Claude Code 类工具的基本思路：Agent 可以读写文件、执行工具、维护上下文，并围绕一个工作区完成任务。
+
+但当前项目的定位更偏团队内部 Agent 平台：
+
+| 维度 | Claude Code 类工具 | ZK Data Agent |
+|------|--------------------|---------------|
+| 使用形态 | 个人本地 CLI/IDE 工作流为主 | 团队共享 Web 服务 |
+| 任务范围 | 代码开发为核心 | 通用任务底座，数据开发只是能力之一 |
+| 能力沉淀 | 个人 prompt、命令、脚本较多 | 项目级 Skill + Tools 统一管理 |
+| 文件空间 | 通常围绕当前代码仓库 | 每个用户/会话独立 `input/scratchpad/output` |
+| 可观测性 | 终端输出为主 | Web UI 展示工具链路、活动、文件、历史 |
+| Python 执行 | 常用 shell 自行管理 | 优先 `python_exec`，走账号级环境和工具约束 |
+| 人工确认 | 通常是权限批准或终端交互 | 更强调业务 review：目标、计划、样本、导出 |
+| 内部系统 | 需要用户自行接脚本 | 可以通过 Skill/Tools 接 ELK、SQL、线上数据 |
+
+因此，它不是要替代 Claude Code 的个人编码体验，而是把 Agent 变成团队可共享、可扩展、可治理的工作台。
+
+## 能力目录
+
+### 通用 Agent 能力
+
+当前底座已经支持：
+
+- OpenAI 兼容模型调用
+- Web UI 多会话管理
+- 模型选择
+- Skill 发现和会话级启用
+- 工具调用展示
+- 文件输入和会话产物管理
+- 后台 run 状态和刷新恢复
+- 总结版思考过程展示
+- Python 执行和包安装工具
+- 用户级 systemd 部署
+
+### 数据开发能力
+
+Skill：`product-data`
+
+适用于从产品定义、标签规则、手写边界或示例 query 生成数据集。
+
+典型流程：
+
+```text
+输入定义/规则/样例
+  -> 抽取 generation goal
+  -> 用户 review
+  -> 生成 generation plan
+  -> 用户确认数量、标签、边界、路径
+  -> 生成 dataset draft text
+  -> 转换为 canonical records
+  -> 校验
+  -> 导出 records.jsonl
+```
+
+默认最终产物：
+
+```text
+当前会话/output/records.jsonl
+```
+
+### 线上挖掘能力
+
+Skill：`online-mining`
+
+适用于从线上 router session 中按 query 特征、domain、设备、日期等条件挖掘候选样本。
+
+典型流程：
+
+```text
+需求/badcase/标签定义
+  -> 构造挖掘策略
+  -> profile 数据
+  -> search 候选
+  -> sample 抽样 review
+  -> 策略调整
+  -> 候选转换为 canonical records
+  -> 导出 records.jsonl
+```
+
+默认线上数据路径：
+
+```text
+/data/online_data/router_session_parquet/date=YYYYMMDD/
+```
+
+重要分支：
+
+- 如果用户要“直接把线上候选作为样本”，只做转换，不生成新 query。
+- 如果用户明确要“补充生成/扩写类似 case”，才切换到数据生成链路。
+
+### 评测修复能力
+
+Skill：`eval-repair`
+
+用于评测错误分析、错误类型归纳和后续补数流程。目前主要是流程占位和约定沉淀，工具还会继续补齐。
+
+### 日志查询能力
+
+Skill：`elk-fetch`
+
+用于按 request id 查询小米内网 ELK 日志，覆盖 NLP 主链路、拒识、免唤醒、小米汽车 OneTrack 等场景。
+
+原则：
+
+- 通过 `python_exec` 执行 skill 内脚本。
+- 不让 Agent 直接用 `bash python ...` 绕过工具链路。
+
+### 数据工场 SQL 能力
+
+Skill：`data-factory-sql`
+
+用于通过 Kyuubi HTTP API 执行 SQL、轮询状态并下载 CSV 结果。
+
+适用于：
+
+- 用户直接给 SQL。
+- 用户要求“跑个 SQL”“数据工场查一下”。
+- Agent 基于表结构和字段说明生成 SQL 草稿，再请求用户确认后执行。
+
+## 团队公约
+
+### 空间公约
+
+业务任务默认在当前会话空间内工作：
+
+```text
+input/       用户上传或指定的输入材料
+scratchpad/  临时脚本、中间文件、抽样缓存
+output/      最终交付文件
+```
+
+约定：
+
+- 最终产物优先写入当前会话 `output/`。
+- 临时脚本和中间文件写入当前会话 `scratchpad/`。
+- 数据 records 默认导出到逻辑路径 `output/records.jsonl`，工具会自动路由到当前会话 output。
+- 不要把业务任务产物写到项目根目录的 `output/`、`tasks/`、`src/`、`skills/`。
+- 读取外部数据可以用明确路径，但写入外部路径前需要用户明确确认。
+- 平台源码目录默认只读。只有用户明确要求开发平台功能时，才修改 `src/`、`frontend/`、`skills/`、`scripts/` 等项目文件。
+
+### Skill 公约
+
+项目级 Skill 统一放在：
+
+```text
+skills/<skill-name>/SKILL.md
+```
+
+Skill 是可以独立维护、独立安装、被 Agent 读取和执行的能力包。它不只是 prompt，也不只是脚本，而是某类任务的“能力入口”：可以包含流程、知识、脚本、配置和模板，但必须清楚说明边界。
+
+#### 适合做成 Skill 的内容
+
+当前项目里的 Skill 大致分为四类：
+
+| 类型 | 代表 | 适合承载 |
+|------|------|----------|
+| 流程编排型 | `product-data`、`online-mining`、`eval-repair` | 分阶段流程、review 门禁、工具调用顺序、产物规范 |
+| 工具封装型 | `elk-fetch`、`data-factory-sql` | 外部系统调用脚本、CLI 参数、依赖说明、返回格式 |
+| 知识增强型 | `model-iteration/knowledge/*`，后续标签知识 Skill | 标签定义、边界规则、案例、决策依据 |
+| 混合工程型 | `model-iteration` | 复杂工程闭环：流程 + 知识 + 脚本 + 配置 |
+
+判断一件事放在哪里：
+
+- **Skill**：告诉 Agent 怎么做、什么时候停、读哪些知识、如何组织流程。
+- **Knowledge / references**：放大段业务知识、规则、案例和字段说明。
+- **Scripts**：放可重复、确定性、容易写错的执行逻辑。
+- **Tools**：放平台级、强约束、需要长期稳定维护的能力，例如 records 转换、校验、线上 parquet 检索。
+
+#### 推荐目录结构
+
+```text
+skills/<skill-name>/
+  SKILL.md          必须，Agent 触发和执行该能力的入口
+  README.md         可选，给维护者看的说明
+  scripts/          可选，确定性脚本或 CLI
+  knowledge/        可选，业务知识、标签规则、案例
+  references/       可选，长文档、字段说明、API 说明
+  assets/           可选，模板、静态资源
+  config.yaml       可选，默认参数
+```
+
+不建议提交：
+
+- `__pycache__/`
+- `.venv/`
+- 临时运行结果
+- 用户私有 token、key、cookie
+- 大体积产物或线上原始数据
+
+#### SKILL.md frontmatter
+
+`SKILL.md` frontmatter 至少包含：
+
+```yaml
+---
+name: skill-name
+description: 简短说明这个 skill 做什么，尽量覆盖触发关键词。
+when_to_use: 说明什么场景应该触发，包含用户常见说法。
+aliases: optional-alias
+allowed_tools: read_file, write_file, python_exec
+---
+```
+
+字段约定：
+
+- `name`：短横线命名，稳定、可读，例如 `online-mining`、`data-factory-sql`。
+- `description`：面向模型召回，说明能力范围和典型触发词。
+- `when_to_use`：面向模型决策，说明什么场景应该使用。
+- `aliases`：兼容旧名字、团队口头叫法。
+- `allowed_tools`：列出该 Skill 合理使用的工具，避免能力越界。
+
+命名建议：
+
+- 用“能力名”而不是项目临时代号，例如 `model-iteration` 优于 `zk-model`。
+- 工具封装型可以用系统名，例如 `elk-fetch`、`data-factory-sql`。
+- 知识型可以用知识域名，例如 `label-master`。
+- 不要用过泛的名字，例如 `helper`、`tools`、`data`。
+
+维护规则：
+
+- 用中文写主要流程说明，方便团队后续维护。
+- Skill 写“怎么做”和“什么时候停下来问用户”。
+- 不要把稳定格式转换、校验、复杂查询长期写在 Skill 里，应沉淀为 Tool。
+- Skill 如果依赖脚本，脚本放在该 Skill 目录下，并通过 `python_exec` 调用。
+- 新增业务 Skill 后，可以在 Web UI Skill 列表中按会话启用或关闭。
+
+#### SKILL.md 内容结构
+
+推荐顺序：
+
+```text
+1. 这个 Skill 解决什么问题
+2. 输入假设
+3. 必要工作流
+4. 需要用户 review 的门禁
+5. 输出目录和产物约束
+6. 可用脚本或知识文件
+7. 常见错误和禁止事项
+```
+
+如果 `SKILL.md` 超过几百行，优先拆分：
+
+- 长业务规则放 `knowledge/`
+- 长 API/字段说明放 `references/`
+- 可执行逻辑放 `scripts/`
+- `SKILL.md` 只保留导航、流程和关键门禁
+
+#### 脚本型 Skill 约定
+
+脚本型 Skill 典型如 `elk-fetch`、`data-factory-sql`、`model-iteration`。
+
+约定：
+
+- Python 脚本优先放在 `scripts/`，少量历史 Skill 可保留根目录脚本，但新 Skill 优先使用 `scripts/`。
+- Agent 调用脚本优先使用 `python_exec`，不要让模型直接 `bash python xxx.py`。
+- 依赖缺失时使用 `python_package` 安装到账号级 Python 环境。
+- 脚本参数要稳定，输出尽量给 JSON 或结构化摘要，方便 Agent 继续分析。
+- 不要在脚本里硬编码 API key、token、个人路径。优先读取环境变量或用户 home 下配置。
+- 长耗时脚本必须考虑超时、分页、采样或断点，不要默认全量扫描。
+
+脚本调用示例：
+
+```json
+{
+  "script_path": "skills/example/scripts/run_task.py",
+  "args": ["--input", "xxx"],
+  "timeout_seconds": 120,
+  "max_output_chars": 20000
+}
+```
+
+#### 知识型 Skill 约定
+
+知识型 Skill 适合承载标签体系、业务规则、字段定义、案例库。
+
+约定：
+
+- `SKILL.md` 只写“什么时候读哪些知识文件”。
+- `knowledge/` 下按主题拆文件，文件名语义化。
+- 每个知识文件开头写清楚适用范围。
+- 不要把所有知识一次性塞进 `SKILL.md`。
+- 面向标签、路由、复杂度等判断时，鼓励输出“候选、依据、排除项、不确定点”，不要过早封装成黑盒单步分类。
+
+例如后续中控标签知识可以先设计为：
+
+```text
+skills/label-master/
+  SKILL.md
+  knowledge/
+    agents.md
+    functions.md
+    complex_rules.md
+    boundary_cases.md
+    examples.md
+  scripts/
+    build_index.py
+```
+
+#### 外部 Skill 仓库安装约定
+
+允许同事把能力打包为独立 git 仓库维护，再安装到本项目：
+
+```text
+skills/<skill-name>/
+```
+
+安装或迁移时需要检查：
+
+- 是否有合法 `SKILL.md` frontmatter。
+- skill 名是否符合项目命名风格。
+- 是否包含不该提交的缓存、运行产物、私钥、token。
+- 脚本是否能通过 `python_exec` 调用。
+- 依赖是否写清楚，缺包时能通过 `python_package` 安装。
+- 输出目录是否遵守当前会话 `output/` / `scratchpad/` 公约。
+- 高风险动作是否有用户确认门禁。
+
+外部仓库可以保留自己的 README，但真正影响 Agent 行为的是 `SKILL.md`。
+
+#### 高风险动作门禁
+
+Skill 中只要涉及下面动作，必须先展示计划并等待用户确认：
+
+- 批量修改训练数据或标签定义。
+- 提交训练、部署模型、启动 CML job。
+- 写入外部路径或覆盖已有产物。
+- 推送 git、改远端配置。
+- 导出包含敏感线上字段的数据。
+
+用户明确说“开始评测”“查一下”“分析一下”时，可以执行只读分析；不要自动升级成训练、部署或批量改数据。
+
+### Tool 公约
+
+工具是稳定执行边界。
+
+约定：
+
+- 强格式输出必须工具化，例如 canonical records、JSONL 导出、数据校验。
+- 需要持久化状态的 review 流程应由工具记录 pending/confirmed 状态。
+- Python 分析优先用 `python_exec`。
+- 缺 Python 包时用 `python_package`。
+- 除非没有专用工具，否则不要让 Agent 通过 `bash` 绕过已有工具。
+
+### Python 公约
+
+Agent 执行 Python 优先使用：
+
+```text
+python_exec
+python_package
+```
+
+原因：
+
+- 可以进入账号级 Python 环境。
+- 可以把临时脚本和输出放在当前会话 scratchpad。
+- Web UI 能看到工具调用过程。
+- 后续更容易加超时、取消、审计和资源限制。
+
+### Git 公约
+
+不要提交：
+
+- `.env.deploy`
+- `.venv/`
+- `.port_sessions/`
+- `router_session_parquet/`
+- 用户数据、模型输出、临时任务产物
+
+可以提交：
+
+- `skills/` 下经过确认的项目级 Skill
+- `src/` 下稳定工具和运行时代码
+- `frontend/app/` 下 Web UI 代码
+- `scripts/` 和 `deploy/` 下部署维护脚本
+- README 中面向团队维护的约定
+
+## 系统组成
+
+```text
+frontend/app/         Next.js Web UI
+backend/              FastAPI Web 后端
+src/                  Agent runtime、提示词、工具实现、会话持久化
+skills/               项目级 Skill，使用 SKILL.md 定义
+scripts/              本地启动、服务器部署、systemd 启动脚本
+deploy/systemd/       用户级 systemd service 模板
+.port_sessions/       本地/服务端运行数据，禁止提交
+.env.deploy           本机私有部署配置，禁止提交
+```
+
+前端负责账号、会话列表、模型选择、对话流式展示、活动面板和会话文件面板。
+
+后端负责 Agent loop、OpenAI 兼容模型调用、工具执行、run 状态、会话持久化和数据开发工具。
+
+## 本地开发启动
+
+首次准备 Python 依赖：
 
 ```bash
-   docker compose --profile build-only build workspace-image
+pyenv install 3.10.14
+pyenv local 3.10.14
+python -m venv .venv
+.venv/bin/python -m pip install --upgrade pip setuptools wheel
+.venv/bin/python -m pip install -e .
 ```
 
-3. Start the stack:
+准备前端依赖：
 
 ```bash
-   docker compose up --build
+cd frontend/app
+npm install
 ```
 
-4. Open <http://localhost:3000>. New users register as `pending` and require
+本地启动 Web UI：
-   approval by the bootstrap administrator.
-
-Provider credentials are intentionally not stored here. Keep them only in the
-protected deployment `.env`.
-
-## Tests
 
 ```bash
-python3 -m venv .venv
+bash scripts/start-webui.sh
-.venv/bin/pip install -e '.[dev]'
-.venv/bin/pytest
 ```
 
-Run the complete local verification path, including the real Docker isolation
+默认地址：
-test, with:
+
+```text
+前端：http://127.0.0.1:3000
+后端：http://127.0.0.1:8765
+```
+
+`scripts/start-webui.sh` 会优先读取 `.env.deploy`，也可以直接使用当前 shell 里的环境变量：
 
 ```bash
-./scripts/verify.sh
+export OPENAI_API_KEY="..."
+export OPENAI_BASE_URL="http://model.mify.ai.srv/v1"
+export OPENAI_MODEL="tongyi/deepseek-v4-pro"
 ```
 
-Run a live Agent evaluation in a unique, disposable, network-disabled Docker
+停止本地 Web UI：
-workspace with:
 
 ```bash
-set -a
+kill $(cat .port_sessions/webui-frontend.pid) $(cat .port_sessions/webui-backend.pid)
-source /path/to/protected/deepseek.env
-set +a
-.venv/bin/python scripts/eval-live-work.py --model deepseek-v4-pro
 ```
 
-The evaluator reports latency, token usage, tool counts, completion evidence,
+## 部署和更新
-and the generated file listing. It removes only the uniquely named evaluation
-container, volume, and network unless `--keep-workspace` is supplied.
 
-Run the disposable six-service integration stack with a deterministic fake
+### 首次部署
-model provider and exercise registration approval, the Agent loop, workspace
+
-isolation, and file delivery with:
+推荐把应用部署在用户目录，不需要把代码放到 `/opt`：
 
 ```bash
-./scripts/verify-e2e.sh
+git clone git@git.n.xiaomi.com:wuyang6/zk-data-agent.git "$HOME/zk-data-agent" || true
+bash "$HOME/zk-data-agent/scripts/install-from-git.sh"
 ```
 
-The E2E stack and its test-only volumes are removed on exit. Set
+首次部署会：
-`E2E_KEEP_STACK=1` only when you need to inspect the running containers.
 
-Audit every finished service and workspace image, requiring consistent Python
+- 拉取 `main` 分支。
-environments, zero known Python vulnerabilities, and zero fixable
+- 交互式生成 `.env.deploy`。
-High/Critical image vulnerabilities, with:
+- 必要时请求 sudo 安装 Ubuntu 系统依赖。
+- 用 pyenv 准备 Python `3.10.14`。
+- 创建项目 `.venv`。
+- 安装前端依赖并构建。
+- 安装并启动用户级 systemd 服务。
+
+如果要启用“平台账号 = Linux 用户”的托管工作区，需要使用 root/systemd system 服务部署：
 
 ```bash
-./scripts/audit-images.sh
+cd "$HOME/zk-data-agent"
+sudo -E env PATH="$PATH" bash scripts/deploy-ubuntu.sh main --system-service --enable-linux-accounts
 ```
 
-Generated files are available from the **工作区文件** button beside the model
+启用后：
-selector. Users can browse directories, download one file, or download the
-current directory as a `.tar.gz` archive. Browser requests are authenticated by
-Open WebUI and re-signed for the Workspace Gateway; no internal key is exposed.
 
-## Documentation
+- 平台注册/登录账号时会同步创建同名 Linux 用户。
+- 平台密码会同步设置为 Linux 用户密码。
+- Linux 用户允许 SSH 登录。
+- 本机托管工作区会使用 `/home/<account_id>/zk-agent/`。
+- `python_exec`、`python_package`、`bash` 会在对应 Linux 用户身份下执行。
+- Jupyter 远程工作区保持现有逻辑，不参与本机 Linux 用户隔离。
 
-- [Architecture and Agent loop](docs/architecture.md)
+`.env.deploy` 会保存：
-- [Open WebUI integration](docs/openwebui-integration.md)
-- [Infrastructure map](docs/infrastructure.md)
-- [Operations runbook](docs/operations.md)
-- [Security model](docs/security.md)
 
-## Deployment
+```text
+OPENAI_API_KEY
+OPENAI_BASE_URL
+OPENAI_MODEL
+OPENAI_TIMEOUT_SECONDS
+CLAW_BACKEND_HOST
+CLAW_BACKEND_PORT
+CLAW_FRONTEND_HOST
+CLAW_FRONTEND_PORT
+CLAW_API_URL
+CLAW_SERVICE_SCOPE
+CLAW_ENABLE_LINUX_ACCOUNTS
+CLAW_NPM_BIN
+CLAW_NPX_BIN
+CLAW_NODE_BIN
+CLAW_NODE_BIN_DIR
+```
 
-Production uses immutable `linux/amd64` images in
+其中 Node.js 相关路径会在部署时自动记录，供 systemd 后端/前端服务以及飞书 MCP 等 Node 生态能力使用。该文件包含敏感信息，只保存在部署机器本地，权限设置为 `600`，并已被 `.gitignore` 忽略。
-`docker.k1412.top/wuyang/*`, an Unraid Compose Manager project, private
-service networking, and a single public HTTPS entry at
-`https://agent.k1412.top`.
 
-User workspaces run through SSH on a dedicated Docker host; application and
+### 日常更新
-database services remain on the NAS.
 
-## License and Open WebUI attribution
+已经完成首次部署后，普通代码更新使用：
 
-The web service is derived from Open WebUI v0.9.6. Open WebUI's copyright,
+```bash
-license, and attribution remain under the upstream Open WebUI License. K1412's
+cd "$HOME/zk-data-agent"
-original Runtime, Gateway, deployment, tests, and documentation are Apache-2.0.
+bash scripts/update-server-fast.sh
-This is therefore a mixed-license source repository; the Open WebUI-derived web
+```
-layer is not relicensed as Apache-2.0.
 
-The production deployment uses Open WebUI's branding-removal exception for
+如果改动包含依赖、systemd 模板或部署脚本，使用完整部署脚本：
-installations with at most 50 end users in a rolling 30-day period. Public
+
-builds retain upstream branding by default. See
+```bash
-[THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md).
+bash scripts/deploy-ubuntu.sh
+```
+
+root/system 服务更新：
+
+```bash
+sudo -E env PATH="$PATH" bash scripts/deploy-ubuntu.sh main --system-service --enable-linux-accounts
+```
+
+部署指定分支：
+
+```bash
+bash scripts/deploy-ubuntu.sh main
+```
+
+强制覆盖服务器工作区：
+
+```bash
+bash scripts/deploy-ubuntu.sh main --force
+```
+
+### 服务管理
+
+部署脚本默认安装用户级 systemd 服务；以 root 执行或指定 `--system-service` 时安装 system 级服务。服务名默认是：
+
+```text
+zk-data-agent-backend
+zk-data-agent-frontend
+```
+
+查看状态：
+
+```bash
+systemctl --user status zk-data-agent-backend
+systemctl --user status zk-data-agent-frontend
+```
+
+system 级服务使用：
+
+```bash
+systemctl status zk-data-agent-backend
+systemctl status zk-data-agent-frontend
+```
+
+查看日志：
+
+```bash
+journalctl --user -u zk-data-agent-backend -f
+journalctl --user -u zk-data-agent-frontend -f
+```
+
+system 级日志使用：
+
+```bash
+journalctl -u zk-data-agent-backend -f
+journalctl -u zk-data-agent-frontend -f
+```
+
+重启服务：
+
+```bash
+systemctl --user restart zk-data-agent-backend zk-data-agent-frontend
+```
+
+system 级重启使用：
+
+```bash
+systemctl restart zk-data-agent-backend zk-data-agent-frontend
+```
+
+停止服务：
+
+```bash
+systemctl --user stop zk-data-agent-backend zk-data-agent-frontend
+```
+
+如果机器要求用户退出 SSH 后服务仍保持运行，可由管理员执行：
+
+```bash
+loginctl enable-linger <username>
+```
+
+## 环境要求
+
+Ubuntu 部署建议：
+
+- `git`
+- `bash`
+- `curl`
+- `systemd`
+- `pyenv`
+- Python `3.10.14`
+- Node.js `20` 或 `22`
+- `npm`
+- 启用 Linux 账号工作区时，还需要 `passwd`、`python3`、`python3-venv`，并要求服务以 root/systemd system 方式运行。
+
+首次安装如果缺 Python 编译依赖，可以执行：
+
+```bash
+bash scripts/deploy-ubuntu.sh --bootstrap-system
+```
+
+`--bootstrap-system` 会使用 `sudo apt-get` 安装系统依赖；普通模式下应用代码、虚拟环境、前端依赖、运行数据和 systemd 用户服务仍然位于当前用户目录。启用 `--enable-linux-accounts` 后，账号工作区位于 `/home/<account_id>/zk-agent/`。
+
+## 开发验证
+
+后端基础校验：
+
+```bash
+.venv/bin/python -m compileall src backend
+```
+
+前端校验：
+
+```bash
+cd frontend/app
+npm run lint
+npx tsc --noEmit
+npm run build
+```
+
+提交前建议确认：
+
+```bash
+git status --short
+git diff --check
+```
+
+## 常见问题
+
+### Web UI 能打开，但模型调用失败
+
+先检查 `.env.deploy`：
+
+```bash
+cat .env.deploy
+```
+
+重点确认：
+
+- `OPENAI_API_KEY`
+- `OPENAI_BASE_URL`
+- `OPENAI_MODEL`
+- `OPENAI_TIMEOUT_SECONDS`
+
+然后看后端日志：
+
+```bash
+journalctl --user -u zk-data-agent-backend -f
+```
+
+### systemd 服务找不到 npm
+
+重新执行完整部署脚本：
+
+```bash
+bash scripts/deploy-ubuntu.sh
+```
+
+脚本会把当前可用的 `npm` 路径写入 `.env.deploy`，避免用户级 systemd 读取不到 zsh/nvm 环境。
+
+### 新增 Skill 后前端看不到
+
+项目级 Skill 放在：
+
+```text
+skills/<skill-name>/SKILL.md
+```
+
+确保 frontmatter 至少包含 `name`、`description`、`when_to_use`。Web UI 会通过 `/api/claw/skills` 读取 Skill 列表。
+
+### Agent 产物没有出现在“聊天中的文件”
+
+最终产物必须写入当前会话 `output/`。数据 records 推荐使用数据工具导出到逻辑路径：
+
+```text
+output/records.jsonl
+```
+
+工具会自动路由到当前会话 output 目录。

THIRD_PARTY_NOTICES.md

@@ -1,24 +0,0 @@
-# Third-party notices
-
-## Open WebUI
-
-The web interface is built from Open WebUI v0.9.6
-(`1a97751e376e00a1897bc3679215ae1c7bd8fd42`). It remains subject to the Open
-WebUI License, including its branding restrictions.
-
-Copyright (c) 2023- Open WebUI Inc. (Created by Timothy Jaeryang Baek).
-All rights reserved.
-
-The complete upstream license is included in the web image at
-`/app/OPEN_WEBUI_LICENSE` and is available from the upstream repository:
-<https://github.com/open-webui/open-webui/blob/v0.9.6/LICENSE>.
-
-Open WebUI is redistributed under its own license. K1412 Agent's original
-runtime and gateway code are not represented as part of Open WebUI.
-
-The repository defaults to retaining upstream user-facing branding. A deployer
-may set `K1412_REMOVE_UPSTREAM_BRANDING=true` only when one of the exceptions
-in section 4 of the Open WebUI License applies. The K1412 private deployment
-uses the exception for installations with no more than 50 end users in a
-rolling 30-day period. The upstream copyright and license notice remain
-available even when visible product branding is replaced.

agent_platform/__init__.py

@@ -1,3 +0,0 @@
-"""K1412 Agent platform."""
-
-__version__ = "0.1.0"

agent_platform/auth.py

@@ -1,70 +0,0 @@
-from __future__ import annotations
-
-import hmac
-from dataclasses import dataclass
-from typing import Annotated
-
-import jwt
-from fastapi import Header, HTTPException, status
-
-from agent_platform.config import get_settings
-
-
-@dataclass(frozen=True, slots=True)
-class UserIdentity:
-    user_id: str
-    email: str
-    name: str
-    role: str
-
-
-def verify_service_bearer(authorization: str | None, expected: str) -> None:
-    if not expected:
-        raise HTTPException(status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail="Service secret is not configured")
-    scheme, _, token = (authorization or "").partition(" ")
-    if scheme.lower() != "bearer" or not hmac.compare_digest(token, expected):
-        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid service credentials")
-
-
-def decode_openwebui_identity(token: str | None, secret: str) -> UserIdentity:
-    if not secret:
-        raise HTTPException(
-            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
-            detail="Identity verification is unavailable",
-        )
-    if not token:
-        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Missing signed user identity")
-    try:
-        claims = jwt.decode(
-            token,
-            secret,
-            algorithms=["HS256"],
-            issuer="open-webui",
-            options={"require": ["sub", "iss", "iat", "exp"]},
-        )
-    except jwt.PyJWTError as exc:
-        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid signed user identity") from exc
-    user_id = str(claims.get("sub", "")).strip()
-    if not user_id:
-        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Signed identity has no subject")
-    return UserIdentity(
-        user_id=user_id,
-        email=str(claims.get("email", "")),
-        name=str(claims.get("name", "")),
-        role=str(claims.get("role", "user")),
-    )
-
-
-def require_gateway_identity(
-    authorization: Annotated[
-        str | None,
-        Header(alias="Authorization", include_in_schema=False),
-    ] = None,
-    user_jwt: Annotated[
-        str | None,
-        Header(alias="X-OpenWebUI-User-Jwt", include_in_schema=False),
-    ] = None,
-) -> UserIdentity:
-    settings = get_settings()
-    verify_service_bearer(authorization, settings.internal_gateway_key)
-    return decode_openwebui_identity(user_jwt, settings.openwebui_forward_jwt_secret)

agent_platform/bootstrap.py

@@ -1,190 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import os
-import sys
-from typing import Any
-
-import httpx
-
-from agent_platform.models import MODEL_SPECS
-
-LEGACY_MODEL_IDS = {
-    f"{mode}-{strength}" for mode in ("chat", "work") for strength in ("light", "medium", "high", "extreme")
-}
-
-
-def _required(name: str) -> str:
-    value = os.getenv(name, "").strip()
-    if not value:
-        raise RuntimeError(f"{name} is required")
-    return value
-
-
-async def _wait_until_ready(client: httpx.AsyncClient, base_url: str) -> None:
-    for _ in range(120):
-        try:
-            response = await client.get(f"{base_url}/health")
-            if response.status_code == 200:
-                return
-        except httpx.HTTPError:
-            pass
-        await asyncio.sleep(2)
-    raise RuntimeError("Open WebUI did not become healthy")
-
-
-def _model_payload() -> list[dict[str, Any]]:
-    public_read = [{"principal_type": "user", "principal_id": "*", "permission": "read"}]
-    items = []
-    for spec in MODEL_SPECS.values():
-        items.append(
-            {
-                "id": spec.public_id,
-                "base_model_id": None,
-                "name": spec.display_name,
-                "params": {},
-                "meta": {
-                    "description": (f"K1412 Agent；思考：{spec.thinking_label}。"),
-                    "toolIds": [],
-                    "capabilities": {
-                        "tool_calling": False,
-                        "builtin_tools": False,
-                        "file_context": False,
-                        "citations": False,
-                        "vision": False,
-                        "file_upload": False,
-                    },
-                    "tags": [{"name": "Agent"}],
-                },
-                "access_grants": public_read,
-                "is_active": True,
-            }
-        )
-    return items
-
-
-async def bootstrap() -> None:
-    base_url = os.getenv("OPENWEBUI_INTERNAL_URL", "http://web:8080").rstrip("/")
-    admin_email = _required("WEBUI_ADMIN_EMAIL")
-    admin_password = _required("WEBUI_ADMIN_PASSWORD")
-    gateway_key = _required("INTERNAL_GATEWAY_KEY")
-    gateway_url = os.getenv("GATEWAY_URL", "http://gateway:8001").rstrip("/")
-
-    async with httpx.AsyncClient(timeout=30) as client:
-        await _wait_until_ready(client, base_url)
-        response = await client.post(
-            f"{base_url}/api/v1/auths/signin",
-            json={"email": admin_email, "password": admin_password},
-        )
-        response.raise_for_status()
-        token = response.json().get("token")
-        if not token:
-            raise RuntimeError("Open WebUI sign-in returned no token")
-        headers = {"Authorization": f"Bearer {token}"}
-
-        config_response = await client.get(f"{base_url}/api/v1/auths/admin/config", headers=headers)
-        config_response.raise_for_status()
-        config = config_response.json()
-        config.update(
-            {
-                "ENABLE_SIGNUP": True,
-                "DEFAULT_USER_ROLE": "pending",
-                "ENABLE_API_KEYS": False,
-                "ENABLE_COMMUNITY_SHARING": False,
-                "ENABLE_MESSAGE_RATING": False,
-                "ENABLE_FOLDERS": False,
-                "ENABLE_AUTOMATIONS": False,
-                "ENABLE_CHANNELS": False,
-                "ENABLE_CALENDAR": False,
-                "ENABLE_MEMORIES": False,
-                "ENABLE_NOTES": False,
-                "ENABLE_USER_WEBHOOKS": False,
-            }
-        )
-        update_response = await client.post(
-            f"{base_url}/api/v1/auths/admin/config",
-            headers=headers,
-            json=config,
-        )
-        update_response.raise_for_status()
-
-        evaluation_response = await client.post(
-            f"{base_url}/api/v1/evaluations/config",
-            headers=headers,
-            json={
-                "ENABLE_EVALUATION_ARENA_MODELS": False,
-                "EVALUATION_ARENA_MODELS": [],
-            },
-        )
-        evaluation_response.raise_for_status()
-
-        tool_server_response = await client.post(
-            f"{base_url}/api/v1/configs/tool_servers",
-            headers=headers,
-            json={
-                "TOOL_SERVER_CONNECTIONS": [
-                    {
-                        "url": gateway_url,
-                        "path": "openapi.json",
-                        "type": "openapi",
-                        "auth_type": "bearer",
-                        "headers": None,
-                        "key": gateway_key,
-                        "config": {
-                            "enable": True,
-                            "access_grants": [
-                                {
-                                    "principal_type": "user",
-                                    "principal_id": "*",
-                                    "permission": "read",
-                                }
-                            ],
-                        },
-                        "info": {
-                            "id": "workspace",
-                            "name": "Workspace",
-                            "description": "当前用户的隔离编码工作区",
-                        },
-                        "spec_type": "url",
-                    }
-                ]
-            },
-        )
-        tool_server_response.raise_for_status()
-
-        for model_id in sorted(LEGACY_MODEL_IDS):
-            existing = await client.get(
-                f"{base_url}/api/v1/models/model",
-                headers=headers,
-                params={"id": model_id},
-            )
-            if existing.status_code == 404:
-                continue
-            existing.raise_for_status()
-            delete_response = await client.post(
-                f"{base_url}/api/v1/models/model/delete",
-                headers=headers,
-                json={"id": model_id},
-            )
-            delete_response.raise_for_status()
-
-        model_response = await client.post(
-            f"{base_url}/api/v1/models/import",
-            headers=headers,
-            json={"models": _model_payload()},
-        )
-        model_response.raise_for_status()
-
-    print("Open WebUI bootstrap complete: auth policy, workspace tools, and four Agent models are ready.")
-
-
-def run() -> None:
-    try:
-        asyncio.run(bootstrap())
-    except Exception as exc:
-        print(f"Open WebUI bootstrap failed: {exc}", file=sys.stderr)
-        raise SystemExit(1) from exc
-
-
-if __name__ == "__main__":
-    run()

agent_platform/config.py

@@ -1,124 +0,0 @@
-from __future__ import annotations
-
-import os
-from dataclasses import dataclass
-from functools import lru_cache
-
-
-def _bool(name: str, default: bool) -> bool:
-    value = os.getenv(name)
-    if value is None:
-        return default
-    return value.strip().lower() in {"1", "true", "yes", "on"}
-
-
-def _int(name: str, default: int) -> int:
-    value = os.getenv(name)
-    return int(value) if value else default
-
-
-def _float(name: str, default: float) -> float:
-    value = os.getenv(name)
-    return float(value) if value else default
-
-
-@dataclass(frozen=True, slots=True)
-class Settings:
-    model_api_base_url: str
-    model_api_key: str
-    deepseek_api_base_url: str
-    deepseek_api_key: str
-    openwebui_forward_jwt_secret: str
-    internal_provider_key: str
-    internal_gateway_key: str
-    database_url: str
-    redis_url: str
-    gateway_url: str
-    execution_provider: str
-    workspace_image: str
-    workspace_network_enabled: bool
-    workspace_memory_limit: str
-    workspace_cpu_limit: float
-    workspace_pids_limit: int
-    workspace_ssh_host: str
-    workspace_download_max_bytes: int
-    model_timeout_seconds: int
-    tool_timeout_seconds: int
-    max_tool_output_chars: int
-
-    @classmethod
-    def from_env(cls) -> Settings:
-        return cls(
-            model_api_base_url=os.getenv("MODEL_API_BASE_URL", "https://api.k1412.top").rstrip("/"),
-            model_api_key=os.getenv("MODEL_API_KEY", ""),
-            deepseek_api_base_url=os.getenv("DEEPSEEK_API_BASE_URL", "https://api.deepseek.com").rstrip("/"),
-            deepseek_api_key=os.getenv("DEEPSEEK_API_KEY", ""),
-            openwebui_forward_jwt_secret=os.getenv("OPENWEBUI_FORWARD_JWT_SECRET", ""),
-            internal_provider_key=os.getenv("INTERNAL_PROVIDER_KEY", ""),
-            internal_gateway_key=os.getenv("INTERNAL_GATEWAY_KEY", ""),
-            database_url=os.getenv("DATABASE_URL", "sqlite+aiosqlite:///./.runtime/agent.db"),
-            redis_url=os.getenv("REDIS_URL", "redis://localhost:6379/0"),
-            gateway_url=os.getenv("GATEWAY_URL", "http://gateway:8001").rstrip("/"),
-            execution_provider=os.getenv("EXECUTION_PROVIDER", "local-docker"),
-            workspace_image=os.getenv("WORKSPACE_IMAGE", "k1412-agent-workspace:dev"),
-            workspace_network_enabled=_bool("WORKSPACE_NETWORK_ENABLED", True),
-            workspace_memory_limit=os.getenv("WORKSPACE_MEMORY_LIMIT", "2g"),
-            workspace_cpu_limit=_float("WORKSPACE_CPU_LIMIT", 2.0),
-            workspace_pids_limit=_int("WORKSPACE_PIDS_LIMIT", 512),
-            workspace_ssh_host=os.getenv("WORKSPACE_SSH_HOST", ""),
-            workspace_download_max_bytes=_int("WORKSPACE_DOWNLOAD_MAX_BYTES", 128 * 1024 * 1024),
-            model_timeout_seconds=_int("MODEL_TIMEOUT_SECONDS", 600),
-            tool_timeout_seconds=_int("TOOL_TIMEOUT_SECONDS", 120),
-            max_tool_output_chars=_int("MAX_TOOL_OUTPUT_CHARS", 24_000),
-        )
-
-    def validate_runtime(self) -> None:
-        missing = [
-            name
-            for name, value in (
-                ("MODEL_API_KEY", self.model_api_key),
-                ("DEEPSEEK_API_KEY", self.deepseek_api_key),
-                ("OPENWEBUI_FORWARD_JWT_SECRET", self.openwebui_forward_jwt_secret),
-                ("INTERNAL_PROVIDER_KEY", self.internal_provider_key),
-                ("INTERNAL_GATEWAY_KEY", self.internal_gateway_key),
-            )
-            if not value
-        ]
-        if missing:
-            raise RuntimeError(f"Missing required runtime secrets: {', '.join(missing)}")
-        self._validate_internal_secret_lengths()
-
-    def validate_gateway(self) -> None:
-        missing = [
-            name
-            for name, value in (
-                ("OPENWEBUI_FORWARD_JWT_SECRET", self.openwebui_forward_jwt_secret),
-                ("INTERNAL_GATEWAY_KEY", self.internal_gateway_key),
-            )
-            if not value
-        ]
-        if missing:
-            raise RuntimeError(f"Missing required gateway secrets: {', '.join(missing)}")
-        self._validate_internal_secret_lengths()
-        if self.execution_provider not in {"local-docker", "ssh-docker"}:
-            raise RuntimeError("EXECUTION_PROVIDER must be local-docker or ssh-docker")
-        if self.execution_provider == "ssh-docker" and not self.workspace_ssh_host:
-            raise RuntimeError("WORKSPACE_SSH_HOST is required for ssh-docker")
-
-    def _validate_internal_secret_lengths(self) -> None:
-        weak = [
-            name
-            for name, value in (
-                ("OPENWEBUI_FORWARD_JWT_SECRET", self.openwebui_forward_jwt_secret),
-                ("INTERNAL_PROVIDER_KEY", self.internal_provider_key),
-                ("INTERNAL_GATEWAY_KEY", self.internal_gateway_key),
-            )
-            if value and len(value.encode("utf-8")) < 32
-        ]
-        if weak:
-            raise RuntimeError(f"Internal secrets must be at least 32 bytes: {', '.join(weak)}")
-
-
-@lru_cache(maxsize=1)
-def get_settings() -> Settings:
-    return Settings.from_env()

agent_platform/gateway/__init__.py

@@ -1 +0,0 @@
-"""Isolated workspace execution gateway."""

agent_platform/gateway/app.py

@@ -1,239 +0,0 @@
-import asyncio
-from contextlib import asynccontextmanager
-from typing import Annotated
-from urllib.parse import quote
-
-import uvicorn
-from fastapi import Depends, FastAPI, Header, HTTPException, Query
-from fastapi.responses import Response, StreamingResponse
-
-from agent_platform.auth import UserIdentity, decode_openwebui_identity, verify_service_bearer
-from agent_platform.config import Settings, get_settings
-from agent_platform.gateway.provider import ExecutionProvider, create_execution_provider
-from agent_platform.gateway.schemas import (
-    ApplyPatchRequest,
-    ExecRequest,
-    GitDiffRequest,
-    GitRequest,
-    ListFilesRequest,
-    ProcessRequest,
-    ReadFileRequest,
-    SearchFilesRequest,
-    StartProcessRequest,
-    ToolResult,
-    WorkspaceListing,
-    WorkspaceStatus,
-    WriteFileRequest,
-)
-
-
-def create_app(
-    settings: Settings | None = None,
-    provider: ExecutionProvider | None = None,
-) -> FastAPI:
-    settings = settings or get_settings()
-
-    @asynccontextmanager
-    async def lifespan(app: FastAPI):
-        settings.validate_gateway()
-        app.state.provider = provider or create_execution_provider(settings)
-        yield
-
-    app = FastAPI(
-        title="K1412 Workspace Gateway",
-        description="Authenticated tools for one isolated workspace per user.",
-        version="0.1.0",
-        lifespan=lifespan,
-    )
-    mutation_locks: dict[str, asyncio.Lock] = {}
-    mutation_locks_guard = asyncio.Lock()
-
-    def require_identity(
-        authorization: Annotated[
-            str | None,
-            Header(alias="Authorization", include_in_schema=False),
-        ] = None,
-        user_jwt: Annotated[
-            str | None,
-            Header(alias="X-OpenWebUI-User-Jwt", include_in_schema=False),
-        ] = None,
-    ) -> UserIdentity:
-        verify_service_bearer(authorization, settings.internal_gateway_key)
-        return decode_openwebui_identity(user_jwt, settings.openwebui_forward_jwt_secret)
-
-    Identity = Annotated[UserIdentity, Depends(require_identity)]
-
-    def executor() -> ExecutionProvider:
-        return app.state.provider
-
-    def translate_value_error(exc: ValueError) -> HTTPException:
-        return HTTPException(status_code=400, detail=str(exc))
-
-    async def mutation_lock(user_id: str) -> asyncio.Lock:
-        async with mutation_locks_guard:
-            return mutation_locks.setdefault(user_id, asyncio.Lock())
-
-    def attachment_header(filename: str) -> str:
-        fallback = "".join(character for character in filename if character.isascii() and character.isalnum())
-        fallback = fallback[:80] or "download"
-        return f"attachment; filename={fallback}; filename*=UTF-8''{quote(filename)}"
-
-    @app.get("/health", include_in_schema=False)
-    async def health() -> dict:
-        return {"status": "ok", "provider": settings.execution_provider}
-
-    @app.post("/v1/tools/workspace_status", response_model=WorkspaceStatus, operation_id="workspace_status")
-    async def workspace_status(identity: Identity) -> WorkspaceStatus:
-        return await executor().status(identity.user_id)
-
-    @app.post("/v1/tools/list_files", response_model=ToolResult, operation_id="list_files")
-    async def list_files(body: ListFilesRequest, identity: Identity) -> ToolResult:
-        try:
-            return await executor().list_files(identity.user_id, body.path, body.max_depth, body.limit)
-        except ValueError as exc:
-            raise translate_value_error(exc) from exc
-
-    @app.get("/v1/files", response_model=WorkspaceListing, operation_id="browse_workspace_files")
-    async def browse_workspace_files(
-        identity: Identity,
-        path: Annotated[str, Query(max_length=4096)] = ".",
-    ) -> WorkspaceListing:
-        try:
-            return await executor().browse_files(identity.user_id, path)
-        except ValueError as exc:
-            raise translate_value_error(exc) from exc
-        except FileNotFoundError as exc:
-            raise HTTPException(status_code=404, detail=str(exc)) from exc
-
-    @app.get("/v1/files/download", operation_id="download_workspace_file")
-    async def download_workspace_file(
-        identity: Identity,
-        path: Annotated[str, Query(max_length=4096)],
-    ) -> Response:
-        try:
-            download = await executor().download_file(identity.user_id, path)
-        except ValueError as exc:
-            raise translate_value_error(exc) from exc
-        except (FileNotFoundError, IsADirectoryError) as exc:
-            raise HTTPException(status_code=404, detail=str(exc)) from exc
-        return Response(
-            content=download.content or b"",
-            media_type=download.media_type,
-            headers={"Content-Disposition": attachment_header(download.filename)},
-        )
-
-    @app.get("/v1/files/archive", operation_id="archive_workspace_files")
-    async def archive_workspace_files(
-        identity: Identity,
-        path: Annotated[str, Query(max_length=4096)] = ".",
-    ) -> StreamingResponse:
-        try:
-            download = await executor().archive_files(identity.user_id, path)
-        except ValueError as exc:
-            raise translate_value_error(exc) from exc
-        except FileNotFoundError as exc:
-            raise HTTPException(status_code=404, detail=str(exc)) from exc
-        return StreamingResponse(
-            download.chunks or iter(()),
-            media_type=download.media_type,
-            headers={"Content-Disposition": attachment_header(download.filename)},
-        )
-
-    @app.post("/v1/tools/read_file", response_model=ToolResult, operation_id="read_file")
-    async def read_file(body: ReadFileRequest, identity: Identity) -> ToolResult:
-        try:
-            return await executor().read_file(identity.user_id, body.path, body.start_line, body.max_lines)
-        except ValueError as exc:
-            raise translate_value_error(exc) from exc
-
-    @app.post("/v1/tools/write_file", response_model=ToolResult, operation_id="write_file")
-    async def write_file(body: WriteFileRequest, identity: Identity) -> ToolResult:
-        try:
-            lock = await mutation_lock(identity.user_id)
-            async with lock:
-                return await executor().write_file(identity.user_id, body.path, body.content)
-        except ValueError as exc:
-            raise translate_value_error(exc) from exc
-
-    @app.post("/v1/tools/search_files", response_model=ToolResult, operation_id="search_files")
-    async def search_files(body: SearchFilesRequest, identity: Identity) -> ToolResult:
-        try:
-            return await executor().search_files(
-                identity.user_id,
-                body.query,
-                body.path,
-                body.glob,
-                body.limit,
-            )
-        except ValueError as exc:
-            raise translate_value_error(exc) from exc
-
-    @app.post("/v1/tools/exec", response_model=ToolResult, operation_id="exec")
-    async def exec_command(body: ExecRequest, identity: Identity) -> ToolResult:
-        try:
-            lock = await mutation_lock(identity.user_id)
-            async with lock:
-                return await executor().exec(
-                    identity.user_id,
-                    body.command,
-                    body.cwd,
-                    min(body.timeout_seconds, settings.tool_timeout_seconds),
-                )
-        except ValueError as exc:
-            raise translate_value_error(exc) from exc
-
-    @app.post("/v1/tools/apply_patch", response_model=ToolResult, operation_id="apply_patch")
-    async def apply_patch(body: ApplyPatchRequest, identity: Identity) -> ToolResult:
-        try:
-            lock = await mutation_lock(identity.user_id)
-            async with lock:
-                return await executor().apply_patch(identity.user_id, body.patch, body.cwd)
-        except ValueError as exc:
-            raise translate_value_error(exc) from exc
-
-    @app.post("/v1/tools/git_status", response_model=ToolResult, operation_id="git_status")
-    async def git_status(body: GitRequest, identity: Identity) -> ToolResult:
-        try:
-            return await executor().exec(identity.user_id, "git status --short --branch", body.cwd, 30)
-        except ValueError as exc:
-            raise translate_value_error(exc) from exc
-
-    @app.post("/v1/tools/git_diff", response_model=ToolResult, operation_id="git_diff")
-    async def git_diff(body: GitDiffRequest, identity: Identity) -> ToolResult:
-        command = "git diff --cached" if body.staged else "git diff"
-        try:
-            return await executor().exec(identity.user_id, command, body.cwd, 30)
-        except ValueError as exc:
-            raise translate_value_error(exc) from exc
-
-    @app.post("/v1/tools/start_process", response_model=ToolResult, operation_id="start_process")
-    async def start_process(body: StartProcessRequest, identity: Identity) -> ToolResult:
-        try:
-            lock = await mutation_lock(identity.user_id)
-            async with lock:
-                return await executor().start_process(identity.user_id, body.command, body.cwd)
-        except ValueError as exc:
-            raise translate_value_error(exc) from exc
-
-    @app.post("/v1/tools/poll_process", response_model=ToolResult, operation_id="poll_process")
-    async def poll_process(body: ProcessRequest, identity: Identity) -> ToolResult:
-        return await executor().poll_process(identity.user_id, body.process_id)
-
-    @app.post("/v1/tools/cancel_process", response_model=ToolResult, operation_id="cancel_process")
-    async def cancel_process(body: ProcessRequest, identity: Identity) -> ToolResult:
-        lock = await mutation_lock(identity.user_id)
-        async with lock:
-            return await executor().cancel_process(identity.user_id, body.process_id)
-
-    return app
-
-
-app = create_app()
-
-
-def run() -> None:
-    uvicorn.run("agent_platform.gateway.app:app", host="0.0.0.0", port=8001)  # noqa: S104
-
-
-if __name__ == "__main__":
-    run()

agent_platform/gateway/provider.py

@@ -1,475 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import hashlib
-import io
-import json
-import mimetypes
-import shlex
-import tarfile
-import uuid
-import zlib
-from collections.abc import Iterator
-from dataclasses import dataclass
-from pathlib import PurePosixPath
-from typing import Protocol
-
-from docker.errors import ImageNotFound, NotFound
-
-import docker
-from agent_platform.config import Settings
-from agent_platform.gateway.schemas import ToolResult, WorkspaceListing, WorkspaceStatus
-from agent_platform.text import truncate_middle
-
-
-def normalize_workspace_path(raw_path: str) -> PurePosixPath:
-    if "\x00" in raw_path:
-        raise ValueError("Path contains a null byte")
-    raw = raw_path.strip() or "."
-    path = PurePosixPath(raw)
-    if path.is_absolute():
-        try:
-            path = path.relative_to("/workspace")
-        except ValueError as exc:
-            raise ValueError("Absolute paths must be under /workspace") from exc
-    if any(part in {"..", ""} for part in path.parts):
-        raise ValueError("Path traversal is not allowed")
-    return PurePosixPath(".") if str(path) in {"", "."} else path
-
-
-def absolute_workspace_path(raw_path: str) -> str:
-    relative = normalize_workspace_path(raw_path)
-    return "/workspace" if relative == PurePosixPath(".") else f"/workspace/{relative}"
-
-
-@dataclass(frozen=True, slots=True)
-class WorkspaceRef:
-    workspace_id: str
-    container_name: str
-    volume_name: str
-    network_name: str
-
-
-@dataclass(slots=True)
-class WorkspaceDownload:
-    filename: str
-    media_type: str
-    content: bytes | None = None
-    chunks: Iterator[bytes] | None = None
-
-
-def workspace_ref(user_id: str) -> WorkspaceRef:
-    digest = hashlib.sha256(user_id.encode("utf-8")).hexdigest()[:20]
-    return WorkspaceRef(
-        workspace_id=digest,
-        container_name=f"k1412-ws-{digest}",
-        volume_name=f"k1412-ws-data-{digest}",
-        network_name=f"k1412-ws-net-{digest}",
-    )
-
-
-class ExecutionProvider(Protocol):
-    provider_name: str
-
-    async def status(self, user_id: str) -> WorkspaceStatus: ...
-
-    async def exec(self, user_id: str, command: str, cwd: str, timeout_seconds: int) -> ToolResult: ...
-
-    async def list_files(self, user_id: str, path: str, max_depth: int, limit: int) -> ToolResult: ...
-
-    async def read_file(self, user_id: str, path: str, start_line: int, max_lines: int) -> ToolResult: ...
-
-    async def write_file(self, user_id: str, path: str, content: str) -> ToolResult: ...
-
-    async def search_files(
-        self,
-        user_id: str,
-        query: str,
-        path: str,
-        glob: str | None,
-        limit: int,
-    ) -> ToolResult: ...
-
-    async def apply_patch(self, user_id: str, patch: str, cwd: str) -> ToolResult: ...
-
-    async def start_process(self, user_id: str, command: str, cwd: str) -> ToolResult: ...
-
-    async def poll_process(self, user_id: str, process_id: str) -> ToolResult: ...
-
-    async def cancel_process(self, user_id: str, process_id: str) -> ToolResult: ...
-
-    async def browse_files(self, user_id: str, path: str) -> WorkspaceListing: ...
-
-    async def download_file(self, user_id: str, path: str) -> WorkspaceDownload: ...
-
-    async def archive_files(self, user_id: str, path: str) -> WorkspaceDownload: ...
-
-
-class DockerExecutionProvider:
-    provider_name = "local-docker"
-
-    def __init__(self, settings: Settings, client: docker.DockerClient | None = None) -> None:
-        self.settings = settings
-        self.client = client or docker.from_env()
-        self._locks: dict[str, asyncio.Lock] = {}
-        self._lock_guard = asyncio.Lock()
-
-    async def _user_lock(self, user_id: str) -> asyncio.Lock:
-        async with self._lock_guard:
-            return self._locks.setdefault(user_id, asyncio.Lock())
-
-    async def _container(self, user_id: str):
-        ref = workspace_ref(user_id)
-
-        def ensure():
-            try:
-                container = self.client.containers.get(ref.container_name)
-            except NotFound:
-                try:
-                    self.client.images.get(self.settings.workspace_image)
-                except ImageNotFound as exc:
-                    raise RuntimeError(f"Workspace image {self.settings.workspace_image!r} is not installed") from exc
-                if self.settings.workspace_network_enabled:
-                    try:
-                        self.client.networks.get(ref.network_name)
-                    except NotFound:
-                        self.client.networks.create(
-                            ref.network_name,
-                            driver="bridge",
-                            check_duplicate=True,
-                            labels={
-                                "app.k1412.component": "user-workspace-network",
-                                "app.k1412.workspace-id": ref.workspace_id,
-                            },
-                        )
-                container = self.client.containers.create(
-                    image=self.settings.workspace_image,
-                    name=ref.container_name,
-                    hostname=f"workspace-{ref.workspace_id}",
-                    command=["sleep", "infinity"],
-                    user="1000:1000",
-                    working_dir="/workspace",
-                    volumes={ref.volume_name: {"bind": "/workspace", "mode": "rw"}},
-                    labels={
-                        "app.k1412.component": "user-workspace",
-                        "app.k1412.workspace-id": ref.workspace_id,
-                    },
-                    detach=True,
-                    read_only=True,
-                    tmpfs={
-                        "/tmp": "rw,noexec,nosuid,size=256m",  # noqa: S108 - isolated container tmpfs
-                        "/run": "rw,noexec,nosuid,size=16m",
-                    },
-                    cap_drop=["ALL"],
-                    security_opt=["no-new-privileges:true"],
-                    mem_limit=self.settings.workspace_memory_limit,
-                    nano_cpus=int(self.settings.workspace_cpu_limit * 1_000_000_000),
-                    pids_limit=self.settings.workspace_pids_limit,
-                    network_mode=ref.network_name if self.settings.workspace_network_enabled else "none",
-                )
-            container.reload()
-            if container.status != "running":
-                container.start()
-                container.reload()
-            return container
-
-        lock = await self._user_lock(user_id)
-        async with lock:
-            return await asyncio.to_thread(ensure)
-
-    async def status(self, user_id: str) -> WorkspaceStatus:
-        container = await self._container(user_id)
-        ref = workspace_ref(user_id)
-        return WorkspaceStatus(
-            workspace_id=ref.workspace_id,
-            provider=self.provider_name,
-            container_name=ref.container_name,
-            state=container.status,
-        )
-
-    async def _exec_argv(
-        self,
-        user_id: str,
-        argv: list[str],
-        *,
-        cwd: str = ".",
-        max_output: int = 128_000,
-    ) -> ToolResult:
-        container = await self._container(user_id)
-        workdir = absolute_workspace_path(cwd)
-
-        def execute() -> ToolResult:
-            result = container.exec_run(
-                argv,
-                workdir=workdir,
-                user="1000:1000",
-                demux=True,
-            )
-            stdout, stderr = result.output if isinstance(result.output, tuple) else (result.output, b"")
-            output = ((stdout or b"") + (stderr or b"")).decode("utf-8", errors="replace")
-            truncated = len(output) > max_output
-            if truncated:
-                output = truncate_middle(output, max_output, "\n… output truncated; middle omitted …\n")
-            return ToolResult(
-                ok=result.exit_code == 0,
-                output=output,
-                exit_code=result.exit_code,
-                truncated=truncated,
-            )
-
-        return await asyncio.to_thread(execute)
-
-    async def exec(self, user_id: str, command: str, cwd: str, timeout_seconds: int) -> ToolResult:
-        normalize_workspace_path(cwd)
-        bounded_timeout = max(1, min(timeout_seconds, 1800))
-        shell = f"timeout --signal=TERM {bounded_timeout}s sh -lc {shlex.quote(command)}"
-        return await self._exec_argv(user_id, ["sh", "-lc", shell], cwd=cwd)
-
-    async def list_files(self, user_id: str, path: str, max_depth: int, limit: int) -> ToolResult:
-        absolute = absolute_workspace_path(path)
-        script = (
-            "import os,sys\n"
-            "root=sys.argv[1]; max_depth=int(sys.argv[2]); limit=int(sys.argv[3]); out=[]\n"
-            "base_depth=root.rstrip('/').count('/')\n"
-            "for current, dirs, files in os.walk(root):\n"
-            " depth=current.rstrip('/').count('/')-base_depth\n"
-            " dirs[:]=sorted(d for d in dirs if d not in {'.git','node_modules','.venv','__pycache__'})\n"
-            " if depth>=max_depth: dirs[:]=[]\n"
-            " rel=os.path.relpath(current,'/workspace')\n"
-            " for name in sorted(dirs): out.append((os.path.join(rel,name) if rel!='.' else name)+'/')\n"
-            " for name in sorted(files): out.append(os.path.join(rel,name) if rel!='.' else name)\n"
-            " if len(out)>=limit: break\n"
-            "print('\\n'.join(out[:limit]))\n"
-        )
-        return await self._exec_argv(
-            user_id,
-            ["python3", "-c", script, absolute, str(max_depth), str(limit)],
-        )
-
-    async def browse_files(self, user_id: str, path: str) -> WorkspaceListing:
-        relative = normalize_workspace_path(path)
-        absolute = absolute_workspace_path(path)
-        script = (
-            "import json,os,pathlib,sys\n"
-            "root=pathlib.Path(sys.argv[1])\n"
-            "if not root.is_dir(): raise SystemExit(f'Not a directory: {root}')\n"
-            "items=[]\n"
-            "for item in root.iterdir():\n"
-            " if item.name=='.agent': continue\n"
-            " try: info=item.lstat()\n"
-            " except OSError: continue\n"
-            " kind='symlink' if item.is_symlink() else ('directory' if item.is_dir() else 'file')\n"
-            " rel=item.relative_to('/workspace').as_posix()\n"
-            " items.append({'name':item.name,'path':rel,'kind':kind,'size':info.st_size,"
-            "'modified_at':int(info.st_mtime)})\n"
-            "items.sort(key=lambda value:(value['kind']!='directory',value['name'].casefold()))\n"
-            "print(json.dumps(items,ensure_ascii=False))\n"
-        )
-        raw = await self._exec_argv(user_id, ["python3", "-c", script, absolute])
-        if not raw.ok:
-            raise FileNotFoundError(raw.output.strip() or f"Directory not found: {relative}")
-        try:
-            entries = json.loads(raw.output)
-        except json.JSONDecodeError as exc:
-            raise RuntimeError("Workspace returned an invalid directory listing") from exc
-        return WorkspaceListing(path="." if relative == PurePosixPath(".") else str(relative), entries=entries)
-
-    async def download_file(self, user_id: str, path: str) -> WorkspaceDownload:
-        relative = normalize_workspace_path(path)
-        if relative == PurePosixPath("."):
-            raise ValueError("A file path is required")
-        container = await self._container(user_id)
-        absolute = absolute_workspace_path(path)
-
-        def collect() -> WorkspaceDownload:
-            chunks, metadata = container.get_archive(absolute)
-            size = int(metadata.get("size", 0))
-            if size > self.settings.workspace_download_max_bytes:
-                raise ValueError(
-                    f"File exceeds the {self.settings.workspace_download_max_bytes // (1024 * 1024)} MB download limit"
-                )
-            archive = io.BytesIO()
-            archive_limit = self.settings.workspace_download_max_bytes + 2 * 1024 * 1024
-            for chunk in chunks:
-                archive.write(chunk)
-                if archive.tell() > archive_limit:
-                    raise ValueError("File archive exceeded the download limit")
-            archive.seek(0)
-            with tarfile.open(fileobj=archive, mode="r:*") as tar:
-                members = tar.getmembers()
-                member = members[0] if members else None
-                if member is None:
-                    raise FileNotFoundError(f"File not found: {relative}")
-                if not member.isfile():
-                    raise IsADirectoryError(f"Not a regular file: {relative}")
-                source = tar.extractfile(member)
-                if source is None:
-                    raise FileNotFoundError(f"File not found: {relative}")
-                content = source.read(self.settings.workspace_download_max_bytes + 1)
-            if len(content) > self.settings.workspace_download_max_bytes:
-                raise ValueError("File exceeds the download limit")
-            media_type = mimetypes.guess_type(relative.name)[0] or "application/octet-stream"
-            return WorkspaceDownload(filename=relative.name, media_type=media_type, content=content)
-
-        return await asyncio.to_thread(collect)
-
-    async def archive_files(self, user_id: str, path: str) -> WorkspaceDownload:
-        relative = normalize_workspace_path(path)
-        container = await self._container(user_id)
-        chunks, _ = await asyncio.to_thread(container.get_archive, absolute_workspace_path(path))
-
-        def compressed_chunks() -> Iterator[bytes]:
-            compressor = zlib.compressobj(level=6, method=zlib.DEFLATED, wbits=31)
-            for chunk in chunks:
-                compressed = compressor.compress(chunk)
-                if compressed:
-                    yield compressed
-            final = compressor.flush()
-            if final:
-                yield final
-
-        basename = "workspace" if relative == PurePosixPath(".") else relative.name
-        return WorkspaceDownload(
-            filename=f"{basename}.tar.gz",
-            media_type="application/gzip",
-            chunks=compressed_chunks(),
-        )
-
-    async def read_file(self, user_id: str, path: str, start_line: int, max_lines: int) -> ToolResult:
-        absolute = absolute_workspace_path(path)
-        script = (
-            "import pathlib,sys\n"
-            "p=pathlib.Path(sys.argv[1]); start=int(sys.argv[2]); count=int(sys.argv[3])\n"
-            "if not p.is_file(): raise SystemExit(f'Not a file: {p}')\n"
-            "with p.open('r',encoding='utf-8',errors='replace') as f:\n"
-            " lines=f.readlines()\n"
-            "for i,line in enumerate(lines[start-1:start-1+count],start): print(f'{i:>6}  {line}',end='')\n"
-        )
-        return await self._exec_argv(
-            user_id,
-            ["python3", "-c", script, absolute, str(start_line), str(max_lines)],
-        )
-
-    async def write_file(self, user_id: str, path: str, content: str) -> ToolResult:
-        relative = normalize_workspace_path(path)
-        if relative == PurePosixPath("."):
-            raise ValueError("A file path is required")
-        parent = str(relative.parent)
-        container = await self._container(user_id)
-        if parent not in {"", "."}:
-            await self._exec_argv(user_id, ["mkdir", "-p", absolute_workspace_path(parent)])
-
-        archive = io.BytesIO()
-        encoded = content.encode("utf-8")
-        with tarfile.open(fileobj=archive, mode="w") as tar:
-            info = tarfile.TarInfo(name=relative.name)
-            info.size = len(encoded)
-            info.mode = 0o644
-            info.uid = 1000
-            info.gid = 1000
-            tar.addfile(info, io.BytesIO(encoded))
-        archive.seek(0)
-        destination = "/workspace" if parent in {"", "."} else absolute_workspace_path(parent)
-        await asyncio.to_thread(container.put_archive, destination, archive.getvalue())
-        return ToolResult(ok=True, output=f"Wrote {len(encoded)} bytes to {relative}")
-
-    async def search_files(
-        self,
-        user_id: str,
-        query: str,
-        path: str,
-        glob: str | None,
-        limit: int,
-    ) -> ToolResult:
-        absolute = absolute_workspace_path(path)
-        argv = ["rg", "--line-number", "--color=never", "--max-count", str(limit), "--", query, absolute]
-        if glob:
-            argv[1:1] = ["--glob", glob]
-        result = await self._exec_argv(user_id, argv)
-        if result.exit_code == 1:
-            return ToolResult(ok=True, output="No matches.", exit_code=0)
-        return result
-
-    async def apply_patch(self, user_id: str, patch: str, cwd: str) -> ToolResult:
-        process_id = uuid.uuid4().hex
-        patch_path = f".agent/tmp/{process_id}.patch"
-        await self.write_file(user_id, patch_path, patch)
-        result = await self.exec(
-            user_id,
-            f"git apply --whitespace=nowarn {shlex.quote(absolute_workspace_path(patch_path))}",
-            cwd,
-            120,
-        )
-        await self._exec_argv(user_id, ["rm", "-f", absolute_workspace_path(patch_path)])
-        return result
-
-    async def start_process(self, user_id: str, command: str, cwd: str) -> ToolResult:
-        normalize_workspace_path(cwd)
-        process_id = uuid.uuid4().hex
-        process_dir = absolute_workspace_path(f".agent/processes/{process_id}")
-        worker = (
-            f"sh -lc {shlex.quote(command)}; "
-            "code=$?; "
-            f'echo "$code" > {shlex.quote(process_dir + "/exit_code")}; '
-            'exit "$code"'
-        )
-        wrapped = (
-            f"mkdir -p {shlex.quote(process_dir)}; "
-            f"setsid sh -lc {shlex.quote(worker)} "
-            f"> {shlex.quote(process_dir + '/output.log')} 2>&1 & "
-            f'pid=$!; echo "$pid" > {shlex.quote(process_dir + "/pid")}; '
-            f"echo {shlex.quote(json.dumps({'process_id': process_id}))}"
-        )
-        result = await self._exec_argv(user_id, ["sh", "-lc", wrapped], cwd=cwd)
-        result.metadata = {"process_id": process_id}
-        return result
-
-    async def poll_process(self, user_id: str, process_id: str) -> ToolResult:
-        process_dir = absolute_workspace_path(f".agent/processes/{process_id}")
-        script = (
-            "import json,os,pathlib,sys\n"
-            "d=pathlib.Path(sys.argv[1]); pid=(d/'pid').read_text().strip() if (d/'pid').exists() else ''\n"
-            "code=(d/'exit_code').read_text().strip() if (d/'exit_code').exists() else None\n"
-            "log=(d/'output.log').read_text(errors='replace')[-50000:] if (d/'output.log').exists() else ''\n"
-            "print(json.dumps({'running': code is None and bool(pid), 'pid': pid, 'exit_code': code, 'output': log}))\n"
-        )
-        raw = await self._exec_argv(user_id, ["python3", "-c", script, process_dir])
-        if not raw.ok:
-            return raw
-        try:
-            data = json.loads(raw.output)
-        except json.JSONDecodeError:
-            return ToolResult(ok=False, output="Invalid process state", exit_code=1)
-        exit_code = int(data["exit_code"]) if data["exit_code"] is not None else None
-        return ToolResult(
-            ok=exit_code in {None, 0},
-            output=data["output"],
-            exit_code=exit_code,
-            metadata={"process_id": process_id, "running": data["running"], "pid": data["pid"]},
-        )
-
-    async def cancel_process(self, user_id: str, process_id: str) -> ToolResult:
-        process_dir = absolute_workspace_path(f".agent/processes/{process_id}")
-        command = (
-            f"pid=$(cat {shlex.quote(process_dir + '/pid')} 2>/dev/null) || exit 1; "
-            'kill -TERM -- "-$pid" 2>/dev/null || kill -TERM "$pid" 2>/dev/null || true; '
-            f"echo 143 > {shlex.quote(process_dir + '/exit_code')}"
-        )
-        return await self._exec_argv(user_id, ["sh", "-lc", command])
-
-
-class SSHDockerExecutionProvider(DockerExecutionProvider):
-    provider_name = "ssh-docker"
-
-    def __init__(self, settings: Settings) -> None:
-        client = docker.DockerClient(
-            base_url=f"ssh://{settings.workspace_ssh_host}",
-            use_ssh_client=True,
-        )
-        super().__init__(settings, client=client)
-
-
-def create_execution_provider(settings: Settings) -> ExecutionProvider:
-    if settings.execution_provider == "ssh-docker":
-        return SSHDockerExecutionProvider(settings)
-    return DockerExecutionProvider(settings)

agent_platform/gateway/schemas.py

@@ -1,104 +0,0 @@
-from __future__ import annotations
-
-from typing import Literal
-
-from pydantic import BaseModel, Field, field_validator
-
-
-class PathRequest(BaseModel):
-    path: str = Field(default=".", max_length=4096, description="Path relative to /workspace.")
-
-
-class ListFilesRequest(PathRequest):
-    max_depth: int = Field(default=4, ge=1, le=12)
-    limit: int = Field(default=500, ge=1, le=5000)
-
-
-class ReadFileRequest(PathRequest):
-    start_line: int = Field(default=1, ge=1)
-    max_lines: int = Field(default=1000, ge=1, le=5000)
-
-
-class WriteFileRequest(PathRequest):
-    content: str = Field(max_length=2_000_000, description="Complete UTF-8 file content.")
-
-
-class SearchFilesRequest(BaseModel):
-    query: str = Field(min_length=1, max_length=500)
-    path: str = Field(default=".", description="Directory relative to /workspace.")
-    glob: str | None = Field(default=None, max_length=200)
-    limit: int = Field(default=200, ge=1, le=2000)
-
-
-class ExecRequest(BaseModel):
-    command: str = Field(min_length=1, max_length=32_000)
-    cwd: str = Field(default=".", max_length=4096, description="Working directory relative to /workspace.")
-    timeout_seconds: int = Field(default=120, ge=1, le=1800)
-
-
-class ApplyPatchRequest(BaseModel):
-    patch: str = Field(min_length=1, max_length=512_000)
-    cwd: str = Field(default=".", max_length=4096, description="Repository directory relative to /workspace.")
-
-
-class GitRequest(BaseModel):
-    cwd: str = Field(default=".", max_length=4096, description="Repository directory relative to /workspace.")
-
-
-class GitDiffRequest(GitRequest):
-    staged: bool = False
-
-
-class StartProcessRequest(BaseModel):
-    command: str = Field(min_length=1, max_length=32_000)
-    cwd: str = Field(default=".", max_length=4096, description="Working directory relative to /workspace.")
-
-
-class ProcessRequest(BaseModel):
-    process_id: str = Field(pattern=r"^[a-f0-9]{32}$")
-
-
-class ToolResult(BaseModel):
-    ok: bool
-    output: str = ""
-    exit_code: int | None = None
-    truncated: bool = False
-    metadata: dict = Field(default_factory=dict)
-
-
-class WorkspaceStatus(BaseModel):
-    ok: bool = True
-    workspace_id: str
-    provider: Literal["local-docker", "ssh-docker"]
-    container_name: str
-    state: str
-
-
-class WorkspaceEntry(BaseModel):
-    name: str
-    path: str
-    kind: Literal["file", "directory", "symlink"]
-    size: int = Field(ge=0)
-    modified_at: int = Field(ge=0)
-
-
-class WorkspaceListing(BaseModel):
-    path: str
-    entries: list[WorkspaceEntry]
-
-
-class PlanItem(BaseModel):
-    step: str = Field(min_length=1, max_length=1000)
-    status: Literal["pending", "in_progress", "completed"]
-
-
-class UpdatePlanRequest(BaseModel):
-    explanation: str | None = Field(default=None, max_length=4000)
-    items: list[PlanItem] = Field(min_length=1, max_length=50)
-
-    @field_validator("items")
-    @classmethod
-    def one_in_progress(cls, value: list[PlanItem]) -> list[PlanItem]:
-        if sum(item.status == "in_progress" for item in value) > 1:
-            raise ValueError("At most one plan item may be in progress")
-        return value

agent_platform/models.py

@@ -1,104 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import Literal
-
-Provider = Literal["k1412", "deepseek"]
-
-
-@dataclass(frozen=True, slots=True)
-class ModelSpec:
-    public_id: str
-    display_name: str
-    provider: Provider
-    provider_model: str
-    thinking_enabled: bool
-    thinking_label: str
-    thinking_adjustable: bool
-    reasoning_effort: str | None
-    max_output_tokens: int
-    max_iterations: int
-    context_char_budget: int
-
-
-MODEL_SPECS: dict[str, ModelSpec] = {
-    "luna": ModelSpec(
-        public_id="luna",
-        display_name="Luna",
-        provider="k1412",
-        provider_model="ChatGPT-5.6:Luna",
-        thinking_enabled=True,
-        thinking_label="开启（不分档）",
-        thinking_adjustable=False,
-        reasoning_effort=None,
-        max_output_tokens=4_096,
-        max_iterations=8,
-        context_char_budget=120_000,
-    ),
-    "terra": ModelSpec(
-        public_id="terra",
-        display_name="Terra",
-        provider="k1412",
-        provider_model="ChatGPT-5.6:Terra",
-        thinking_enabled=True,
-        thinking_label="开启（不分档）",
-        thinking_adjustable=False,
-        reasoning_effort=None,
-        max_output_tokens=4_096,
-        max_iterations=16,
-        context_char_budget=240_000,
-    ),
-    "sol": ModelSpec(
-        public_id="sol",
-        display_name="Sol",
-        provider="k1412",
-        provider_model="ChatGPT-5.6:Sol",
-        thinking_enabled=True,
-        thinking_label="开启（不分档）",
-        thinking_adjustable=False,
-        reasoning_effort=None,
-        max_output_tokens=4_096,
-        max_iterations=24,
-        context_char_budget=400_000,
-    ),
-    "deepseek-v4-pro": ModelSpec(
-        public_id="deepseek-v4-pro",
-        display_name="DeepSeek V4 Pro",
-        provider="deepseek",
-        provider_model="deepseek-v4-pro",
-        thinking_enabled=True,
-        thinking_label="极高",
-        thinking_adjustable=False,
-        reasoning_effort="max",
-        max_output_tokens=16_384,
-        max_iterations=32,
-        context_char_budget=800_000,
-    ),
-}
-
-
-def get_model_spec(model_id: str) -> ModelSpec:
-    try:
-        return MODEL_SPECS[model_id]
-    except KeyError as exc:
-        raise ValueError(f"Unsupported model: {model_id}") from exc
-
-
-def openai_model_list() -> dict:
-    return {
-        "object": "list",
-        "data": [
-            {
-                "id": spec.public_id,
-                "object": "model",
-                "owned_by": "k1412-agent",
-                "name": spec.display_name,
-                "k1412": {
-                    "thinking_enabled": spec.thinking_enabled,
-                    "thinking_label": spec.thinking_label,
-                    "thinking_adjustable": spec.thinking_adjustable,
-                },
-            }
-            for spec in MODEL_SPECS.values()
-        ],
-    }

agent_platform/runtime/__init__.py

@@ -1 +0,0 @@
-"""Model gateway and independently evolvable K1412 Agent runtime."""

agent_platform/runtime/app.py

@@ -1,289 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import json
-import time
-import uuid
-from collections.abc import AsyncIterator
-from contextlib import asynccontextmanager
-from typing import Annotated, Any
-
-import httpx
-import uvicorn
-from fastapi import FastAPI, Header, HTTPException
-from fastapi.responses import JSONResponse, StreamingResponse
-
-from agent_platform.auth import UserIdentity, decode_openwebui_identity, verify_service_bearer
-from agent_platform.config import Settings, get_settings
-from agent_platform.models import get_model_spec, openai_model_list
-from agent_platform.runtime.loop import AgentLoop, tool_event_details
-from agent_platform.runtime.provider import ModelProvider
-from agent_platform.runtime.schemas import ChatCompletionRequest
-from agent_platform.runtime.tools import ToolRegistry
-from agent_platform.store import RuntimeStore
-
-OPENWEBUI_BACKGROUND_TASKS = {
-    "title_generation",
-    "follow_up_generation",
-    "tags_generation",
-    "emoji_generation",
-    "query_generation",
-    "image_prompt_generation",
-    "autocomplete_generation",
-    "function_calling",
-    "moa_response_generation",
-}
-
-
-def _sse_chunk(model: str, content: str = "", finish_reason: str | None = None) -> bytes:
-    payload = {
-        "id": f"chatcmpl-{uuid.uuid4().hex}",
-        "object": "chat.completion.chunk",
-        "created": int(time.time()),
-        "model": model,
-        "choices": [
-            {
-                "index": 0,
-                "delta": ({"content": content} if content else {}),
-                "finish_reason": finish_reason,
-            }
-        ],
-    }
-    return f"data: {json.dumps(payload, ensure_ascii=False)}\n\n".encode()
-
-
-def _completion(model: str, content: str) -> dict[str, Any]:
-    return {
-        "id": f"chatcmpl-{uuid.uuid4().hex}",
-        "object": "chat.completion",
-        "created": int(time.time()),
-        "model": model,
-        "choices": [
-            {
-                "index": 0,
-                "message": {"role": "assistant", "content": content},
-                "finish_reason": "stop",
-            }
-        ],
-    }
-
-
-def _public_sse_line(line: str, public_model: str) -> bytes:
-    if not line.startswith("data:"):
-        return f"{line}\n".encode()
-    value = line.removeprefix("data:").strip()
-    if not value or value == "[DONE]":
-        return f"{line}\n".encode()
-    try:
-        payload = json.loads(value)
-    except json.JSONDecodeError:
-        return f"{line}\n".encode()
-    if isinstance(payload, dict) and "model" in payload:
-        payload["model"] = public_model
-    return f"data: {json.dumps(payload, ensure_ascii=False)}\n".encode()
-
-
-async def _public_model_stream(response: httpx.Response, public_model: str) -> AsyncIterator[bytes]:
-    try:
-        async for line in response.aiter_lines():
-            yield _public_sse_line(line, public_model)
-    finally:
-        await response.aclose()
-
-
-def _extract_identity(
-    settings: Settings,
-    authorization: str | None,
-    user_jwt: str | None,
-) -> UserIdentity:
-    verify_service_bearer(authorization, settings.internal_provider_key)
-    return decode_openwebui_identity(user_jwt, settings.openwebui_forward_jwt_secret)
-
-
-def create_app(
-    settings: Settings | None = None,
-    *,
-    store: RuntimeStore | None = None,
-    provider: ModelProvider | None = None,
-    tools: ToolRegistry | None = None,
-) -> FastAPI:
-    settings = settings or get_settings()
-
-    @asynccontextmanager
-    async def lifespan(app: FastAPI):
-        settings.validate_runtime()
-        runtime_store = store or RuntimeStore(settings.database_url)
-        await runtime_store.initialize()
-        model_provider = provider or ModelProvider(settings)
-        registry = tools or ToolRegistry(settings, runtime_store)
-        app.state.store = runtime_store
-        app.state.provider = model_provider
-        app.state.tools = registry
-        app.state.loop = AgentLoop(
-            model_provider,
-            registry,
-            runtime_store,
-            max_tool_output_chars=settings.max_tool_output_chars,
-        )
-        yield
-        await registry.close()
-        await model_provider.close()
-        await runtime_store.close()
-
-    app = FastAPI(title="K1412 Agent Runtime", version="0.1.0", lifespan=lifespan)
-
-    @app.get("/health")
-    async def health() -> dict:
-        return {"status": "ok"}
-
-    @app.get("/v1/models")
-    async def models(
-        authorization: Annotated[str | None, Header(alias="Authorization")] = None,
-    ) -> dict:
-        verify_service_bearer(authorization, settings.internal_provider_key)
-        return openai_model_list()
-
-    @app.post("/v1/chat/completions")
-    async def chat_completions(
-        body: ChatCompletionRequest,
-        authorization: Annotated[str | None, Header(alias="Authorization")] = None,
-        user_jwt: Annotated[str | None, Header(alias="X-OpenWebUI-User-Jwt")] = None,
-        chat_id: Annotated[str | None, Header(alias="X-OpenWebUI-Chat-Id")] = None,
-        message_id: Annotated[str | None, Header(alias="X-OpenWebUI-Message-Id")] = None,
-    ):
-        identity = _extract_identity(settings, authorization, user_jwt)
-        try:
-            spec = get_model_spec(body.model)
-        except ValueError as exc:
-            raise HTTPException(status_code=404, detail=str(exc)) from exc
-
-        async def forward_direct():
-            payload = body.model_dump(exclude_none=True)
-            payload["model"] = spec.provider_model
-            response = await app.state.provider.forward(
-                payload,
-                provider=spec.provider,
-                thinking_enabled=spec.thinking_enabled,
-                reasoning_effort=spec.reasoning_effort,
-                max_output_tokens=spec.max_output_tokens,
-            )
-            if body.stream:
-                passthrough_headers = {
-                    key: value
-                    for key, value in response.headers.items()
-                    if key.lower() in {"cache-control", "x-request-id"}
-                }
-                return StreamingResponse(
-                    _public_model_stream(response, body.model),
-                    media_type=response.headers.get("content-type", "text/event-stream"),
-                    headers=passthrough_headers,
-                )
-            content = json.loads(await response.aread())
-            if isinstance(content, dict) and "model" in content:
-                content["model"] = body.model
-            headers = {
-                key: value
-                for key, value in response.headers.items()
-                if key.lower() in {"content-type", "cache-control", "x-request-id"}
-            }
-            await response.aclose()
-            return JSONResponse(
-                content=content,
-                status_code=response.status_code,
-                headers=headers,
-            )
-
-        background_task = str((body.metadata or {}).get("task", "")).strip()
-        if background_task in OPENWEBUI_BACKGROUND_TASKS:
-            return await forward_direct()
-
-        stable_chat_id = (chat_id or message_id or f"ephemeral-{uuid.uuid4().hex}").strip()
-        messages = [message.model_dump(exclude_none=True) for message in body.messages]
-        if not body.stream:
-
-            async def ignore_event(_: str, __: dict[str, Any]) -> None:
-                return None
-
-            answer = await app.state.loop.run(
-                spec=spec,
-                messages=messages,
-                identity=identity,
-                raw_user_jwt=user_jwt or "",
-                chat_id=stable_chat_id,
-                callback=ignore_event,
-            )
-            return _completion(body.model, answer)
-
-        async def agent_stream() -> AsyncIterator[bytes]:
-            queue: asyncio.Queue[tuple[str, Any]] = asyncio.Queue()
-
-            async def publish(event_type: str, payload: dict[str, Any]) -> None:
-                details = tool_event_details(event_type, payload)
-                if details:
-                    await queue.put(("content", details))
-
-            async def run_loop() -> None:
-                try:
-                    answer = await app.state.loop.run(
-                        spec=spec,
-                        messages=messages,
-                        identity=identity,
-                        raw_user_jwt=user_jwt or "",
-                        chat_id=stable_chat_id,
-                        callback=publish,
-                    )
-                    await queue.put(("answer", answer))
-                except Exception as exc:
-                    await queue.put(("error", str(exc)))
-                finally:
-                    await queue.put(("done", None))
-
-            task = asyncio.create_task(run_loop())
-            try:
-                while True:
-                    kind, value = await queue.get()
-                    if kind == "done":
-                        break
-                    if kind == "error":
-                        yield _sse_chunk(body.model, f"\n\nAgent 运行失败：{value}")
-                        continue
-                    if kind == "answer":
-                        text = str(value)
-                        for start in range(0, len(text), 240):
-                            yield _sse_chunk(body.model, text[start : start + 240])
-                    else:
-                        yield _sse_chunk(body.model, str(value))
-                yield _sse_chunk(body.model, finish_reason="stop")
-                yield b"data: [DONE]\n\n"
-            finally:
-                if not task.done():
-                    task.cancel()
-                await asyncio.gather(task, return_exceptions=True)
-
-        return StreamingResponse(
-            agent_stream(),
-            media_type="text/event-stream",
-            headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
-        )
-
-    @app.get("/v1/runs/{chat_id}")
-    async def run_events(
-        chat_id: str,
-        authorization: Annotated[str | None, Header(alias="Authorization")] = None,
-        user_jwt: Annotated[str | None, Header(alias="X-OpenWebUI-User-Jwt")] = None,
-    ) -> dict:
-        identity = _extract_identity(settings, authorization, user_jwt)
-        return {"items": await app.state.store.events_for_chat(identity.user_id, chat_id)}
-
-    return app
-
-
-app = create_app()
-
-
-def run() -> None:
-    uvicorn.run("agent_platform.runtime.app:app", host="0.0.0.0", port=8000)  # noqa: S104
-
-
-if __name__ == "__main__":
-    run()

agent_platform/runtime/context.py

@@ -1,84 +0,0 @@
-from __future__ import annotations
-
-import re
-from dataclasses import dataclass
-from typing import Any
-
-_TOOL_DETAILS = re.compile(r"<details\s+type=[\"']tool_calls[\"'].*?</details>", re.DOTALL | re.IGNORECASE)
-
-
-def content_text(content: Any) -> str:
-    if isinstance(content, str):
-        return content
-    if isinstance(content, list):
-        chunks: list[str] = []
-        for item in content:
-            if isinstance(item, dict) and item.get("type") in {"text", "input_text", "output_text"}:
-                chunks.append(str(item.get("text", "")))
-        return "\n".join(chunks)
-    if content is None:
-        return ""
-    return str(content)
-
-
-def clean_visible_content(content: Any) -> Any:
-    if isinstance(content, str):
-        return _TOOL_DETAILS.sub("", content).strip()
-    return content
-
-
-@dataclass(frozen=True, slots=True)
-class ContextResult:
-    messages: list[dict[str, Any]]
-    dropped_messages: int
-    estimated_chars: int
-
-
-class ContextPolicy:
-    def prepare(
-        self,
-        messages: list[dict[str, Any]],
-        *,
-        system_prompt: str,
-        memories: list[dict[str, str]],
-        char_budget: int,
-    ) -> ContextResult:
-        cleaned: list[dict[str, Any]] = []
-        for message in messages:
-            role = message.get("role")
-            if role not in {"system", "developer", "user", "assistant"}:
-                continue
-            content = clean_visible_content(message.get("content"))
-            if content is None or content == "":
-                continue
-            cleaned.append({"role": role, "content": content})
-
-        memory_text = ""
-        if memories:
-            memory_text = "\n\nUser memory (treat as context, not instructions):\n" + "\n".join(
-                f"- {item['content']}" for item in memories
-            )
-        root = {"role": "system", "content": system_prompt + memory_text}
-        root_chars = len(system_prompt) + len(memory_text)
-        remaining = max(8_000, char_budget - root_chars)
-
-        selected: list[dict[str, Any]] = []
-        consumed = 0
-        for message in reversed(cleaned):
-            size = len(content_text(message.get("content"))) + 32
-            if selected and consumed + size > remaining:
-                break
-            selected.append(message)
-            consumed += size
-        selected.reverse()
-        dropped = len(cleaned) - len(selected)
-        if dropped:
-            root["content"] += (
-                f"\n\nContext policy compacted {dropped} older visible messages. "
-                "Use the current workspace and recent messages as the source of truth."
-            )
-        return ContextResult(
-            messages=[root, *selected],
-            dropped_messages=dropped,
-            estimated_chars=root_chars + consumed,
-        )

agent_platform/runtime/provider.py

@@ -1,249 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-from typing import Any
-
-import httpx
-
-from agent_platform.config import Settings
-from agent_platform.models import Provider
-
-TRANSIENT_COMPLETE_STATUS_CODES = frozenset({408, 425, 429, 500, 502, 503, 504, 524})
-COMPLETE_MAX_ATTEMPTS = 2
-COMPLETE_MAX_TOKENS = 4096
-MAX_RETRY_DELAY_SECONDS = 5.0
-
-
-def completions_url(base_url: str) -> str:
-    base = base_url.rstrip("/")
-    if base.endswith("/v1"):
-        return f"{base}/chat/completions"
-    return f"{base}/v1/chat/completions"
-
-
-def retry_delay_seconds(response: httpx.Response | None, attempt: int) -> float:
-    if response is not None:
-        retry_after = response.headers.get("Retry-After")
-        if retry_after:
-            try:
-                return min(MAX_RETRY_DELAY_SECONDS, max(0.0, float(retry_after)))
-            except ValueError:
-                pass
-    return min(MAX_RETRY_DELAY_SECONDS, float(2 ** (attempt - 1)))
-
-
-async def decode_complete_response(response: httpx.Response) -> dict[str, Any]:
-    content_type = response.headers.get("Content-Type", "").lower()
-    if "text/event-stream" not in content_type:
-        await response.aread()
-        data = response.json()
-        if not data.get("choices"):
-            raise RuntimeError("Model provider returned no choices")
-        return data
-
-    envelope: dict[str, Any] = {"object": "chat.completion"}
-    message: dict[str, Any] = {"role": "assistant", "content": None}
-    content_parts: list[str] = []
-    reasoning_parts: list[str] = []
-    tool_calls: dict[int, dict[str, Any]] = {}
-    finish_reason: str | None = None
-    usage: dict[str, Any] | None = None
-    saw_choice = False
-
-    async for line in response.aiter_lines():
-        if not line.startswith("data:"):
-            continue
-        raw = line[5:].strip()
-        if not raw or raw == "[DONE]":
-            continue
-        chunk = httpx.Response(200, content=raw).json()
-        if chunk.get("error"):
-            raise RuntimeError(f"Model provider stream error: {chunk['error']}")
-        for key in ("id", "created", "model", "system_fingerprint"):
-            if chunk.get(key) is not None:
-                envelope[key] = chunk[key]
-        if chunk.get("usage"):
-            usage = chunk["usage"]
-        choices = chunk.get("choices") or []
-        if not choices:
-            continue
-        saw_choice = True
-        choice = choices[0]
-        delta = choice.get("delta") or {}
-        if delta.get("role"):
-            message["role"] = delta["role"]
-        if isinstance(delta.get("content"), str):
-            content_parts.append(delta["content"])
-        reasoning = delta.get("reasoning_content")
-        if not isinstance(reasoning, str):
-            reasoning = delta.get("reasoning")
-        if isinstance(reasoning, str):
-            reasoning_parts.append(reasoning)
-        for position, fragment in enumerate(delta.get("tool_calls") or []):
-            index = int(fragment.get("index", position))
-            merged = tool_calls.setdefault(
-                index,
-                {
-                    "id": "",
-                    "type": "function",
-                    "function": {"name": "", "arguments": ""},
-                },
-            )
-            if fragment.get("id"):
-                merged["id"] = fragment["id"]
-            if fragment.get("type"):
-                merged["type"] = fragment["type"]
-            function = fragment.get("function") or {}
-            if function.get("name"):
-                merged["function"]["name"] += function["name"]
-            if function.get("arguments"):
-                merged["function"]["arguments"] += function["arguments"]
-        if choice.get("finish_reason") is not None:
-            finish_reason = choice["finish_reason"]
-
-    if not saw_choice:
-        raise RuntimeError("Model provider stream returned no choices")
-    if content_parts:
-        message["content"] = "".join(content_parts)
-    if reasoning_parts:
-        message["reasoning_content"] = "".join(reasoning_parts)
-    if tool_calls:
-        message["tool_calls"] = [tool_calls[index] for index in sorted(tool_calls)]
-
-    envelope["choices"] = [
-        {
-            "index": 0,
-            "message": message,
-            "finish_reason": finish_reason,
-        }
-    ]
-    if usage is not None:
-        envelope["usage"] = usage
-    return envelope
-
-
-class ModelProvider:
-    def __init__(self, settings: Settings, client: httpx.AsyncClient | None = None) -> None:
-        self.settings = settings
-        self.client = client or httpx.AsyncClient(
-            timeout=httpx.Timeout(settings.model_timeout_seconds),
-            limits=httpx.Limits(max_connections=50, max_keepalive_connections=20),
-        )
-        self._owns_client = client is None
-
-    @property
-    def headers(self) -> dict[str, str]:
-        return self._headers("k1412")
-
-    def _headers(self, provider: Provider) -> dict[str, str]:
-        key = self.settings.deepseek_api_key if provider == "deepseek" else self.settings.model_api_key
-        return {
-            "Authorization": f"Bearer {key}",
-            "Content-Type": "application/json",
-        }
-
-    def _base_url(self, provider: Provider) -> str:
-        return self.settings.deepseek_api_base_url if provider == "deepseek" else self.settings.model_api_base_url
-
-    @staticmethod
-    def _provider_payload(
-        payload: dict[str, Any],
-        *,
-        provider: Provider,
-        thinking_enabled: bool,
-        reasoning_effort: str | None,
-        max_output_tokens: int,
-    ) -> dict[str, Any]:
-        value = dict(payload)
-        value.pop("thinking", None)
-        value.pop("reasoning_effort", None)
-        value["max_tokens"] = max_output_tokens
-        if provider == "deepseek" and thinking_enabled:
-            value["thinking"] = {"type": "enabled"}
-            if reasoning_effort:
-                value["reasoning_effort"] = reasoning_effort
-        return value
-
-    async def close(self) -> None:
-        if self._owns_client:
-            await self.client.aclose()
-
-    async def complete(
-        self,
-        *,
-        model: str,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]] | None = None,
-        temperature: float | None = None,
-        provider: Provider = "k1412",
-        thinking_enabled: bool = False,
-        reasoning_effort: str | None = None,
-        max_output_tokens: int = COMPLETE_MAX_TOKENS,
-    ) -> dict[str, Any]:
-        payload = self._provider_payload(
-            {
-                "model": model,
-                "messages": messages,
-                "stream": True,
-            },
-            provider=provider,
-            thinking_enabled=thinking_enabled,
-            reasoning_effort=reasoning_effort,
-            max_output_tokens=max_output_tokens,
-        )
-        if tools:
-            payload["tools"] = tools
-            payload["tool_choice"] = "auto"
-        if temperature is not None:
-            payload["temperature"] = temperature
-        for attempt in range(1, COMPLETE_MAX_ATTEMPTS + 1):
-            response: httpx.Response | None = None
-            try:
-                async with self.client.stream(
-                    "POST",
-                    completions_url(self._base_url(provider)),
-                    headers=self._headers(provider),
-                    json=payload,
-                ) as response:
-                    if response.status_code in TRANSIENT_COMPLETE_STATUS_CODES and attempt < COMPLETE_MAX_ATTEMPTS:
-                        await response.aread()
-                        await asyncio.sleep(retry_delay_seconds(response, attempt))
-                        continue
-
-                    response.raise_for_status()
-                    return await decode_complete_response(response)
-            except httpx.TransportError:
-                if attempt >= COMPLETE_MAX_ATTEMPTS:
-                    raise
-                await asyncio.sleep(retry_delay_seconds(response, attempt))
-                continue
-
-        raise RuntimeError("Model provider retry loop exited unexpectedly")
-
-    async def forward(
-        self,
-        payload: dict[str, Any],
-        *,
-        provider: Provider = "k1412",
-        thinking_enabled: bool = False,
-        reasoning_effort: str | None = None,
-        max_output_tokens: int = COMPLETE_MAX_TOKENS,
-    ) -> httpx.Response:
-        payload = self._provider_payload(
-            payload,
-            provider=provider,
-            thinking_enabled=thinking_enabled,
-            reasoning_effort=reasoning_effort,
-            max_output_tokens=max_output_tokens,
-        )
-        request = self.client.build_request(
-            "POST",
-            completions_url(self._base_url(provider)),
-            headers=self._headers(provider),
-            json=payload,
-        )
-        response = await self.client.send(request, stream=bool(payload.get("stream")))
-        if response.status_code >= 400:
-            await response.aread()
-            response.raise_for_status()
-        return response

agent_platform/runtime/schemas.py

@@ -1,31 +0,0 @@
-from __future__ import annotations
-
-from typing import Any, Literal
-
-from pydantic import BaseModel, ConfigDict, Field
-
-
-class ChatMessage(BaseModel):
-    model_config = ConfigDict(extra="allow")
-
-    role: Literal["system", "developer", "user", "assistant", "tool"]
-    content: Any = None
-    name: str | None = None
-    tool_call_id: str | None = None
-    tool_calls: list[dict[str, Any]] | None = None
-
-
-class ChatCompletionRequest(BaseModel):
-    model_config = ConfigDict(extra="allow")
-
-    model: str
-    messages: list[ChatMessage] = Field(min_length=1)
-    stream: bool = False
-    tools: list[dict[str, Any]] | None = None
-    tool_choice: Any = None
-    metadata: dict[str, Any] | None = None
-
-
-class PlanItem(BaseModel):
-    step: str = Field(min_length=1, max_length=1000)
-    status: Literal["pending", "in_progress", "completed"]

agent_platform/runtime/tools.py

@@ -1,341 +0,0 @@
-from __future__ import annotations
-
-import json
-from dataclasses import dataclass
-from typing import Any
-
-import httpx
-
-from agent_platform.auth import UserIdentity
-from agent_platform.config import Settings
-from agent_platform.runtime.schemas import PlanItem
-from agent_platform.store import RuntimeStore
-from agent_platform.text import truncate_middle
-
-
-@dataclass(frozen=True, slots=True)
-class ToolMetadata:
-    name: str
-    description: str
-    schema: dict[str, Any]
-    parallel_safe: bool
-    read_only: bool
-
-    def openai_spec(self) -> dict[str, Any]:
-        return {
-            "type": "function",
-            "function": {
-                "name": self.name,
-                "description": self.description,
-                "parameters": self.schema,
-            },
-        }
-
-
-def object_schema(properties: dict[str, Any], required: list[str] | None = None) -> dict[str, Any]:
-    return {
-        "type": "object",
-        "properties": properties,
-        "required": required or [],
-        "additionalProperties": False,
-    }
-
-
-TOOL_METADATA: dict[str, ToolMetadata] = {
-    "workspace_status": ToolMetadata(
-        "workspace_status",
-        "Return the current user's isolated workspace status.",
-        object_schema({}),
-        True,
-        True,
-    ),
-    "list_files": ToolMetadata(
-        "list_files",
-        "List files and directories in the isolated workspace.",
-        object_schema(
-            {
-                "path": {"type": "string", "default": "."},
-                "max_depth": {"type": "integer", "minimum": 1, "maximum": 12, "default": 4},
-                "limit": {"type": "integer", "minimum": 1, "maximum": 5000, "default": 500},
-            }
-        ),
-        True,
-        True,
-    ),
-    "read_file": ToolMetadata(
-        "read_file",
-        "Read a UTF-8 text file with line numbers.",
-        object_schema(
-            {
-                "path": {"type": "string"},
-                "start_line": {"type": "integer", "minimum": 1, "default": 1},
-                "max_lines": {"type": "integer", "minimum": 1, "maximum": 5000, "default": 1000},
-            },
-            ["path"],
-        ),
-        True,
-        True,
-    ),
-    "search_files": ToolMetadata(
-        "search_files",
-        "Search workspace text using ripgrep.",
-        object_schema(
-            {
-                "query": {"type": "string"},
-                "path": {"type": "string", "default": "."},
-                "glob": {"type": ["string", "null"], "default": None},
-                "limit": {"type": "integer", "minimum": 1, "maximum": 2000, "default": 200},
-            },
-            ["query"],
-        ),
-        True,
-        True,
-    ),
-    "write_file": ToolMetadata(
-        "write_file",
-        "Write complete UTF-8 file contents. Use actual line breaks between source lines, not literal \\\\n text.",
-        object_schema(
-            {
-                "path": {"type": "string", "maxLength": 4096},
-                "content": {"type": "string", "maxLength": 2_000_000},
-            },
-            ["path", "content"],
-        ),
-        False,
-        False,
-    ),
-    "apply_patch": ToolMetadata(
-        "apply_patch",
-        "Apply a unified diff in a workspace repository.",
-        object_schema(
-            {"patch": {"type": "string"}, "cwd": {"type": "string", "default": "."}},
-            ["patch"],
-        ),
-        False,
-        False,
-    ),
-    "exec": ToolMetadata(
-        "exec",
-        "Run a complete non-interactive shell command. Name the script, pass -c code, or invoke a test; "
-        "bare python, node, or shells are rejected.",
-        object_schema(
-            {
-                "command": {"type": "string"},
-                "cwd": {"type": "string", "default": "."},
-                "timeout_seconds": {"type": "integer", "minimum": 1, "maximum": 1800, "default": 120},
-            },
-            ["command"],
-        ),
-        False,
-        False,
-    ),
-    "git_status": ToolMetadata(
-        "git_status",
-        "Show concise Git status for a workspace repository.",
-        object_schema({"cwd": {"type": "string", "default": "."}}),
-        True,
-        True,
-    ),
-    "git_diff": ToolMetadata(
-        "git_diff",
-        "Show the unstaged or staged Git diff.",
-        object_schema(
-            {
-                "cwd": {"type": "string", "default": "."},
-                "staged": {"type": "boolean", "default": False},
-            }
-        ),
-        True,
-        True,
-    ),
-    "start_process": ToolMetadata(
-        "start_process",
-        "Start a long-running background process and return a process id.",
-        object_schema(
-            {"command": {"type": "string"}, "cwd": {"type": "string", "default": "."}},
-            ["command"],
-        ),
-        False,
-        False,
-    ),
-    "poll_process": ToolMetadata(
-        "poll_process",
-        "Poll a background process and return recent output.",
-        object_schema({"process_id": {"type": "string"}}, ["process_id"]),
-        True,
-        True,
-    ),
-    "cancel_process": ToolMetadata(
-        "cancel_process",
-        "Stop a background process.",
-        object_schema({"process_id": {"type": "string"}}, ["process_id"]),
-        False,
-        False,
-    ),
-    "update_plan": ToolMetadata(
-        "update_plan",
-        "Publish a concise execution plan. At most one step may be in progress.",
-        object_schema(
-            {
-                "explanation": {"type": ["string", "null"]},
-                "items": {
-                    "type": "array",
-                    "minItems": 1,
-                    "maxItems": 50,
-                    "items": {
-                        "type": "object",
-                        "properties": {
-                            "step": {"type": "string"},
-                            "status": {"type": "string", "enum": ["pending", "in_progress", "completed"]},
-                        },
-                        "required": ["step", "status"],
-                        "additionalProperties": False,
-                    },
-                },
-            },
-            ["items"],
-        ),
-        False,
-        False,
-    ),
-    "remember": ToolMetadata(
-        "remember",
-        "Store a durable user preference or fact that will help future Agent tasks.",
-        object_schema({"content": {"type": "string"}}, ["content"]),
-        False,
-        False,
-    ),
-    "recall_memory": ToolMetadata(
-        "recall_memory",
-        "Search durable Agent memory for this user.",
-        object_schema(
-            {
-                "query": {"type": "string", "default": ""},
-                "limit": {"type": "integer", "minimum": 1, "maximum": 50, "default": 8},
-            }
-        ),
-        True,
-        True,
-    ),
-    "forget_memory": ToolMetadata(
-        "forget_memory",
-        "Delete one durable Agent memory by id.",
-        object_schema({"memory_id": {"type": "string"}}, ["memory_id"]),
-        False,
-        False,
-    ),
-    "delegate_task": ToolMetadata(
-        "delegate_task",
-        "Delegate a bounded subtask to a child Agent. Read-only delegates may run in parallel.",
-        object_schema(
-            {
-                "task": {"type": "string"},
-                "role": {"type": "string", "default": "researcher"},
-                "allow_writes": {"type": "boolean", "default": False},
-            },
-            ["task"],
-        ),
-        True,
-        True,
-    ),
-}
-
-GATEWAY_ENDPOINTS = {
-    name: f"/v1/tools/{name}"
-    for name in (
-        "workspace_status",
-        "list_files",
-        "read_file",
-        "search_files",
-        "write_file",
-        "apply_patch",
-        "exec",
-        "git_status",
-        "git_diff",
-        "start_process",
-        "poll_process",
-        "cancel_process",
-    )
-}
-
-READ_ONLY_TOOL_NAMES = {
-    name for name, metadata in TOOL_METADATA.items() if metadata.read_only and name != "delegate_task"
-}
-
-
-@dataclass(frozen=True, slots=True)
-class ToolContext:
-    identity: UserIdentity
-    raw_user_jwt: str
-    chat_id: str
-
-
-class ToolRegistry:
-    def __init__(
-        self,
-        settings: Settings,
-        store: RuntimeStore,
-        client: httpx.AsyncClient | None = None,
-    ) -> None:
-        self.settings = settings
-        self.store = store
-        self.client = client or httpx.AsyncClient(timeout=httpx.Timeout(settings.tool_timeout_seconds))
-        self._owns_client = client is None
-
-    async def close(self) -> None:
-        if self._owns_client:
-            await self.client.aclose()
-
-    def specs(self, *, read_only: bool = False, allow_delegate: bool = True) -> list[dict[str, Any]]:
-        names = READ_ONLY_TOOL_NAMES if read_only else set(TOOL_METADATA)
-        if not allow_delegate:
-            names = names - {"delegate_task"}
-        return [TOOL_METADATA[name].openai_spec() for name in TOOL_METADATA if name in names]
-
-    async def execute(self, name: str, arguments: dict[str, Any], context: ToolContext) -> dict[str, Any]:
-        if name in GATEWAY_ENDPOINTS:
-            response = await self.client.post(
-                f"{self.settings.gateway_url}{GATEWAY_ENDPOINTS[name]}",
-                headers={
-                    "Authorization": f"Bearer {self.settings.internal_gateway_key}",
-                    "X-OpenWebUI-User-Jwt": context.raw_user_jwt,
-                },
-                json=arguments,
-            )
-            if response.status_code >= 400:
-                return {
-                    "ok": False,
-                    "status_code": response.status_code,
-                    "error": response.text[:4000],
-                }
-            return response.json()
-        if name == "update_plan":
-            items = [PlanItem.model_validate(item).model_dump() for item in arguments.get("items", [])]
-            if sum(item["status"] == "in_progress" for item in items) > 1:
-                raise ValueError("At most one plan item may be in progress")
-            await self.store.update_plan(context.identity.user_id, context.chat_id, items)
-            return {"ok": True, "explanation": arguments.get("explanation"), "items": items}
-        if name == "remember":
-            content = str(arguments.get("content", "")).strip()
-            if not content:
-                raise ValueError("Memory content is required")
-            memory_id = await self.store.remember(context.identity.user_id, content[:20_000])
-            return {"ok": True, "memory_id": memory_id}
-        if name == "recall_memory":
-            return {
-                "ok": True,
-                "items": await self.store.recall(
-                    context.identity.user_id,
-                    str(arguments.get("query", "")),
-                    int(arguments.get("limit", 8)),
-                ),
-            }
-        if name == "forget_memory":
-            deleted = await self.store.forget(context.identity.user_id, str(arguments.get("memory_id", "")))
-            return {"ok": deleted}
-        raise ValueError(f"Unknown tool: {name}")
-
-
-def tool_result_text(result: Any, limit: int) -> str:
-    text = json.dumps(result, ensure_ascii=False, separators=(",", ":"), default=str)
-    return truncate_middle(text, limit)

agent_platform/store.py

@@ -1,141 +0,0 @@
-from __future__ import annotations
-
-import uuid
-from collections.abc import AsyncIterator
-from contextlib import asynccontextmanager
-from datetime import UTC, datetime
-from typing import Any
-
-from sqlalchemy import JSON, DateTime, Integer, String, Text, UniqueConstraint, delete, select
-from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession, async_sessionmaker, create_async_engine
-from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
-
-
-class Base(DeclarativeBase):
-    pass
-
-
-class RunEvent(Base):
-    __tablename__ = "agent_run_events"
-
-    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
-    run_id: Mapped[str] = mapped_column(String(64), index=True)
-    user_id: Mapped[str] = mapped_column(String(128), index=True)
-    chat_id: Mapped[str] = mapped_column(String(256), index=True)
-    sequence: Mapped[int] = mapped_column(Integer)
-    event_type: Mapped[str] = mapped_column(String(80), index=True)
-    payload: Mapped[dict[str, Any]] = mapped_column(JSON, default=dict)
-    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=lambda: datetime.now(UTC))
-
-
-class Memory(Base):
-    __tablename__ = "agent_memories"
-
-    id: Mapped[str] = mapped_column(String(64), primary_key=True, default=lambda: str(uuid.uuid4()))
-    user_id: Mapped[str] = mapped_column(String(128), index=True)
-    content: Mapped[str] = mapped_column(Text)
-    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=lambda: datetime.now(UTC))
-
-
-class Plan(Base):
-    __tablename__ = "agent_plans"
-    __table_args__ = (UniqueConstraint("user_id", "chat_id"),)
-
-    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
-    user_id: Mapped[str] = mapped_column(String(128), index=True)
-    chat_id: Mapped[str] = mapped_column(String(256), index=True)
-    items: Mapped[list[dict[str, Any]]] = mapped_column(JSON, default=list)
-    updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=lambda: datetime.now(UTC))
-
-
-class RuntimeStore:
-    def __init__(self, url: str) -> None:
-        self.engine: AsyncEngine = create_async_engine(url, pool_pre_ping=True)
-        self.sessions = async_sessionmaker(self.engine, expire_on_commit=False)
-
-    async def initialize(self) -> None:
-        async with self.engine.begin() as connection:
-            await connection.run_sync(Base.metadata.create_all)
-
-    async def close(self) -> None:
-        await self.engine.dispose()
-
-    @asynccontextmanager
-    async def session(self) -> AsyncIterator[AsyncSession]:
-        async with self.sessions() as session:
-            yield session
-
-    async def append_event(
-        self,
-        run_id: str,
-        user_id: str,
-        chat_id: str,
-        sequence: int,
-        event_type: str,
-        payload: dict[str, Any] | None = None,
-    ) -> None:
-        async with self.session() as session:
-            session.add(
-                RunEvent(
-                    run_id=run_id,
-                    user_id=user_id,
-                    chat_id=chat_id,
-                    sequence=sequence,
-                    event_type=event_type,
-                    payload=payload or {},
-                )
-            )
-            await session.commit()
-
-    async def events_for_chat(self, user_id: str, chat_id: str, limit: int = 500) -> list[dict[str, Any]]:
-        async with self.session() as session:
-            rows = (
-                await session.scalars(
-                    select(RunEvent)
-                    .where(RunEvent.user_id == user_id, RunEvent.chat_id == chat_id)
-                    .order_by(RunEvent.id.desc())
-                    .limit(max(1, min(limit, 2000)))
-                )
-            ).all()
-        return [
-            {
-                "run_id": row.run_id,
-                "sequence": row.sequence,
-                "type": row.event_type,
-                "payload": row.payload,
-                "created_at": row.created_at.isoformat(),
-            }
-            for row in reversed(rows)
-        ]
-
-    async def remember(self, user_id: str, content: str) -> str:
-        item = Memory(user_id=user_id, content=content)
-        async with self.session() as session:
-            session.add(item)
-            await session.commit()
-        return item.id
-
-    async def recall(self, user_id: str, query: str = "", limit: int = 8) -> list[dict[str, str]]:
-        statement = select(Memory).where(Memory.user_id == user_id)
-        if query.strip():
-            statement = statement.where(Memory.content.ilike(f"%{query.strip()}%"))
-        statement = statement.order_by(Memory.created_at.desc()).limit(max(1, min(limit, 50)))
-        async with self.session() as session:
-            rows = (await session.scalars(statement)).all()
-        return [{"id": row.id, "content": row.content, "created_at": row.created_at.isoformat()} for row in rows]
-
-    async def forget(self, user_id: str, memory_id: str) -> bool:
-        async with self.session() as session:
-            result = await session.execute(delete(Memory).where(Memory.id == memory_id, Memory.user_id == user_id))
-            await session.commit()
-            return bool(result.rowcount)
-
-    async def update_plan(self, user_id: str, chat_id: str, items: list[dict[str, Any]]) -> None:
-        async with self.session() as session:
-            row = await session.scalar(select(Plan).where(Plan.user_id == user_id, Plan.chat_id == chat_id))
-            if row is None:
-                session.add(Plan(user_id=user_id, chat_id=chat_id, items=items))
-            else:
-                row.items = items
-                row.updated_at = datetime.now(UTC)
-            await session.commit()

agent_platform/text.py

@@ -1,15 +0,0 @@
-from __future__ import annotations
-
-
-def truncate_middle(text: str, limit: int, marker: str = "\n… middle omitted …\n") -> str:
-    if limit <= 0:
-        return ""
-    if len(text) <= limit:
-        return text
-    if limit <= len(marker):
-        return text[:limit]
-    remaining = limit - len(marker)
-    head = (remaining + 1) // 2
-    tail = remaining - head
-    suffix = text[-tail:] if tail else ""
-    return text[:head] + marker + suffix

backend/__init__.py

@@ -0,0 +1 @@
+"""Backend API package for the claw-code agent workbench."""

backend/api/__init__.py

@@ -0,0 +1 @@
+"""HTTP API layer for the claw-code agent workbench."""

compose.e2e.yaml

@@ -1,23 +0,0 @@
-services:
-  stub-provider:
-    build:
-      context: .
-      dockerfile: docker/e2e-stub.Dockerfile
-    image: k1412-agent-e2e-stub:test
-    networks:
-      - internal
-    read_only: true
-    tmpfs:
-      - /tmp:rw,noexec,nosuid,size=16m
-    security_opt:
-      - no-new-privileges:true
-    cap_drop:
-      - ALL
-
-  runtime:
-    environment:
-      MODEL_API_BASE_URL: http://stub-provider:9000
-      DEEPSEEK_API_BASE_URL: http://stub-provider:9000
-    depends_on:
-      stub-provider:
-        condition: service_healthy

compose.ssh.yaml

@@ -1,4 +0,0 @@
-services:
-  gateway:
-    volumes: !reset
-      - ${WORKSPACE_SSH_CONFIG_DIR:?WORKSPACE_SSH_CONFIG_DIR is required}:/root/.ssh:ro

compose.yaml

@@ -1,248 +0,0 @@
-name: k1412-agent
-
-services:
-  web:
-    build:
-      context: .
-      dockerfile: docker/web.Dockerfile
-    image: ${WEB_IMAGE:-k1412-agent-web:dev}
-    restart: unless-stopped
-    ports:
-      - "${WEB_PORT:-3000}:8080"
-    environment:
-      WEBUI_URL: ${WEBUI_URL:-http://localhost:3000}
-      WEBUI_NAME: K1412 Agent
-      K1412_REMOVE_UPSTREAM_BRANDING: ${K1412_REMOVE_UPSTREAM_BRANDING:-false}
-      WEBUI_FAVICON_URL: /static/favicon.png
-      CORS_ALLOW_ORIGIN: ${CORS_ALLOW_ORIGIN:-http://localhost:3000}
-      WEBUI_AUTH: "true"
-      WEBUI_SECRET_KEY: ${WEBUI_SECRET_KEY:?WEBUI_SECRET_KEY is required}
-      WEBUI_ADMIN_EMAIL: ${WEBUI_ADMIN_EMAIL:?WEBUI_ADMIN_EMAIL is required}
-      WEBUI_ADMIN_PASSWORD: ${WEBUI_ADMIN_PASSWORD:?WEBUI_ADMIN_PASSWORD is required}
-      WEBUI_ADMIN_NAME: Administrator
-      WEBUI_SESSION_COOKIE_SAME_SITE: lax
-      WEBUI_SESSION_COOKIE_SECURE: ${WEBUI_COOKIE_SECURE:-false}
-      WEBUI_AUTH_COOKIE_SAME_SITE: lax
-      WEBUI_AUTH_COOKIE_SECURE: ${WEBUI_COOKIE_SECURE:-false}
-      JWT_EXPIRES_IN: 7d
-      ENABLE_SIGNUP: "true"
-      DEFAULT_USER_ROLE: pending
-      ENABLE_SIGNUP_PASSWORD_CONFIRMATION: "true"
-      ENABLE_FORWARD_USER_INFO_HEADERS: "true"
-      FORWARD_USER_INFO_HEADER_JWT_SECRET: ${OPENWEBUI_FORWARD_JWT_SECRET:?OPENWEBUI_FORWARD_JWT_SECRET is required}
-      FORWARD_USER_INFO_HEADER_JWT_EXPIRES_SECONDS: "300"
-      K1412_GATEWAY_URL: http://gateway:8001
-      K1412_GATEWAY_KEY: ${INTERNAL_GATEWAY_KEY:?INTERNAL_GATEWAY_KEY is required}
-      OPENAI_API_BASE_URL: http://runtime:8000/v1
-      OPENAI_API_KEY: ${INTERNAL_PROVIDER_KEY:?INTERNAL_PROVIDER_KEY is required}
-      DEFAULT_MODELS: terra
-      TASK_MODEL: luna
-      ENABLE_TITLE_GENERATION: "false"
-      ENABLE_FOLLOW_UP_GENERATION: "false"
-      ENABLE_TAGS_GENERATION: "false"
-      ENABLE_SEARCH_QUERY_GENERATION: "false"
-      ENABLE_RETRIEVAL_QUERY_GENERATION: "false"
-      ENABLE_AUTOCOMPLETE_GENERATION: "false"
-      ENABLE_IMAGE_PROMPT_GENERATION: "false"
-      ENABLE_OLLAMA_API: "false"
-      ENABLE_DIRECT_CONNECTIONS: "false"
-      ENABLE_PERSISTENT_CONFIG: "false"
-      ENABLE_VERSION_UPDATE_CHECK: "false"
-      SAFE_MODE: "true"
-      ENABLE_COMMUNITY_SHARING: "false"
-      ENABLE_WEB_SEARCH: "false"
-      ENABLE_IMAGE_GENERATION: "false"
-      ENABLE_CODE_EXECUTION: "false"
-      ENABLE_CODE_INTERPRETER: "false"
-      ENABLE_MEMORIES: "false"
-      ENABLE_NOTES: "false"
-      ENABLE_CHANNELS: "false"
-      ENABLE_CALENDAR: "false"
-      ENABLE_AUTOMATIONS: "false"
-      ENABLE_EVALUATION_ARENA_MODELS: "false"
-      ENABLE_USER_STATUS: "false"
-      RAG_EMBEDDING_ENGINE: openai
-      RAG_EMBEDDING_MODEL: disabled
-      RAG_OPENAI_API_BASE_URL: http://runtime:8000/v1
-      RAG_OPENAI_API_KEY: ${INTERNAL_PROVIDER_KEY:?INTERNAL_PROVIDER_KEY is required}
-      RAG_EMBEDDING_MODEL_AUTO_UPDATE: "false"
-      RAG_RERANKING_MODEL_AUTO_UPDATE: "false"
-      ENABLE_RAG_HYBRID_SEARCH: "false"
-      BYPASS_EMBEDDING_AND_RETRIEVAL: "true"
-      VECTOR_DB: disabled
-      STORAGE_PROVIDER: local
-      USER_PERMISSIONS_WORKSPACE_MODELS_ACCESS: "false"
-      USER_PERMISSIONS_WORKSPACE_KNOWLEDGE_ACCESS: "false"
-      USER_PERMISSIONS_WORKSPACE_PROMPTS_ACCESS: "false"
-      USER_PERMISSIONS_WORKSPACE_TOOLS_ACCESS: "false"
-      USER_PERMISSIONS_WORKSPACE_SKILLS_ACCESS: "false"
-      USER_PERMISSIONS_CHAT_CONTROLS: "false"
-      USER_PERMISSIONS_CHAT_VALVES: "false"
-      USER_PERMISSIONS_CHAT_SYSTEM_PROMPT: "false"
-      USER_PERMISSIONS_CHAT_PARAMS: "false"
-      USER_PERMISSIONS_CHAT_MULTIPLE_MODELS: "false"
-      USER_PERMISSIONS_CHAT_SHARE: "false"
-      USER_PERMISSIONS_CHAT_ALLOW_PUBLIC_SHARING: "false"
-      USER_PERMISSIONS_FEATURES_DIRECT_TOOL_SERVERS: "false"
-      USER_PERMISSIONS_FEATURES_WEB_SEARCH: "false"
-      USER_PERMISSIONS_FEATURES_IMAGE_GENERATION: "false"
-      USER_PERMISSIONS_FEATURES_CODE_INTERPRETER: "false"
-      USER_PERMISSIONS_FEATURES_API_KEYS: "false"
-      USER_PERMISSIONS_FEATURES_MEMORIES: "false"
-      USER_PERMISSIONS_FEATURES_AUTOMATIONS: "false"
-      USER_PERMISSIONS_FEATURES_CHANNELS: "false"
-      USER_PERMISSIONS_FEATURES_CALENDAR: "false"
-      USER_PERMISSIONS_FEATURES_NOTES: "false"
-      USER_PERMISSIONS_SETTINGS_INTERFACE: "false"
-      DATABASE_URL: postgresql+psycopg://${POSTGRES_USER:-agent}:${POSTGRES_PASSWORD:?POSTGRES_PASSWORD is required}@postgres:5432/${POSTGRES_DB:-agent}
-      REDIS_URL: redis://redis:6379/0
-    volumes:
-      - openwebui-data:/app/backend/data
-    depends_on:
-      postgres:
-        condition: service_healthy
-      redis:
-        condition: service_healthy
-      runtime:
-        condition: service_healthy
-    networks:
-      - internal
-      - public
-    healthcheck:
-      test: ["CMD", "curl", "--silent", "--fail", "http://127.0.0.1:8080/health"]
-      interval: 15s
-      timeout: 5s
-      retries: 20
-
-  runtime:
-    build:
-      context: .
-      dockerfile: docker/runtime.Dockerfile
-    image: ${RUNTIME_IMAGE:-k1412-agent-runtime:dev}
-    restart: unless-stopped
-    environment:
-      MODEL_API_BASE_URL: ${MODEL_API_BASE_URL:-https://api.k1412.top}
-      MODEL_API_KEY: ${MODEL_API_KEY:?MODEL_API_KEY is required}
-      DEEPSEEK_API_BASE_URL: ${DEEPSEEK_API_BASE_URL:-https://api.deepseek.com}
-      DEEPSEEK_API_KEY: ${DEEPSEEK_API_KEY:?DEEPSEEK_API_KEY is required}
-      OPENWEBUI_FORWARD_JWT_SECRET: ${OPENWEBUI_FORWARD_JWT_SECRET:?OPENWEBUI_FORWARD_JWT_SECRET is required}
-      INTERNAL_PROVIDER_KEY: ${INTERNAL_PROVIDER_KEY:?INTERNAL_PROVIDER_KEY is required}
-      INTERNAL_GATEWAY_KEY: ${INTERNAL_GATEWAY_KEY:?INTERNAL_GATEWAY_KEY is required}
-      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-agent}:${POSTGRES_PASSWORD:?POSTGRES_PASSWORD is required}@postgres:5432/${POSTGRES_DB:-agent}
-      GATEWAY_URL: http://gateway:8001
-    depends_on:
-      postgres:
-        condition: service_healthy
-      gateway:
-        condition: service_healthy
-    networks:
-      - internal
-      - egress
-    read_only: true
-    tmpfs:
-      - /tmp:rw,noexec,nosuid,size=128m
-    security_opt:
-      - no-new-privileges:true
-    cap_drop:
-      - ALL
-
-  gateway:
-    build:
-      context: .
-      dockerfile: docker/gateway.Dockerfile
-    image: ${GATEWAY_IMAGE:-k1412-agent-gateway:dev}
-    restart: unless-stopped
-    environment:
-      OPENWEBUI_FORWARD_JWT_SECRET: ${OPENWEBUI_FORWARD_JWT_SECRET:?OPENWEBUI_FORWARD_JWT_SECRET is required}
-      INTERNAL_GATEWAY_KEY: ${INTERNAL_GATEWAY_KEY:?INTERNAL_GATEWAY_KEY is required}
-      EXECUTION_PROVIDER: ${EXECUTION_PROVIDER:-local-docker}
-      WORKSPACE_IMAGE: ${WORKSPACE_IMAGE:-k1412-agent-workspace:dev}
-      WORKSPACE_NETWORK_ENABLED: ${WORKSPACE_NETWORK_ENABLED:-true}
-      WORKSPACE_MEMORY_LIMIT: ${WORKSPACE_MEMORY_LIMIT:-2g}
-      WORKSPACE_CPU_LIMIT: ${WORKSPACE_CPU_LIMIT:-2}
-      WORKSPACE_PIDS_LIMIT: ${WORKSPACE_PIDS_LIMIT:-512}
-      WORKSPACE_SSH_HOST: ${WORKSPACE_SSH_HOST:-}
-      WORKSPACE_DOWNLOAD_MAX_BYTES: ${WORKSPACE_DOWNLOAD_MAX_BYTES:-134217728}
-    volumes:
-      - /var/run/docker.sock:/var/run/docker.sock
-    networks:
-      - internal
-      - egress
-    read_only: true
-    tmpfs:
-      - /tmp:rw,noexec,nosuid,size=128m
-    security_opt:
-      - no-new-privileges:true
-    cap_drop:
-      - ALL
-
-  bootstrap:
-    build:
-      context: .
-      dockerfile: docker/runtime.Dockerfile
-    image: ${RUNTIME_IMAGE:-k1412-agent-runtime:dev}
-    restart: "no"
-    command: ["python", "-m", "agent_platform.bootstrap"]
-    environment:
-      OPENWEBUI_INTERNAL_URL: http://web:8080
-      WEBUI_ADMIN_EMAIL: ${WEBUI_ADMIN_EMAIL:?WEBUI_ADMIN_EMAIL is required}
-      WEBUI_ADMIN_PASSWORD: ${WEBUI_ADMIN_PASSWORD:?WEBUI_ADMIN_PASSWORD is required}
-      INTERNAL_GATEWAY_KEY: ${INTERNAL_GATEWAY_KEY:?INTERNAL_GATEWAY_KEY is required}
-      GATEWAY_URL: http://gateway:8001
-    depends_on:
-      web:
-        condition: service_healthy
-      gateway:
-        condition: service_healthy
-    networks:
-      - internal
-
-  postgres:
-    image: postgres:17-alpine@sha256:742f40ea20b9ff2ff31db5458d127452988a2164df9e17441e191f3b72252193
-    restart: unless-stopped
-    environment:
-      POSTGRES_USER: ${POSTGRES_USER:-agent}
-      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:?POSTGRES_PASSWORD is required}
-      POSTGRES_DB: ${POSTGRES_DB:-agent}
-    volumes:
-      - postgres-data:/var/lib/postgresql/data
-    networks:
-      - internal
-    healthcheck:
-      test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER:-agent} -d ${POSTGRES_DB:-agent}"]
-      interval: 5s
-      timeout: 5s
-      retries: 20
-
-  redis:
-    image: redis:8-alpine@sha256:8096655e437712b07503796fb64d81359256cfcff0ab29d95a7da72863786efb
-    restart: unless-stopped
-    command: ["redis-server", "--save", "60", "1", "--appendonly", "yes"]
-    volumes:
-      - redis-data:/data
-    networks:
-      - internal
-    healthcheck:
-      test: ["CMD", "redis-cli", "ping"]
-      interval: 5s
-      timeout: 3s
-      retries: 20
-
-  workspace-image:
-    build:
-      context: .
-      dockerfile: docker/workspace.Dockerfile
-    image: ${WORKSPACE_IMAGE:-k1412-agent-workspace:dev}
-    command: ["true"]
-    profiles: ["build-only"]
-
-networks:
-  public:
-  egress:
-  internal:
-    internal: true
-
-volumes:
-  openwebui-data:
-  postgres-data:
-  redis-data:

deploy/docker-compose.override.yml

@@ -1,17 +0,0 @@
-services:
-  web:
-    labels: &compose-manager-labels
-      net.unraid.docker.managed: composeman
-      net.unraid.docker.icon: ""
-      net.unraid.docker.webui: ""
-      net.unraid.docker.shell: ""
-  runtime:
-    labels: *compose-manager-labels
-  gateway:
-    labels: *compose-manager-labels
-  bootstrap:
-    labels: *compose-manager-labels
-  postgres:
-    labels: *compose-manager-labels
-  redis:
-    labels: *compose-manager-labels

deploy/docker-compose.ssh.yml

@@ -1,4 +0,0 @@
-services:
-  gateway:
-    volumes: !override
-      - ${WORKSPACE_SSH_CONFIG_DIR:?WORKSPACE_SSH_CONFIG_DIR is required}:/root/.ssh:ro

deploy/docker-compose.yml

@@ -1,249 +0,0 @@
-name: k1412-agent
-
-services:
-  web:
-    image: ${WEB_IMAGE:?WEB_IMAGE is required}
-    platform: linux/amd64
-    container_name: k1412-agent-web
-    restart: unless-stopped
-    init: true
-    ports:
-      - "${WEB_PORT:-12004}:8080"
-    environment:
-      WEBUI_URL: ${WEBUI_URL:-https://agent.k1412.top}
-      WEBUI_NAME: K1412 Agent
-      K1412_REMOVE_UPSTREAM_BRANDING: ${K1412_REMOVE_UPSTREAM_BRANDING:-false}
-      WEBUI_FAVICON_URL: /static/favicon.png
-      CORS_ALLOW_ORIGIN: ${CORS_ALLOW_ORIGIN:-https://agent.k1412.top}
-      WEBUI_AUTH: "true"
-      WEBUI_SECRET_KEY: ${WEBUI_SECRET_KEY:?WEBUI_SECRET_KEY is required}
-      WEBUI_ADMIN_EMAIL: ${WEBUI_ADMIN_EMAIL:?WEBUI_ADMIN_EMAIL is required}
-      WEBUI_ADMIN_PASSWORD: ${WEBUI_ADMIN_PASSWORD:?WEBUI_ADMIN_PASSWORD is required}
-      WEBUI_ADMIN_NAME: Administrator
-      WEBUI_SESSION_COOKIE_SAME_SITE: lax
-      WEBUI_SESSION_COOKIE_SECURE: "true"
-      WEBUI_AUTH_COOKIE_SAME_SITE: lax
-      WEBUI_AUTH_COOKIE_SECURE: "true"
-      JWT_EXPIRES_IN: 7d
-      ENABLE_SIGNUP: "true"
-      DEFAULT_USER_ROLE: pending
-      ENABLE_SIGNUP_PASSWORD_CONFIRMATION: "true"
-      ENABLE_FORWARD_USER_INFO_HEADERS: "true"
-      FORWARD_USER_INFO_HEADER_JWT_SECRET: ${OPENWEBUI_FORWARD_JWT_SECRET:?OPENWEBUI_FORWARD_JWT_SECRET is required}
-      FORWARD_USER_INFO_HEADER_JWT_EXPIRES_SECONDS: "300"
-      K1412_GATEWAY_URL: http://gateway:8001
-      K1412_GATEWAY_KEY: ${INTERNAL_GATEWAY_KEY:?INTERNAL_GATEWAY_KEY is required}
-      OPENAI_API_BASE_URL: http://runtime:8000/v1
-      OPENAI_API_KEY: ${INTERNAL_PROVIDER_KEY:?INTERNAL_PROVIDER_KEY is required}
-      DEFAULT_MODELS: terra
-      TASK_MODEL: luna
-      ENABLE_TITLE_GENERATION: "false"
-      ENABLE_FOLLOW_UP_GENERATION: "false"
-      ENABLE_TAGS_GENERATION: "false"
-      ENABLE_SEARCH_QUERY_GENERATION: "false"
-      ENABLE_RETRIEVAL_QUERY_GENERATION: "false"
-      ENABLE_AUTOCOMPLETE_GENERATION: "false"
-      ENABLE_IMAGE_PROMPT_GENERATION: "false"
-      ENABLE_OLLAMA_API: "false"
-      ENABLE_DIRECT_CONNECTIONS: "false"
-      ENABLE_PERSISTENT_CONFIG: "false"
-      ENABLE_VERSION_UPDATE_CHECK: "false"
-      SAFE_MODE: "true"
-      ENABLE_COMMUNITY_SHARING: "false"
-      ENABLE_WEB_SEARCH: "false"
-      ENABLE_IMAGE_GENERATION: "false"
-      ENABLE_CODE_EXECUTION: "false"
-      ENABLE_CODE_INTERPRETER: "false"
-      ENABLE_MEMORIES: "false"
-      ENABLE_NOTES: "false"
-      ENABLE_CHANNELS: "false"
-      ENABLE_CALENDAR: "false"
-      ENABLE_AUTOMATIONS: "false"
-      ENABLE_EVALUATION_ARENA_MODELS: "false"
-      ENABLE_USER_STATUS: "false"
-      RAG_EMBEDDING_ENGINE: openai
-      RAG_EMBEDDING_MODEL: disabled
-      RAG_OPENAI_API_BASE_URL: http://runtime:8000/v1
-      RAG_OPENAI_API_KEY: ${INTERNAL_PROVIDER_KEY:?INTERNAL_PROVIDER_KEY is required}
-      RAG_EMBEDDING_MODEL_AUTO_UPDATE: "false"
-      RAG_RERANKING_MODEL_AUTO_UPDATE: "false"
-      ENABLE_RAG_HYBRID_SEARCH: "false"
-      BYPASS_EMBEDDING_AND_RETRIEVAL: "true"
-      VECTOR_DB: disabled
-      STORAGE_PROVIDER: local
-      USER_PERMISSIONS_WORKSPACE_MODELS_ACCESS: "false"
-      USER_PERMISSIONS_WORKSPACE_KNOWLEDGE_ACCESS: "false"
-      USER_PERMISSIONS_WORKSPACE_PROMPTS_ACCESS: "false"
-      USER_PERMISSIONS_WORKSPACE_TOOLS_ACCESS: "false"
-      USER_PERMISSIONS_WORKSPACE_SKILLS_ACCESS: "false"
-      USER_PERMISSIONS_CHAT_CONTROLS: "false"
-      USER_PERMISSIONS_CHAT_VALVES: "false"
-      USER_PERMISSIONS_CHAT_SYSTEM_PROMPT: "false"
-      USER_PERMISSIONS_CHAT_PARAMS: "false"
-      USER_PERMISSIONS_CHAT_MULTIPLE_MODELS: "false"
-      USER_PERMISSIONS_CHAT_SHARE: "false"
-      USER_PERMISSIONS_CHAT_ALLOW_PUBLIC_SHARING: "false"
-      USER_PERMISSIONS_FEATURES_DIRECT_TOOL_SERVERS: "false"
-      USER_PERMISSIONS_FEATURES_WEB_SEARCH: "false"
-      USER_PERMISSIONS_FEATURES_IMAGE_GENERATION: "false"
-      USER_PERMISSIONS_FEATURES_CODE_INTERPRETER: "false"
-      USER_PERMISSIONS_FEATURES_API_KEYS: "false"
-      USER_PERMISSIONS_FEATURES_MEMORIES: "false"
-      USER_PERMISSIONS_FEATURES_AUTOMATIONS: "false"
-      USER_PERMISSIONS_FEATURES_CHANNELS: "false"
-      USER_PERMISSIONS_FEATURES_CALENDAR: "false"
-      USER_PERMISSIONS_FEATURES_NOTES: "false"
-      USER_PERMISSIONS_SETTINGS_INTERFACE: "false"
-      DATABASE_URL: postgresql+psycopg://${POSTGRES_USER:-agent}:${POSTGRES_PASSWORD:?POSTGRES_PASSWORD is required}@postgres:5432/${POSTGRES_DB:-agent}
-      REDIS_URL: redis://redis:6379/0
-    volumes:
-      - openwebui-data:/app/backend/data
-    depends_on:
-      postgres:
-        condition: service_healthy
-      redis:
-        condition: service_healthy
-      runtime:
-        condition: service_healthy
-    networks:
-      - internal
-      - public
-    healthcheck:
-      test: ["CMD", "curl", "--silent", "--fail", "http://127.0.0.1:8080/health"]
-      interval: 15s
-      timeout: 5s
-      retries: 20
-
-  runtime:
-    image: ${RUNTIME_IMAGE:?RUNTIME_IMAGE is required}
-    platform: linux/amd64
-    container_name: k1412-agent-runtime
-    restart: unless-stopped
-    init: true
-    environment:
-      MODEL_API_BASE_URL: ${MODEL_API_BASE_URL:-https://api.k1412.top}
-      MODEL_API_KEY: ${MODEL_API_KEY:?MODEL_API_KEY is required}
-      DEEPSEEK_API_BASE_URL: ${DEEPSEEK_API_BASE_URL:-https://api.deepseek.com}
-      DEEPSEEK_API_KEY: ${DEEPSEEK_API_KEY:?DEEPSEEK_API_KEY is required}
-      OPENWEBUI_FORWARD_JWT_SECRET: ${OPENWEBUI_FORWARD_JWT_SECRET:?OPENWEBUI_FORWARD_JWT_SECRET is required}
-      INTERNAL_PROVIDER_KEY: ${INTERNAL_PROVIDER_KEY:?INTERNAL_PROVIDER_KEY is required}
-      INTERNAL_GATEWAY_KEY: ${INTERNAL_GATEWAY_KEY:?INTERNAL_GATEWAY_KEY is required}
-      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-agent}:${POSTGRES_PASSWORD:?POSTGRES_PASSWORD is required}@postgres:5432/${POSTGRES_DB:-agent}
-      GATEWAY_URL: http://gateway:8001
-    depends_on:
-      postgres:
-        condition: service_healthy
-      gateway:
-        condition: service_healthy
-    networks:
-      - internal
-      - egress
-    read_only: true
-    tmpfs:
-      - /tmp:rw,noexec,nosuid,size=128m
-    security_opt:
-      - no-new-privileges:true
-    cap_drop:
-      - ALL
-
-  gateway:
-    image: ${GATEWAY_IMAGE:?GATEWAY_IMAGE is required}
-    platform: linux/amd64
-    container_name: k1412-agent-gateway
-    restart: unless-stopped
-    init: true
-    environment:
-      OPENWEBUI_FORWARD_JWT_SECRET: ${OPENWEBUI_FORWARD_JWT_SECRET:?OPENWEBUI_FORWARD_JWT_SECRET is required}
-      INTERNAL_GATEWAY_KEY: ${INTERNAL_GATEWAY_KEY:?INTERNAL_GATEWAY_KEY is required}
-      EXECUTION_PROVIDER: ${EXECUTION_PROVIDER:-local-docker}
-      WORKSPACE_IMAGE: ${WORKSPACE_IMAGE:?WORKSPACE_IMAGE is required}
-      WORKSPACE_NETWORK_ENABLED: ${WORKSPACE_NETWORK_ENABLED:-true}
-      WORKSPACE_MEMORY_LIMIT: ${WORKSPACE_MEMORY_LIMIT:-2g}
-      WORKSPACE_CPU_LIMIT: ${WORKSPACE_CPU_LIMIT:-2}
-      WORKSPACE_PIDS_LIMIT: ${WORKSPACE_PIDS_LIMIT:-512}
-      WORKSPACE_SSH_HOST: ${WORKSPACE_SSH_HOST:-}
-      WORKSPACE_DOWNLOAD_MAX_BYTES: ${WORKSPACE_DOWNLOAD_MAX_BYTES:-134217728}
-    volumes:
-      - /var/run/docker.sock:/var/run/docker.sock
-    networks:
-      - internal
-      - egress
-    read_only: true
-    tmpfs:
-      - /tmp:rw,noexec,nosuid,size=128m
-    security_opt:
-      - no-new-privileges:true
-    cap_drop:
-      - ALL
-
-  bootstrap:
-    image: ${RUNTIME_IMAGE:?RUNTIME_IMAGE is required}
-    platform: linux/amd64
-    container_name: k1412-agent-bootstrap
-    restart: "no"
-    command: ["python", "-m", "agent_platform.bootstrap"]
-    environment:
-      OPENWEBUI_INTERNAL_URL: http://web:8080
-      WEBUI_ADMIN_EMAIL: ${WEBUI_ADMIN_EMAIL:?WEBUI_ADMIN_EMAIL is required}
-      WEBUI_ADMIN_PASSWORD: ${WEBUI_ADMIN_PASSWORD:?WEBUI_ADMIN_PASSWORD is required}
-      INTERNAL_GATEWAY_KEY: ${INTERNAL_GATEWAY_KEY:?INTERNAL_GATEWAY_KEY is required}
-      GATEWAY_URL: http://gateway:8001
-    depends_on:
-      web:
-        condition: service_healthy
-      gateway:
-        condition: service_healthy
-    networks:
-      - internal
-
-  postgres:
-    image: postgres:17-alpine@sha256:742f40ea20b9ff2ff31db5458d127452988a2164df9e17441e191f3b72252193
-    platform: linux/amd64
-    container_name: k1412-agent-postgres
-    restart: unless-stopped
-    environment:
-      POSTGRES_USER: ${POSTGRES_USER:-agent}
-      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:?POSTGRES_PASSWORD is required}
-      POSTGRES_DB: ${POSTGRES_DB:-agent}
-    volumes:
-      - postgres-data:/var/lib/postgresql/data
-    networks:
-      - internal
-    healthcheck:
-      test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER:-agent} -d ${POSTGRES_DB:-agent}"]
-      interval: 5s
-      timeout: 5s
-      retries: 20
-
-  redis:
-    image: redis:8-alpine@sha256:8096655e437712b07503796fb64d81359256cfcff0ab29d95a7da72863786efb
-    platform: linux/amd64
-    container_name: k1412-agent-redis
-    restart: unless-stopped
-    command: ["redis-server", "--save", "60", "1", "--appendonly", "yes"]
-    volumes:
-      - redis-data:/data
-    networks:
-      - internal
-    healthcheck:
-      test: ["CMD", "redis-cli", "ping"]
-      interval: 5s
-      timeout: 3s
-      retries: 20
-
-  workspace-image:
-    image: ${WORKSPACE_IMAGE:?WORKSPACE_IMAGE is required}
-    platform: linux/amd64
-    command: ["true"]
-    profiles: ["build-only"]
-
-networks:
-  public:
-  egress:
-  internal:
-    internal: true
-
-volumes:
-  openwebui-data:
-  postgres-data:
-  redis-data:

deploy/systemd/zk-data-agent-backend.service.template

@@ -0,0 +1,15 @@
+[Unit]
+Description=ZK Data Agent backend
+After=network-online.target
+Wants=network-online.target
+
+[Service]
+Type=simple
+WorkingDirectory=__PROJECT_ROOT__
+ExecStart=__PROJECT_ROOT__/scripts/start-backend.sh
+Restart=always
+RestartSec=5
+Environment=PYTHONUNBUFFERED=1
+
+[Install]
+WantedBy=default.target

deploy/systemd/zk-data-agent-frontend.service.template

@@ -0,0 +1,14 @@
+[Unit]
+Description=ZK Data Agent frontend
+After=network-online.target __BACKEND_SERVICE__.service
+Wants=network-online.target
+
+[Service]
+Type=simple
+WorkingDirectory=__PROJECT_ROOT__/frontend/app
+ExecStart=__PROJECT_ROOT__/scripts/start-frontend.sh
+Restart=always
+RestartSec=5
+
+[Install]
+WantedBy=default.target

docker/e2e-stub.Dockerfile

@@ -1,14 +0,0 @@
-FROM python:3.12-slim-bookworm@sha256:d50fb7611f86d04a3b0471b46d7557818d88983fc3136726336b2a4c657aa30b
-
-ENV PYTHONDONTWRITEBYTECODE=1 \
-    PYTHONUNBUFFERED=1
-
-WORKDIR /app
-RUN python -m pip install --no-cache-dir --upgrade pip==26.1.2
-COPY --chown=65534:65534 --chmod=0444 e2e/stub_provider.py ./stub_provider.py
-
-USER 65534:65534
-EXPOSE 9000
-HEALTHCHECK --interval=2s --timeout=2s --retries=20 CMD \
-  python -c "import urllib.request; urllib.request.urlopen('http://127.0.0.1:9000/health', timeout=1)"
-CMD ["python", "/app/stub_provider.py"]

docker/gateway.Dockerfile

@@ -1,24 +0,0 @@
-FROM python:3.12-slim-bookworm@sha256:d50fb7611f86d04a3b0471b46d7557818d88983fc3136726336b2a4c657aa30b
-
-ENV PYTHONDONTWRITEBYTECODE=1 \
-    PYTHONUNBUFFERED=1
-
-WORKDIR /app
-
-RUN apt-get update \
-    && apt-get install --yes --no-install-recommends openssh-client \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY pyproject.toml README.md ./
-COPY agent_platform ./agent_platform
-RUN --mount=type=cache,target=/root/.cache/pip \
-    python -m pip install --upgrade pip==26.1.2 \
-    && python -m pip install .
-
-EXPOSE 8001
-HEALTHCHECK --interval=15s --timeout=5s --retries=10 CMD \
-  python -c "import urllib.request; urllib.request.urlopen('http://127.0.0.1:8001/health', timeout=3)"
-
-# Access to the Docker API is the gateway's purpose. It is never shared with
-# Open WebUI, the model runtime, or user workspace containers.
-CMD ["uvicorn", "agent_platform.gateway.app:app", "--host", "0.0.0.0", "--port", "8001"]

docker/runtime.Dockerfile

@@ -1,22 +0,0 @@
-FROM python:3.12-slim-bookworm@sha256:d50fb7611f86d04a3b0471b46d7557818d88983fc3136726336b2a4c657aa30b
-
-ENV PYTHONDONTWRITEBYTECODE=1 \
-    PYTHONUNBUFFERED=1
-
-WORKDIR /app
-
-COPY pyproject.toml README.md ./
-COPY agent_platform ./agent_platform
-RUN --mount=type=cache,target=/root/.cache/pip \
-    python -m pip install --upgrade pip==26.1.2 \
-    && python -m pip install .
-
-RUN useradd --create-home --uid 10001 agent
-WORKDIR /srv
-USER 10001:10001
-
-EXPOSE 8000
-HEALTHCHECK --interval=15s --timeout=5s --retries=10 CMD \
-  python -c "import urllib.request; urllib.request.urlopen('http://127.0.0.1:8000/health', timeout=3)"
-
-CMD ["uvicorn", "agent_platform.runtime.app:app", "--host", "0.0.0.0", "--port", "8000", "--proxy-headers", "--forwarded-allow-ips=*"]

docker/web-requirements.txt

@@ -1,70 +0,0 @@
-# Open WebUI minimal backend dependencies for the K1412 Chat/Work surface.
-# Optional RAG, media, browser automation, cloud storage, and vector providers
-# are intentionally excluded.
-fastapi==0.140.0
-uvicorn[standard]==0.51.0
-pydantic==2.13.4
-python-multipart==0.0.31
-itsdangerous==2.2.0
-typer==0.21.0
-ldap3==2.9.1
-validators==0.35.0
-
-python-socketio==5.16.2
-cryptography==48.0.1
-bcrypt==5.0.0
-argon2-cffi==25.1.0
-PyJWT[crypto]==2.13.0
-authlib==1.6.12
-
-requests==2.34.2
-aiohttp==3.14.1
-async-timeout==5.0.1
-aiocache==0.12.3
-aiofiles==25.1.0
-starlette-compress==1.7.0
-Brotli==1.2.0
-brotlicffi==1.2.0.1
-httpx[socks,http2,zstd,cli,brotli]==0.28.1
-starsessions[redis]==2.2.1
-python-mimeparse==2.0.0
-
-sqlalchemy[asyncio]==2.0.51
-aiosqlite==0.21.0
-psycopg[binary]==3.2.9
-alembic==1.18.4
-peewee==3.19.0
-peewee-migrate==1.14.3
-
-pycrdt==0.12.47
-redis==7.4.0
-APScheduler==3.11.2
-RestrictedPython==8.1
-pytz==2026.1.post1
-loguru==0.7.3
-asgiref==3.11.1
-python-dateutil==2.9.0.post0
-
-tiktoken==0.12.0
-mcp==1.28.1
-openai==2.29.0
-anthropic==0.87.0
-huggingface-hub==1.16.1
-
-langchain==1.3.9
-langchain-community==0.4.1
-langchain-classic==1.0.7
-langchain-text-splitters==1.1.2
-langsmith==0.8.18
-
-fake-useragent==2.2.0
-black==26.3.1
-pydub==0.25.1
-chardet==5.2.0
-beautifulsoup4==4.14.3
-nltk==3.10.0
-pypdf==6.14.2
-pymdown-extensions==11.0.0
-pillow==12.3.0
-ddgs==9.11.3
-fpdf2==2.8.7

docker/web.Dockerfile

@@ -1,92 +0,0 @@
-FROM node:22-bookworm-slim@sha256:6c74791e557ce11fc957704f6d4fe134a7bc8d6f5ca4403205b2966bd488f6b3 AS web-build
-
-ARG OPEN_WEBUI_REF=v0.9.6
-ARG OPEN_WEBUI_COMMIT=1a97751e376e00a1897bc3679215ae1c7bd8fd42
-
-RUN apt-get update && apt-get install -y --no-install-recommends git ca-certificates librsvg2-bin \
-    && rm -rf /var/lib/apt/lists/*
-
-WORKDIR /src
-RUN git clone --depth 1 --branch "${OPEN_WEBUI_REF}" https://github.com/open-webui/open-webui.git . \
-    && test "$(git rev-parse HEAD)" = "${OPEN_WEBUI_COMMIT}"
-
-COPY docker/web/ModelSelector.svelte src/lib/components/chat/ModelSelector.svelte
-COPY docker/web/WorkspaceFiles.svelte src/lib/components/chat/WorkspaceFiles.svelte
-COPY docker/web/k1412_workspace.py backend/open_webui/routers/k1412_workspace.py
-COPY docker/web/k1412-logo.svg /tmp/k1412-logo.svg
-COPY docker/web/openwebui-no-rag.patch /tmp/openwebui-no-rag.patch
-COPY docker/web/openwebui-local-storage.patch /tmp/openwebui-local-storage.patch
-COPY docker/web/openwebui-lazy-azure.patch /tmp/openwebui-lazy-azure.patch
-COPY docker/web/openwebui-product-ui.patch /tmp/openwebui-product-ui.patch
-COPY docker/web/openwebui-workspace-router.patch /tmp/openwebui-workspace-router.patch
-COPY docker/web/openwebui-brand.patch /tmp/openwebui-brand.patch
-RUN git apply /tmp/openwebui-no-rag.patch \
-    && git apply /tmp/openwebui-local-storage.patch \
-    && git apply /tmp/openwebui-lazy-azure.patch \
-    && git apply /tmp/openwebui-product-ui.patch \
-    && git apply /tmp/openwebui-workspace-router.patch \
-    && git apply /tmp/openwebui-brand.patch
-RUN cp /tmp/k1412-logo.svg static/static/favicon.svg \
-    && rsvg-convert --width 512 --height 512 /tmp/k1412-logo.svg --output static/favicon.png \
-    && rsvg-convert --width 512 --height 512 /tmp/k1412-logo.svg --output static/static/favicon.png \
-    && rsvg-convert --width 500 --height 500 /tmp/k1412-logo.svg --output static/static/logo.png \
-    && rsvg-convert --width 500 --height 500 /tmp/k1412-logo.svg --output static/static/splash.png \
-    && rsvg-convert --width 500 --height 500 /tmp/k1412-logo.svg --output static/static/splash-dark.png \
-    && rsvg-convert --width 180 --height 180 /tmp/k1412-logo.svg --output static/static/apple-touch-icon.png \
-    && rsvg-convert --width 192 --height 192 /tmp/k1412-logo.svg --output static/static/web-app-manifest-192x192.png \
-    && rsvg-convert --width 512 --height 512 /tmp/k1412-logo.svg --output static/static/web-app-manifest-512x512.png
-RUN npm ci --no-audit --no-fund \
-    && npx vite build
-
-FROM python:3.12-slim-bookworm@sha256:d50fb7611f86d04a3b0471b46d7557818d88983fc3136726336b2a4c657aa30b AS backend-deps
-
-RUN apt-get update \
-    && apt-get upgrade --yes --no-install-recommends \
-    && apt-get install --yes --no-install-recommends build-essential \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY docker/web-requirements.txt /tmp/web-requirements.txt
-RUN --mount=type=cache,target=/root/.cache/pip \
-    python -m pip install --upgrade pip==26.1.2 \
-    && python -m pip download --only-binary=:all: --dest /wheels pip==26.1.2 \
-    && python -m pip wheel --wheel-dir /wheels --requirement /tmp/web-requirements.txt
-
-FROM python:3.12-slim-bookworm@sha256:d50fb7611f86d04a3b0471b46d7557818d88983fc3136726336b2a4c657aa30b
-
-ENV PYTHONUNBUFFERED=1 \
-    ENV=prod \
-    PORT=8080 \
-    USE_OLLAMA_DOCKER=false \
-    USE_CUDA_DOCKER=false \
-    USE_SLIM_DOCKER=true \
-    SCARF_NO_ANALYTICS=true \
-    DO_NOT_TRACK=true \
-    ANONYMIZED_TELEMETRY=false \
-    VECTOR_DB=disabled \
-    DOCKER=true
-
-RUN apt-get update \
-    && apt-get upgrade --yes --no-install-recommends \
-    && apt-get install --yes --no-install-recommends bash curl tini \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY --from=backend-deps /wheels /wheels
-COPY docker/web-requirements.txt /tmp/web-requirements.txt
-RUN python -m pip install --no-cache-dir --no-index --find-links=/wheels --upgrade pip==26.1.2 \
-    && python -m pip install --no-cache-dir --no-index --find-links=/wheels --requirement /tmp/web-requirements.txt \
-    && rm -rf /wheels /tmp/web-requirements.txt
-
-WORKDIR /app/backend
-COPY --from=web-build /src/backend /app/backend
-COPY --from=web-build /src/build /app/build
-COPY --from=web-build /src/CHANGELOG.md /app/CHANGELOG.md
-
-COPY --from=web-build /src/LICENSE /app/OPEN_WEBUI_LICENSE
-COPY THIRD_PARTY_NOTICES.md /app/THIRD_PARTY_NOTICES.md
-
-EXPOSE 8080
-HEALTHCHECK --interval=15s --timeout=5s --retries=20 CMD \
-  curl --silent --fail http://127.0.0.1:8080/health
-
-ENTRYPOINT ["/usr/bin/tini", "--"]
-CMD ["bash", "start.sh"]

docker/web/ModelSelector.svelte

@@ -1,361 +0,0 @@
-<script lang="ts">
-	import { models } from '$lib/stores';
-	import { flyAndScale } from '$lib/utils/transitions';
-	import WorkspaceFiles from './WorkspaceFiles.svelte';
-
-	export let selectedModels = [''];
-	export let disabled = false;
-	export let showSetDefault = true;
-
-	const modelOptions = [
-		{
-			id: 'luna',
-			label: 'Luna',
-			description: '快速、轻量的本地 Agent',
-			location: '本地',
-			accent: 'bg-violet-100 text-violet-700 dark:bg-violet-500/15 dark:text-violet-300',
-			thinking: '开启',
-			thinkingTitle: '思考模式',
-			thinkingHelp: '当前固定开启。此模型只支持思考开关，不提供轻、中、高强度档位。'
-		},
-		{
-			id: 'terra',
-			label: 'Terra',
-			description: '速度与任务能力均衡',
-			location: '本地',
-			accent: 'bg-sky-100 text-sky-700 dark:bg-sky-500/15 dark:text-sky-300',
-			thinking: '开启',
-			thinkingTitle: '思考模式',
-			thinkingHelp: '当前固定开启。此模型只支持思考开关，不提供轻、中、高强度档位。'
-		},
-		{
-			id: 'sol',
-			label: 'Sol',
-			description: '面向复杂任务的本地 Agent',
-			location: '本地',
-			accent: 'bg-amber-100 text-amber-700 dark:bg-amber-500/15 dark:text-amber-300',
-			thinking: '开启',
-			thinkingTitle: '思考模式',
-			thinkingHelp: '当前固定开启。此模型只支持思考开关，不提供轻、中、高强度档位。'
-		},
-		{
-			id: 'deepseek-v4-pro',
-			label: 'DeepSeek V4 Pro',
-			description: '云端高能力 Agent',
-			location: '云端',
-			accent: 'bg-emerald-100 text-emerald-700 dark:bg-emerald-500/15 dark:text-emerald-300',
-			thinking: '极高',
-			thinkingTitle: '推理强度',
-			thinkingHelp: '当前固定为极高（max），由 Agent 服务端管理。'
-		}
-	];
-	const allowed = new Set(modelOptions.map((item) => item.id));
-
-	let showWorkspace = false;
-	let showModels = false;
-	let showThinking = false;
-	let modelTrigger: HTMLElement | null = null;
-	let modelPanel: HTMLElement | null = null;
-	let thinkingTrigger: HTMLElement | null = null;
-	let thinkingPanel: HTMLElement | null = null;
-	let modelPosition = { top: 0, left: 0, width: 320 };
-	let thinkingPosition = { top: 0, left: 0, width: 288 };
-
-	$: selected = allowed.has(selectedModels?.[0]) ? selectedModels[0] : 'terra';
-	$: selectedOption = modelOptions.find((item) => item.id === selected) ?? modelOptions[1];
-	$: availableIds = new Set($models.map((model) => model.id));
-
-	$: if ($models.length > 0 && !availableIds.has(selectedModels?.[0])) {
-		const fallback = availableIds.has('terra')
-			? 'terra'
-			: modelOptions.find((item) => availableIds.has(item.id))?.id;
-		if (fallback) selectedModels = [fallback];
-	}
-
-	const portal = (node: HTMLElement) => {
-		document.body.appendChild(node);
-		return {
-			destroy() {
-				node.remove();
-			}
-		};
-	};
-
-	const positionFor = (trigger: HTMLElement | null, preferredWidth: number) => {
-		if (!trigger || typeof window === 'undefined') {
-			return { top: 0, left: 0, width: preferredWidth };
-		}
-
-		const rect = trigger.getBoundingClientRect();
-		const width = Math.min(preferredWidth, window.innerWidth - 16);
-		return {
-			top: rect.bottom + 8,
-			left: Math.max(8, Math.min(rect.left, window.innerWidth - width - 8)),
-			width
-		};
-	};
-
-	const updatePositions = () => {
-		if (showModels) modelPosition = positionFor(modelTrigger, 320);
-		if (showThinking) thinkingPosition = positionFor(thinkingTrigger, 288);
-	};
-
-	const toggleModels = () => {
-		if (disabled) return;
-		showThinking = false;
-		showModels = !showModels;
-		if (showModels) modelPosition = positionFor(modelTrigger, 320);
-	};
-
-	const toggleThinking = () => {
-		showModels = false;
-		showThinking = !showThinking;
-		if (showThinking) thinkingPosition = positionFor(thinkingTrigger, 288);
-	};
-
-	const chooseModel = (modelId: string) => {
-		if (disabled || ($models.length > 0 && !availableIds.has(modelId))) return;
-		selectedModels = [modelId];
-		showModels = false;
-	};
-
-	const closePopovers = (event: PointerEvent) => {
-		const target = event.target as Node;
-		if (
-			(modelTrigger?.contains(target) ?? false) ||
-			(modelPanel?.contains(target) ?? false) ||
-			(thinkingTrigger?.contains(target) ?? false) ||
-			(thinkingPanel?.contains(target) ?? false)
-		) {
-			return;
-		}
-		showModels = false;
-		showThinking = false;
-	};
-
-	const handleKeydown = (event: KeyboardEvent) => {
-		if (event.key !== 'Escape') return;
-		const focusTarget = showThinking ? thinkingTrigger : modelTrigger;
-		showModels = false;
-		showThinking = false;
-		focusTarget?.focus();
-	};
-</script>
-
-<svelte:window
-	on:pointerdown={closePopovers}
-	on:keydown={handleKeydown}
-	on:resize={updatePositions}
-	on:scroll={updatePositions}
-/>
-
-<div class="flex max-w-full select-none items-center gap-0.5">
-	<button
-		bind:this={modelTrigger}
-		type="button"
-		class="group flex min-w-0 max-w-[13rem] items-center gap-1.5 rounded-xl px-2.5 py-2 text-sm font-semibold text-gray-900 outline-hidden transition hover:bg-gray-100 focus-visible:ring-2 focus-visible:ring-gray-300 disabled:cursor-not-allowed disabled:opacity-50 dark:text-white dark:hover:bg-gray-800 dark:focus-visible:ring-gray-600 sm:max-w-[16rem]"
-		on:click={toggleModels}
-		disabled={disabled}
-		aria-label="选择模型，当前为 {selectedOption.label}"
-		aria-haspopup="listbox"
-		aria-expanded={showModels}
-	>
-		<span class="truncate">{selectedOption.label}</span>
-		<svg
-			viewBox="0 0 20 20"
-			class="size-3.5 shrink-0 text-gray-400 transition-transform duration-150 {showModels
-				? 'rotate-180'
-				: ''}"
-			fill="none"
-			stroke="currentColor"
-			stroke-width="1.8"
-			aria-hidden="true"
-		>
-			<path d="m6 8 4 4 4-4" stroke-linecap="round" stroke-linejoin="round" />
-		</svg>
-	</button>
-
-	<div class="mx-0.5 h-4 w-px bg-gray-200 dark:bg-gray-700" aria-hidden="true" />
-
-	<button
-		bind:this={thinkingTrigger}
-		type="button"
-		class="group flex shrink-0 items-center gap-1.5 rounded-xl px-2.5 py-2 text-xs font-medium text-gray-500 outline-hidden transition hover:bg-gray-100 hover:text-gray-900 focus-visible:ring-2 focus-visible:ring-gray-300 dark:text-gray-400 dark:hover:bg-gray-800 dark:hover:text-white dark:focus-visible:ring-gray-600"
-		on:click={toggleThinking}
-		aria-label="{selectedOption.thinkingTitle}：{selectedOption.thinking}"
-		aria-haspopup="dialog"
-		aria-expanded={showThinking}
-	>
-		<svg
-			viewBox="0 0 20 20"
-			class="size-3.5 text-gray-400 group-hover:text-gray-600 dark:group-hover:text-gray-200"
-			fill="none"
-			stroke="currentColor"
-			stroke-width="1.6"
-			aria-hidden="true"
-		>
-			<path
-				d="M10 2.5c.3 2.4 1.5 3.6 4 4-2.5.4-3.7 1.6-4 4-.4-2.4-1.6-3.6-4-4 2.4-.4 3.6-1.6 4-4Z"
-				stroke-linecap="round"
-				stroke-linejoin="round"
-			/>
-			<path d="M4.5 11.5c.2 1.7 1 2.5 2.7 2.7-1.7.3-2.5 1.1-2.7 2.8-.3-1.7-1.1-2.5-2.8-2.8 1.7-.2 2.5-1 2.8-2.7Z" />
-			<path d="M15 12.5c.2 1.2.8 1.8 2 2-1.2.2-1.8.8-2 2-.2-1.2-.8-1.8-2-2 1.2-.2 1.8-.8 2-2Z" />
-		</svg>
-		<span class="hidden sm:inline">{selectedOption.thinkingTitle}</span>
-		<span class="text-gray-800 dark:text-gray-200">{selectedOption.thinking}</span>
-		<svg
-			viewBox="0 0 20 20"
-			class="size-3 shrink-0 text-gray-400 transition-transform duration-150 {showThinking
-				? 'rotate-180'
-				: ''}"
-			fill="none"
-			stroke="currentColor"
-			stroke-width="1.8"
-			aria-hidden="true"
-		>
-			<path d="m6 8 4 4 4-4" stroke-linecap="round" stroke-linejoin="round" />
-		</svg>
-	</button>
-
-	<button
-		type="button"
-		class="ml-0.5 rounded-xl p-2 text-gray-500 outline-hidden transition hover:bg-gray-100 hover:text-gray-900 focus-visible:ring-2 focus-visible:ring-gray-300 dark:text-gray-400 dark:hover:bg-gray-800 dark:hover:text-white dark:focus-visible:ring-gray-600"
-		on:click={() => (showWorkspace = true)}
-		title="工作区文件"
-		aria-label="工作区文件"
-	>
-		<svg
-			viewBox="0 0 24 24"
-			class="size-4"
-			fill="none"
-			stroke="currentColor"
-			stroke-width="1.8"
-			aria-hidden="true"
-		>
-			<path d="M3.5 6.5h6l2 2h9v10h-17z" stroke-linejoin="round" />
-		</svg>
-	</button>
-</div>
-
-{#if showModels}
-	<div
-		use:portal
-		bind:this={modelPanel}
-		class="fixed z-[9999]"
-		style="top: {modelPosition.top}px; left: {modelPosition.left}px; width: {modelPosition.width}px;"
-		transition:flyAndScale
-	>
-		<div
-			class="overflow-hidden rounded-2xl border border-gray-200/70 bg-white p-1.5 text-gray-900 shadow-xl shadow-black/10 dark:border-gray-700/70 dark:bg-gray-850 dark:text-white dark:shadow-black/30"
-			role="listbox"
-			aria-label="可用模型"
-		>
-			<div class="px-2.5 pb-1.5 pt-1 text-xs font-medium text-gray-400 dark:text-gray-500">
-				选择模型
-			</div>
-			{#each modelOptions as item}
-				{@const unavailable = $models.length > 0 && !availableIds.has(item.id)}
-				<button
-					type="button"
-					role="option"
-					aria-selected={selected === item.id}
-					class="group flex w-full items-center gap-3 rounded-xl px-2.5 py-2.5 text-left outline-hidden transition hover:bg-gray-100 focus-visible:bg-gray-100 disabled:cursor-not-allowed disabled:opacity-40 dark:hover:bg-gray-800 dark:focus-visible:bg-gray-800"
-					on:click={() => chooseModel(item.id)}
-					disabled={disabled || unavailable}
-				>
-					<span
-						class="flex size-8 shrink-0 items-center justify-center rounded-full text-xs font-bold {item.accent}"
-						aria-hidden="true"
-					>
-						{item.label.slice(0, 1)}
-					</span>
-					<span class="min-w-0 flex-1">
-						<span class="flex items-center gap-2">
-							<span class="truncate text-sm font-medium">{item.label}</span>
-							<span
-								class="rounded-full bg-gray-100 px-1.5 py-0.5 text-[10px] font-medium text-gray-500 dark:bg-gray-800 dark:text-gray-400"
-							>
-								{item.location}
-							</span>
-						</span>
-						<span class="mt-0.5 block truncate text-xs text-gray-500 dark:text-gray-400">
-							{unavailable ? '当前不可用' : item.description}
-						</span>
-					</span>
-					{#if selected === item.id}
-						<svg
-							viewBox="0 0 20 20"
-							class="size-4 shrink-0 text-gray-900 dark:text-white"
-							fill="none"
-							stroke="currentColor"
-							stroke-width="2"
-							aria-hidden="true"
-						>
-							<path d="m5 10 3 3 7-7" stroke-linecap="round" stroke-linejoin="round" />
-						</svg>
-					{/if}
-				</button>
-			{/each}
-		</div>
-	</div>
-{/if}
-
-{#if showThinking}
-	<div
-		use:portal
-		bind:this={thinkingPanel}
-		class="fixed z-[9999]"
-		style="top: {thinkingPosition.top}px; left: {thinkingPosition.left}px; width: {thinkingPosition.width}px;"
-		transition:flyAndScale
-	>
-		<div
-			class="rounded-2xl border border-gray-200/70 bg-white p-2 text-gray-900 shadow-xl shadow-black/10 dark:border-gray-700/70 dark:bg-gray-850 dark:text-white dark:shadow-black/30"
-			role="dialog"
-			aria-label="{selectedOption.thinkingTitle}说明"
-		>
-			<div class="flex items-center gap-3 rounded-xl bg-gray-50 px-3 py-2.5 dark:bg-gray-800/70">
-				<span
-					class="flex size-8 shrink-0 items-center justify-center rounded-full bg-gray-900 text-white dark:bg-white dark:text-gray-900"
-					aria-hidden="true"
-				>
-					<svg
-						viewBox="0 0 20 20"
-						class="size-4"
-						fill="none"
-						stroke="currentColor"
-						stroke-width="1.6"
-					>
-						<path
-							d="M10 2.5c.3 2.4 1.5 3.6 4 4-2.5.4-3.7 1.6-4 4-.4-2.4-1.6-3.6-4-4 2.4-.4 3.6-1.6 4-4Z"
-							stroke-linecap="round"
-							stroke-linejoin="round"
-						/>
-						<path d="M5 12c.2 1.7 1 2.5 2.7 2.7C6 15 5.2 15.8 5 17.5c-.3-1.7-1.1-2.5-2.8-2.8C3.9 14.5 4.7 13.7 5 12Z" />
-					</svg>
-				</span>
-				<span class="min-w-0 flex-1">
-					<span class="block text-xs text-gray-500 dark:text-gray-400">
-						{selectedOption.thinkingTitle}
-					</span>
-					<span class="block text-sm font-semibold">{selectedOption.thinking}</span>
-				</span>
-				<svg
-					viewBox="0 0 20 20"
-					class="size-4 shrink-0"
-					fill="none"
-					stroke="currentColor"
-					stroke-width="2"
-					aria-hidden="true"
-				>
-					<path d="m5 10 3 3 7-7" stroke-linecap="round" stroke-linejoin="round" />
-				</svg>
-			</div>
-			<p class="px-2 pb-1 pt-2.5 text-xs leading-5 text-gray-500 dark:text-gray-400">
-				{selectedOption.thinkingHelp}
-			</p>
-		</div>
-	</div>
-{/if}
-
-<WorkspaceFiles bind:show={showWorkspace} />

docker/web/WorkspaceFiles.svelte

@@ -1,299 +0,0 @@
-<script lang="ts">
-	import Modal from '$lib/components/common/Modal.svelte';
-	import { WEBUI_API_BASE_URL } from '$lib/constants';
-
-	export let show = false;
-
-	type WorkspaceEntry = {
-		name: string;
-		path: string;
-		kind: 'file' | 'directory' | 'symlink';
-		size: number;
-		modified_at: number;
-	};
-
-	let path = '.';
-	let entries: WorkspaceEntry[] = [];
-	let loading = false;
-	let downloading = '';
-	let error = '';
-	let opened = false;
-
-	$: pathParts = path === '.' ? [] : path.split('/');
-	$: breadcrumbs = [
-		{ label: '工作区', path: '.' },
-		...pathParts.map((part, index) => ({
-			label: part,
-			path: pathParts.slice(0, index + 1).join('/')
-		}))
-	];
-
-	$: if (show && !opened) {
-		opened = true;
-		void loadFiles(path);
-	}
-	$: if (!show) opened = false;
-
-	const authHeaders = () => ({
-		Authorization: `Bearer ${localStorage.token}`
-	});
-
-	async function responseError(response: Response): Promise<string> {
-		try {
-			const payload = await response.json();
-			return typeof payload?.detail === 'string'
-				? payload.detail
-				: JSON.stringify(payload?.detail ?? payload);
-		} catch {
-			return `请求失败（${response.status}）`;
-		}
-	}
-
-	async function loadFiles(nextPath: string) {
-		loading = true;
-		error = '';
-		try {
-			const response = await fetch(
-				`${WEBUI_API_BASE_URL}/k1412/workspace/files?path=${encodeURIComponent(nextPath)}`,
-				{ headers: authHeaders() }
-			);
-			if (!response.ok) throw new Error(await responseError(response));
-			const payload = await response.json();
-			path = payload.path || '.';
-			entries = payload.entries || [];
-		} catch (exception) {
-			error = exception instanceof Error ? exception.message : String(exception);
-		} finally {
-			loading = false;
-		}
-	}
-
-	function downloadName(response: Response, fallback: string): string {
-		const disposition = response.headers.get('content-disposition') ?? '';
-		const encoded = disposition.match(/filename\*=UTF-8''([^;]+)/i)?.[1];
-		if (encoded) {
-			try {
-				return decodeURIComponent(encoded);
-			} catch {
-				return fallback;
-			}
-		}
-		return fallback;
-	}
-
-	async function saveDownload(
-		endpoint: 'download' | 'archive',
-		itemPath: string,
-		fallback: string
-	) {
-		downloading = itemPath;
-		error = '';
-		try {
-			const response = await fetch(
-				`${WEBUI_API_BASE_URL}/k1412/workspace/${endpoint}?path=${encodeURIComponent(itemPath)}`,
-				{ headers: authHeaders() }
-			);
-			if (!response.ok) throw new Error(await responseError(response));
-			const blob = await response.blob();
-			const url = URL.createObjectURL(blob);
-			const link = document.createElement('a');
-			link.href = url;
-			link.download = downloadName(response, fallback);
-			document.body.appendChild(link);
-			link.click();
-			link.remove();
-			URL.revokeObjectURL(url);
-		} catch (exception) {
-			error = exception instanceof Error ? exception.message : String(exception);
-		} finally {
-			downloading = '';
-		}
-	}
-
-	function formatSize(size: number): string {
-		if (size < 1024) return `${size} B`;
-		if (size < 1024 * 1024) return `${(size / 1024).toFixed(1)} KB`;
-		if (size < 1024 * 1024 * 1024) return `${(size / 1024 / 1024).toFixed(1)} MB`;
-		return `${(size / 1024 / 1024 / 1024).toFixed(1)} GB`;
-	}
-</script>
-
-<Modal bind:show size="lg" className="bg-white dark:bg-gray-900 rounded-3xl overflow-hidden">
-	<div class="flex h-[min(74vh,46rem)] min-h-[28rem] flex-col">
-		<header
-			class="flex items-center justify-between gap-3 border-b border-gray-100 px-5 py-4 dark:border-gray-800"
-		>
-			<div>
-				<h2 class="text-base font-semibold text-gray-900 dark:text-white">工作区文件</h2>
-				<p class="mt-0.5 text-xs text-gray-500">这里是当前用户 Agent 生成和修改的文件</p>
-			</div>
-			<div class="flex items-center gap-2">
-				<button
-					type="button"
-					class="rounded-xl border border-gray-200 px-3 py-2 text-xs font-medium text-gray-700 transition hover:bg-gray-50 disabled:opacity-50 dark:border-gray-700 dark:text-gray-200 dark:hover:bg-gray-800"
-					on:click={() =>
-						saveDownload(
-							'archive',
-							path,
-							path === '.' ? 'workspace.tar.gz' : `${pathParts.at(-1)}.tar.gz`
-						)}
-					disabled={loading || Boolean(downloading)}
-				>
-					{downloading === path ? '打包中…' : '打包下载'}
-				</button>
-				<button
-					type="button"
-					class="rounded-xl p-2 text-gray-500 transition hover:bg-gray-100 hover:text-gray-900 dark:hover:bg-gray-800 dark:hover:text-white"
-					on:click={() => (show = false)}
-					aria-label="关闭"
-				>
-					<svg
-						viewBox="0 0 24 24"
-						class="size-5"
-						fill="none"
-						stroke="currentColor"
-						stroke-width="1.8"
-					>
-						<path d="M6 6l12 12M18 6 6 18" stroke-linecap="round" />
-					</svg>
-				</button>
-			</div>
-		</header>
-
-		<nav
-			class="flex min-h-12 items-center gap-1 overflow-x-auto border-b border-gray-100 px-5 dark:border-gray-800"
-		>
-			{#each breadcrumbs as crumb, index}
-				{#if index > 0}<span class="text-gray-300 dark:text-gray-700">/</span>{/if}
-				<button
-					type="button"
-					class="shrink-0 rounded-lg px-2 py-1 text-xs {index === breadcrumbs.length - 1
-						? 'font-medium text-gray-900 dark:text-white'
-						: 'text-gray-500 hover:bg-gray-100 dark:hover:bg-gray-800'}"
-					on:click={() => loadFiles(crumb.path)}
-				>
-					{crumb.label}
-				</button>
-			{/each}
-			<button
-				type="button"
-				class="ml-auto shrink-0 rounded-lg p-2 text-gray-400 transition hover:bg-gray-100 hover:text-gray-700 dark:hover:bg-gray-800 dark:hover:text-gray-200"
-				on:click={() => loadFiles(path)}
-				aria-label="刷新"
-			>
-				<svg
-					viewBox="0 0 24 24"
-					class="size-4"
-					fill="none"
-					stroke="currentColor"
-					stroke-width="1.8"
-				>
-					<path
-						d="M20 11a8 8 0 1 0-2.34 5.66M20 4v7h-7"
-						stroke-linecap="round"
-						stroke-linejoin="round"
-					/>
-				</svg>
-			</button>
-		</nav>
-
-		<div class="min-h-0 flex-1 overflow-y-auto px-3 py-3">
-			{#if error}
-				<div
-					class="mx-2 mb-3 rounded-xl bg-red-50 px-3 py-2 text-sm text-red-700 dark:bg-red-950/40 dark:text-red-300"
-				>
-					{error}
-				</div>
-			{/if}
-			{#if loading}
-				<div class="flex h-40 items-center justify-center text-sm text-gray-400">
-					正在读取工作区…
-				</div>
-			{:else if entries.length === 0}
-				<div class="flex h-40 flex-col items-center justify-center text-gray-400">
-					<svg
-						viewBox="0 0 24 24"
-						class="mb-3 size-9"
-						fill="none"
-						stroke="currentColor"
-						stroke-width="1.5"
-					>
-						<path d="M3.5 6.5h6l2 2h9v10h-17z" stroke-linejoin="round" />
-					</svg>
-					<span class="text-sm">这个目录还是空的</span>
-				</div>
-			{:else}
-				<div class="space-y-1">
-					{#each entries as entry}
-						<div
-							class="group flex items-center gap-3 rounded-xl px-3 py-2.5 transition hover:bg-gray-50 dark:hover:bg-gray-800/70"
-						>
-							<div class={entry.kind === 'directory' ? 'text-amber-500' : 'text-gray-400'}>
-								{#if entry.kind === 'directory'}
-									<svg viewBox="0 0 24 24" class="size-5" fill="currentColor">
-										<path d="M3 5.5h7l2 2h9v11H3z" />
-									</svg>
-								{:else}
-									<svg
-										viewBox="0 0 24 24"
-										class="size-5"
-										fill="none"
-										stroke="currentColor"
-										stroke-width="1.7"
-									>
-										<path d="M6 2.8h8l4 4v14.4H6zM14 2.8v4h4" stroke-linejoin="round" />
-									</svg>
-								{/if}
-							</div>
-							<button
-								type="button"
-								class="min-w-0 flex-1 text-left"
-								on:click={() =>
-									entry.kind === 'directory'
-										? loadFiles(entry.path)
-										: saveDownload('download', entry.path, entry.name)}
-								disabled={entry.kind === 'symlink'}
-							>
-								<div class="truncate text-sm font-medium text-gray-800 dark:text-gray-100">
-									{entry.name}
-								</div>
-								<div class="mt-0.5 text-[11px] text-gray-400">
-									{entry.kind === 'directory'
-										? '文件夹'
-										: entry.kind === 'symlink'
-											? '符号链接'
-											: formatSize(entry.size)}
-									<span class="mx-1.5">·</span>
-									{new Date(entry.modified_at * 1000).toLocaleString()}
-								</div>
-							</button>
-							{#if entry.kind === 'file'}
-								<button
-									type="button"
-									class="rounded-lg p-2 text-gray-400 opacity-0 transition hover:bg-white hover:text-gray-900 group-hover:opacity-100 dark:hover:bg-gray-700 dark:hover:text-white"
-									on:click={() => saveDownload('download', entry.path, entry.name)}
-									disabled={Boolean(downloading)}
-									aria-label={`下载 ${entry.name}`}
-								>
-									<svg
-										viewBox="0 0 24 24"
-										class="size-4"
-										fill="none"
-										stroke="currentColor"
-										stroke-width="1.8"
-									>
-										<path
-											d="M12 3v12m0 0 4-4m-4 4-4-4M5 20h14"
-											stroke-linecap="round"
-											stroke-linejoin="round"
-										/>
-									</svg>
-								</button>
-							{/if}
-						</div>
-					{/each}
-				</div>
-			{/if}
-		</div>
-	</div>
-</Modal>

docker/web/k1412-logo.svg

@@ -1,17 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" role="img" aria-label="K1412 Agent">
-  <defs>
-    <linearGradient id="surface" x1="48" y1="32" x2="464" y2="480" gradientUnits="userSpaceOnUse">
-      <stop stop-color="#171922"/>
-      <stop offset="1" stop-color="#08090d"/>
-    </linearGradient>
-    <linearGradient id="accent" x1="152" y1="112" x2="384" y2="400" gradientUnits="userSpaceOnUse">
-      <stop stop-color="#a78bfa"/>
-      <stop offset="0.55" stop-color="#7c3aed"/>
-      <stop offset="1" stop-color="#4f46e5"/>
-    </linearGradient>
-  </defs>
-  <rect x="24" y="24" width="464" height="464" rx="128" fill="url(#surface)"/>
-  <path d="M154 116v280" fill="none" stroke="#f8fafc" stroke-width="58" stroke-linecap="round"/>
-  <path d="M357 125 190 256l167 131" fill="none" stroke="url(#accent)" stroke-width="62" stroke-linecap="round" stroke-linejoin="round"/>
-  <circle cx="403" cy="99" r="24" fill="#c4b5fd"/>
-</svg>

docker/web/k1412_workspace.py

@@ -1,104 +0,0 @@
-from __future__ import annotations
-
-import os
-from collections.abc import AsyncIterator
-from typing import Annotated
-
-import httpx
-from fastapi import APIRouter, Depends, HTTPException, Query
-from fastapi.responses import StreamingResponse
-from open_webui.utils.auth import get_verified_user
-from open_webui.utils.headers import include_user_info_headers
-
-router = APIRouter()
-
-GATEWAY_URL = os.getenv("K1412_GATEWAY_URL", "http://gateway:8001").rstrip("/")
-GATEWAY_KEY = os.getenv("K1412_GATEWAY_KEY", "")
-REQUEST_TIMEOUT = httpx.Timeout(120, connect=10)
-STREAM_TIMEOUT = httpx.Timeout(1800, connect=10)
-VerifiedUser = Annotated[object, Depends(get_verified_user)]
-
-
-def gateway_headers(user) -> dict[str, str]:
-    if not GATEWAY_KEY:
-        raise HTTPException(status_code=503, detail="Workspace service is not configured")
-    return include_user_info_headers(
-        {"Authorization": f"Bearer {GATEWAY_KEY}"},
-        user,
-    )
-
-
-async def gateway_error(response: httpx.Response) -> HTTPException:
-    try:
-        payload = response.json()
-        detail = payload.get("detail", payload)
-    except Exception:
-        detail = (await response.aread()).decode("utf-8", errors="replace")[:2000]
-    return HTTPException(status_code=response.status_code, detail=detail)
-
-
-@router.get("/files")
-async def list_workspace_files(
-    user: VerifiedUser,
-    path: str = Query(default=".", max_length=4096),
-):
-    async with httpx.AsyncClient(timeout=REQUEST_TIMEOUT) as client:
-        response = await client.get(
-            f"{GATEWAY_URL}/v1/files",
-            params={"path": path},
-            headers=gateway_headers(user),
-        )
-        if response.status_code >= 400:
-            raise await gateway_error(response)
-        return response.json()
-
-
-async def close_after_stream(response: httpx.Response, client: httpx.AsyncClient) -> AsyncIterator[bytes]:
-    try:
-        async for chunk in response.aiter_bytes():
-            yield chunk
-    finally:
-        await response.aclose()
-        await client.aclose()
-
-
-async def stream_workspace_response(endpoint: str, path: str, user) -> StreamingResponse:
-    client = httpx.AsyncClient(timeout=STREAM_TIMEOUT)
-    request = client.build_request(
-        "GET",
-        f"{GATEWAY_URL}{endpoint}",
-        params={"path": path},
-        headers=gateway_headers(user),
-    )
-    response = await client.send(request, stream=True)
-    if response.status_code >= 400:
-        error = await gateway_error(response)
-        await response.aclose()
-        await client.aclose()
-        raise error
-    headers = {
-        key: value
-        for key, value in response.headers.items()
-        if key.lower() in {"content-disposition", "content-length"}
-    }
-    return StreamingResponse(
-        close_after_stream(response, client),
-        media_type=response.headers.get("content-type", "application/octet-stream"),
-        headers=headers,
-    )
-
-
-@router.get("/download")
-async def download_workspace_file(
-    user: VerifiedUser,
-    path: str = Query(min_length=1, max_length=4096),
-):
-    return await stream_workspace_response("/v1/files/download", path, user)
-
-
-@router.get("/archive")
-async def archive_workspace_files(
-    user: VerifiedUser,
-    path: str = Query(default=".", max_length=4096),
-):
-    return await stream_workspace_response("/v1/files/archive", path, user)

docker/web/openwebui-brand.patch

@@ -1,46 +0,0 @@
-diff --git a/backend/open_webui/env.py b/backend/open_webui/env.py
-index 3d85f30..524f623 100644
---- a/backend/open_webui/env.py
-+++ b/backend/open_webui/env.py
-@@ -769,10 +769,13 @@ TRUSTED_SIGNATURE_KEY = os.getenv('TRUSTED_SIGNATURE_KEY', '')
- ####################################
- 
- WEBUI_NAME = os.getenv('WEBUI_NAME', 'Open WebUI')
--if WEBUI_NAME != 'Open WebUI':
-+K1412_REMOVE_UPSTREAM_BRANDING = (
-+    os.getenv('K1412_REMOVE_UPSTREAM_BRANDING', 'false').lower() == 'true'
-+)
-+if WEBUI_NAME != 'Open WebUI' and not K1412_REMOVE_UPSTREAM_BRANDING:
-     WEBUI_NAME += ' (Open WebUI)'
- 
--WEBUI_FAVICON_URL = 'https://openwebui.com/favicon.png'
-+WEBUI_FAVICON_URL = os.getenv('WEBUI_FAVICON_URL', '/static/favicon.png')
- WEBUI_BUILD_HASH = os.getenv('WEBUI_BUILD_HASH', 'dev-build')
- TRUSTED_SIGNATURE_KEY = os.getenv('TRUSTED_SIGNATURE_KEY', '')
- 
-diff --git a/src/app.html b/src/app.html
-index bc3fc37..c2a42a1 100644
---- a/src/app.html
-+++ b/src/app.html
-@@ -115,7 +115,7 @@
- 			})();
- 		</script>
- 
--		<title>Open WebUI</title>
-+		<title>K1412 Agent</title>
- 
- 		%sveltekit.head%
- 	</head>
-diff --git a/src/lib/constants.ts b/src/lib/constants.ts
-index 2926448..d1a4c47 100644
---- a/src/lib/constants.ts
-+++ b/src/lib/constants.ts
-@@ -1,7 +1,7 @@
- import { browser, dev } from '$app/environment';
- // import { version } from '../../package.json';
- 
--export const APP_NAME = 'Open WebUI';
-+export const APP_NAME = 'K1412 Agent';
- 
- export const WEBUI_HOSTNAME = browser ? (dev ? `${location.hostname}:8080` : ``) : '';
- export const WEBUI_BASE_URL = browser ? (dev ? `http://${WEBUI_HOSTNAME}` : ``) : ``;

docker/web/openwebui-lazy-azure.patch

@@ -1,21 +0,0 @@
-diff --git a/backend/open_webui/routers/openai.py b/backend/open_webui/routers/openai.py
-index 9c1325e..a19f128 100644
---- a/backend/open_webui/routers/openai.py
-+++ b/backend/open_webui/routers/openai.py
-@@ -10,7 +10,6 @@ from urllib.parse import quote, urlparse
- 
- import aiohttp
- from aiocache import cached
--from azure.identity import DefaultAzureCredential, get_bearer_token_provider
- from fastapi import APIRouter, Depends, HTTPException, Request, status
- from fastapi.responses import (
-     FileResponse,
-@@ -218,6 +217,8 @@ def get_microsoft_entra_id_access_token():
-     Get Microsoft Entra ID access token using DefaultAzureCredential for Azure OpenAI.
-     Returns the token string or None if authentication fails.
-     """
-+    from azure.identity import DefaultAzureCredential, get_bearer_token_provider
-+
-     try:
-         token_provider = get_bearer_token_provider(
-             DefaultAzureCredential(), 'https://cognitiveservices.azure.com/.default'

docker/web/openwebui-local-storage.patch

@@ -1,44 +0,0 @@
-diff --git a/backend/open_webui/storage/provider.py b/backend/open_webui/storage/provider.py
-index 261f010..048ea4b 100644
---- a/backend/open_webui/storage/provider.py
-+++ b/backend/open_webui/storage/provider.py
-@@ -6,14 +6,6 @@ import shutil
- from abc import ABC, abstractmethod
- from typing import BinaryIO, Dict, Tuple
- 
--import boto3
--from azure.core.exceptions import ResourceNotFoundError
--from azure.identity import DefaultAzureCredential
--from azure.storage.blob import BlobServiceClient
--from botocore.config import Config
--from botocore.exceptions import ClientError
--from google.cloud import storage
--from google.cloud.exceptions import GoogleCloudError, NotFound
- from open_webui.config import (
-     AZURE_STORAGE_CONTAINER_NAME,
-     AZURE_STORAGE_ENDPOINT,
-@@ -335,10 +327,24 @@ def get_storage_provider(storage_provider: str):
-     if storage_provider == 'local':
-         Storage = LocalStorageProvider()
-     elif storage_provider == 's3':
-+        global boto3, Config, ClientError
-+        import boto3
-+        from botocore.config import Config
-+        from botocore.exceptions import ClientError
-+
-         Storage = S3StorageProvider()
-     elif storage_provider == 'gcs':
-+        global storage, GoogleCloudError, NotFound
-+        from google.cloud import storage
-+        from google.cloud.exceptions import GoogleCloudError, NotFound
-+
-         Storage = GCSStorageProvider()
-     elif storage_provider == 'azure':
-+        global ResourceNotFoundError, DefaultAzureCredential, BlobServiceClient
-+        from azure.core.exceptions import ResourceNotFoundError
-+        from azure.identity import DefaultAzureCredential
-+        from azure.storage.blob import BlobServiceClient
-+
-         Storage = AzureStorageProvider()
-     else:
-         raise RuntimeError(f'Unsupported storage provider: {storage_provider}')

docker/web/openwebui-no-rag.patch

@@ -1,71 +0,0 @@
-diff --git a/backend/open_webui/retrieval/vector/dbs/disabled.py b/backend/open_webui/retrieval/vector/dbs/disabled.py
-new file mode 100644
-index 0000000..7e1ab23
---- /dev/null
-+++ b/backend/open_webui/retrieval/vector/dbs/disabled.py
-@@ -0,0 +1,50 @@
-+from typing import Dict, List, Optional, Union
-+
-+from open_webui.retrieval.vector.main import (
-+    GetResult,
-+    SearchResult,
-+    VectorDBBase,
-+    VectorItem,
-+)
-+
-+
-+class DisabledVectorClient(VectorDBBase):
-+    """No-op vector backend used when all RAG surfaces are disabled."""
-+
-+    def has_collection(self, collection_name: str) -> bool:
-+        return False
-+
-+    def delete_collection(self, collection_name: str) -> None:
-+        return None
-+
-+    def insert(self, collection_name: str, items: List[VectorItem]) -> None:
-+        raise RuntimeError("Vector storage is disabled")
-+
-+    def upsert(self, collection_name: str, items: List[VectorItem]) -> None:
-+        raise RuntimeError("Vector storage is disabled")
-+
-+    def search(
-+        self,
-+        collection_name: str,
-+        vectors: List[List[Union[float, int]]],
-+        filter: Optional[Dict] = None,
-+        limit: int = 10,
-+    ) -> Optional[SearchResult]:
-+        return None
-+
-+    def query(
-+        self,
-+        collection_name: str,
-+        filter: Dict,
-+        limit: Optional[int] = None,
-+    ) -> Optional[GetResult]:
-+        return None
-+
-+    def get(self, collection_name: str) -> Optional[GetResult]:
-+        return None
-+
-+    def delete(self, collection_name: str, ids=None, filter=None) -> None:
-+        return None
-+
-+    def reset(self) -> None:
-+        return None
-diff --git a/backend/open_webui/retrieval/vector/factory.py b/backend/open_webui/retrieval/vector/factory.py
-index af10c5d..35c3266 100644
---- a/backend/open_webui/retrieval/vector/factory.py
-+++ b/backend/open_webui/retrieval/vector/factory.py
-@@ -74,6 +74,10 @@ class Vector:
-             case VectorType.CHROMA:
-                 from open_webui.retrieval.vector.dbs.chroma import ChromaClient
- 
-                 return ChromaClient()
-+            case 'disabled':
-+                from open_webui.retrieval.vector.dbs.disabled import DisabledVectorClient
-+
-+                return DisabledVectorClient()
-             case VectorType.ORACLE23AI:
-                 from open_webui.retrieval.vector.dbs.oracle23ai import Oracle23aiClient

docker/web/openwebui-product-ui.patch

@@ -1,15 +0,0 @@
-diff --git a/src/routes/(app)/+layout.svelte b/src/routes/(app)/+layout.svelte
-index 95bf320e..461618ed 100644
---- a/src/routes/(app)/+layout.svelte
-+++ b/src/routes/(app)/+layout.svelte
-@@ -316,9 +316,7 @@
- 		};
- 		setupKeyboardShortcuts();
- 
--		if ($user?.role === 'admin' && ($settings?.showChangelog ?? true)) {
--			showChangelog.set($settings?.version !== $config.version);
--		}
-+		showChangelog.set(false);
- 
- 		if ($user?.role === 'admin' || ($user?.permissions?.chat?.temporary ?? true)) {
- 			if ($page.url.searchParams.get('temporary-chat') === 'true') {

docker/web/openwebui-workspace-router.patch

@@ -1,18 +0,0 @@
-diff --git a/backend/open_webui/main.py b/backend/open_webui/main.py
-index 6bab8a6..2a04f66 100644
---- a/backend/open_webui/main.py
-+++ b/backend/open_webui/main.py
-@@ -495,5 +495,6 @@ from open_webui.routers import (
-     images,
-     knowledge,
-+    k1412_workspace,
-     memories,
-     models,
-     notes,
-@@ -1423,5 +1424,6 @@ app.include_router(retrieval.router, prefix='/api/v1/retrieval', tags=['retrieva
- 
- app.include_router(configs.router, prefix='/api/v1/configs', tags=['configs'])
-+app.include_router(k1412_workspace.router, prefix='/api/v1/k1412/workspace', tags=['workspace'])
- 
- app.include_router(auths.router, prefix='/api/v1/auths', tags=['auths'])
- app.include_router(users.router, prefix='/api/v1/users', tags=['users'])

docker/workspace.Dockerfile

@@ -1,51 +0,0 @@
-FROM node:22-bookworm-slim@sha256:6c74791e557ce11fc957704f6d4fe134a7bc8d6f5ca4403205b2966bd488f6b3 AS node-runtime
-
-FROM python:3.12-slim-bookworm@sha256:d50fb7611f86d04a3b0471b46d7557818d88983fc3136726336b2a4c657aa30b
-
-ENV DEBIAN_FRONTEND=noninteractive
-
-RUN apt-get update && apt-get upgrade -y --no-install-recommends \
-    && apt-get install -y --no-install-recommends \
-      bash \
-      build-essential \
-      ca-certificates \
-      curl \
-      git \
-      jq \
-      less \
-      libatomic1 \
-      openssh-client \
-      patch \
-      ripgrep \
-      tini \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY --from=node-runtime /usr/local/bin/node /usr/local/bin/node
-COPY --from=node-runtime /usr/local/include/node /usr/local/include/node
-COPY --from=node-runtime /usr/local/lib/node_modules /usr/local/lib/node_modules
-RUN ln -s ../lib/node_modules/npm/bin/npm-cli.js /usr/local/bin/npm \
-    && ln -s ../lib/node_modules/npm/bin/npx-cli.js /usr/local/bin/npx \
-    && ln -s ../lib/node_modules/corepack/dist/corepack.js /usr/local/bin/corepack \
-    && npm install --global npm@12.0.1 \
-    && npm pack --silent brace-expansion@5.0.8 --pack-destination /tmp \
-    && rm -rf /usr/local/lib/node_modules/npm/node_modules/brace-expansion \
-    && mkdir -p /usr/local/lib/node_modules/npm/node_modules/brace-expansion \
-    && tar -xzf /tmp/brace-expansion-5.0.8.tgz \
-        -C /usr/local/lib/node_modules/npm/node_modules/brace-expansion \
-        --strip-components=1 \
-    && rm /tmp/brace-expansion-5.0.8.tgz \
-    && npm cache clean --force \
-    && node --version \
-    && npm --version
-
-RUN python -m pip install --no-cache-dir --upgrade pip==26.1.2
-
-RUN groupadd --gid 1000 agent \
-    && useradd --uid 1000 --gid 1000 --create-home --shell /bin/bash agent \
-    && mkdir -p /workspace \
-    && chown 1000:1000 /workspace
-
-WORKDIR /workspace
-USER 1000:1000
-ENTRYPOINT ["/usr/bin/tini", "--"]
-CMD ["sleep", "infinity"]

docs/architecture.md

@@ -1,74 +0,0 @@
-# Architecture
-
-## Single Agent path
-
-Open WebUI owns authenticated users, conversation history, and rendering.
-Every normal model request is delegated to the K1412 Runtime. There is no
-second Chat loop and no per-conversation mode state.
-
-## Agent loop
-
-The first strategy is intentionally modular:
-
-- `ModelProvider` owns provider transport.
-- `ContextPolicy` owns visible-message cleanup and compaction.
-- `ToolRegistry` owns tool schemas and dispatch.
-- `AgentLoop` owns iteration, safe parallel scheduling, and delegation.
-- `RuntimeStore` owns events, plans, and durable Agent memory.
-- `ExecutionProvider` owns local or SSH Docker execution.
-
-Every run records its model ID, strategy version, scheduler version, context
-policy, model/tool events, failures, and completion. This event stream is the
-foundation for replay, evaluation, and future experiment assignment.
-
-Luna, Terra, and Sol are three independent Ollama models. Each advertises
-boolean thinking support, but none advertises adjustable reasoning effort.
-DeepSeek V4 Pro is a fourth model configured with thinking enabled and maximum
-reasoning effort. Provider selection and credentials remain server-side.
-Thinking state is preserved across tool turns when the provider requires the
-assistant's `reasoning_content` to be returned with following tool results.
-The Agent never publishes hidden reasoning as its own user-facing output.
-
-Completion is evidence-gated. Requests for scripts, reports, code, or
-other concrete artifacts are not accepted as complete until a file mutation
-has succeeded and a later read, diff, or command has verified the latest
-mutation. Bare interactive shell commands are rejected. If the iteration
-budget is exhausted without evidence, Runtime returns an explicit incomplete
-checkpoint instead of repeating an unverified model claim.
-
-Read-only tools and read-only child Agents may run concurrently. Workspace
-mutations are serialized. A child Agent cannot delegate again, and read-only
-children receive only read-only tools.
-
-## Execution providers
-
-`local-docker` connects the Workspace Gateway to the local Docker daemon.
-`ssh-docker` connects the same Docker SDK operations to a remote physical
-machine through SSH. Both create the same workspace image, persistent per-user
-volume, and dedicated per-user bridge network, so moving execution off the web
-host changes configuration rather than Agent behavior.
-
-For SSH mode, layer `compose.ssh.yaml` over the development Compose file, or
-`deploy/docker-compose.ssh.yml` over the two production Compose files. The
-override removes the local Docker socket and mounts only the selected SSH
-configuration directory read-only.
-
-Only Open WebUI publishes a host port. Runtime, Gateway, Postgres, and Redis
-remain unpublishing services. Runtime and Gateway also join an egress-only
-bridge so the model API and a future SSH Docker host are reachable without
-exposing either service on the host.
-
-## Workspace file path
-
-```text
-browser with Open WebUI session
-  -> /api/v1/k1412/workspace/*
-  -> Open WebUI backend verifies the user
-  -> backend mints a short-lived signed identity JWT
-  -> Workspace Gateway verifies service key + user JWT
-  -> provider selects only that user's hashed container and volume
-```
-
-The file API exposes read-only browse, file download, and streamed directory
-archive operations. Paths pass through the same `/workspace` normalization as
-Agent tools. The browser never talks to Gateway or Docker directly.

docs/assets/planning-data-driven-loop-v1.svg

@@ -0,0 +1,148 @@
+<svg width="1600" height="900" viewBox="0 0 1600 900" xmlns="http://www.w3.org/2000/svg">
+  <defs>
+    <marker id="arrow" markerWidth="12" markerHeight="12" refX="10" refY="6" orient="auto">
+      <path d="M2,2 L10,6 L2,10 Z" fill="#64748b"/>
+    </marker>
+    <marker id="arrowBlue" markerWidth="12" markerHeight="12" refX="10" refY="6" orient="auto">
+      <path d="M2,2 L10,6 L2,10 Z" fill="#2563eb"/>
+    </marker>
+    <filter id="softShadow" x="-10%" y="-10%" width="120%" height="130%">
+      <feDropShadow dx="0" dy="8" stdDeviation="10" flood-color="#0f172a" flood-opacity="0.10"/>
+    </filter>
+    <style>
+      .title { font: 700 38px -apple-system, BlinkMacSystemFont, "PingFang SC", "Microsoft YaHei", sans-serif; fill: #0f172a; }
+      .subtitle { font: 400 20px -apple-system, BlinkMacSystemFont, "PingFang SC", "Microsoft YaHei", sans-serif; fill: #475569; }
+      .sectionTitle { font: 700 22px -apple-system, BlinkMacSystemFont, "PingFang SC", "Microsoft YaHei", sans-serif; fill: #0f172a; }
+      .stepNo { font: 700 17px -apple-system, BlinkMacSystemFont, "PingFang SC", "Microsoft YaHei", sans-serif; fill: #64748b; }
+      .stepTitle { font: 700 19px -apple-system, BlinkMacSystemFont, "PingFang SC", "Microsoft YaHei", sans-serif; fill: #0f172a; }
+      .stepText { font: 400 14px -apple-system, BlinkMacSystemFont, "PingFang SC", "Microsoft YaHei", sans-serif; fill: #475569; }
+      .badge { font: 700 13px -apple-system, BlinkMacSystemFont, "PingFang SC", "Microsoft YaHei", sans-serif; fill: #9a3412; }
+      .baseTitle { font: 700 24px -apple-system, BlinkMacSystemFont, "PingFang SC", "Microsoft YaHei", sans-serif; fill: #0f172a; }
+      .baseMain { font: 700 28px -apple-system, BlinkMacSystemFont, "PingFang SC", "Microsoft YaHei", sans-serif; fill: #1e293b; }
+      .baseSub { font: 400 18px -apple-system, BlinkMacSystemFont, "PingFang SC", "Microsoft YaHei", sans-serif; fill: #475569; }
+      .chip { font: 600 15px -apple-system, BlinkMacSystemFont, "PingFang SC", "Microsoft YaHei", sans-serif; fill: #334155; }
+      .loopLabel { font: 600 17px -apple-system, BlinkMacSystemFont, "PingFang SC", "Microsoft YaHei", sans-serif; fill: #2563eb; }
+    </style>
+  </defs>
+
+  <rect width="1600" height="900" fill="#f8fafc"/>
+
+  <text x="800" y="72" text-anchor="middle" class="title">planning 模型数据驱动迭代链路</text>
+  <text x="800" y="108" text-anchor="middle" class="subtitle">从问题发现到模型改进：数据开发 + AutoResearch + Agent 平台底座的全流程提效</text>
+
+  <!-- Main entry: problem discovery -->
+  <g filter="url(#softShadow)">
+    <rect x="68" y="220" width="190" height="130" rx="18" fill="#ffffff" stroke="#dbeafe" stroke-width="2"/>
+    <text x="95" y="253" class="stepNo">1</text>
+    <text x="163" y="283" text-anchor="middle" class="stepTitle">问题发现</text>
+    <text x="163" y="314" text-anchor="middle" class="stepText">线上 badcase</text>
+    <text x="163" y="337" text-anchor="middle" class="stepText">评测错误 / 指标异常</text>
+  </g>
+
+  <!-- Data development module -->
+  <g filter="url(#softShadow)">
+    <rect x="300" y="170" width="690" height="230" rx="24" fill="#eef6ff" stroke="#bfdbfe" stroke-width="2"/>
+    <text x="645" y="205" text-anchor="middle" class="sectionTitle">数据开发提效</text>
+    <text x="645" y="232" text-anchor="middle" class="stepText">把问题转成可训练、可评测的数据资产</text>
+  </g>
+
+  <!-- AutoResearch module -->
+  <g filter="url(#softShadow)">
+    <rect x="1040" y="170" width="455" height="230" rx="24" fill="#f5f3ff" stroke="#ddd6fe" stroke-width="2"/>
+    <text x="1267" y="205" text-anchor="middle" class="sectionTitle">AutoResearch</text>
+    <text x="1267" y="232" text-anchor="middle" class="stepText">planning 模型迭代与结构分析</text>
+  </g>
+
+  <!-- Step boxes: data development -->
+  <g>
+    <rect x="330" y="265" width="140" height="95" rx="16" fill="#ffffff" stroke="#bfdbfe" stroke-width="1.5"/>
+    <text x="350" y="292" class="stepNo">2</text>
+    <text x="400" y="316" text-anchor="middle" class="stepTitle">线上数据挖掘</text>
+    <text x="400" y="342" text-anchor="middle" class="stepText">rid / session</text>
+    <text x="400" y="362" text-anchor="middle" class="stepText">prompt / output</text>
+  </g>
+
+  <g>
+    <rect x="500" y="265" width="150" height="95" rx="16" fill="#fff7ed" stroke="#fed7aa" stroke-width="1.8"/>
+    <text x="520" y="292" class="stepNo">3</text>
+    <text x="575" y="316" text-anchor="middle" class="stepTitle">问题分析</text>
+    <text x="575" y="342" text-anchor="middle" class="stepText">归因 / 标签边界</text>
+    <rect x="536" y="352" width="78" height="24" rx="12" fill="#ffedd5" stroke="#fdba74"/>
+    <text x="575" y="369" text-anchor="middle" class="badge">人工 Review</text>
+  </g>
+
+  <g>
+    <rect x="680" y="265" width="140" height="95" rx="16" fill="#ffffff" stroke="#bfdbfe" stroke-width="1.5"/>
+    <text x="700" y="292" class="stepNo">4</text>
+    <text x="750" y="316" text-anchor="middle" class="stepTitle">数据构造</text>
+    <text x="750" y="342" text-anchor="middle" class="stepText">训练样本</text>
+    <text x="750" y="362" text-anchor="middle" class="stepText">评测样本 / 边界样例</text>
+  </g>
+
+  <g>
+    <rect x="850" y="265" width="115" height="95" rx="16" fill="#ffffff" stroke="#bfdbfe" stroke-width="1.5"/>
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+    <text x="907" y="316" text-anchor="middle" class="stepTitle">格式转换</text>
+    <text x="907" y="342" text-anchor="middle" class="stepText">CSV / JSONL</text>
+    <text x="907" y="362" text-anchor="middle" class="stepText">eval set</text>
+  </g>
+
+  <!-- Step boxes: autoresearch -->
+  <g>
+    <rect x="1072" y="265" width="155" height="95" rx="16" fill="#ffffff" stroke="#ddd6fe" stroke-width="1.5"/>
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+    <text x="1149" y="316" text-anchor="middle" class="stepTitle">planning 模型迭代</text>
+    <text x="1149" y="342" text-anchor="middle" class="stepText">训练 / 评测</text>
+    <text x="1149" y="362" text-anchor="middle" class="stepText">配置实验</text>
+  </g>
+
+  <g>
+    <rect x="1262" y="265" width="200" height="95" rx="16" fill="#fff7ed" stroke="#fed7aa" stroke-width="1.8"/>
+    <text x="1282" y="292" class="stepNo">7</text>
+    <text x="1362" y="316" text-anchor="middle" class="stepTitle">结构分析 / 结果分析</text>
+    <text x="1362" y="342" text-anchor="middle" class="stepText">指标拆解 / 回归验证</text>
+    <rect x="1323" y="352" width="78" height="24" rx="12" fill="#ffedd5" stroke="#fdba74"/>
+    <text x="1362" y="369" text-anchor="middle" class="badge">人工 Review</text>
+  </g>
+
+  <!-- Straight arrows -->
+  <path d="M258 285 L318 285" stroke="#64748b" stroke-width="3" fill="none" marker-end="url(#arrow)"/>
+  <path d="M470 312 L492 312" stroke="#64748b" stroke-width="3" fill="none" marker-end="url(#arrow)"/>
+  <path d="M650 312 L672 312" stroke="#64748b" stroke-width="3" fill="none" marker-end="url(#arrow)"/>
+  <path d="M820 312 L842 312" stroke="#64748b" stroke-width="3" fill="none" marker-end="url(#arrow)"/>
+  <path d="M965 312 L1064 312" stroke="#64748b" stroke-width="3" fill="none" marker-end="url(#arrow)"/>
+  <path d="M1227 312 L1254 312" stroke="#64748b" stroke-width="3" fill="none" marker-end="url(#arrow)"/>
+
+  <!-- Feedback loop -->
+  <path d="M1362 402 C1362 500 1070 505 820 505 C465 505 163 485 163 365" stroke="#2563eb" stroke-width="3" stroke-dasharray="8 8" fill="none" marker-end="url(#arrowBlue)"/>
+  <text x="760" y="486" text-anchor="middle" class="loopLabel">新 badcase / 新边界 / 新样本 / 新策略回流</text>
+
+  <!-- Platform base -->
+  <g filter="url(#softShadow)">
+    <rect x="90" y="570" width="1410" height="210" rx="28" fill="#ffffff" stroke="#cbd5e1" stroke-width="2"/>
+    <text x="795" y="615" text-anchor="middle" class="baseTitle">ZK Data Agent 平台底座</text>
+    <text x="795" y="663" text-anchor="middle" class="baseMain">Agent Loop + Skill + 工具链 + 记忆 + Jupyter 工作区</text>
+    <text x="795" y="699" text-anchor="middle" class="baseSub">通过 Agent Loop 调度 Skill 和工具，连接线上数据、执行环境与产物沉淀</text>
+
+    <g>
+      <rect x="205" y="725" width="140" height="34" rx="17" fill="#f1f5f9"/>
+      <text x="275" y="748" text-anchor="middle" class="chip">线上日志工具</text>
+      <rect x="375" y="725" width="120" height="34" rx="17" fill="#f1f5f9"/>
+      <text x="435" y="748" text-anchor="middle" class="chip">标签知识</text>
+      <rect x="525" y="725" width="150" height="34" rx="17" fill="#f1f5f9"/>
+      <text x="600" y="748" text-anchor="middle" class="chip">数据格式转换</text>
+      <rect x="705" y="725" width="120" height="34" rx="17" fill="#f1f5f9"/>
+      <text x="765" y="748" text-anchor="middle" class="chip">会话沉淀</text>
+      <rect x="855" y="725" width="130" height="34" rx="17" fill="#f1f5f9"/>
+      <text x="920" y="748" text-anchor="middle" class="chip">运行态观测</text>
+      <rect x="1015" y="725" width="155" height="34" rx="17" fill="#f1f5f9"/>
+      <text x="1092" y="748" text-anchor="middle" class="chip">账号 / 工作区隔离</text>
+      <rect x="1200" y="725" width="160" height="34" rx="17" fill="#f1f5f9"/>
+      <text x="1280" y="748" text-anchor="middle" class="chip">训练评测工具链</text>
+    </g>
+  </g>
+
+  <!-- Thin connector from business flow to base -->
+  <path d="M795 526 L795 565" stroke="#94a3b8" stroke-width="2" stroke-dasharray="5 6"/>
+  <text x="825" y="548" class="stepText">平台能力支撑全链路</text>
+</svg>

docs/assets/planning-data-driven-loop-v2.svg

@@ -0,0 +1,133 @@
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+  <rect width="1600" height="900" fill="#f8fafc"/>
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+  <text x="800" y="76" text-anchor="middle" class="title">planning 模型数据驱动迭代链路</text>
+  <text x="800" y="112" text-anchor="middle" class="subtitle">从问题发现到模型改进：数据开发 + AutoResearch 的全流程提效</text>
+
+  <!-- Step 1 -->
+  <g filter="url(#softShadow)">
+    <rect x="70" y="265" width="185" height="120" rx="18" fill="#ffffff" stroke="#dbeafe" stroke-width="2"/>
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+    <text x="162" y="351" text-anchor="middle" class="stepText">线上 badcase</text>
+    <text x="162" y="373" text-anchor="middle" class="stepText">评测错误 / 指标异常</text>
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+  <!-- Data development module -->
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+    <text x="620" y="252" text-anchor="middle" class="sectionSub">把问题转成可训练、可评测的数据资产</text>
+  </g>
+
+  <!-- Data development steps -->
+  <g>
+    <rect x="335" y="300" width="150" height="105" rx="16" fill="#ffffff" stroke="#bfdbfe" stroke-width="1.5"/>
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+    <text x="410" y="379" text-anchor="middle" class="stepText">rid / session</text>
+    <text x="410" y="399" text-anchor="middle" class="stepText">prompt / output</text>
+  </g>
+
+  <g>
+    <rect x="535" y="300" width="155" height="105" rx="16" fill="#fff7ed" stroke="#fed7aa" stroke-width="1.8"/>
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+    <text x="612" y="352" text-anchor="middle" class="stepTitle">问题分析</text>
+    <text x="612" y="379" text-anchor="middle" class="stepText">归因 / 标签边界</text>
+    <rect x="573" y="389" width="78" height="24" rx="12" fill="#ffedd5" stroke="#fdba74"/>
+    <text x="612" y="406" text-anchor="middle" class="badge">人工 Review</text>
+  </g>
+
+  <g>
+    <rect x="740" y="300" width="165" height="105" rx="16" fill="#ffffff" stroke="#bfdbfe" stroke-width="1.5"/>
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+    <text x="822" y="352" text-anchor="middle" class="stepTitle">数据构造</text>
+    <text x="822" y="379" text-anchor="middle" class="stepText">样本生成 / 格式转换</text>
+    <text x="822" y="399" text-anchor="middle" class="stepText">训练集 / 评测集</text>
+  </g>
+
+  <!-- AutoResearch module -->
+  <g filter="url(#softShadow)">
+    <rect x="990" y="190" width="450" height="250" rx="24" fill="#f5f3ff" stroke="#ddd6fe" stroke-width="2"/>
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+    <text x="1215" y="252" text-anchor="middle" class="sectionSub">planning 模型迭代与结构分析</text>
+  </g>
+
+  <!-- AutoResearch steps -->
+  <g>
+    <rect x="1028" y="300" width="165" height="105" rx="16" fill="#ffffff" stroke="#ddd6fe" stroke-width="1.5"/>
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+    <text x="1110" y="352" text-anchor="middle" class="stepTitle">planning 模型迭代</text>
+    <text x="1110" y="379" text-anchor="middle" class="stepText">训练 / 评测</text>
+    <text x="1110" y="399" text-anchor="middle" class="stepText">配置实验</text>
+  </g>
+
+  <g>
+    <rect x="1248" y="300" width="160" height="105" rx="16" fill="#fff7ed" stroke="#fed7aa" stroke-width="1.8"/>
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+    <text x="1328" y="352" text-anchor="middle" class="stepTitle">结构分析 / 结果分析</text>
+    <text x="1328" y="379" text-anchor="middle" class="stepText">指标拆解 / 回归验证</text>
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+  </g>
+
+  <!-- Step 7 online -->
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+    <text x="1152" y="550" class="stepNo">7</text>
+    <text x="1215" y="576" text-anchor="middle" class="stepTitle">模型上线</text>
+    <text x="1215" y="604" text-anchor="middle" class="stepText">发布 / 观察 / 回流</text>
+  </g>
+
+  <!-- Main arrows -->
+  <path d="M255 325 L326 325" stroke="#64748b" stroke-width="3" fill="none" marker-end="url(#arrow)"/>
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+  <path d="M690 352 L731 352" stroke="#64748b" stroke-width="3" fill="none" marker-end="url(#arrow)"/>
+  <path d="M905 352 L1019 352" stroke="#64748b" stroke-width="3" fill="none" marker-end="url(#arrow)"/>
+
+  <!-- Iteration loop between 5 and 6 -->
+  <path d="M1195 325 C1222 292 1250 292 1275 319" stroke="#7c3aed" stroke-width="3" fill="none" marker-end="url(#arrowPurple)"/>
+  <path d="M1276 385 C1248 424 1218 424 1194 391" stroke="#7c3aed" stroke-width="3" fill="none" marker-end="url(#arrowPurple)"/>
+  <text x="1218" y="286" text-anchor="middle" class="iterLabel">循环迭代</text>
+
+  <!-- From 6 to 7 -->
+  <path d="M1328 405 C1328 480 1260 498 1225 514" stroke="#64748b" stroke-width="3" fill="none" marker-end="url(#arrow)"/>
+
+  <!-- Feedback loop from 7 to 1 -->
+  <path d="M1128 570 C920 690 520 690 270 585 C175 545 142 470 154 395" stroke="#2563eb" stroke-width="3" stroke-dasharray="8 8" fill="none" marker-end="url(#arrowBlue)"/>
+  <text x="650" y="660" text-anchor="middle" class="loopLabel">新 badcase / 新边界 / 新样本 / 新策略回流</text>
+
+  <!-- Light platform support bar -->
+  <g>
+    <rect x="140" y="752" width="1320" height="58" rx="18" fill="#f1f5f9" stroke="#e2e8f0" stroke-width="1"/>
+    <text x="800" y="788" text-anchor="middle" class="base">ZK Data Agent 支撑：通过 Agent Loop 调度 Skill 和工具，连接线上数据、执行环境与产物沉淀</text>
+  </g>
+</svg>

docs/infrastructure.md

@@ -1,76 +0,0 @@
-# Infrastructure map
-
-This document separates shared infrastructure from the parts that define
-K1412 Agent behavior.
-
-## Request path
-
-```text
-Internet
-  -> DNS / TLS
-  -> Nginx Proxy Manager
-  -> Unraid NAS : K1412 web stack
-  -> Tailscale + SSH
-  -> home-node-itx : per-user Docker workspaces
-```
-
-## Shared infrastructure
-
-These services make the application reachable or deployable, but changing
-them does not change the Agent loop:
-
-| Service | Responsibility |
-| --- | --- |
-| `git.k1412.top` | Public source repository |
-| `docker.k1412.top` | Private immutable container images |
-| Nginx Proxy Manager | HTTPS entry and reverse proxy |
-| k1412 homepage | Service discovery entry |
-| Tailscale | Private NAS-to-execution-host network |
-| Unraid Compose Manager | Application lifecycle on the NAS |
-| Ollama + 32 GB NVIDIA GPU | Luna, Terra, and Sol model serving |
-
-Other NAS applications are independent tenants. They are not dependencies of
-K1412 Agent merely because they share the host.
-
-## K1412 platform services on the NAS
-
-| Container | Responsibility | Agent core? |
-| --- | --- | --- |
-| `k1412-agent-web` | Open WebUI auth, RBAC, history, and UI | Product shell |
-| `k1412-agent-runtime` | Agent loop and provider gateway | Yes |
-| `k1412-agent-gateway` | Identity, execution policy, workspace lifecycle | Yes |
-| `k1412-agent-postgres` | Open WebUI and Runtime durable state | State |
-| `k1412-agent-redis` | Open WebUI coordination/cache | State |
-| `k1412-agent-bootstrap` | Idempotent admin/tool bootstrap job | Operations |
-
-Only Web publishes a host port. Runtime, Gateway, PostgreSQL, and Redis stay on
-private Compose networks.
-
-## Physical execution host
-
-`home-node-itx` is a dedicated, replaceable Docker execution node:
-
-- Intel Core i3-12100F, 4 cores / 8 threads;
-- approximately 14 GiB usable RAM;
-- 120 GB NVMe system disk;
-- 500 GB SSD formatted ext4 and mounted at `/srv/k1412-data`;
-- Docker root at `/srv/k1412-data/docker`;
-- user workspace volumes stored on the same data disk.
-
-The NAS Gateway connects with a dedicated Ed25519 key and strict host-key
-checking. The execution host stores no web/database secrets. Replacing it
-requires migrating Docker volumes and changing SSH configuration, not changing
-the Agent loop.
-
-## Agent core
-
-The code intended for continuous experimentation is:
-
-- `agent_platform/runtime/loop.py`: loop and evidence policy;
-- `agent_platform/runtime/context.py`: context policy;
-- `agent_platform/runtime/tools.py`: tools and scheduling metadata;
-- `agent_platform/store.py`: events, plans, and memory;
-- `agent_platform/gateway/`: isolated execution providers.
-
-Frontend branding, reverse proxy configuration, and registry automation are
-important product/operations work, but they are not Agent intelligence.

docs/openwebui-integration.md

@@ -1,58 +0,0 @@
-# Open WebUI integration
-
-## Responsibility split
-
-Open WebUI is the product shell. It owns account registration, login sessions,
-RBAC, administrator approval, conversation history, and rendering. K1412 does
-not fork those responsibilities into a second authentication or chat database.
-
-K1412 Runtime owns the only user-facing Agent loop: context selection, durable
-memory, planning, tool schemas, scheduling, parallel reads, child Agents,
-evidence gating, experiments, and run-event persistence. Workspace Gateway
-owns identity-bound execution policy and Docker lifecycle.
-
-This split makes the expensive experimental surface small. A new scheduler or
-context policy changes Runtime, not the account system or entire frontend.
-
-## Tool and progress rendering
-
-Open WebUI natively understands tool-call and reasoning detail blocks. It also
-has a richer live status mechanism for loops that execute inside its own
-backend. K1412 Runtime is an external OpenAI-compatible provider, so it cannot mutate
-Open WebUI's internal socket status objects directly.
-
-K1412 therefore emits Open WebUI-compatible completed tool detail blocks. Each
-tool appears once with:
-
-- a localized action name;
-- sanitized arguments (file contents and patches are omitted);
-- success or failure state;
-- concise plain output instead of the internal JSON envelope.
-
-K1412 deliberately does not expose hidden chain-of-thought. The UI shows
-execution progress, tool evidence, completion checks, and concise Agent
-summaries. This is enough to inspect behavior without treating private model
-reasoning as a product API.
-
-The earlier implementation appended one unfinished and one completed
-`<details>` element for every tool call. Since standard provider streaming is
-append-only, Open WebUI rendered them as duplicate cards. Runtime now emits
-only the completed card; the normal generation indicator covers the active
-wait.
-
-## Patches and upgrade cost
-
-Open WebUI is pinned to v0.9.6 and an exact commit. The web image applies a
-small patch set during a reproducible build. Product-specific Svelte
-components and the authenticated workspace router live under `docker/web/`.
-
-Upgrading Open WebUI requires:
-
-1. updating the tag and commit pin;
-2. replaying every patch with `git apply`;
-3. building the Svelte production bundle;
-4. running authentication, Agent-loop, isolation, and file-browser E2E tests;
-5. reviewing upstream license changes.
-
-The K1412 Agent loop is not embedded into Open WebUI, so most Agent experiments
-do not incur this upgrade cost.

docs/operations.md

@@ -1,124 +0,0 @@
-# Operations runbook
-
-## Production layout
-
-- Public URL: `https://agent.k1412.top`
-- NAS project: `/boot/config/plugins/compose.manager/projects/k1412-agent`
-- Public host port: `12004`
-- Execution provider: `ssh-docker`
-- Execution host Docker root: `/srv/k1412-data/docker`
-- Runtime model API route: `http://172.31.0.1:3000` (NAS host gateway
-  from the `k1412-agent_egress` network, bypassing the public proxy)
-- Execution host workspace volumes: Docker volumes named
-  `k1412-ws-data-<hashed-user-id>`
-
-No credential belongs in this document or repository.
-
-## Deployment
-
-Build and push immutable `linux/amd64` images:
-
-```bash
-docker buildx build --platform linux/amd64 -f docker/web.Dockerfile \
-  -t docker.k1412.top/wuyang/k1412-agent-web:<release> --push .
-docker buildx build --platform linux/amd64 -f docker/runtime.Dockerfile \
-  -t docker.k1412.top/wuyang/k1412-agent-runtime:<release> --push .
-docker buildx build --platform linux/amd64 -f docker/gateway.Dockerfile \
-  -t docker.k1412.top/wuyang/k1412-agent-gateway:<release> --push .
-docker buildx build --platform linux/amd64 -f docker/workspace.Dockerfile \
-  -t docker.k1412.top/wuyang/k1412-agent-workspace:<release> --push .
-```
-
-Copy `deploy/docker-compose.yml`, `deploy/docker-compose.override.yml`, and
-`deploy/docker-compose.ssh.yml` to the NAS project. Update only immutable image
-tags and non-secret settings in the protected `.env`, validate with
-`docker compose config`, pull, and recreate.
-
-On the NAS deployment, set `MODEL_API_BASE_URL` to the internal model API route
-listed above. `https://api.k1412.top` remains the external API entrypoint, but
-Runtime must not send long-running Agent inference through the public reverse
-proxy.
-
-Set `DEEPSEEK_API_BASE_URL=https://api.deepseek.com` and place
-`DEEPSEEK_API_KEY` only in the protected deployment `.env`. The key is used
-only by Runtime for DeepSeek V4 Pro and must never be added to a browser,
-Compose file, image, repository, or log.
-
-The SSH override removes `/var/run/docker.sock` from Gateway and mounts the
-dedicated SSH directory read-only at `/root/.ssh`.
-
-## Ollama model residency
-
-The NAS Ollama service keeps Luna, Terra, and Sol resident with:
-
-- `OLLAMA_KEEP_ALIVE=-1`;
-- `OLLAMA_MAX_LOADED_MODELS=3`;
-- `OLLAMA_NUM_PARALLEL=1`;
-- `OLLAMA_GPU_OVERHEAD=2147483648`;
-- Flash Attention and `q8_0` KV cache enabled.
-
-The three 32K-context runners were measured at approximately 2.88 GB, 4.12 GB,
-and 18.44 GB of GPU allocation respectively. Their combined allocation is
-about 25.44 GB on the 32 GB GPU. Do not increase context size or per-model
-parallelism without repeating the simultaneous-load test and leaving capacity
-for inference overhead.
-
-The persistent Unraid template is
-`/boot/config/plugins/dockerMan/templates-user/my-ollama.xml`. A locked,
-idempotent five-minute check in
-`/boot/config/plugins/dynamix/k1412-ollama.cron` prewarms only missing models
-after a host or container restart. Confirm `/api/ps` lists all three with an
-infinite expiry after recovery.
-
-## Workspace migration
-
-Before changing execution hosts:
-
-1. stop Gateway so no new workspace mutation can begin;
-2. enumerate containers and volumes with the
-   `app.k1412.component=user-workspace` label/name prefix;
-3. stop each workspace container;
-4. stream a tar archive of each named volume to an identically named volume on
-   the new Docker host;
-5. pull the exact workspace image on the new host;
-6. start Gateway with the new SSH target;
-7. verify file listing and one read for every migrated volume;
-8. retain the old volumes until the verification window ends.
-
-Volume names contain only a user-ID hash, not email or display name.
-
-## Verification
-
-For every release:
-
-```bash
-.venv/bin/pytest
-RUN_DOCKER_INTEGRATION=1 .venv/bin/pytest tests/test_docker_workspace.py
-./scripts/verify-e2e.sh
-./scripts/audit-images.sh
-```
-
-Production smoke checks must cover:
-
-- anonymous requests are redirected/rejected;
-- an approved user sees exactly four Agent models;
-- each model routes through the custom Agent loop;
-- the Agent creates and verifies a real file;
-- the file browser lists and downloads that file;
-- a second user cannot see it;
-- Gateway health reports `ssh-docker`;
-- the workspace container exists on the physical execution host, not the NAS.
-
-## Backup priorities
-
-Back up:
-
-1. PostgreSQL;
-2. the Open WebUI data volume;
-3. Runtime state in PostgreSQL;
-4. per-user workspace volumes on the execution host;
-5. the protected deployment `.env` and SSH directory through the private
-   infrastructure backup process.
-
-Redis is reconstructible coordination state and is lower priority than the
-database and workspace volumes.

docs/security.md

@@ -1,73 +0,0 @@
-# Security model
-
-## Trust boundaries
-
-Only Open WebUI is public. Runtime, Gateway, PostgreSQL, and Redis are on an
-internal Compose network.
-
-Open WebUI forwards a short-lived HS256 user JWT. Runtime and Gateway verify
-the signature, issuer, expiry, and subject. They also require independent
-service bearer keys, so a copied user identity token alone cannot call either
-service.
-
-The browser never receives:
-
-- the upstream provider URL or API key;
-- provider model IDs;
-- the Workspace Gateway service key;
-- the user identity signing key;
-- Docker, SSH, MCP/OpenAPI, prompt, or experiment configuration.
-
-## Workspace isolation
-
-Each user gets a dedicated container and named volume derived from a
-SHA-256 hash of the immutable Open WebUI user ID. Containers:
-
-- run as UID/GID 1000;
-- have a read-only root filesystem and writable `/workspace` volume;
-- use a dedicated per-user bridge network when egress is enabled;
-- drop every Linux capability;
-- enable `no-new-privileges`;
-- receive memory, CPU, and PID limits;
-- do not receive the Docker socket;
-- expose no ports to the host.
-
-Only Workspace Gateway receives the Docker socket in local mode. Runtime and
-Open WebUI never receive it. Remote mode removes the socket mount, mounts the
-chosen SSH configuration read-only, and moves Docker access to an SSH-connected
-execution host.
-
-Workspace paths are normalized and rejected if they escape `/workspace`.
-File browsing and downloads use the same identity boundary. Single-file
-downloads are bounded to 128 MiB by default; directory archives are streamed.
-
-## Deployment secrets
-
-Secrets live only in a mode-600 deployment `.env` outside Git. Images contain
-no provider or infrastructure credentials. Provider credentials are never
-returned to the browser, logged by application code, or committed to Git.
-
-The Web image is rebuilt from a commit-pinned Open WebUI release on
-digest-pinned Node and Python bases. RAG/vector, cloud storage, media model,
-browser automation, and unused cryptographic dependencies are excluded.
-Release acceptance audits both the repository environment and the finished Web
-image. `scripts/audit-web-image.sh` requires:
-
-- `pip check` to report a consistent Python environment;
-- `pip-audit` to report zero known Python vulnerabilities, with no ignored IDs;
-- Trivy to report zero fixable High/Critical OS or library vulnerabilities.
-
-`scripts/audit-images.sh` applies the fixable High/Critical gate to Web,
-Runtime, Gateway, and the user Workspace image. As of 2026-07-26 all four pass.
-Trivy also reports 38 High/Critical Debian findings in the Web image for which
-the distribution publishes no fix; the release gate records these separately
-through `--ignore-unfixed` rather than pretending an application change can
-remediate them.
-
-## Remaining hardening before hostile public use
-
-Workspaces run on a dedicated physical Docker host, separated from the public
-web and database services. The Docker daemon remains a high-value boundary.
-Before treating the service as hostile multi-tenant infrastructure, add
-per-workspace egress policy, image signing, central audit retention,
-backup/restore drills, quotas, and resource-abuse alerts.

docs/share-report-v1.md

@@ -0,0 +1,145 @@
+# 从个人 Agent 到团队 Agent：沉淀可复用的 AI 提效探索
+
+## 平台地址
+
+- 平台入口：http://10.189.47.6/
+- 平台文档：http://10.189.47.6/doc
+
+## Git 地址
+
+- 仓库：https://git.n.xiaomi.com/wuyang6/zk-data-agent.git
+
+现有 Skill 数量 12 个，涵盖线上数据挖掘、数据生成、日志分析、标签知识、模型打标、训练发起 / 评测等业务功能，提升组内工作效率。
+
+## 1. 为什么要做这个
+
+个人 AI 提效已经发生了，下一步要解决的是团队复用和流程沉淀。
+
+[图片]
+
+最初的目标，是提升中控 planning 模型迭代中的数据开发效率。
+
+- 日志挖掘、问题分析、数据构造、数据格式化....
+
+做的过程中发现，我们发现低效并不只是某个步骤慢，而是三类能力分散在个人手里：
+
+- 业务知识分散：哪些日志表可查、哪些字段有用、标签边界怎么判断、问题应该怎么归因。
+- 执行工具分散：脚本、参数、路径、格式转换逻辑和产物规范各自维护。
+- AI 经验分散：有效的 AI 流程、会话历史、修正经验、个人偏好难以共享和继承。
+
+类似情况也存在于评测、训练、结果分析、问题排查、线上配置修改等工作中，用于数据开发提效的逻辑也同样适用...
+
+所以，这个项目逐渐从一个数据开发提效实践，演化成一个中控数据 Agent：把已经验证有效的业务知识、执行工具和 AI 经验，整理成可以共享、组合和继续迭代的 Skill 与工具链。
+
+## 2. 目前做了什么
+
+中控数据 Agent 以 Agent Loop 作为执行底座，以业务 Skill 作为核心组织方式，再通过工作区、工具链和记忆机制，把个人经验沉淀成团队可以继续使用和改造的能力。
+
+系统的架构：http://10.189.47.6/doc
+
+[图片]
+
+### 2.1 Skill 热更新与同步
+
+平台支持在页面中更新 Skill：当有人新增或修改 Skill 后，可以通过 Skill 面板同步到当前服务，让新的能力进入模型可见列表。这样业务能力可以跟随实际需求快速迭代，不需要每次都改核心 Agent 代码。
+
+这一点适合团队协作：
+
+- 个人可以先在自己的分支里开发 Skill；
+- 稳定后同步到平台；
+- 其他人可以直接启用和复用；
+- Skill 可以继续被修改、补充和演进。
+
+[图片]
+
+### 2.2 记忆机制
+
+平台目前把记忆分成两类：用户记忆和 Skill 记忆。
+
+- 用户记忆：记录个人偏好、常用环境、输出习惯等。
+- Skill 记忆：记录某个 Skill 使用过程中积累的经验、边界和常见坑。
+
+这类记忆可以在页面中查看和编辑。使用次数越多，某个 Skill 相关的经验越容易被沉淀下来，后续类似任务可以少重复解释。
+
+和常见的“全局用户偏好记忆”相比，这里的重点是把记忆绑定到业务能力上。
+
+例如线上挖掘中，哪些字段更可靠、session 应该怎么抽、哪些输出格式容易出错，这些经验更适合沉淀到对应 Skill，而不是混在一个全局记忆里。
+
+[图片]
+
+典型记忆方法的对比：
+
+| 记忆方案 | 主要解决什么 | 典型做法 | 对我们的启发 | ZK Data Agent 的取舍 |
+|---|---|---|---|---|
+| ChatGPT Memory | 个人助手的长期个性化 | 从用户对话、历史聊天、文件、记忆摘要等来源形成个性化上下文；用户可以查看 Memory Sources、纠正、删除或关闭记忆。OpenAI 文档也强调，删除某条记忆并不等于删除所有来源，完整删除需要处理过去聊天、文件、连接应用等源头。(OpenAI Help Center) | 记忆必须用户可管理、可纠正、可删除、最好能追踪来源。 | 保留“用户记忆”，记录个人偏好、输出习惯、常用约定 |
+| Claude Code / CLAUDE.md + Auto Memory | 项目规则、工程约定、代码库经验沉淀 | CLAUDE.md 保存显式项目规则；Auto Memory 使用 MEMORY.md 作为索引，并把详细内容拆到 debugging.md、api-conventions.md 等主题文件。Claude Code 会在启动时加载 MEMORY.md 前 200 行或 25KB，主题文件按需读取；这些 memory 文件是 Markdown，可编辑、可删除。(Claude Platform Docs) | 记忆应该透明、文件化、可编辑、可版本化；同时要用“索引 + 主题文件”避免全量注入。 | 记忆正文落到 Markdown，用户可在页面编辑 |
+| LangGraph / LangMem | Agent 的会话状态、长期记忆、异步整理 | LangGraph 区分 thread-scoped short-term memory 和跨 session 的 long-term memory；长期记忆通过 namespace 隔离。LangMem 则提供从对话中抽取、合并、更新长期记忆的工具，并支持 hot path 写入和 background memory manager。(LangChain 文档) (LangChain AI) | 记忆需要分层：会话状态、用户长期记忆、Skill 规则、历史经验不是一类东西；写记忆也应区分同步和异步。 | 第一阶段不先上复杂检索框架，但借鉴其结构：主链路只记录候选记忆事件，后台异步整理；注入时按 user_id + skill_id + task_type 精准选择。 |
+| Mem0 | 通用 AI 应用 / Agent 的持久记忆层 | Mem0 把记忆分为 conversation、session、user、organizational 等层；通过 user_id、run_id、metadata 做作用域隔离，并在查询时合并召回不同层级的记忆。(Mem0) | 记忆的 scope 和生命周期应该是数据模型的一等字段，而不是临时写进 prompt。 | 当前先按用户和 Skill 精准注入，不先上复杂检索层 |
+| Zep | 企业级 Agent Memory、时序知识图谱、动态事实更新 | Zep 从聊天、业务数据、文档、JSON 等来源构建 temporal Context Graph；图中包含实体、关系和事实，事实变更后会 invalidating outdated facts，同时保留历史。它还会生成 token-efficient Context Block 给 Agent 使用。(Zep) | 对业务 Agent 很重要的一点是：记忆不能只追加，还要能处理事实过期、口径变化、历史保留、当前有效状态。 | 采用后台异步整理，避免影响主任务执行 |
+| ZK Data Agent | 团队业务 Skill 的持续复用 | 用户记忆 + Skill 核心记忆 + Skill 归档记忆 + 异步整理队列 + Markdown 可编辑。主任务执行时，只注入当前用户和当前 Skill 相关的高优先级记忆。 | 核心目标不是“什么都记住”，而是把反复纠正、反复复用、业务相关的经验沉淀到对应 Skill 上。 | 第一阶段采用轻量、可控方案：Markdown 可编辑、按 Skill 精准注入、后台异步整理、作用域隔离。 |
+
+### 2.3 Jupyter 工作区入口
+
+很多实际工作最终还是要落到远端环境里执行，例如：
+
+- 修改线上配置
+- 发起模型训练-cloudml / 分析训练结果
+- 读取 juiceFS 数据和产物
+
+平台支持把 Jupyter / 远端工作区绑定到当前用户或会话。Agent 在执行任务时，可以围绕当前工作区读写文件、运行脚本、整理结果。
+
+这个能力的价值在于：AI 不只停留在本地聊天和文本生成，而是可以进入真实工作环境，衔接已有的数据、脚本和训练流程。
+
+[图片]
+
+## 3. 我们在怎么用
+
+### 3.1 数据挖掘的例子
+
+会话链接：http://10.189.47.6/session/__LOCALID_0lj28j5i
+
+[图片]
+
+[图片]
+
+### 3.2 标签大师的例子
+
+会话链接：
+
+- http://10.189.47.6/session/__LOCALID_HP8pkRE
+- http://10.189.47.6/session/__LOCALID_bextxgj0
+
+[图片]
+
+### 3.3 线上正则干预的例子
+
+- http://10.189.47.6/session/__LOCALID_dgjr9shm
+
+## 4. 实际效果
+
+| 场景 | 原流程 | 当前流程 | 粗略提效 | 产出 |
+|---|---:|---:|---:|---|
+| 线上问题分析 / 评测集构建 | 1～2 天 | 30 分钟内 | 约 16～32 倍 | 目前已构造 5 个评测集，2000+ 数据 |
+| 数据构造与格式转换 | 1～2 小时 | 10 分钟内 | 约 6～12 倍 |  |
+| 线上配置修改（熟练工） | 20 分钟 | 2 分钟 | 约 10 倍 |  |
+
+[图片]
+
+## 5. 总结
+
+这次实践的起点很具体：提升中控 planning 模型迭代中的数据开发效率。
+
+做下来之后，我们验证了一个更通用的方向：很多 AI 提效能力，真正有复用价值的部分，往往不是某一次对话结果，而是背后的业务知识、执行脚本、流程经验和修正记录。
+
+中控数据 Agent 目前做的事情，就是把这些分散能力整理成团队可以复用的 Skill 和工具链：
+
+- 个人经验可以沉淀成 Skill。
+- Skill 可以被同步、启用和组合。
+- 使用过程中的修正经验可以进入记忆。
+- Jupyter 工作区让 Agent 能连接真实执行环境。
+
+后续继续迭代的重点可以放在三件事：
+
+- 扩展更多高频业务 Skill。
+- 提升 Skill 记忆和团队经验沉淀质量。
+- 把数据开发、评测、训练、线上问题分析这些流程打通得更稳定。

docs/technical-architecture/01-base-runtime.md

@@ -0,0 +1,221 @@
+# 01. 基座 Runtime 架构
+
+![基座 Runtime 架构](assets/01-base-runtime.png)
+
+## 1. 项目定位
+
+ZK Data Agent 的定位不是通用聊天机器人，也不是单人本地 IDE Agent，而是面向团队业务流程的 Web Agent 工作台。
+
+它基于已有的本地工程 Agent 能力继续往上搭：
+
+- `LocalCodingAgent` 提供多轮 Agent Loop。
+- OpenAI-compatible client 提供模型调用适配。
+- Tool registry 和 handler 提供可控执行能力。
+- Session workspace 提供每个会话独立的输入、中间文件和产物目录。
+- Skill system 把流程协议、业务知识、脚本和样例打包成可复用能力。
+- Memory worker 把用户偏好和 Skill 使用经验异步整理为可编辑记忆。
+
+这个项目要解决的核心痛点是：团队里的数据开发、线上挖掘、标签判断等流程，往往散在口头经验、临时 prompt、个人脚本、聊天记录和本地文件里。每个人都能临时做一次，但很难让别人稳定复用、持续维护、形成可审计的产物链。
+
+当前已经验证过的主要业务场景包括：
+
+- `product-data`：从产品定义、标签边界、样例 query 或 badcase 出发，生成 canonical records，并导出流转 CSV、训练 JSONL、评测 CSV。
+- `online-mining-v2`：基于 ELK 日志挖掘线上样本，保留 request_id、timestamp、session、模型 prompt、模型输出、domain 等信息，并接入后续数据规范。
+- `label-master`：把复杂度、多指令、自动任务、标签定义、function 输出和边界经验整理为可检索知识体系。
+- 外部系统 Skill：ELK、SQL、模型迭代、飞书在线文档转换等能力以 Skill 方式接入，不侵入基座。
+
+因此，基座 Runtime 的目标不是把某一条业务链路写死，而是提供一套稳定的承载层，让不同业务能力都能以 Skill 的方式运行、交付、沉淀和更新。
+
+## 2. 基座解决的问题
+
+基座不绑定某一个业务流程。它提供的是通用 Agent runtime：
+
+- 多用户 Web 入口。
+- 多会话状态管理。
+- 模型选择和 OpenAI-compatible 调用。
+- Tool registry 和 tool handler 执行。
+- Skill 发现、启用和提示词注入。
+- 当前会话工作区。
+- 运行态、事件流、活动区和刷新恢复。
+- 用户记忆和 Skill 记忆后台。
+
+业务能力例如 `product-data`、`online-mining-v2`、`label-master` 都跑在这个基座上。
+
+## 3. 文字架构图
+
+```text
+浏览器 / Web UI
+  |
+  | 用户消息、模型选择、Skill 启用、文件面板、停止运行
+  v
+frontend/app
+  |
+  | /api/chat、/api/claw/*、/admin
+  v
+backend/api/server.py
+  |
+  | 账号配置、会话目录、run manager、run_state_store、memory_manager
+  v
+LocalCodingAgent
+  |
+  | build_session、prompt sections、tool_specs、Agent loop
+  v
+OpenAICompatClient
+  |
+  | messages + tools -> model backend
+  v
+模型
+  |
+  | assistant text 或 tool_calls
+  v
+Tool Runtime
+  |
+  | read/write/python_exec/data_agent/MCP/search/bash
+  v
+session workspace
+  |
+  | input / scratchpad / output / session.json
+  v
+前端活动区和文件面板
+
+运行结束后：
+
+AgentRunResult
+  -> session_store 持久化
+  -> memory_manager.enqueue_interaction
+  -> memory worker 异步整理
+  -> 下一轮 render_injection 注入
+```
+
+## 4. 关键代码入口
+
+### 4.1 Web 后端入口
+
+主要文件：
+
+```text
+backend/api/server.py
+```
+
+关键职责：
+
+- 维护账号和会话配置。
+- 创建或复用 `LocalCodingAgent`。
+- 给 Agent 注入 `runtime_context`、记忆、Jupyter 上下文。
+- 记录 run event，并通过 NDJSON streaming 返回前端。
+- 运行结束后写入 elapsed、标题、memory event。
+
+关键函数和位置：
+
+```text
+backend/api/server.py:691  enabled_skill_names(...)
+backend/api/server.py:822  _build_agent(...)
+backend/api/server.py:853  agent_for(...)
+backend/api/server.py:866  run_lock_for(...)
+backend/api/server.py:1720 /api/chat 运行链路开始
+backend/api/server.py:1739 memory_manager.render_injection(...)
+backend/api/server.py:1751 emit_agent_event(...)
+backend/api/server.py:1897 序列化运行结果和 elapsed
+backend/api/server.py:1920 memory_manager.enqueue_interaction(...)
+backend/api/server.py:2034 StreamingResponse NDJSON
+```
+
+### 4.2 Agent Runtime
+
+主要文件：
+
+```text
+src/agent_runtime.py
+```
+
+关键职责：
+
+- 初始化 tool registry、plugin runtime、MCP runtime、search runtime 等。
+- 新会话用 `run(...)`，旧会话用 `resume(...)`。
+- 在 `_run_prompt(...)` 中执行完整 Agent loop。
+- 维护 usage、cost、tool_calls、events、file_history。
+- 在结束时持久化 session。
+
+关键函数和位置：
+
+```text
+src/agent_runtime.py:158  class LocalCodingAgent
+src/agent_runtime.py:195  __post_init__
+src/agent_runtime.py:413  run(...)
+src/agent_runtime.py:441  resume(...)
+src/agent_runtime.py:520  _run_prompt 主链路
+src/agent_runtime.py:552  tool_specs = [tool.to_openai_tool() ...]
+src/agent_runtime.py:619  for turn_index in range(...)
+src/agent_runtime.py:1008 遍历模型返回的 tool_calls
+src/agent_runtime.py:1490 _query_model(...)
+```
+
+### 4.3 系统提示词
+
+主要文件：
+
+```text
+src/agent_prompting.py
+```
+
+关键职责：
+
+- 定义中控 Agent 身份。
+- 注入工具使用策略。
+- 注入工作空间边界。
+- 注入 Skill 列表。
+- 注入 ask_user 等 runtime 指导。
+
+关键函数和位置：
+
+```text
+src/agent_prompting.py:135 get_intro_section
+src/agent_prompting.py:155 get_doing_tasks_section
+src/agent_prompting.py:182 get_actions_section
+src/agent_prompting.py:196 get_workspace_boundary_section
+src/agent_prompting.py:210 get_using_your_tools_section
+src/agent_prompting.py:274 get_skill_guidance_section
+src/agent_prompting.py:414 get_ask_user_guidance_section
+```
+
+## 5. 基座的分层职责
+
+```text
+Web UI
+  负责交互、展示、文件面板、活动区、Skill 勾选、管理后台。
+
+Backend API
+  负责账号、会话、模型配置、运行态、服务端事件流。
+
+Agent Runtime
+  负责 Agent loop、模型调用、工具调用、预算和持久化。
+
+Prompting
+  负责把规则、工具、公约、Skill 列表转成模型可见上下文。
+
+Tool Runtime
+  负责稳定执行动作，并把结果结构化回传给模型。
+
+Skill System
+  负责让业务流程、知识、脚本可被 Agent 发现和使用。
+
+Workspace
+  负责隔离每个用户和每个会话的输入、临时文件和交付产物。
+
+Memory Worker
+  负责从交互历史中异步整理长期偏好和 Skill 使用经验。
+```
+
+## 6. 设计边界
+
+基座只应该承载跨业务复用的稳定能力，例如：
+
+- 工具执行。
+- 会话状态。
+- 运行态事件。
+- 工作区路径。
+- Skill 发现和启用。
+- 模型适配。
+- 记忆后台。
+
+业务流程不应该写死在基座里。数据生成、线上挖掘、标签判断等变化快的流程应该沉到 Skill；确定性脚本应该放在对应 Skill 的 `scripts/` 下。

docs/technical-architecture/02-agent-loop.md

@@ -0,0 +1,246 @@
+# 02. Agent Loop 执行机制
+
+![Agent Loop 执行机制](assets/02-agent-loop.png)
+
+## 1. Agent Loop 的基本形态
+
+Agent 每轮不是只调用一次模型，而是一个循环：
+
+```text
+用户输入
+  -> 构造 session 和 prompt
+  -> 调模型
+  -> 模型返回 assistant text 或 tool_calls
+  -> 如果没有 tool_calls：输出最终回复，结束
+  -> 如果有 tool_calls：执行工具
+  -> 工具结果写回 session
+  -> 下一轮模型继续读取工具结果
+  -> 直到最终回复、预算超限、取消、max_turns 或 review 停止
+```
+
+这个循环允许 Agent 做“观察-执行-再观察”的任务，例如：
+
+- 先读文件，再决定是否需要抽取。
+- 先生成 plan，等待用户 review。
+- 先运行脚本，再根据校验结果修复。
+- 先查询线上数据，再抽样展示。
+
+## 2. 新会话和恢复会话
+
+新会话入口：
+
+```text
+src/agent_runtime.py:413 run(...)
+```
+
+关键动作：
+
+```text
+1. 清理当前 managed_agent_id 和 resume_source_session_id。
+2. 创建 session_id。
+3. 创建 scratchpad_directory。
+4. 绑定 plan_runtime/task_runtime 到 scratchpad。
+5. 调 _run_prompt(...)
+6. 累计 usage。
+```
+
+恢复会话入口：
+
+```text
+src/agent_runtime.py:441 resume(...)
+```
+
+关键动作：
+
+```text
+1. 从 StoredAgentSession 恢复 AgentSessionState。
+2. 回放 file_history 和 compaction 信息。
+3. 设置 active_session_id 和 last_session_path。
+4. 恢复 plugin state。
+5. 复用已有 scratchpad_directory。
+6. 调 _run_prompt(...)
+```
+
+这解释了为什么刷新页面、回到旧会话后，Agent 理论上可以继续同一个 session 的上下文，而不是新建一个任务。
+
+## 3. `_run_prompt` 主链路
+
+核心位置：
+
+```text
+src/agent_runtime.py:520 _run_prompt 主体
+```
+
+主链路关键步骤：
+
+```text
+1. slash command 预处理。
+2. hook policy / plugin hook 修改 prompt。
+3. agent_manager.start_agent(...) 记录运行。
+4. 新建或复用 AgentSessionState。
+5. runtime_context prepend 到模型可见的用户消息。
+6. session.append_user(...)。
+7. tool_context 注入 scratchpad、plan_runtime、task_runtime。
+8. 生成 tool_specs。
+9. 初始化 usage、cost、tool_calls、events。
+10. 进入 turn loop。
+```
+
+对应代码点：
+
+```text
+src/agent_runtime.py:523 agent_manager.start_agent(...)
+src/agent_runtime.py:531 session = base_session or build_session(...)
+src/agent_runtime.py:539 _prepend_runtime_context(...)
+src/agent_runtime.py:543 session.append_user(...)
+src/agent_runtime.py:546 replace(self.tool_context, scratchpad_directory=...)
+src/agent_runtime.py:552 tool_specs = [tool.to_openai_tool() ...]
+src/agent_runtime.py:582 stream_events = _RuntimeEventBuffer(event_sink)
+src/agent_runtime.py:619 for turn_index in range(...)
+```
+
+## 4. 模型调用和 tool_calls 判断
+
+模型调用入口：
+
+```text
+src/agent_runtime.py:1490 _query_model(...)
+```
+
+非流式路径中：
+
+```text
+turn = self.client.complete(
+  session.to_openai_messages(),
+  tool_specs,
+  output_schema=...
+)
+```
+
+模型能否返回 `tool_calls` 取决于：
+
+- 当前 `messages`。
+- 系统提示词。
+- Skill 列表。
+- 工具描述和参数 schema。
+- 模型自身 tool calling 能力。
+
+基座不会强制某个工具被调用。基座只把工具能力暴露给模型，并在模型返回 `tool_calls` 后负责执行。
+
+## 5. 没有 tool_calls 时
+
+如果模型本轮没有工具调用：
+
+```text
+src/agent_runtime.py:763 if not turn.tool_calls
+```
+
+后续可能有三种情况：
+
+1. 直接输出最终回复。
+2. 如果模型输出被截断，自动追加 continuation prompt。
+3. 如果达到 continuation 限制，则追加提示并结束。
+
+最终会：
+
+```text
+session.append_assistant(...)
+_append_final_text_stream_events(...)
+AgentRunResult(...)
+_persist_session(...)
+```
+
+## 6. 有 tool_calls 时
+
+如果模型返回工具调用：
+
+```text
+src/agent_runtime.py:1008 for tool_call in turn.tool_calls
+```
+
+每个工具调用会：
+
+```text
+1. tool_calls 计数 +1。
+2. 检查预算。
+3. session.start_tool(...) 写入工具开始消息。
+4. stream_events 追加 tool_start。
+5. 根据工具名执行 handler。
+6. 工具结果写回 session。
+7. stream_events 追加工具结果。
+8. 下一轮模型读取工具结果继续判断。
+```
+
+关键代码点：
+
+```text
+src/agent_runtime.py:1054 session.start_tool(...)
+src/agent_runtime.py:1060 stream_events.append(type='tool_start')
+src/agent_tools.py:60 execute_tool(...)
+src/agent_tools.py:76 execute_tool_streaming(...)
+```
+
+## 7. 为什么 review 可以暂停流程
+
+review 门禁不是特殊 UI 魔法，而是 Agent loop 的自然结果：
+
+1. Skill 要求模型在某一步调用 review 工具。
+2. 工具创建 pending state，并返回需要展示的信息。
+3. Agent 生成回复，告诉用户需要确认。
+4. 当前 run 结束。
+5. 用户下一轮回复“确认”。
+6. Agent resume 旧 session，调用 confirm 工具。
+7. 后续流程继续。
+
+例如 `product-data`：
+
+```text
+data_agent_prepare_generation_goal
+  -> pending goal
+  -> 停止等待用户确认
+
+data_agent_confirm_generation_goal
+  -> confirmed_goal_id
+  -> data_agent_prepare_generation_plan
+  -> pending plan
+  -> 停止等待用户确认
+
+data_agent_confirm_generation_plan
+  -> confirmed_plan_id
+  -> 允许生成 draft 和转换 records
+```
+
+## 8. 预算、取消和 max_turns
+
+基座会在模型调用前后、工具请求前检查预算：
+
+```text
+src/agent_runtime.py:592 initial_budget = self._check_budget(...)
+src/agent_runtime.py:732 budget_after_model = self._check_budget(...)
+src/agent_runtime.py:1014 budget_after_tool_request = self._check_budget(...)
+```
+
+如果达到最大轮次：
+
+```text
+src/agent_runtime.py:1440 _build_max_turns_output(...)
+```
+
+输出会包含最后一次阶段说明，提醒用户可以继续补充指令。
+
+取消由后端 run manager 和 tool process registry 处理。Web 点停止后，后端将 run 标记取消，并让工具执行上下文感知 cancel_event。
+
+## 9. 事件流和前端活动区
+
+Agent loop 内会不断追加 `stream_events`，后端用 `event_sink` 把事件推给前端。
+
+后端关键代码：
+
+```text
+backend/api/server.py:1751 emit_agent_event(event)
+backend/api/server.py:1756 state.run_manager.record_event(...)
+backend/api/server.py:1758 state.run_state_store.record_event(...)
+backend/api/server.py:2034 StreamingResponse(..., media_type='application/x-ndjson')
+```
+
+前端活动区展示的阶段说明、工具开始、工具完成、最终文本，本质上都来自这些 runtime events 或 session replay。

docs/technical-architecture/03-tools.md

@@ -0,0 +1,298 @@
+# 03. 工具体系和 Tool Handler
+
+![工具体系和 Tool Handler](assets/03-tools.png)
+
+## 1. 工具体系的职责
+
+Tool 是执行层。模型只能“请求调用工具”，不能直接执行工具。
+后端根据工具名找到 handler，校验参数，执行动作，并把结果写回 session。
+
+工具适合承载：
+
+- 稳定文件读写。
+- Python 执行。
+- Python 包安装。
+- 数据格式转换和校验。
+- review 状态机。
+- 外部系统访问。
+- 需要权限、取消、路径约束或审计的动作。
+
+## 2. Tool registry 构成
+
+入口：
+
+```text
+src/agent_tools.py:105 default_tool_registry()
+```
+
+它会合并多类工具：
+
+```text
+build_file_tools(...)
+build_execution_tools(...)
+LSP / web_fetch / search / account / config / task / team / workflow 等
+Skill 工具
+data_agent 工具
+```
+
+关键片段：
+
+```text
+src/agent_tools.py:107 build_file_tools
+src/agent_tools.py:116 build_execution_tools
+src/agent_tools.py:1012 AgentTool(name='Skill')
+src/agent_tools.py:1033 build_data_agent_tools
+```
+
+返回结果是：
+
+```python
+return {tool.name: tool for tool in tools}
+```
+
+## 3. Tool spec 注入模型
+
+在 Agent loop 中：
+
+```text
+src/agent_runtime.py:552
+tool_specs = [tool.to_openai_tool() for tool in self.tool_registry.values()]
+```
+
+每个工具包含：
+
+```text
+name
+description
+parameters JSON schema
+handler
+```
+
+模型看到的是 name、description、parameters。handler 不暴露给模型，只在后端执行。
+
+## 4. Tool handler 执行路径
+
+工具执行入口：
+
+```text
+src/agent_tools.py:60 execute_tool(...)
+src/agent_tools.py:76 execute_tool_streaming(...)
+```
+
+执行逻辑：
+
+```text
+1. 从 tool_registry 取 tool。
+2. 找不到则返回 Unknown tool。
+3. bash 走 streaming。
+4. 其他工具调用 tool.execute(arguments, context)。
+5. 工具结果序列化回模型和前端。
+```
+
+## 5. 文件工具
+
+定义位置：
+
+```text
+src/agent_tool_specs/files.py
+```
+
+主要工具：
+
+- `list_dir`
+- `read_file`
+- `write_file`
+- `edit_file`
+- `notebook_edit`
+- `glob_search`
+- `grep_search`
+
+### 5.1 `write_file` 的边界
+
+`write_file` 当前明确定位为“小文件工具”：
+
+```text
+src/agent_tool_specs/files.py
+  Write or append a SMALL UTF-8 file inside the workspace.
+```
+
+适合：
+
+- 短 Markdown。
+- 少量配置。
+- 小 JSON。
+- 少量 JSONL。
+- 小 CSV。
+
+不适合：
+
+- 长 Python 脚本。
+- 大 JSON。
+- JSONL 数据集。
+- 长 CSV。
+- 大段包含引号和换行的内容。
+
+原因是模型生成工具参数时，需要把内容嵌入 JSON 参数。长文本、引号、换行会显著增加非法 JSON 参数概率。
+
+因此系统提示词也要求：
+
+```text
+复杂写文件优先用 python_exec 通过 pathlib/json/csv 写入。
+```
+
+## 6. Python 执行工具
+
+定义位置：
+
+```text
+src/agent_tool_specs/execution.py
+```
+
+主要工具：
+
+- `python_exec`
+- `python_package`
+- `bash`
+- `sleep`
+
+### 6.1 `python_exec`
+
+定位：
+
+```text
+结构化文件分析、JSON/JSONL 处理、批量校验、数据抽样、快速计算。
+```
+
+支持两种模式：
+
+```text
+code
+  一次性 Python 代码。
+
+script_path
+  调用项目或 Skill 内已有脚本。
+```
+
+重要约束：
+
+- 不用 `bash python ...`。
+- 不在 `python_exec.code` 里用 subprocess 二次调用 Python 脚本。
+- 临时文件写入 `PYTHON_EXEC_SCRATCHPAD`。
+- 交付产物写入 session/output。
+
+### 6.2 `python_package`
+
+定位：
+
+```text
+给当前用户独立 Python venv 安装缺失包。
+```
+
+典型场景：
+
+- `pandas`
+- `pyarrow`
+- `openpyxl`
+- `elasticsearch`
+- `urllib3`
+
+设计上避免安装到项目 `.venv` 或系统 Python。
+
+### 6.3 `bash`
+
+`bash` 是兜底工具，不是默认 Python 执行方式。
+
+适合：
+
+- git 只读检查。
+- 系统命令。
+- 进程控制。
+- 用户确认后的依赖安装或服务管理。
+
+不适合：
+
+- Python 数据分析。
+- JSON/CSV 转换。
+- 包安装。
+
+## 7. 路径解析和 session 路由
+
+关键实现：
+
+```text
+src/agent_tools.py:1589 _data_agent_output_path(...)
+src/agent_tools.py:1624 _data_agent_session_output_root(...)
+src/agent_tools.py:1630 _resolve_path(...)
+src/agent_tools.py:1658 _session_logical_path(...)
+src/agent_tools.py:1685 _execution_cwd(...)
+```
+
+逻辑路径会被映射到当前 session：
+
+```text
+output/...     -> session/output/...
+outputs/...    -> session/output/...
+scratchpad/... -> session/scratchpad/...
+scratch/...    -> session/scratchpad/...
+input/...      -> session/input/...
+inputs/...     -> session/input/...
+```
+
+`python_exec` 的执行 cwd 优先是当前会话 scratchpad：
+
+```text
+src/agent_tools.py:1685 _execution_cwd(...)
+```
+
+## 8. data_agent 工具
+
+声明位置：
+
+```text
+src/agent_tool_specs/data_agent.py
+```
+
+handler 注册位置：
+
+```text
+src/agent_tools.py:1033 build_data_agent_tools(...)
+```
+
+主要工具分两类：
+
+### 8.1 前链路和 review 状态机
+
+- `data_agent_load_input_sources`
+- `data_agent_render_source_context`
+- `data_agent_extract_case_evidence`
+- `data_agent_prepare_generation_goal`
+- `data_agent_confirm_generation_goal`
+- `data_agent_prepare_generation_plan`
+- `data_agent_show_generation_plan`
+- `data_agent_update_generation_plan`
+- `data_agent_confirm_generation_plan`
+
+这类工具当前仍属于平台工具，因为它们维护 pending/confirmed 状态，以及产品数据生成的 review 门禁。
+
+### 8.2 历史格式转换工具
+
+- `data_agent_normalize_dataset_draft`
+- `data_agent_validate_dataset_records`
+- `data_agent_export_dataset_records`
+- `data_agent_export_training_jsonl`
+- `data_agent_export_planning_eval_csv`
+
+这部分能力已经逐步迁移到 `skills/product-data/scripts/`，平台工具更多是历史兼容和适配层。
+
+## 9. 工具设计原则
+
+当前工具体系的技术取舍：
+
+```text
+模型负责决策。
+工具负责执行。
+Skill 负责流程经验。
+脚本负责可迁移确定性能力。
+```
+
+工具描述要足够明确，否则模型会选错工具；参数 schema 要尽量简单，否则不同模型后端可能不兼容。

docs/technical-architecture/04-skills.md

@@ -0,0 +1,256 @@
+# 04. Skill 体系和能力包约定
+
+![Skill 体系和能力包约定](assets/04-skills.png)
+
+## 1. Skill 的定位
+
+Skill 是经验层。它不是单纯 prompt，也不是单纯脚本。
+
+一个 Skill 应该回答：
+
+- 什么场景触发。
+- 输入材料是什么。
+- Agent 应该按什么流程做。
+- 哪些地方必须让用户 review。
+- 应该调用哪些工具或脚本。
+- 产物应该写到哪里。
+- 哪些做法是禁止的。
+
+稳定可执行逻辑不应该长期写在 Skill 文本里，而应该进入：
+
+```text
+skills/<skill-name>/scripts/
+```
+
+或者平台级工具。
+
+## 2. Skill loader 实现
+
+关键文件：
+
+```text
+src/bundled_skills.py
+```
+
+项目级 Skill 目录：
+
+```text
+skills/<skill-name>/SKILL.md
+```
+
+核心数据结构：
+
+```text
+src/bundled_skills.py:28 BundledSkill
+```
+
+字段：
+
+```text
+name
+description
+when_to_use
+aliases
+allowed_tools
+user_invocable
+source
+path
+get_prompt
+```
+
+## 3. SKILL.md 解析
+
+解析逻辑：
+
+```text
+src/bundled_skills.py:147 _parse_front_matter
+src/bundled_skills.py:176 _load_directory_skill
+```
+
+`SKILL.md` 必须有 frontmatter：
+
+```yaml
+---
+name: product-data
+description: 从产品/标签定义、手写边界规则或示例 query 中提取标签边界...
+when_to_use: 当用户提供产品定义、标签规则...
+aliases: definition-data, label-data
+allowed_tools: read_file, write_file, python_exec
+---
+```
+
+解析后：
+
+- frontmatter 进入 `BundledSkill` 元数据。
+- body 作为真正的 Skill prompt。
+- 如果调用 Skill 时带 args，会追加到 `## Invocation Arguments`。
+
+对应实现：
+
+```text
+src/bundled_skills.py:138 _directory_skill_prompt
+```
+
+## 4. Skill 发现顺序
+
+项目 Skill 发现入口：
+
+```text
+src/bundled_skills.py:199 load_directory_skills
+src/bundled_skills.py:215 load_project_skills
+```
+
+系统提示词中可见 Skill 列表由：
+
+```text
+src/bundled_skills.py:270 format_skills_for_system_prompt
+```
+
+生成。
+
+Web 后端会按当前账号和 session 配置计算启用 Skill：
+
+```text
+backend/api/server.py:691 enabled_skill_names
+backend/api/server.py:706 set_skill_enabled
+backend/api/server.py:738 set_all_skills_enabled
+```
+
+这意味着：
+
+- Skill 可以存在于项目中，但不一定对某个 session 启用。
+- 启用状态影响系统提示词里的 Skill 列表。
+- 被禁用的 Skill 不应该被模型主动选择。
+
+## 5. Skill 工具
+
+Skill 本身也是一个工具：
+
+```text
+src/agent_tools.py:1012 AgentTool(name='Skill')
+```
+
+模型调用：
+
+```json
+{
+  "skill": "product-data",
+  "args": "用户原始需求或显式参数"
+}
+```
+
+执行后，Skill body 会被加入对话，让模型按 Skill 中的流程继续做任务。
+
+## 6. Skill 和 Agent Loop 的关系
+
+Skill 不会替代 Agent loop，而是改变 Agent loop 的下一步决策依据。
+
+典型模式：
+
+```text
+用户提出任务
+  -> 模型从 Skill 列表中选择某个 Skill
+  -> 调用 Skill 工具
+  -> Skill.md 正文进入上下文
+  -> 模型按 Skill 指令调用 read_file/python_exec/data_agent 等工具
+  -> 工具结果进入上下文
+  -> 模型继续按 Skill 流程推进
+```
+
+因此 Skill 的好坏直接影响：
+
+- 模型能否召回正确能力。
+- 是否会过早执行。
+- 是否能在 review 门禁停下来。
+- 是否能使用正确工具而不是手写不稳定逻辑。
+
+## 7. 推荐 Skill 目录结构
+
+```text
+skills/<skill-name>/
+  SKILL.md
+  README.md
+  knowledge/
+  scripts/
+  examples/
+  schemas/
+  tools.yaml
+  requirements.txt
+```
+
+各部分职责：
+
+```text
+SKILL.md
+  运行时入口，写流程、门禁、工具调用方式和禁止事项。
+
+README.md
+  给维护者看的说明，不一定进入模型上下文。
+
+knowledge/
+  业务知识、标签规则、字段说明、边界案例。
+
+scripts/
+  确定性脚本，优先通过 python_exec.script_path 执行。
+
+examples/
+  示例输入输出，用于回归和讲解。
+
+schemas/
+  JSON schema 或字段约定。
+
+tools.yaml
+  描述 portable scripts 如何注册为工具，便于迁移到其他 Agent。
+
+requirements.txt
+  Skill 脚本的 Python 依赖。
+```
+
+## 8. Skill 更新
+
+Web 后端提供 Skill 更新能力：
+
+```text
+backend/api/server.py:765 sync_skills_from_git
+```
+
+核心行为：
+
+```text
+1. 检查当前目录是否是 git 仓库。
+2. 检查 tracked 文件是否干净。
+3. git fetch origin。
+4. git pull --ff-only origin 当前分支。
+5. 清理当前账号 agent cache。
+6. 重新读取 get_bundled_skills。
+```
+
+这让新增或修改 Skill 后，不一定需要重启服务才能让 Skill 列表刷新。
+
+## 9. Skill 设计边界
+
+适合写在 Skill：
+
+- 工作流。
+- 何时提问。
+- 何时 review。
+- 哪些工具优先。
+- 输出位置约定。
+- 常见失败经验。
+
+不适合长期写在 Skill：
+
+- 大段可检索知识。
+- 复杂代码。
+- 格式转换。
+- 查询外部系统的具体实现。
+- 需要校验的稳定数据结构。
+
+这些应该分别放到：
+
+```text
+knowledge/
+scripts/
+schemas/
+platform tools
+```

docs/technical-architecture/05-workspace-memory-observability.md

@@ -0,0 +1,243 @@
+# 05. 会话工作区、运行态和记忆
+
+![会话工作区、运行态和记忆](assets/05-workspace-memory-observability.png)
+
+## 1. 会话工作区
+
+每个用户、每个会话都有独立目录：
+
+```text
+.port_sessions/accounts/<account_id>/sessions/<session_id>/
+  input/
+  scratchpad/
+  output/
+  session.json
+```
+
+目录职责：
+
+```text
+input/
+  用户上传或明确提供的输入资料。
+
+scratchpad/
+  临时脚本、中间文件、抽样缓存、断点记录。
+
+output/
+  最终交付产物。
+
+session.json
+  会话消息、工具调用、usage、events、file_history、runtime metadata。
+```
+
+## 2. 工作区路径路由
+
+路径解析实现：
+
+```text
+src/agent_tools.py:1658 _session_logical_path
+```
+
+逻辑路径：
+
+```text
+output/...     -> session/output/...
+outputs/...    -> session/output/...
+scratchpad/... -> session/scratchpad/...
+scratch/...    -> session/scratchpad/...
+input/...      -> session/input/...
+inputs/...     -> session/input/...
+```
+
+Python 执行 cwd：
+
+```text
+src/agent_tools.py:1685 _execution_cwd
+```
+
+优先使用当前 session scratchpad。这是为了避免临时脚本和缓存污染项目根目录。
+
+## 3. 平台代码写保护
+
+相关实现：
+
+```text
+src/agent_tools.py:1118 _PLATFORM_READONLY_DIRS
+src/agent_tools.py:1698 _is_platform_code_path
+src/agent_tools.py:1708 _ensure_not_platform_code_write
+```
+
+当前平台目录：
+
+```text
+src
+backend
+frontend
+scripts
+```
+
+在数据 Agent 会话中默认视为只读。除非用户明确进入平台开发任务，否则业务任务不应修改平台代码。
+
+## 4. Run 状态和活动区
+
+后端在 `/api/chat` 运行时创建 run record：
+
+```text
+backend/api/server.py:1720 run_record = state.run_manager.start(...)
+backend/api/server.py:1722 state.run_state_store.start(...)
+```
+
+运行事件通过 `emit_agent_event` 记录：
+
+```text
+backend/api/server.py:1751 emit_agent_event
+backend/api/server.py:1756 run_manager.record_event
+backend/api/server.py:1758 run_state_store.record_event
+```
+
+返回前端：
+
+```text
+backend/api/server.py:2034 StreamingResponse
+```
+
+前端活动区看到的内容主要来自：
+
+- `run_started`
+- `tool_start`
+- `tool_result`
+- 模型阶段说明
+- final text stream events
+- run finish/error/cancel 状态
+
+## 5. 会话持久化
+
+运行结束后，后端序列化结果：
+
+```text
+backend/api/server.py:2056 _serialize_run_result
+backend/api/server.py:2069 _normalize_transcript_entry
+```
+
+会话读取：
+
+```text
+backend/api/server.py:2094 _serialize_stored_session
+```
+
+`agent_runtime` 在多个结束路径都会调用：
+
+```text
+_persist_session(session, result)
+```
+
+这让刷新后可以恢复：
+
+- 用户消息。
+- assistant 文本。
+- tool_calls。
+- tool result。
+- elapsed。
+- file_history。
+
+## 6. 记忆体系
+
+实现位置：
+
+```text
+src/personal_memory.py
+```
+
+### 6.1 文件和数据库
+
+```text
+memory.db
+user.md
+skills/<skill-name>.md
+```
+
+常量：
+
+```text
+src/personal_memory.py:26 MEMORY_DB_FILENAME
+src/personal_memory.py:27 USER_MEMORY_FILENAME
+src/personal_memory.py:28 SKILL_MEMORY_DIRNAME
+```
+
+### 6.2 注入逻辑
+
+```text
+src/personal_memory.py:102 render_injection
+```
+
+注入规则：
+
+```text
+1. 读取 user.md。
+2. 根据 enabled_skill_names 读取对应 skills/<skill>.md。
+3. 拼成 # 个性化记忆。
+4. 如果用户本轮要求冲突，以本轮要求为准。
+```
+
+后端调用：
+
+```text
+backend/api/server.py:1739 memory_manager.render_injection(...)
+```
+
+### 6.3 入队逻辑
+
+运行结束后：
+
+```text
+backend/api/server.py:1920 memory_manager.enqueue_interaction(...)
+```
+
+记忆模块内：
+
+```text
+src/personal_memory.py:138 enqueue_interaction
+src/personal_memory.py:635 detect_memory_signals
+```
+
+会检测：
+
+- 显式记忆词：记住、以后、下次、默认、总是、不要、应该、固定。
+- 纠错词：不对、不是这样、格式错、之前说过、还是不行。
+- Skill/工具/流程/格式相关表述。
+- 工具参数非法 JSON 等工具经验。
+
+### 6.4 后台整理
+
+核心逻辑：
+
+```text
+src/personal_memory.py:378 _consolidate_events
+src/personal_memory.py:419 _generate_memory_updates
+src/personal_memory.py:471 _fallback_memory_updates
+```
+
+设计取舍：
+
+- 主链路不直接生成记忆。
+- 事件先进入 SQLite 队列。
+- 后台 worker 批量整理。
+- LLM 失败时有 fallback 规则。
+- Markdown 是最终可编辑记忆正文。
+
+## 7. 可观测性设计
+
+当前可观测性来自三个层次：
+
+```text
+运行态
+  run_manager + run_state_store，支持运行中刷新、停止、恢复活动区。
+
+会话态
+  session.json，保存完整消息和工具调用。
+
+产物态
+  session/input、scratchpad、output，文件面板可查看和下载。
+```
+
+这个设计让用户不仅看到最终回复，也能看到 Agent 做了什么、文件在哪里、失败在哪个工具或阶段。

docs/technical-architecture/06-product-data.md

@@ -0,0 +1,292 @@
+# 06. product-data Skill 实现
+
+![product-data Skill 实现](assets/06-product-data.png)
+
+## 1. 定位
+
+`product-data` 是数据开发链路的核心 Skill，负责：
+
+```text
+产品定义 / 标签规则 / 手写边界 / 示例 query / badcase
+  -> 输入文本化
+  -> generation goal
+  -> 用户 review
+  -> generation plan
+  -> 用户确认数量、标签、边界、路径
+  -> dataset draft text
+  -> canonical records
+  -> validate
+  -> export records / table / training / eval
+```
+
+位置：
+
+```text
+skills/product-data/SKILL.md
+skills/product-data/knowledge/
+skills/product-data/scripts/
+```
+
+## 2. Skill 目录结构
+
+当前 `SKILL.md` 中定义的能力组织：
+
+```text
+skills/product-data/
+  SKILL.md
+  tools.yaml
+  requirements.txt
+  knowledge/
+    dataset_draft_v1.md
+    canonical_record_v1.md
+    portable_skill_contract.md
+  schemas/
+  scripts/
+    normalize_dataset_draft.py
+    validate_dataset_records.py
+    export_dataset_records.py
+    export_dataset_table.py
+    export_training_jsonl.py
+    export_planning_eval_csv.py
+```
+
+分工：
+
+```text
+SKILL.md
+  流程协议、门禁、工具调用顺序、禁止事项。
+
+knowledge/
+  数据草稿、canonical record 和 portable skill contract。
+
+scripts/
+  确定性转换、校验和导出。
+
+data_agent_* 平台工具
+  负责输入文本化和 review 状态机。
+```
+
+## 3. 输入类型
+
+Skill 将输入分成三类：
+
+```text
+文件定义型
+  产品定义、标签定义、路由规则、表格、Markdown、CSV。
+
+手写规则型
+  用户直接描述边界，例如“找附近美食给餐饮服务，导航去某地给地图导航”。
+
+示例归纳型
+  用户只给 query/example/badcase，需要先归纳边界和标签倾向。
+```
+
+三类输入最后统一整理成：
+
+```text
+dataset_label
+target / target_definitions
+plan_hint
+coverage
+exclusions
+open_questions
+source_refs
+complex 规则
+```
+
+这一步称为 `generation_goal`。
+
+## 4. Review 门禁
+
+`product-data` 有两个门禁模式。
+
+### 4.1 一次确认模式
+
+适用：
+
+- 用户直接给出清晰手写规则。
+- target 表达明确。
+- 用户已经希望生成数据。
+
+链路：
+
+```text
+data_agent_prepare_generation_plan(direct_review=true)
+  -> 展示目标 + 数量 + 路径
+  -> 等待“确认，开始生成”
+```
+
+### 4.2 两段确认模式
+
+适用：
+
+- 用户提供文件、表格、badcase、长文档。
+- 标签、边界、字段有歧义。
+- 需要先从资料中抽取 generation goal。
+
+链路：
+
+```text
+data_agent_load_input_sources
+  -> data_agent_render_source_context
+  -> Agent 整理 generation_goal
+  -> data_agent_prepare_generation_goal
+  -> 用户确认目标
+  -> data_agent_confirm_generation_goal
+  -> data_agent_prepare_generation_plan
+  -> 用户确认计划
+  -> data_agent_confirm_generation_plan
+```
+
+## 5. 和 Agent Loop 的关系
+
+`product-data` 明确利用 Agent loop 做分阶段控制。
+
+```text
+第一轮：
+  模型选择 product-data
+  调输入工具
+  调 prepare_generation_goal 或 prepare_generation_plan
+  输出 review 信息
+  停止
+
+第二轮：
+  用户确认目标或计划
+  Agent resume session
+  调 confirm 工具
+  继续下一阶段
+
+第三轮：
+  用户确认计划
+  Agent 生成 dataset draft
+  每批调用 normalize 脚本
+  调 validate 脚本
+  调 export 脚本
+  输出最终文件路径
+```
+
+重点是：确认状态不是靠自由文本记忆，而是由工具维护 `confirmed_goal_id` 和 `confirmed_plan_id`。
+
+## 6. 数据格式设计
+
+### 6.1 dataset draft text
+
+面向模型生成，要求模型用较低结构负担描述：
+
+- 用户 / 小爱 对话。
+- 当前 query。
+- target。
+- complex。
+- 场景说明。
+
+设计目标是降低模型直接写 JSONL 的难度。
+
+### 6.2 canonical record
+
+面向工具处理，字段稳定。
+
+用于：
+
+- 校验结构。
+- 导出流转 CSV。
+- 导出训练 JSONL。
+- 导出评测 planningPrompt CSV。
+
+### 6.3 complex 独立维度
+
+`complex` 不属于 target。
+
+内部保存：
+
+```text
+complex: false
+target: Agent(tag="地图导航")
+```
+
+训练输出组合：
+
+```text
+complex=false
+Agent(tag="地图导航")
+```
+
+评测 CSV 里：
+
+```text
+code标签: Agent(tag="地图导航")
+complex: FALSE
+```
+
+## 7. 脚本链路
+
+生成式数据确认后，Skill 要求使用 `python_exec.script_path` 调脚本。
+
+典型顺序：
+
+```text
+normalize_dataset_draft.py
+  输入 draft + confirmed_plan_id
+  输出/追加 scratchpad/normalized_records.jsonl
+
+validate_dataset_records.py
+  读取 normalized_records.jsonl
+  校验字段、target、complex、时间戳、多轮上下文
+
+export_dataset_records.py
+  导出 output/records.jsonl
+  同时生成 output/records.csv
+
+export_training_jsonl.py
+  导出 output/training.jsonl
+
+export_planning_eval_csv.py
+  导出 output/eval_planning.csv
+```
+
+为什么不用 `write_file`：
+
+- records、CSV、JSONL 都是强格式数据。
+- 模型手写容易出错。
+- 脚本能统一 timestamp、prev_session、context、complex/target 组合。
+
+## 8. 输出约束
+
+固定逻辑输出：
+
+```text
+output/records.jsonl
+output/records.csv
+output/training.jsonl
+output/eval_planning.csv
+```
+
+通过路径路由，实际写入：
+
+```text
+.port_sessions/accounts/<account>/sessions/<session>/output/
+```
+
+Skill 明确禁止：
+
+- 按数据集名创建随机子目录。
+- 把 records 写到项目根目录。
+- 用 `write_file` 手写最终 JSONL/CSV。
+- 在未确认 plan 前生成数据。
+
+## 9. 设计边界
+
+`product-data` 负责：
+
+- 数据目标对齐。
+- 生成计划 review。
+- dataset draft 生成协议。
+- canonical records 转换和导出。
+
+不负责：
+
+- 判断所有标签知识。
+- 查询线上数据。
+- ELK 检索。
+- 模型训练或 git 数据仓库提交。
+
+标签知识应由 `label-master` 辅助，线上数据由 `online-mining-v2` 或相关 Skill 获取。

docs/technical-architecture/07-online-mining-v2.md

@@ -0,0 +1,177 @@
+# 07. online-mining-v2 Skill 实现
+
+![online-mining-v2 Skill 实现](assets/07-online-mining-v2.png)
+
+## 1. 定位
+
+`online-mining-v2` 用于从线上 ELK 日志中挖掘 case，并把候选样本转换成 `product-data` 兼容的标准数据。
+
+典型链路：
+
+```text
+线上挖掘需求
+  -> 明确目标标签、complex、筛选特征
+  -> 探索 ELK 表字段
+  -> 搜索候选
+  -> 抽样 review
+  -> 调整策略
+  -> 直接转 canonical records
+  -> 或转 product-data 生成补数
+```
+
+位置：
+
+```text
+skills/online-mining-v2/SKILL.md
+skills/online-mining-v2/knowledge/
+skills/online-mining-v2/scripts/
+```
+
+## 2. 能力组织
+
+`online-mining-v2` 不新增平台注册工具。它把能力放在 Skill 目录下，通过 `python_exec.script_path` 执行。
+
+```text
+scripts/
+  online_mining_common.py
+  elk_profile_index.py
+  elk_search_cases.py
+  elk_fetch_by_request_ids.py
+  elk_join_request_logs.py
+  build_dataset_draft.py
+  build_online_records.py
+```
+
+这样做的原因：
+
+- ELK 查询逻辑属于该 Skill 的业务能力。
+- 迁移到其他 Agent 时可以直接跑脚本。
+- 平台只需要提供通用 `python_exec` 和 `python_package`。
+
+## 3. 默认数据源
+
+当前重点支持两类索引：
+
+```text
+pre-processing*
+  前处理日志。
+  适合拿模型 prompt、模型输出、候选 domain、excellent_domains_result。
+
+arch-flat-nlp-log-f-*
+  主 NLP 日志。
+  适合拿 query、domain、func、request_id、device_id、device、tts/text/to_speak。
+```
+
+Skill 文档中会引导 Agent：
+
+- 先用 `elk_profile_index.py` 看字段和样本。
+- 再用 `elk_search_cases.py` 根据 query/domain/model output 搜索。
+- 必要时用 request_id 做双表 join。
+
+## 4. 和 Agent Loop 的关系
+
+这个 Skill 的交互不是“一次查询结束”，而是策略迭代：
+
+```text
+用户描述需求
+  -> Agent 整理目标标签和筛选特征
+  -> 如缺目标标签或字段，先问用户
+  -> python_exec 调 elk_profile_index.py
+  -> 模型观察字段和样本
+  -> python_exec 调 elk_search_cases.py
+  -> 模型观察候选质量
+  -> 抽样展示给用户 review
+  -> 用户说哪里不对
+  -> 调整 filters / regex / domain / model output 条件
+  -> 再搜索
+```
+
+Agent loop 的价值在这里很明显：每次工具结果都会进入上下文，模型可以基于真实候选调整策略。
+
+## 5. 两条分支
+
+`online-mining-v2` 最重要的设计是强制区分两个分支。
+
+### 5.1 直接线上样本分支
+
+适用：
+
+```text
+用户想把线上筛出来的真实 case 作为评测集或专项集。
+```
+
+行为：
+
+```text
+不生成新 query。
+不进入 product-data generation plan。
+用 build_online_records.py 直接转 canonical records。
+```
+
+典型输出：
+
+```text
+output/records.jsonl
+output/records.csv
+```
+
+### 5.2 补充生成分支
+
+适用：
+
+```text
+用户想围绕线上问题扩写更多类似 case。
+```
+
+行为：
+
+```text
+先分析线上 badcase。
+整理错误类型和覆盖目标。
+再转 product-data 的 generation goal / plan / draft / records 流程。
+```
+
+这条边界避免了旧流程里出现的错误：用户只是想“把候选转样本”，Agent 却误走“生成新数据”。
+
+## 6. 和 product-data 的关系
+
+`online-mining-v2` 后处理复用 `product-data` 的标准：
+
+```text
+canonical record v1
+records.jsonl
+records.csv
+training.jsonl
+eval_planning.csv
+```
+
+复用脚本：
+
+```text
+skills/product-data/scripts/normalize_dataset_draft.py
+skills/product-data/scripts/validate_dataset_records.py
+skills/product-data/scripts/export_dataset_records.py
+skills/product-data/scripts/export_training_jsonl.py
+skills/product-data/scripts/export_planning_eval_csv.py
+```
+
+因此线上挖掘和产品定义生成最终可以进入同一套数据格式。
+
+## 7. 设计边界
+
+`online-mining-v2` 负责：
+
+- ELK 字段探索。
+- ELK 条件搜索。
+- request_id 补全。
+- 候选样本 review。
+- 线上候选转 canonical records。
+
+不负责：
+
+- 定义 canonical record 标准。
+- 生成全新补数。
+- 判断复杂标签知识。
+- 训练数据提交。
+
+这些分别交给 `product-data`、`label-master` 或后续数据仓库 Skill。

docs/technical-architecture/08-label-master.md

@@ -0,0 +1,231 @@
+# 08. label-master Skill 实现
+
+![label-master Skill 实现](assets/08-label-master.png)
+
+## 1. 定位
+
+`label-master` 是标签知识和边界分析 Skill。
+
+它不把“给 query 打标签”做成一个黑盒工具，而是让 Agent 逐步读取知识、比较候选、解释依据，并在必要时调用脚本校验最终输出格式。
+
+典型链路：
+
+```text
+query / 标签边界问题 / target 校验需求
+  -> 读取决策流程
+  -> 读取候选召回索引
+  -> 找 2-5 个候选标签
+  -> 读取候选标签卡片
+  -> 命中混淆时读取边界卡
+  -> 判断 complex / 多指令 / 自动任务等结构维度
+  -> 判断输出形态
+  -> 给出推荐、候选、依据、排除项和不确定点
+  -> 如要落数据，调用 validate_label_output.py
+```
+
+位置：
+
+```text
+skills/label-master/SKILL.md
+skills/label-master/knowledge/
+skills/label-master/scripts/
+```
+
+## 2. 知识组织
+
+核心目录：
+
+```text
+knowledge/
+  标签总览.md
+  决策流程.md
+  索引/
+    候选召回索引.md
+    标签索引.md
+    维度索引.md
+    label_manifest.json
+  判断维度/
+    复杂度/
+    多指令/
+    自动任务/
+    标注输出形态.md
+  输出能力/
+  标签/
+  边界/
+    边界索引.md
+    高频混淆/
+    领域概览/
+  迁移记录.md
+```
+
+设计原则：
+
+```text
+索引先行
+  不直接读全量标签卡。
+
+维度分离
+  complex、多指令、自动任务不是业务标签。
+
+标签卡片中文维护
+  方便人工编辑。
+
+输出能力独立
+  function、intent、object、Agent 包装放在输出能力层。
+
+边界卡优先人工维护
+  高频混淆写清楚，不只依赖自动迁移总结。
+```
+
+## 3. Agent 使用方式
+
+`label-master` 的关键在于利用 Agent 的多轮阅读和规划能力。
+
+典型 Agent loop：
+
+```text
+模型选择 label-master
+  -> read_file knowledge/决策流程.md
+  -> read_file knowledge/索引/候选召回索引.md
+  -> read_file 候选标签卡片
+  -> read_file 高频混淆边界卡
+  -> 必要时 read_file 判断维度/复杂度/*
+  -> 必要时 read_file 输出能力/*
+  -> 输出可 review 判断
+```
+
+为什么不做成单个 `classify_query(query) -> label` 工具：
+
+- 标签判断常常需要比较候选和排除项。
+- function / intent / Agent 包装要看迁移状态。
+- complex、多指令、自动任务是独立维度。
+- 人类 review 需要看到依据，而不是只看到最终标签。
+
+因此脚本只做索引和校验，不替代语义判断。
+
+## 4. 输出形态
+
+标签知识中存在多种输出形态：
+
+```text
+Agent(tag="xxx")
+function program
+intent
+object + function 组合
+多指令 JSON
+自动任务 JSON
+complex=true/false
+```
+
+`complex` 是独立维度，不应该混在 target 里。
+
+例如训练输出可以组合成：
+
+```text
+complex=false
+Agent(tag="地图导航")
+```
+
+但内部判断要拆成：
+
+```text
+complex: false
+target: Agent(tag="地图导航")
+```
+
+## 5. 脚本能力
+
+### 5.1 build_label_manifest.py
+
+用途：
+
+```text
+把 Markdown 知识生成机器索引。
+```
+
+位置：
+
+```text
+skills/label-master/scripts/build_label_manifest.py
+```
+
+输出：
+
+```text
+skills/label-master/knowledge/索引/label_manifest.json
+```
+
+使用场景：
+
+- 新增标签卡。
+- 修改输出能力。
+- 修改判断维度。
+- 修改边界知识。
+
+### 5.2 validate_label_output.py
+
+用途：
+
+```text
+校验 target/function/intent/Agent/多指令/自动任务输出格式。
+```
+
+位置：
+
+```text
+skills/label-master/scripts/validate_label_output.py
+```
+
+典型调用：
+
+```text
+python_exec(script_path="skills/label-master/scripts/validate_label_output.py", args=["--target", "Agent(tag=\"地图导航\")"])
+```
+
+批量校验：
+
+```text
+--file output/records.jsonl --field target
+```
+
+## 6. 和 product-data 的关系
+
+`product-data` 在需要确认 target 或生成边界数据时，可以读取 `label-master` 的知识。
+
+分工：
+
+```text
+label-master
+  判断标签知识、边界、输出形态、target 合法性。
+
+product-data
+  做数据生成 review、draft、canonical records、导出。
+```
+
+当用户给出模糊标签名时，应该先用 `label-master` 辅助确认：
+
+```text
+标签是否存在
+使用 Agent 包装还是 function
+complex 默认是什么
+是否有高频混淆边界
+```
+
+再进入 `product-data` 的生成计划。
+
+## 7. 设计边界
+
+`label-master` 负责：
+
+- 标签知识检索。
+- 候选标签比较。
+- 边界解释。
+- 输出形态判断。
+- target 格式校验。
+
+不负责：
+
+- 生成数据集。
+- 线上日志挖掘。
+- 导出训练/评测格式。
+- 直接替用户确认争议边界。

docs/technical-architecture/09-external-skills.md

@@ -0,0 +1,186 @@
+# 09. 外部系统 Skill：ELK、SQL、模型迭代
+
+![外部系统 Skill 接入模式](assets/09-external-skills.png)
+
+## 1. 这类 Skill 的共同特征
+
+外部系统 Skill 的核心不是复杂 prompt，而是把某个内部系统的调用方式、参数约定、依赖和输出格式打包起来。
+
+典型形态：
+
+```text
+SKILL.md
+  写清楚什么时候用、参数怎么选、哪些动作需要确认。
+
+scripts/
+  执行真实外部系统调用。
+
+python_exec
+  作为统一执行入口。
+
+python_package
+  处理依赖缺失。
+
+output/
+  查询结果或报表写入当前 session output。
+```
+
+## 2. elk-fetch
+
+位置：
+
+```text
+skills/elk-fetch/SKILL.md
+skills/elk-fetch/elk_query.py
+```
+
+定位：
+
+```text
+按 request id 或查询条件读取小米内网 ELK 日志。
+```
+
+实现方式：
+
+- `SKILL.md` 写明支持的 profile 和查询方式。
+- 实际执行必须使用 `python_exec.script_path`。
+- 缺 `elasticsearch`、`urllib3` 时用 `python_package`。
+- 不让模型用 bash 手写临时 Python 查询脚本。
+
+典型价值：
+
+```text
+把“怎么查 ELK”这类个人经验变成团队共享能力。
+```
+
+和 `online-mining-v2` 的区别：
+
+```text
+elk-fetch
+  更偏单次日志查询和调试。
+
+online-mining-v2
+  更偏批量挖掘、策略迭代、样本转换。
+```
+
+## 3. data-factory-sql
+
+位置：
+
+```text
+skills/data-factory-sql/SKILL.md
+skills/data-factory-sql/run_sql.py
+```
+
+定位：
+
+```text
+通过 Kyuubi HTTP API 执行数据工场 SQL，轮询状态并下载 CSV 结果。
+```
+
+实现方式：
+
+- 用户直接给 SQL 时，可以确认后执行。
+- 用户只给分析需求时，Agent 可以先生成 SQL 草稿，再让用户确认。
+- 执行必须通过 `python_exec.script_path`。
+- 输出默认写入当前 session output。
+
+这个 Skill 的门禁重点是：
+
+```text
+SQL 执行前确认。
+控制查询范围和 limit。
+输出路径清晰。
+失败时展示错误和可调整建议。
+```
+
+## 4. model-iteration
+
+位置：
+
+```text
+skills/model-iteration/SKILL.md
+skills/model-iteration/scripts/
+```
+
+定位：
+
+```text
+模型训练、评估、数据准备和迭代流程辅助。
+```
+
+这类 Skill 属于混合工程型：
+
+- 有流程。
+- 有脚本。
+- 有配置。
+- 可能调用外部训练平台。
+- 高风险动作较多。
+
+因此它更需要明确：
+
+```text
+哪些步骤只是分析。
+哪些步骤会启动训练。
+哪些步骤需要用户确认。
+输出目录和实验记录在哪里。
+```
+
+## 5. 外部 Skill 的通用约定
+
+### 5.1 调用方式
+
+优先：
+
+```json
+{
+  "script_path": "skills/<skill-name>/scripts/run.py",
+  "stdin": "{...}",
+  "timeout_seconds": 120
+}
+```
+
+或者脚本在 Skill 根目录时：
+
+```json
+{
+  "script_path": "skills/elk-fetch/elk_query.py",
+  "args": ["..."]
+}
+```
+
+### 5.2 依赖处理
+
+依赖缺失时：
+
+```text
+python_package(action="install", packages=[...])
+```
+
+不要：
+
+```text
+bash pip install ...
+bash python ...
+```
+
+### 5.3 输出处理
+
+外部系统查询结果应该：
+
+- stdout 给结构化摘要。
+- 大结果写入 `output/` 或 `scratchpad/`。
+- 返回实际文件路径。
+- 避免把大量数据直接塞进最终回复。
+
+### 5.4 安全和确认
+
+需要确认的动作：
+
+- 执行大范围 SQL。
+- 启动训练。
+- 写外部路径。
+- 推送代码或数据仓库。
+- 导出可能包含敏感字段的线上数据。
+
+只读、小范围、用户明确指定 rid 或 SQL 的查询，可以直接执行，但仍要控制结果规模。

docs/technical-architecture/10-memory-research.md

@@ -0,0 +1,478 @@
+# 10. Agent 记忆机制调研与 ZK Data Agent 对比
+
+本文整理主流 Agent / AI 产品的记忆实现方式，并对照 ZK Data Agent 当前实现。目标不是判断哪一种“最好”，而是说明不同记忆机制分别解决什么问题，以及为什么我们当前选择“用户记忆 + Skill 使用记忆 + 异步整理队列 + Markdown 可编辑文件”的路线。
+
+调研时间：2026-05-19。
+
+## 1. 结论摘要
+
+主流记忆实现大致分为六类：
+
+| 类型 | 代表 | 核心做法 | 适合场景 |
+|------|------|----------|----------|
+| 产品级长期记忆 | ChatGPT Memory | 平台自动保存用户偏好和事实，并在后续对话中注入 | 通用个人助手 |
+| 会话状态记忆 | OpenAI Agents SDK Sessions、AutoGen Memory | 自动保存历史消息或把外部记忆注入上下文 | 线程连续对话 |
+| 文件化项目记忆 | Claude Code `CLAUDE.md`、Claw 基座 memory files | 通过项目/用户级 Markdown 文件向 Agent 注入稳定规则 | 工程项目、团队约定 |
+| 图/向量检索记忆 | LangGraph Store、Mem0、Zep/Graphiti | 抽取事实，存入向量库或知识图谱，按语义检索 | 长期、跨会话、海量事实 |
+| Agent 自主管理记忆 | Letta / MemGPT | Agent 有显式 memory blocks 和 archival memory，可读写管理 | 状态型 Agent、长期角色 |
+| 框架内置任务记忆 | CrewAI | 短期、长期、实体、上下文记忆组合 | 多 Agent 任务协作 |
+
+ZK Data Agent 当前更接近：
+
+```text
+文件化项目记忆
+  + 产品级用户记忆
+  + Skill 作用域记忆
+  + 异步记忆整理队列
+```
+
+它没有优先做向量库或知识图谱，而是选择 Markdown 文件作为最终记忆正文。这个取舍适合当前团队场景：记忆内容需要能被用户看到、编辑、删除，并且要按 Skill 作用域精准注入。
+
+## 2. 主流实现机制
+
+### 2.1 ChatGPT Memory：产品级个人长期记忆
+
+ChatGPT Memory 的核心是平台级用户记忆。它会保存用户偏好、事实和历史对话中有持续价值的信息，并在后续对话中使用。用户可以查看、管理、删除保存的记忆，也可以关闭相关能力。
+
+机制特点：
+
+- 记忆作用域是用户账号。
+- 由产品后台判断哪些内容值得保存。
+- 注入方式对用户透明，用户看到的是“助手更了解我”。
+- 适合通用个人助手，不适合表达复杂业务流程结构。
+
+和我们的关系：
+
+ZK Data Agent 的“用户记忆”借鉴了这个方向，但没有把全部记忆做成黑盒。我们把最终正文落到 `user.md`，并在 UI 里允许用户编辑。
+
+### 2.2 OpenAI Agents SDK Sessions：会话状态记忆
+
+OpenAI Agents SDK 的 Sessions 主要解决“同一个会话线程里自动保留历史上下文”。开发者不需要每轮手动传入完整历史，Session 会保存对话项，并在下一轮运行时自动带上。
+
+机制特点：
+
+- 更偏 conversation state，而不是长期个人偏好。
+- 适合多轮会话连续执行。
+- 常见实现是 SQLite / SQLAlchemy / 自定义 session backend。
+- 记忆对象主要是消息历史，不是抽象后的长期知识。
+
+和我们的关系：
+
+ZK Data Agent 也有 session 持久化，但我们把它和“长期记忆”分开：
+
+```text
+session.json
+  保存当前会话消息、工具调用、产物和运行事件。
+
+memory/user.md、memory/skills/*.md
+  保存跨会话长期偏好和 Skill 使用经验。
+```
+
+这个区分很重要：会话历史服务“恢复当前任务”，长期记忆服务“下次任务更懂用户和业务”。
+
+### 2.3 Claude Code / OpenClaw / Claw：文件化项目记忆
+
+Claude Code 使用 `CLAUDE.md` 作为项目或用户级记忆文件，常用于保存仓库规则、构建命令、代码风格、项目约定等。OpenClaw / Claw 类 Code Agent 基座通常也会保留这条路线：从全局或工作目录发现记忆文件，并把内容注入上下文。
+
+在当前仓库里，对应实现主要是：
+
+```text
+src/agent_context.py
+src/session_memory_compact.py
+```
+
+其中 `agent_context.py` 负责发现全局和目录级 memory files，`session_memory_compact.py` 负责会话压缩场景下的 session memory 摘要。
+
+机制特点：
+
+- 记忆是文本文件，天然可读、可版本化。
+- 非常适合工程项目规则和团队约定。
+- 注入通常按目录/项目作用域进行。
+- 记忆更新更多依赖人工维护，而不是完全自动。
+
+和我们的关系：
+
+ZK Data Agent 继承了“文件化、可编辑、可解释”的优点，但把作用域进一步细分：
+
+```text
+user.md
+  用户级偏好和稳定习惯。
+
+skills/<skill-name>.md
+  某个 Skill 的使用经验、踩坑、格式偏好和边界修正。
+```
+
+也就是说，我们不是只有“项目记忆”，而是增加了“Skill 记忆”这一层。
+
+### 2.4 LangGraph：线程状态 + 长期 Memory Store
+
+LangGraph 把 memory 分成 short-term memory 和 long-term memory。短期记忆通常跟 thread 绑定，用来维持一次会话；长期记忆通过 store 按 namespace 保存，可以跨 thread 召回。它还把长期记忆进一步拆成 semantic、episodic、procedural 等类型。
+
+机制特点：
+
+- thread state 解决会话内上下文。
+- store 解决跨会话长期信息。
+- 支持按 user id / namespace 组织记忆。
+- 长期记忆可以由应用逻辑或 Agent 写入、搜索、更新。
+
+和我们的关系：
+
+ZK Data Agent 当前没有引入通用 Store / VectorStore，而是用文件系统和 SQLite 队列实现一个轻量版本：
+
+```text
+namespace = account_id + memory kind + skill_name
+storage = Markdown files + SQLite queue
+retrieval = user memory always considered, skill memory按启用 Skill 精准注入
+```
+
+这比 LangGraph Store 简单，但更直接服务我们当前的 Skill 工作台。
+
+### 2.5 Mem0：独立记忆层
+
+Mem0 更像一个独立 memory layer。典型链路是：从对话中抽取事实，存入记忆系统；后续根据 query 检索相关记忆，再注入给模型。它强调 add / search / update / delete 这类记忆 API，也支持面向用户、Agent、session 等维度组织。
+
+机制特点：
+
+- 记忆层和 Agent 框架解耦。
+- 常见存储后端是向量、图或混合检索。
+- 强调自动抽取、去重、更新和语义召回。
+- 适合大规模个性化 Agent 或跨应用记忆服务。
+
+和我们的关系：
+
+ZK Data Agent 目前没有把记忆做成独立检索服务。原因是我们的高频需求不是“从海量事实里语义搜索”，而是“把少量稳定经验准确注入到对应 Skill”。如果未来 Skill 记忆膨胀，可以在 Markdown 之外增加 Mem0 类似的检索层。
+
+### 2.6 Letta / MemGPT：Agent 自主管理内存
+
+Letta 延续 MemGPT 思路，把 Agent 看成有长期状态的主体。它通常区分 core memory 和 archival memory：core memory 是短小、常驻上下文的重要信息；archival memory 是更大的外部记忆空间，Agent 可以通过工具读写。
+
+机制特点：
+
+- Agent 可以主动管理自己的记忆。
+- core memory 常驻，archival memory 需要检索。
+- 适合长期角色 Agent、个人助理、需要自我状态连续性的 Agent。
+- 复杂度更高，需要更强的记忆写入约束和审计。
+
+和我们的关系：
+
+ZK Data Agent 没有让主 Agent 在执行链路里自由修改记忆。我们把记忆写入放到后台 worker，避免主任务因为记忆整理变慢或出错。这是一个更保守的团队平台取舍。
+
+### 2.7 Zep / Graphiti：时间感知知识图谱记忆
+
+Zep / Graphiti 代表的是 temporal knowledge graph 路线：从对话或事件中抽取实体和关系，形成带时间属性的知识图谱。它解决的问题不是简单偏好记忆，而是“事实如何随时间变化”“实体关系如何演进”。
+
+机制特点：
+
+- 记忆结构是实体、关系、事件、时间。
+- 适合复杂事实网络和时间演化。
+- 检索结果可以包含关系路径和上下文。
+- 实现成本和运维复杂度高于 Markdown 或向量检索。
+
+和我们的关系：
+
+标签边界、业务规则、Skill 使用经验目前更适合文本化规则，不一定需要图谱。但如果未来要做“用户、Skill、数据集、标签、错误类型、修复策略”之间的关系分析，图谱路线会有价值。
+
+### 2.8 CrewAI：多 Agent 任务记忆
+
+CrewAI 的记忆体系主要服务多 Agent 协作，通常包含 short-term memory、long-term memory、entity memory 和 contextual memory。它关注的是任务过程中多个 Agent 如何共享上下文和持续改进。
+
+机制特点：
+
+- 和 Crew / Agent / Task 结构绑定。
+- 强调任务协作过程中的上下文复用。
+- 对实体、任务经验有独立组织方式。
+
+和我们的关系：
+
+ZK Data Agent 当前不是多 Agent 编排优先，而是单个工作台 Agent + Skill 能力包优先。Skill 记忆在某种程度上承担了“任务经验记忆”的角色。
+
+### 2.9 AutoGen：Memory 组件注入上下文
+
+AutoGen 的 AgentChat 提供 Memory 抽象，可以把 list memory、vector memory 等组件挂到 AssistantAgent 上。运行时 Memory 会根据消息更新上下文，或把检索结果添加到模型输入。
+
+机制特点：
+
+- Memory 是 Agent 可插拔组件。
+- 可以使用简单列表，也可以接向量检索。
+- 更偏框架扩展点，而不是产品级记忆管理 UI。
+
+和我们的关系：
+
+ZK Data Agent 的记忆也可以理解为一个可插拔上下文组件，但我们额外做了用户 UI、Skill 作用域和后台队列。
+
+## 3. ZK Data Agent 当前实现
+
+实现入口：
+
+```text
+src/personal_memory.py
+backend/api/server.py
+frontend/app/components/assistant-ui/threadlist-sidebar.tsx
+```
+
+### 3.1 存储结构
+
+每个账号有独立记忆目录：
+
+```text
+.port_sessions/accounts/<account_id>/memory/
+  user.md
+  skills/
+    <skill-name>.md
+  memory.db
+```
+
+其中：
+
+- `user.md`：用户级长期记忆。
+- `skills/<skill>.md`：某个 Skill 的使用记忆。
+- `memory.db`：事件队列、状态和 revision 账本。
+
+### 3.2 注入逻辑
+
+模型调用前，后端调用：
+
+```text
+memory_manager.render_injection(account_id, enabled_skill_names)
+```
+
+注入规则：
+
+```text
+用户记忆
+  账号级，作为长期偏好注入。
+
+Skill 使用记忆
+  只读取当前启用 Skill 对应的 skills/<skill>.md。
+
+冲突优先级
+  用户本轮明确要求 > 个性化记忆。
+```
+
+这避免了一个常见问题：所有记忆都无差别注入导致上下文污染。
+
+### 3.3 生成时机
+
+每次交互结束后，后端调用：
+
+```text
+memory_manager.enqueue_interaction(...)
+```
+
+系统不会每轮同步整理记忆，而是先检测信号：
+
+```text
+显式记忆词：
+记住、以后、下次、默认、总是、不要、应该、固定
+
+纠错词：
+不对、不是这样、格式错、之前说过、还是不行
+
+Skill 经验：
+skill、工具、流程、格式
+
+工具经验：
+模型返回的工具参数不是合法 JSON
+```
+
+命中后写入 SQLite pending 队列。显式记忆优先级更高。
+
+### 3.4 异步整理
+
+后台 worker 每 5 秒扫描账号事件，每次最多处理 8 条 pending event：
+
+```text
+pending -> processing -> done / failed
+```
+
+整理方式：
+
+1. 读取已有 `user.md` 和相关 `skills/<skill>.md`。
+2. 把一批事件交给模型做“整理式合并”。
+3. 模型必须输出 JSON：
+
+```json
+{
+  "user_memory": "完整 Markdown 或空字符串",
+  "skill_memories": {
+    "skill-name": "完整 Markdown"
+  }
+}
+```
+
+4. 如果模型输出不可解析，则走 fallback 规则。
+5. 写入 Markdown 文件，并更新 revision。
+
+### 3.5 用户可编辑
+
+前端左下角“记忆”入口支持：
+
+- 查看用户记忆行数。
+- 查看 Skill 记忆列表。
+- 编辑用户记忆。
+- 编辑某个 Skill 记忆。
+- 查看记忆队列状态。
+
+管理后台只看队列、用量等统计，不展示其他用户具体记忆内容。
+
+## 4. 对比表
+
+| 维度 | ChatGPT | Claude Code / OpenClaw | LangGraph / Mem0 / Zep | Letta | ZK Data Agent |
+|------|---------|--------------------|-------------------------|-------|---------------|
+| 主要目标 | 个人助手个性化 | 项目规则注入 | 长期检索记忆 | 状态型长期 Agent | 团队 Skill 工作台 |
+| 记忆粒度 | 用户 | 用户/项目/目录 | 用户/线程/实体/namespace | Agent memory block | 用户 + Skill |
+| 存储形态 | 平台内部 | Markdown 文件 | Store / 向量 / 图 | Core + archival memory | Markdown + SQLite queue |
+| 生成时机 | 产品后台自动 | 多为人工维护 | 自动抽取 / API 写入 | Agent 主动管理 | 交互结束后异步整理 |
+| 检索方式 | 平台决定 | 直接注入文件 | 语义搜索 / 图检索 | Core 常驻 + archival 检索 | 用户记忆 + 当前 Skill 记忆注入 |
+| 可编辑性 | 用户可管理 | 文件可编辑 | 取决于产品/API | 通常需要工具/API | UI 可编辑 Markdown |
+| 适合业务流程沉淀 | 中 | 中 | 高，但工程复杂 | 高，但复杂 | 高，且轻量 |
+| 风险 | 黑盒、难按业务作用域隔离 | 容易依赖人工维护 | 检索和更新复杂 | 主链路复杂度高 | 暂无语义召回和图谱能力 |
+
+## 5. 为什么当前方案适合我们
+
+### 5.1 我们需要的是 Skill 使用经验，而不只是用户偏好
+
+通用记忆多关注“用户是谁、用户喜欢什么”。我们的高频需求更像：
+
+```text
+product-data 生成数据时，用户偏好什么确认流程？
+标签大师判断时，哪些边界经常被纠正？
+online-mining-v2 查询线上日志时，哪些字段和表更稳定？
+某个 Skill 写文件时，模型容易踩什么坑？
+```
+
+这些经验天然和 Skill 绑定。因此 `skills/<skill>.md` 比单一用户记忆更准确。
+
+### 5.2 我们需要可审计、可编辑，而不是完全黑盒
+
+团队平台里，记忆不能只存在模型或向量库内部。用户需要能看到：
+
+- 记住了什么。
+- 为什么下一次会注入。
+- 哪里可以手动修改。
+- 哪些记忆是用户级，哪些是 Skill 级。
+
+Markdown 文件在这点上比纯向量库更直接。
+
+### 5.3 主链路不能被记忆整理拖慢
+
+数据生成、线上挖掘、标签判断本身就是长任务。记忆整理如果同步放在主链路里，会增加延迟和失败面。
+
+当前设计是：
+
+```text
+主链路：只读取已有记忆 + 入队事件
+后台：异步整理、合并、失败重试/记录
+```
+
+这和团队生产工具的稳定性要求更匹配。
+
+### 5.4 Skill 作用域注入能降低上下文污染
+
+如果所有记忆每次都注入，模型会被无关偏好干扰。当前只注入启用 Skill 的记忆：
+
+```text
+启用 product-data -> 注入 product-data 使用记忆
+启用 label-master -> 注入 label-master 使用记忆
+未启用某 Skill -> 不注入该 Skill 记忆
+```
+
+这使记忆更像“能力使用手册的增量补丁”，而不是一坨全局上下文。
+
+## 6. 当前不足和后续方向
+
+### 6.1 缺少语义召回
+
+当前 Skill 记忆是按 Skill 文件整体注入，不做向量检索。如果某个 Skill 记忆变得很长，可能需要：
+
+- 按章节拆分。
+- 引入轻量 embedding 检索。
+- 只注入和当前 query 相关的片段。
+
+### 6.2 缺少结构化 schema
+
+Markdown 易编辑，但不方便做强约束。后续可以让 Skill 记忆同时存在：
+
+```text
+human.md
+structured.json
+```
+
+其中 Markdown 给人看，JSON 给程序做筛选和校验。
+
+### 6.3 缺少记忆质量评估
+
+目前能看到队列状态，但还没有系统评估：
+
+- 哪些记忆被注入。
+- 注入后是否减少纠错。
+- 哪些记忆过期。
+- 哪些 Skill 记忆导致误导。
+
+后续可以把 memory revision 与 session outcome 关联起来。
+
+### 6.4 缺少跨用户团队记忆
+
+当前是账号级记忆。团队共性经验仍主要沉淀在 Skill 本体里。未来可以区分：
+
+```text
+个人 Skill 记忆
+  某个用户自己的偏好和使用习惯。
+
+团队 Skill 记忆
+  多人使用后沉淀的稳定经验，经 review 后合入 Skill。
+```
+
+这样可以形成从“个人经验”到“团队 Skill 知识”的晋升路径。
+
+## 7. 建议的技术路线
+
+短期保持当前架构：
+
+```text
+Markdown 可编辑记忆
+SQLite 异步队列
+按 Skill 注入
+UI 可查看可修改
+后台可观测队列
+```
+
+中期增强：
+
+```text
+记忆片段化
+记忆注入日志
+过期/冲突检测
+记忆质量指标
+```
+
+长期可选：
+
+```text
+向量检索：解决 Skill 记忆膨胀后的相关片段召回。
+图谱记忆：解决用户、Skill、标签、数据集、错误类型之间的关系分析。
+团队记忆晋升：把多用户共性 Skill 经验 review 后写回 Git Skill。
+```
+
+## 8. 资料来源
+
+- OpenAI Help：ChatGPT Memory FAQ
+  https://help.openai.com/en/articles/8590148-memory-faq
+- OpenAI Agents SDK：Sessions
+  https://openai.github.io/openai-agents-python/sessions/
+- Anthropic Claude Code：Memory
+  https://docs.anthropic.com/en/docs/claude-code/memory
+- LangChain / LangGraph：Memory concepts
+  https://docs.langchain.com/oss/python/concepts/memory
+- Mem0 documentation
+  https://docs.mem0.ai/
+- Letta documentation
+  https://docs.letta.com/
+- Zep / Graphiti documentation
+  https://help.getzep.com/
+- CrewAI Memory concepts
+  https://docs.crewai.com/concepts/memory
+- Microsoft AutoGen AgentChat Memory
+  https://microsoft.github.io/autogen/dev/user-guide/agentchat-user-guide/memory.html
+- ZK Data Agent 当前实现
+  `src/personal_memory.py`、`docs/technical-architecture/05-workspace-memory-observability.md`

docs/technical-architecture/11-workspace-runtime.md

@@ -0,0 +1,663 @@
+# Workspace Runtime 设计稿
+
+## 背景
+
+当前项目已经有了平台账号、会话目录、Jupyter 远程工作区、Skill/Tools 和文件产物管理，但这些能力还没有被一个统一的“执行环境”概念串起来。
+
+现在的问题不是单纯缺少登录账号，而是需要回答：
+
+```text
+谁在使用 Agent
+  -> 当前会话绑定到哪个工作区
+    -> 工具以什么身份、在什么目录、用什么权限执行
+      -> 产物在哪里保存、展示和下载
+```
+
+因此，账户体系升级不应该只看账号密码，而应该引入 `Workspace Runtime` 作为平台账号和工具执行之间的核心抽象。
+
+## 核心结论
+
+账户体系分两层：
+
+```text
+平台账号 Account
+  负责登录、角色、会话、Skill 配置、模型配置、记忆和集成状态。
+
+工作区运行时 Workspace Runtime
+  负责执行身份、工作目录、文件读写、Python 环境、远程连接和进程管理。
+```
+
+平台账号不直接等价于 Linux 账号，也不直接等价于 Jupyter 账号。平台账号可以绑定不同类型的 runtime。
+
+## 目标
+
+1. 统一本机工作区、Linux 子账户工作区、Jupyter 工作区和未来 SSH 工作区。
+2. 让 Tool handler 不关心执行位置，只面向统一 runtime 执行。
+3. 明确权限来源，避免把远程工作区误认为平台托管沙盒。
+4. 让每个 session 的输入、输出、scratchpad、Python 环境和文件下载有稳定归属。
+5. 为后续多用户、资源限制、审计、团队空间和远程执行打基础。
+
+## 非目标
+
+1. 不在第一阶段实现完整企业 SSO。
+2. 不把所有账号体系直接迁移到 Linux PAM。
+3. 不强制所有远程工作区都变成平台托管沙盒。
+4. 不要求 Skill 感知 runtime 的具体实现细节。
+
+## 对象模型
+
+### Account
+
+平台账号是 Web 产品层的身份。
+
+```text
+Account
+  id
+  username
+  display_name
+  role
+  status
+  created_at
+  updated_at
+```
+
+职责：
+
+- 登录和会话 token。
+- 模型选择。
+- Skill 启用状态。
+- 用户记忆。
+- 第三方集成状态。
+- 默认 workspace runtime 策略。
+
+### Session
+
+Session 是一次 Agent 对话任务。
+
+```text
+Session
+  id
+  account_id
+  runtime_id
+  title
+  status
+  created_at
+  updated_at
+```
+
+职责：
+
+- 保存对话历史。
+- 绑定一个 runtime。
+- 保存工具调用、活动步骤和最终结果。
+- 关联输入文件和输出 artifact。
+
+Session 一旦绑定远程 runtime，刷新页面后也应该恢复到同一个 runtime。
+
+### Workspace Runtime
+
+Workspace Runtime 是工具执行的真实环境。
+
+```text
+WorkspaceRuntime
+  id
+  account_id
+  session_id
+  type
+  root
+  permissions_source
+  status
+  created_at
+  updated_at
+```
+
+`type` 可以是：
+
+```text
+local_process
+local_linux_user
+remote_jupyter
+remote_ssh
+```
+
+职责：
+
+- 决定 bash/python/file 工具在哪里执行。
+- 决定输入输出文件在哪里。
+- 决定 Python 环境在哪里。
+- 决定进程如何启动、停止和清理。
+- 决定文件如何展示、下载和转在线文档。
+
+### Artifact
+
+Artifact 是输入和输出文件的统一抽象。
+
+```text
+Artifact
+  id
+  account_id
+  session_id
+  runtime_id
+  kind: input | output | scratchpad
+  uri
+  name
+  size
+  mime
+  created_at
+```
+
+`uri` 可以是：
+
+```text
+file:///home/<account_id>/zk-agent/sessions/<session_id>/output/a.jsonl
+jupyter://<session_id>/root/zk_agent_workspaces/<session_id>/output/a.jsonl
+ssh://<runtime_id>/home/user/zk_agent_workspaces/<session_id>/output/a.jsonl
+```
+
+文件列表只需要展示 metadata。下载或转在线文档时，再通过 runtime 拉取内容。
+
+### Executor
+
+Executor 是 Tool handler 和 Runtime 之间的执行适配层。
+
+```text
+Executor
+  run_bash(command, cwd, timeout)
+  run_python(code_or_file, cwd, timeout)
+  read_file(path)
+  write_file(path, content)
+  list_files(path)
+  open_file_stream(path)
+  cancel(run_id)
+```
+
+Tool handler 不应该自己判断是在本地、Jupyter 还是 SSH。它只调用当前 session 的 executor。
+
+## Runtime 类型
+
+### local_process
+
+当前已有的默认模式。工具在服务进程所在机器上执行，目录由平台约定。
+
+```text
+.port_sessions/accounts/<account_id>/sessions/<session_id>/
+```
+
+适合：
+
+- 本地开发。
+- 单用户调试。
+- 早期兼容。
+
+问题：
+
+- 多用户隔离主要靠代码路径约束。
+- 工具进程和平台服务权限一致，风险较高。
+
+### local_linux_user
+
+平台托管的标准多用户工作区。
+
+```text
+平台账号: banisherwy
+Linux runtime user: banisherwy
+workspace root: /home/banisherwy/zk-agent/sessions/<session_id>
+```
+
+服务进程可以是 root，工具进程切换到普通 Linux 用户执行。
+
+```text
+root backend
+  -> runuser -u banisherwy -- <runner command>
+```
+
+职责分工：
+
+```text
+root 服务
+  创建 runtime 用户
+  初始化 workspace
+  设置 owner 和权限
+  启停进程
+  管理平台账号和 session
+
+普通 Linux 用户
+  执行 bash/python
+  拥有自己的 workspace
+  拥有自己的 Python 虚拟环境
+  只能写自己的目录
+```
+
+推荐目录：
+
+```text
+/home/<account_id>/zk-agent/
+  sessions/
+    <session_id>/
+      input/
+      output/
+      scratchpad/
+      session.json
+  python/
+    .venv/
+  memory/
+  integrations/
+```
+
+推荐约定：
+
+```text
+平台用户名 = Linux 用户名
+平台密码 = Linux 用户密码
+Linux 用户允许 SSH 登录
+```
+
+这样用户体验更直接：
+
+- 在平台创建账号时，同步创建同名 Linux 用户。
+- 用户可以使用同一套账号密码登录 Web 平台和 SSH。
+- Agent 工具执行时也使用同一个 Linux 用户身份。
+- 文件 owner、进程 owner、SSH 登录用户和平台用户名一致，便于排查和审计。
+
+但两者在架构语义上仍然保留分层：
+
+```text
+平台账号体系
+  登录、角色、session、Skill、模型配置。
+
+Linux 用户体系
+  执行隔离、文件权限、进程权限、资源限制。
+```
+
+也就是说，账号名和密码保持一致，但平台仍然保留自己的登录态、session、角色和配置管理。Linux 账号负责机器级登录和执行权限。
+
+需要注意：
+
+- 用户名必须同时满足平台账号规范和 Linux 用户名规范。
+- 修改平台密码时必须同步修改 Linux 密码。
+- 禁用平台账号时，也应该禁用 Linux 登录或锁定 Linux 用户。
+- 删除平台账号时，需要明确是否保留 `/home/<account_id>/zk-agent/` 数据。
+- root 服务创建用户和改密码时必须走受控 helper，不能把用户输入拼成 shell 命令。
+
+### remote_jupyter
+
+用户授权的远程工作区。
+
+语义是：
+
+```text
+用户把自己已有权限的 Jupyter 环境接入平台。
+平台代替用户在这个环境里执行。
+```
+
+这不是平台托管沙盒。权限边界来自用户提供的 Jupyter 凭证。
+
+```text
+Account: banisherwy
+Session: xxx
+Runtime: remote_jupyter
+Root: /root/zk_agent_workspaces/<session_id>
+Permissions source: Jupyter password/token 对应的远程用户权限
+```
+
+平台需要保证：
+
+- Jupyter 凭证只绑定当前 account/session。
+- 刷新后 runtime 状态可恢复。
+- 文件列表 metadata-only。
+- 下载时通过 Jupyter API 流式拉取。
+- 转在线文档时按需拉取，不默认同步大文件。
+- 用户明确知道 Agent 在远程环境里的权限等同于该 Jupyter 用户。
+
+平台不能保证：
+
+- 远程机器上的文件权限隔离。
+- 远程 Jupyter 用户不是 root。
+- 远程挂载目录的访问范围。
+
+短期建议：`remote_jupyter` 先保持当前逻辑，不作为账户体系升级的主战场。
+
+当前已经具备：
+
+- session 级 Jupyter 绑定。
+- 刷新后恢复远程工作区状态。
+- 输出文件 metadata-only 展示。
+- 下载时通过 Jupyter API 流式读取。
+- 转在线文档时按需拉取。
+
+因此下一步账户体系升级优先处理本机托管 runtime 和平台账号，不主动重构 Jupyter 执行链路。后续只需要让 Jupyter 工作区在概念上挂到 `WorkspaceRuntime` 模型下。
+
+### remote_ssh
+
+未来可扩展的用户授权远程工作区。
+
+语义和 remote_jupyter 类似：
+
+```text
+用户提供 SSH 连接能力。
+平台代替用户在远程机器上执行。
+权限边界来自 SSH 凭证对应的远程用户。
+```
+
+remote_ssh 更适合：
+
+- 远程机器没有 Jupyter。
+- 需要更完整 shell 能力。
+- 需要使用远程开发机的挂载盘、GPU、模型目录。
+
+但它也更复杂：
+
+- SSH 凭证管理。
+- 长连接和心跳。
+- relay / OTP / 扫码登录。
+- 文件传输和断线恢复。
+- 进程树管理。
+
+因此优先级应低于 `local_linux_user` 和已有 `remote_jupyter`。
+
+## 权限边界
+
+需要在 UI 和文档中明确区分两类工作区：
+
+```text
+平台托管工作区
+  平台负责权限隔离。
+  典型类型: local_linux_user。
+
+用户授权工作区
+  用户提供凭证。
+  平台不创建权限边界，只复用用户已有权限。
+  典型类型: remote_jupyter, remote_ssh。
+```
+
+UI 可以显示：
+
+```text
+当前工作区：Jupyter 远程工作区
+权限来源：用户提供的 Jupyter 凭证
+Agent 权限：等同于该远程环境当前登录用户
+```
+
+或者：
+
+```text
+当前工作区：平台托管工作区
+执行身份：banisherwy
+Agent 权限：普通 Linux 用户权限
+```
+
+## Tool 调用关系
+
+目标关系：
+
+```text
+Agent Loop
+  -> Tool handler
+    -> RuntimeResolver(session_id)
+      -> Executor
+        -> local process / linux user / jupyter / ssh
+```
+
+工具不应该散落处理路径和远程协议。
+
+例如：
+
+```text
+python_exec
+  -> executor.run_python(...)
+
+write_file
+  -> executor.write_file(...)
+
+download_artifact
+  -> executor.open_file_stream(...)
+```
+
+这样后续新增 runtime 时，尽量只新增 executor，不重写每个工具。
+
+## 文件策略
+
+### 输入文件
+
+输入文件应该同步到当前 runtime 的 `input/`。
+
+```text
+local_linux_user
+  上传文件 -> /home/<account_id>/zk-agent/sessions/<session_id>/input/
+
+remote_jupyter
+  上传文件 -> 通过 Jupyter API 写入 /root/zk_agent_workspaces/<session_id>/input/
+```
+
+### 输出文件
+
+输出文件默认放到当前 runtime 的 `output/`。
+
+```text
+output/
+  records.jsonl
+  report.md
+  samples.csv
+```
+
+对远程 runtime，平台只保存 metadata。
+
+```text
+name
+size
+mtime
+uri
+runtime_id
+```
+
+点击下载时再流式读取。点击转在线文档时再按需拉取，并设置大小限制。
+
+## Python 环境策略
+
+每个 runtime 应有自己的 Python 环境。
+
+```text
+local_linux_user
+  /home/<account_id>/zk-agent/python/.venv
+
+remote_jupyter
+  /root/zk_agent_workspaces/.zk-agent-python/.venv
+```
+
+初始化时只做最小准备：
+
+- 创建 venv。
+- 配置 pip 源。
+- 不预装大量包。
+
+缺包时由 Agent 根据任务安装，安装也发生在当前 runtime 内。
+
+## 进程管理
+
+每个工具执行必须有 run id 和 process group。
+
+```text
+run_id
+account_id
+session_id
+runtime_id
+executor_pid 或 remote_execution_id
+status
+started_at
+updated_at
+```
+
+停止任务时：
+
+- local_process：杀本地进程组。
+- local_linux_user：杀对应 runtime 用户下该 run 的进程组。
+- remote_jupyter：中断 kernel 或关闭对应执行任务。
+- remote_ssh：杀远程进程组。
+
+不能只停止 Web 请求，否则会出现“前端以为停了，后台 Python 还在跑”的问题。
+
+## 持久化建议
+
+建议把当前 JSON 账号体系逐步迁到 SQLite。
+
+第一阶段可新增这些表：
+
+```text
+accounts
+  id
+  username
+  password_hash
+  role
+  status
+  created_at
+  updated_at
+
+account_sessions
+  token_hash
+  account_id
+  created_at
+  updated_at
+  expires_at
+
+workspace_runtimes
+  id
+  account_id
+  session_id
+  type
+  root
+  status
+  config_json
+  created_at
+  updated_at
+
+artifacts
+  id
+  account_id
+  session_id
+  runtime_id
+  kind
+  uri
+  name
+  size
+  mime
+  created_at
+```
+
+敏感信息不要直接明文落库。Jupyter 密码、SSH key、token 至少需要加密或放入受控 secret store。
+
+## 与现有实现的关系
+
+当前已有能力可以映射到新模型：
+
+```text
+frontend/app/lib/claw-auth.ts
+  Account 登录态原型。
+
+.port_sessions/accounts/<account_id>
+  local_process 模式下的 account workspace。
+
+backend/api/server.py::account_paths
+  Runtime path resolver 的雏形。
+
+src/jupyter_runtime.py
+  remote_jupyter executor 的雏形。
+
+RunManager / RunStateStore
+  run id、活动状态、停止任务的雏形。
+
+frontend 文件面板
+  Artifact list/download 的雏形。
+```
+
+所以这不是推翻重来，而是把已有能力抽象成更稳定的边界。
+
+## 演进路线
+
+### Phase 0：明确概念，不改执行路径
+
+- 在代码和文档中引入 Workspace Runtime 术语。
+- 把现有 `.port_sessions/accounts/<account_id>` 视为 `local_process` runtime。
+- UI 显示当前工作区类型。
+- 对 Jupyter 工作区补充权限提示。
+
+### Phase 1：抽象 RuntimeResolver 和 Executor
+
+- 新增 `RuntimeResolver`，根据 account/session 找当前 runtime。
+- 新增统一 `Executor` 接口。
+- 先把 `python_exec`、`bash`、文件工具迁到 executor。
+- 保持现有 local 和 Jupyter 行为不变。
+
+### Phase 2：账号存储升级
+
+- 把 `users.json` 和 `auth_sessions.json` 迁到 SQLite。
+- 增加 `account_id`、`role`、`status`、`expires_at`。
+- 增加 session token 清理。
+- 管理后台去掉 `admin/admin` 和默认 `123456`。
+
+### Phase 3：local_linux_user runtime
+
+- root 服务创建与平台账号同名的 Linux 用户。
+- 平台密码同步设置为 Linux 用户密码。
+- Linux 用户允许 SSH 登录。
+- 初始化 `/home/<account_id>/zk-agent/`。
+- 工具执行切到普通 Linux 用户。
+- Python venv、session、output 全部进入用户 home。
+- 停止任务时按 process group 清理。
+
+### Phase 4：资源限制和审计
+
+- ulimit / cgroup。
+- 每账号磁盘 quota。
+- 工具执行审计。
+- 大文件下载限流。
+- session/output 清理策略。
+
+### Phase 5：remote_ssh runtime
+
+- 在 remote_jupyter 稳定后再考虑。
+- 重点解决认证、relay、长连接、文件传输和远程进程清理。
+
+## 关键待决问题
+
+1. 平台账号是否允许用户自注册，还是只允许管理员创建？
+2. 用户自注册时，是否允许自动创建同名 Linux 用户？
+3. 删除账号时，是否删除 Linux 用户，是否保留 home 目录？
+4. 本机平台托管 workspace 是否统一迁到 `/home/<account_id>/zk-agent/`？
+5. Jupyter 凭证如何加密保存？
+6. 远程 workspace 产物保留多久？
+7. 大文件下载、在线文档转换和文件预览的大小限制是多少？
+8. 是否需要团队空间：一个 workspace 被多个账号共享？
+
+## 推荐决策
+
+短期建议：
+
+```text
+保留平台账号体系。
+引入 Workspace Runtime 抽象。
+继续稳定 remote_jupyter。
+账号存储从 JSON 迁到 SQLite。
+开始设计 local_linux_user，但不要立即替换所有执行路径。
+```
+
+中期建议：
+
+```text
+服务可以 root 运行。
+平台账号创建时同步创建同名普通 Linux 用户。
+平台密码和 Linux 密码保持一致。
+Linux 用户允许 SSH 登录。
+工具执行统一通过 runtime executor。
+本机默认工作区逐步迁到 /home/<account_id>/zk-agent。
+```
+
+长期建议：
+
+```text
+平台账号负责产品身份。
+Workspace Runtime 负责执行环境。
+Artifact 负责跨 runtime 文件抽象。
+Executor 负责工具执行适配。
+```
+
+这样账户体系、Linux 子账户、Jupyter/SSH 远程工作区、文件下载、Python 环境和工具执行可以合到一个统一设计里，而不是继续各自生长。

docs/technical-architecture/12-runtime-guidance-queue.md

@@ -0,0 +1,154 @@
+# 运行中输入队列与 Runtime Guidance 注入
+
+## 背景
+
+用户在一个会话运行中继续输入，是 Agent 产品的基本能力。这个输入不能直接当成普通 user message 写入当前模型历史，否则会产生三个问题：
+
+1. **串台**：前端切换 session 或 URL 状态滞后时，新输入可能被写进旧 session。
+2. **取消误伤**：新建任务或继续输入会触发新的 run，从而取消当前正在运行的 run。
+3. **上下文污染**：运行中的输入如果直接进入 `model_messages`，会破坏当前 tool_use/tool_result 顺序，甚至触发 Bedrock/Anthropic 的 tool_result 校验错误。
+
+正确做法是把运行中输入先作为 UI 和 runtime 的外部事件持久化，等 Agent loop 进入安全边界时再决定如何注入。
+
+## 主流方案对比
+
+| 方案 | 关键机制 | 对本项目的启发 |
+|------|----------|----------------|
+| [OpenAI Codex long-horizon tasks](https://developers.openai.com/blog/run-long-horizon-tasks-with-codex) | 长任务依赖计划、验证、修复和可持续的外部状态，而不是单轮大 prompt | 会话运行态要可恢复；用户中途修正不能重置整轮任务 |
+| [Claude Code hooks](https://code.claude.com/docs/en/hooks-guide) | `UserPromptSubmit`、`PreToolUse`、`PostToolUse`、`Stop` 等生命周期点允许注入上下文或阻断动作 | runtime guidance 应只在明确生命周期边界注入，不直接改写当前消息流 |
+| [Building AI Coding Agents for the Terminal](https://arxiv.org/html/2603.05344v1) | Agent harness 把输入层、工具层、上下文层和执行层拆开；输入可通过线程安全队列进入执行循环 | 运行中输入应先入队，再由 Agent loop 主线程消费 |
+| [Event-driven agentic loops](https://boundaryml.com/podcast/2025-11-05-event-driven-agents) | 用户输入、LLM chunk、tool call、interrupt 都是事件；UI、LLM、持久化各自投影 | `display_messages`、`model_messages`、`run_events` 必须分离，避免一个状态源服务所有场景 |
+
+## 目标设计
+
+```text
+用户输入
+  -> 如果当前 session idle: 正常发送，创建 run
+  -> 如果当前 session running: 写入 agent_input_queue
+       -> UI 展示 pending chip
+       -> 用户可编辑、删除、引导
+       -> 引导: kind=guidance，绑定当前 run_id
+       -> Agent loop 在安全插入点消费 guidance
+       -> guidance 以 display=false 的 user message 注入 model_messages
+```
+
+## 数据分层
+
+| 数据 | 作用 | 是否允许 compact 覆盖 |
+|------|------|-----------------------|
+| `model_messages` | 给模型推理用，可压缩、可摘要、可隐藏注入 | 允许 |
+| `display_messages` / `agent_display_messages` | 给 UI 回放用，append-only，不因为 compact 丢历史 | 不允许 |
+| `run_states` | 当前 run 的状态、耗时、取消能力 | 不允许用前端内存替代 |
+| `run_events` | 右侧活动区事件流 | 不允许只存在 SSE 内存里 |
+| `agent_input_queue` | 运行中输入、guidance、待处理后续输入 | 不允许直接写进 display/model messages |
+
+## 后端实现约定
+
+### 状态读取
+
+前端优先读取：
+
+```text
+GET /api/sessions/{session_id}/state
+```
+
+返回：
+
+- `messages`: DB 中 `agent_display_messages` 的增量或全量。
+- `activity_events`: DB 中 `run_events` 的增量或全量。
+- `run`: `run_states` 最新状态。
+- `input_queue`: 当前 pending 输入队列。
+
+旧接口 `GET /api/sessions/{session_id}` 只作为兼容兜底，不应该再作为实时 UI 的主状态源。
+
+### 输入队列
+
+```text
+POST   /api/sessions/{session_id}/input-queue
+GET    /api/sessions/{session_id}/input-queue
+PATCH  /api/sessions/{session_id}/input-queue/{item_id}
+DELETE /api/sessions/{session_id}/input-queue/{item_id}
+```
+
+字段约定：
+
+| 字段 | 说明 |
+|------|------|
+| `kind=next_turn` | 运行中输入的默认状态，只展示在 composer 上方，不进入模型 |
+| `kind=guidance` | 用户显式点击“引导”后进入当前 run |
+| `run_id` | guidance 应绑定当前 active run；未绑定时由后端尝试绑定 latest active run |
+| `status=pending` | UI 可见，等待处理 |
+| `status=consumed` | 已被 runtime 注入 |
+| `status=cancelled` | 用户编辑/删除/取消 run 后不再处理 |
+
+### Runtime 注入
+
+`LocalCodingAgent` 提供 `runtime_guidance_provider`，在 Agent loop 的安全边界消费 pending guidance：
+
+```text
+agent loop safe boundary
+  -> consume_session_guidance(session_id, run_id)
+  -> append hidden user message:
+     <runtime-guidance>
+     用户在任务运行中补充了以下引导...
+     </runtime-guidance>
+  -> display=false
+  -> 继续模型调用
+```
+
+可用插入点：
+
+| 插入点 | 时机 | 处理策略 |
+|--------|------|----------|
+| `before_model` | 每次模型调用前 | 常规消费，适合上一轮工具完成后的补充 |
+| `before_tools` | 模型已经给出工具计划，但工具还没开始执行 | 运行时先为上一批未执行工具补 synthetic `tool_result`，标记为 `runtime_guidance_replan`，再注入 guidance，让模型重新判断是否继续原计划、调整参数或换计划 |
+| `during_tool_interrupted` | 工具已经开始执行，且用户引导明显要求停止、改目标、换参数、纠错 | 运行时给当前工具传入单工具 interrupt event，并通过 process registry 终止当前工具进程；当前工具结果落盘后注入 guidance，让模型重规划 |
+| `after_tool` | 工具执行期间或刚完成后收到补充型 guidance，或工具没有中间输出无法及时中断 | 保留当前工具结果，停止继续执行同批旧工具计划，注入 guidance 让模型判断继续、补充或重跑 |
+| `before_finish` | 模型准备给最终回复前 | 注入 guidance，让模型判断是修正最终输出、补充信息，还是转为后续任务 |
+
+约束：
+
+- 不把 guidance 插在 `tool_use` 和 `tool_result` 中间。
+- `before_tools` 不直接删除 assistant 的工具计划，而是补一组“未执行、被 runtime 跳过”的 tool result，保证 Anthropic/Bedrock 的消息顺序合法。
+- `during_tool_interrupted` 不复用整轮 run cancel event，而是构造“整轮取消 OR 当前工具中断”的组合 cancel event 传给当前工具，避免把用户引导误判成整轮取消。
+- 立即中断依赖工具合作：bash / python / Jupyter / 远端执行等接入 cancel_event 或 process registry 的工具可以被终止；纯同步且没有中间输出的工具只能在返回后进入 `after_tool` 重规划。
+
+### 引导策略判断
+
+前端不暴露复杂按钮，用户仍然只点击“引导”。系统内部按安全点自动处理：
+
+| 用户引导类型 | 默认策略 |
+|--------------|----------|
+| 工具未开始前的纠偏、改目标、改参数 | `before_tools` 重规划 |
+| 工具完成后的补充要求 | `before_model` 注入下一次模型调用 |
+| 即将结束前的格式、总结、补充输出要求 | `before_finish` 注入并继续一轮 |
+| 已经运行中的长工具纠偏 | 明显停止/改目标/纠错类引导触发 `during_tool_interrupted`；补充输出类引导进入 `after_tool` |
+
+模型负责在收到 `<runtime-guidance>` 后判断如何吸收：继续原计划、调整计划、说明冲突或转为后续任务；运行时只负责选择合法插入点和维护消息协议。
+
+## 前端交互
+
+1. 当前 session idle：输入框 Enter 仍然正常发送。
+2. 当前 session running：输入框不禁用；Enter 写入 queue，清空输入框。
+3. pending 输入显示为 composer 上方 chip：
+   - **编辑**：取消 queue item，把文本恢复到输入框。
+   - **引导**：改成 `kind=guidance` 并绑定 active `run_id`。
+   - **删除**：取消 queue item。
+4. 停止 run 时，后端同时取消该 session 下 pending queue item，避免下一轮误消费。
+
+## 不做的事
+
+- 不在运行中输入时自动创建新 run。
+- 不把 pending 输入直接写入 `display_messages`。
+- 不把 guidance 展示成普通用户消息；它是运行中的控制信号，不是对话历史。
+- 不依赖前端内存判断最终状态；刷新后必须能从 DB 完整恢复。
+
+## 验收点
+
+1. 同一个账号同时打开两个 session，分别运行任务，输入不会串台。
+2. A session 运行中切到 B session 输入，B 的输入只进入 B 的 queue。
+3. A session 运行中输入后刷新，pending chip 仍存在。
+4. 点击“引导”后，最近的安全插入点能消费 guidance，并在活动区记录注入事件。
+5. 如果 guidance 在工具执行前到达，旧工具计划不执行，并产生 `runtime_guidance_replan_before_tools` 活动事件。
+6. 点击停止后，对应 run 的进程和 pending queue 都被取消。
+7. 触发 compact 后，UI 仍能从 `display_messages` 回放完整历史；模型只使用 compact 后的 `model_messages`。

docs/technical-architecture/13-skill-evaluation-workbench.md

@@ -0,0 +1,451 @@
+# 13. Skill 评测实验台设计
+
+## 1. 背景
+
+当前快慢分流优化需要完成一条完整链路：
+
+```text
+已有评测集
+  -> 使用现成 Skill 判断快慢
+  -> 支持单条和批量运行
+  -> 对比人工标签和模型判断
+  -> 查看分歧及判定依据
+  -> 更新 Skill 或切换模型
+  -> 重新评测并比较变化
+```
+
+这里讨论的是离线优化和评估场景。评测过程允许 Agent 使用完整 Agent Loop，重点是判断质量、依据可追溯和标准持续收敛。线上快慢路由的耗时、成本和部署形态属于后续独立问题。
+
+本设计假设快慢分流 Skill 已经存在。实验台负责稳定调用和评估 Skill，不负责定义 Skill 内部的分类标准。
+
+## 2. 目标
+
+实验台需要支持：
+
+- 单条输入调用指定 Skill，返回判断、理由和执行记录。
+- 上传 CSV、XLSX 或 JSONL，配置字段映射后批量运行。
+- 选择模型和不可变的 Skill 版本。
+- 后台并发执行，支持暂停、继续、取消和失败重试。
+- 自动计算总体、分类别和分垂域指标。
+- 集中查看人工与模型分歧，并进行人工复核。
+- 查看模型声明的 Skill 判定依据和实际访问记录。
+- 对比不同 Skill 版本或模型版本的修正与退化。
+- 导出原始结果、复核结果和版本对比结果。
+
+实验台不承担：
+
+- 在线请求的实时路由。
+- 快系统能力或分类原则的编写。
+- 将每个普通 Skill 自动转成页面应用。
+- 修改现有聊天 Agent 的会话和运行流程。
+
+## 3. 总体架构
+
+```mermaid
+flowchart LR
+    UI["快慢分流评测页面"] --> EXP["Evaluation Service"]
+    API["Skill Run API"] --> EXP
+
+    EXP --> MAP["Dataset Mapping"]
+    EXP --> SNAP["Skill Snapshot"]
+    EXP --> SCHED["Batch Scheduler"]
+
+    SCHED --> RUNNER["Headless Skill Runner"]
+    RUNNER --> AGENT["LocalCodingAgent"]
+    RUNNER --> MODEL["指定模型"]
+    RUNNER --> SKILL["固定 Skill 快照"]
+
+    EXP --> STORE["Evaluation Store"]
+    STORE --> RESULT["指标 / 分歧 / 复核 / 版本对比"]
+```
+
+新增能力由五个部分组成：
+
+1. `Evaluation Service`：管理数据集、实验配置、运行和结果。
+2. `Dataset Mapping`：把不同格式的数据转换为统一 case。
+3. `Skill Snapshot`：固定每次实验使用的 Skill 内容。
+4. `Headless Skill Runner`：使用现有 Agent Core 在后台执行指定 Skill。
+5. `Batch Scheduler`：拆分、并发和恢复批量任务。
+
+## 4. 与现有 Agent 的隔离
+
+现有聊天链路保持不变：
+
+```text
+/api/chat
+  -> AgentState
+  -> account/session-scoped LocalCodingAgent
+  -> 聊天 session 和活动流
+```
+
+评测链路新增独立入口：
+
+```text
+/api/evaluations
+  -> EvaluationRuntime
+  -> evaluation-scoped LocalCodingAgent
+  -> 评测任务、case 结果和内部执行记录
+```
+
+两条链路可以复用：
+
+- `LocalCodingAgent`
+- `ModelConfig`
+- Skill loader
+- Tool handler
+- `OutputSchemaConfig`
+- 模型兼容层
+
+评测链路必须独立管理：
+
+- Agent 实例。
+- Session 命名空间。
+- 数据库记录。
+- 运行队列。
+- 并发和资源配额。
+- Skill 快照。
+
+评测产生的内部 Agent session 不进入左侧聊天会话列表，也不读取聊天历史、用户记忆或当前聊天 session 状态。
+
+代码隔离只能保证功能不互相污染。批量任务仍可能争用模型服务资源，因此评测任务需要独立 worker pool、并发上限和低于聊天请求的调度优先级。有条件时可以为评测配置独立模型地址。
+
+## 5. 数据集字段映射
+
+评测文件格式由数据来源决定，不应写进 Skill，也不应要求用户临时组织成提示词。
+
+上传文件后，系统先读取表头和样本，用户把源字段映射到统一字段：
+
+| 统一字段 | 说明 | 必填 |
+|---|---|---|
+| `case_id` | case 唯一标识 | 否，可自动生成 |
+| `query` | 当前用户 query | 是 |
+| `gold_route` | 人工快慢标签 | 评测时必填 |
+| `history` | 对话历史 | 否 |
+| `context` | 设备、位置等上下文 | 否 |
+| `domain` | 垂域 | 否 |
+| `request_id` | 线上 RID | 否 |
+| `metadata` | 其他保留字段 | 否 |
+| `source_row` | 原始行 | 自动保留 |
+
+页面需要支持：
+
+- 列名自动推荐。
+- 手动选择源字段。
+- JSON path，例如 `data.query`。
+- 标签值转换，例如 `快/慢`、`0/1`、`fast/slow`。
+- 必要的字段组合和简单转换。
+- 转换后样本预览。
+- 缺失字段、非法标签和重复 ID 校验。
+
+确认后的映射保存为可复用的 `Dataset Mapping Profile`。Skill Runner 只接收统一 case，不感知原始文件的列名和格式。
+
+统一 case 示例：
+
+```json
+{
+  "case_id": "001",
+  "query": "到目的地电量够不够",
+  "gold_route": "fast",
+  "history": [],
+  "context": {},
+  "domain": "地图导航",
+  "request_id": "",
+  "metadata": {},
+  "source_row": {}
+}
+```
+
+## 6. Skill 版本与快照
+
+实验不能只记录 Skill 名称。Skill 内容会持续更新，同一个名称在不同时间可能代表不同标准。
+
+每次创建实验时生成不可变快照，并记录：
+
+```json
+{
+  "skill_name": "fast-slow-routing",
+  "repository": "zk-data-agent",
+  "git_commit": "abc1234",
+  "content_hash": "sha256:...",
+  "snapshot_id": "skill_snapshot_xxx",
+  "status": "committed"
+}
+```
+
+版本来源可以是：
+
+- 当前部署版本。
+- 指定 Git commit。
+- 指定 Git tag。
+- 尚未提交的草稿快照。
+
+快照建议存放在：
+
+```text
+.port_sessions/evaluations/skill_snapshots/
+  <skill-name>/
+    <commit-or-content-hash>/
+```
+
+运行旧版本时不能切换当前项目分支，也不能覆盖线上 Skill。对于 Git 中的历史版本，可以通过 `git archive` 提取单个 Skill 目录到快照缓存。
+
+草稿版本允许参与实验，但页面必须明确标记“未提交”，并使用内容哈希保证同一次实验可复现。
+
+评测 Agent 始终从快照加载 Skill；聊天 Agent 继续从当前部署目录加载 Skill，两者互不影响。
+
+## 7. Headless Skill Runner
+
+`Headless` 表示没有聊天页面，不表示减少 Agent 能力。
+
+Runner 接收：
+
+```json
+{
+  "skill_snapshot_id": "skill_snapshot_xxx",
+  "model_id": "model_xxx",
+  "case": {
+    "case_id": "001",
+    "query": "到目的地电量够不够"
+  }
+}
+```
+
+Runner 的执行过程：
+
+1. 从快照读取指定 Skill。
+2. 创建独立的评测 Agent 实例和 session。
+3. 直接把指定 Skill 注入当前任务，不等待模型自行召回 Skill。
+4. 把统一 case 作为任务输入。
+5. 按 Skill 约定执行完整 Agent Loop。
+6. 保存最终结果、执行事件、工具调用和用量。
+7. 将最终输出归一化为评测结果。
+
+统一评测结果至少包含：
+
+```json
+{
+  "case_id": "001",
+  "prediction": "fast",
+  "reason": "当前快系统已有对应能力",
+  "confidence": 0.95,
+  "evidence_refs": [
+    "knowledge/地图能力.md#到达电量预估"
+  ],
+  "status": "completed",
+  "raw_output": "",
+  "agent_session_id": ""
+}
+```
+
+单条判断创建一个 Agent Run。批量判断把每条 case 拆成独立 Agent Run，避免样本间上下文污染。
+
+## 8. 批量调度
+
+批量调度器负责：
+
+- 把数据集拆成独立 case 任务。
+- 使用受控并发执行。
+- 保存每条 case 的状态和重试次数。
+- 单条失败不终止整个实验。
+- 支持暂停、继续和取消。
+- 服务重启后从数据库恢复未完成任务。
+- 只重跑失败项、分歧项或用户选中的 case。
+
+任务状态：
+
+```text
+pending
+running
+completed
+failed
+cancelled
+```
+
+聊天请求和评测请求需要独立并发池。评测批量任务默认低优先级，避免影响当前在线使用者。
+
+## 9. 判定依据与可追溯性
+
+页面需要回答“模型依据 Skill 的哪部分作出判断”，但不能把完整模型思考当作可靠因果证据。
+
+实验台展示两类事实：
+
+1. **实际访问记录**：Agent 读取了哪些 Skill 文件、知识文件和章节。
+2. **模型声明的判定依据**：最终结果中的 `evidence_refs`。
+
+生成 Skill 快照时，系统为 Markdown 标题和知识文件建立稳定引用，例如：
+
+```text
+SKILL.md#判断流程
+knowledge/地图能力.md#到达电量预估
+knowledge/边界.md#开放式行程规划
+```
+
+评测 Runner 的统一任务约定要求最终结果返回引用。页面将引用解析为可点击的原文片段。
+
+如果模型没有引用明确规则，页面展示“未引用具体依据”，并允许按此条件筛选。这类 case 本身就是标准缺失或模型未正确使用 Skill 的候选问题。
+
+## 10. 页面信息架构
+
+页面采用“实验配置、结果页签、单条详情”三层结构。
+
+### 10.1 实验配置
+
+顶部配置区包含：
+
+- 数据集。
+- 字段映射。
+- Skill 和版本。
+- 模型。
+- case 数量。
+- 并发配置。
+- 开始、暂停、继续、取消、克隆实验。
+
+运行开始后配置区折叠，避免占用结果空间。
+
+### 10.2 结果页签
+
+| 页签 | 主要内容 |
+|---|---|
+| 概览 | 运行进度、总体指标、混淆矩阵、快慢分布、分垂域指标 |
+| 分歧 | 人工标签与模型判断不一致、低置信、无明确依据的 case |
+| 全部 Case | 全量结果、状态、筛选、排序和导出 |
+| 版本对比 | 不同 Skill 或模型实验之间的修正、退化和指标变化 |
+
+### 10.3 单条详情抽屉
+
+点击一条 case 后展示：
+
+1. 原始数据和统一后的输入。
+2. 人工标签、模型判断、置信度和理由。
+3. Skill 判定依据及原文片段。
+4. 实际读取文件和工具调用。
+5. 默认折叠的完整 Agent 执行记录。
+6. 人工复核结果和备注。
+
+人工复核至少支持：
+
+- 人工原标签正确。
+- 模型判断正确。
+- 分类标准存在歧义。
+- 快系统能力信息缺失。
+- 暂不确定。
+
+## 11. 实验与版本对比
+
+一个实验由以下对象共同确定：
+
+```text
+数据集版本
++ 字段映射版本
++ Skill 快照
++ 模型版本
++ 执行参数
+```
+
+同一数据集可以克隆实验并替换 Skill 或模型：
+
+```text
+实验 A：Skill abc1234 + 模型 A
+实验 B：Skill def5678 + 模型 A
+实验 C：Skill def5678 + 模型 B
+```
+
+版本对比需要展示：
+
+- 总体指标变化。
+- 各垂域指标变化。
+- 从错误变正确的 case。
+- 从正确变错误的 case。
+- 判断未变化但理由变化的 case。
+- 新增或消失的无依据结果。
+
+这样才能形成“评测、发现分歧、更新 Skill、重新评测”的优化闭环。
+
+## 12. 数据存储
+
+评测运行状态适合使用独立 SQLite 数据库，例如：
+
+```text
+.port_sessions/evaluations/evaluations.db
+```
+
+核心实体：
+
+```text
+evaluation_datasets
+dataset_mapping_profiles
+skill_snapshots
+evaluation_experiments
+evaluation_cases
+evaluation_case_runs
+evaluation_results
+evaluation_reviews
+```
+
+数据库保存可查询状态、关系和人工复核结果。上传原文件、Skill 快照、导出文件和较大的 Agent transcript 保存在实验目录，数据库记录路径和摘要。
+
+## 13. API 轮廓
+
+```text
+POST /api/evaluations/datasets
+POST /api/evaluations/datasets/{id}/mapping
+
+POST /api/evaluations/skill-snapshots
+GET  /api/evaluations/skill-snapshots
+
+POST /api/evaluations
+GET  /api/evaluations/{id}
+POST /api/evaluations/{id}/start
+POST /api/evaluations/{id}/pause
+POST /api/evaluations/{id}/resume
+POST /api/evaluations/{id}/cancel
+
+GET  /api/evaluations/{id}/results
+POST /api/evaluations/{id}/retry
+POST /api/evaluations/{id}/reviews
+GET  /api/evaluations/{id}/export
+
+POST /api/evaluations/compare
+```
+
+API 同时服务 Web 页面和外部批量调用。单条调用可以创建只包含一个 case 的轻量实验，复用相同运行与记录机制。
+
+## 14. 实施顺序
+
+### P1：后台单条运行
+
+- Skill 快照。
+- Headless Skill Runner。
+- 单条输入和统一结果。
+- 独立于聊天 session 的运行记录。
+
+### P2：数据集与批量调度
+
+- 文件上传和字段映射。
+- 批量拆分、并发、暂停、恢复和重试。
+- 基础指标和导出。
+
+### P3：评测页面
+
+- 实验配置。
+- 概览、分歧、全部 Case。
+- 单条详情和人工复核。
+- Skill 依据引用。
+
+### P4：版本对比
+
+- 克隆实验。
+- Skill 和模型版本矩阵。
+- 修正、退化和理由变化分析。
+
+## 15. 验收标准
+
+- 开启评测任务后，现有聊天 Agent 可以正常创建、继续和停止会话。
+- 600 条批量任务不会出现在聊天会话列表。
+- 不同输入格式可以通过字段映射转换为统一 case。
+- 每次实验可以确认具体 Skill commit 或内容哈希。
+- Skill 更新后，历史实验仍能读取原快照并复现配置。
+- 单条失败不会中断全量任务，服务重启后可以恢复。
+- 页面可以定位人工与模型分歧，并查看简短理由和 Skill 引用。
+- 可以比较两个实验的修正项和退化项。
+- 批量评测的并发不会明显拖慢聊天 Agent。

docs/technical-architecture/README.md

@@ -0,0 +1,62 @@
+# ZK Data Agent 技术架构说明
+
+这组文档先服务于技术 review 和后续讲解材料沉淀，不做宣传表达，不做产品比较。
+每个章节尽量对应当前代码里的真实模块、函数和 Skill 实现，后续 `/doc` 页面可以从这里抽取内容做视觉化呈现。
+
+## 阅读顺序
+
+1. [基座 Runtime 架构](01-base-runtime.md)
+2. [Agent Loop 执行机制](02-agent-loop.md)
+3. [工具体系和 Tool Handler](03-tools.md)
+4. [Skill 体系和能力包约定](04-skills.md)
+5. [会话工作区、运行态和记忆](05-workspace-memory-observability.md)
+6. [product-data Skill 实现](06-product-data.md)
+7. [online-mining-v2 Skill 实现](07-online-mining-v2.md)
+8. [label-master Skill 实现](08-label-master.md)
+9. [外部系统 Skill：ELK、SQL、模型迭代](09-external-skills.md)
+10. [Agent 记忆机制调研与对比](10-memory-research.md)
+11. [运行中输入队列与 Runtime Guidance 注入](12-runtime-guidance-queue.md)
+12. [Skill 评测实验台设计](13-skill-evaluation-workbench.md)
+
+## 一句话定位
+
+ZK Data Agent 的基座是一个 Web 化、多用户、可观测的 Agent runtime。
+业务能力通过 `skills/<skill-name>/SKILL.md`、`knowledge/` 和 `scripts/` 组织；稳定执行能力通过 Tool handler 或 Skill 内 portable scripts 承载；每次运行通过 Agent loop 让模型在“判断、调用工具、观察结果、继续判断”之间循环。
+
+## 当前技术主线
+
+```text
+Web UI
+  -> backend/api/server.py
+  -> LocalCodingAgent
+  -> agent_prompting 组装系统提示词和 Skill 列表
+  -> OpenAICompatClient 调模型
+  -> 模型返回文本或 tool_calls
+  -> agent_tools 执行 handler
+  -> session workspace 保存 input/scratchpad/output/session.json
+  -> run_state_store / event stream 推给前端活动区
+  -> personal_memory 后台异步整理用户记忆和 Skill 记忆
+```
+
+## 代码入口速查
+
+| 主题 | 主要代码 |
+|------|----------|
+| Agent runtime | `src/agent_runtime.py` |
+| 系统提示词 | `src/agent_prompting.py` |
+| Tool registry / handler | `src/agent_tools.py`、`src/agent_tool_specs/` |
+| Skill loader | `src/bundled_skills.py` |
+| 模型兼容层 | `src/openai_compat.py` |
+| Web 后端 | `backend/api/server.py` |
+| 会话持久化 | `src/agent_session.py`、`src/session_store.py` |
+| 记忆后台 | `src/personal_memory.py` |
+| 数据生成 Skill | `skills/product-data/` |
+| 线上挖掘 Skill | `skills/online-mining-v2/` |
+| 标签知识 Skill | `skills/label-master/` |
+
+## 后续维护原则
+
+- 如果是在讲“基座怎么运行”，优先改 01-05。
+- 如果是在讲“某个 Skill 怎么做事”，优先改 06-09。
+- 如果代码实现发生变化，先更新对应章节，再考虑同步 README 或 `/doc` 页面。
+- 页面表达、图形素材、演示脚本不写在这里；这里只保留技术事实和设计边界。

e2e/stub_provider.py

@@ -1,179 +0,0 @@
-from __future__ import annotations
-
-import json
-import time
-import uuid
-from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
-from typing import Any
-
-
-def _last_user_text(messages: list[dict[str, Any]]) -> str:
-    for message in reversed(messages):
-        if message.get("role") != "user":
-            continue
-        content = message.get("content", "")
-        if isinstance(content, str):
-            return content
-        if isinstance(content, list):
-            return " ".join(str(part.get("text", "")) for part in content if isinstance(part, dict))
-    return ""
-
-
-def _completion(model: str, message: dict[str, Any], finish_reason: str) -> dict[str, Any]:
-    return {
-        "id": f"chatcmpl-stub-{uuid.uuid4().hex}",
-        "object": "chat.completion",
-        "created": int(time.time()),
-        "model": model,
-        "choices": [{"index": 0, "message": message, "finish_reason": finish_reason}],
-        "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
-    }
-
-
-class StubHandler(BaseHTTPRequestHandler):
-    server_version = "K1412E2EStub/1"
-
-    def log_message(self, format: str, *args: Any) -> None:
-        return
-
-    def _json(self, status: int, payload: dict[str, Any]) -> None:
-        encoded = json.dumps(payload, ensure_ascii=False).encode()
-        self.send_response(status)
-        self.send_header("Content-Type", "application/json")
-        self.send_header("Content-Length", str(len(encoded)))
-        self.end_headers()
-        self.wfile.write(encoded)
-
-    def do_GET(self) -> None:
-        if self.path == "/health":
-            self._json(200, {"status": "ok"})
-            return
-        self._json(404, {"error": "not found"})
-
-    def do_POST(self) -> None:
-        if self.path != "/v1/chat/completions":
-            self._json(404, {"error": "not found"})
-            return
-        length = int(self.headers.get("Content-Length", "0"))
-        payload = json.loads(self.rfile.read(length) or b"{}")
-        model = str(payload.get("model", "stub"))
-        messages = payload.get("messages") or []
-        tools = payload.get("tools") or []
-        has_tool_result = any(message.get("role") == "tool" for message in messages)
-        called_tools = {
-            str((call.get("function") or {}).get("name", ""))
-            for message in messages
-            if message.get("role") == "assistant"
-            for call in (message.get("tool_calls") or [])
-        }
-        available_names = {
-            str((tool.get("function") or {}).get("name", "")) for tool in tools if isinstance(tool, dict)
-        }
-        is_agent = "update_plan" in available_names
-
-        if tools and not has_tool_result:
-            arguments = json.dumps(
-                {"path": "work-proof.txt", "content": _last_user_text(messages) or "e2e"},
-                ensure_ascii=False,
-            )
-            message = {
-                "role": "assistant",
-                "content": None,
-                "tool_calls": [
-                    {
-                        "id": "call_e2e_write",
-                        "type": "function",
-                        "function": {"name": "write_file", "arguments": arguments},
-                    }
-                ],
-            }
-            self._respond(payload, _completion(model, message, "tool_calls"))
-            return
-
-        if is_agent and "write_file" in called_tools and "read_file" not in called_tools:
-            message = {
-                "role": "assistant",
-                "content": None,
-                "tool_calls": [
-                    {
-                        "id": "call_e2e_verify",
-                        "type": "function",
-                        "function": {
-                            "name": "read_file",
-                            "arguments": json.dumps({"path": "work-proof.txt"}),
-                        },
-                    }
-                ],
-            }
-            self._respond(payload, _completion(model, message, "tool_calls"))
-            return
-
-        text = "E2E provider completed after tool execution." if has_tool_result else "E2E provider response."
-        self._respond(payload, _completion(model, {"role": "assistant", "content": text}, "stop"))
-
-    def _respond(self, request: dict[str, Any], completion: dict[str, Any]) -> None:
-        if not request.get("stream"):
-            self._json(200, completion)
-            return
-
-        message = completion["choices"][0]["message"]
-        finish_reason = completion["choices"][0]["finish_reason"]
-        chunks: list[dict[str, Any]] = []
-        if message.get("tool_calls"):
-            call = message["tool_calls"][0]
-            chunks.append(
-                {
-                    "id": completion["id"],
-                    "object": "chat.completion.chunk",
-                    "created": completion["created"],
-                    "model": completion["model"],
-                    "choices": [
-                        {
-                            "index": 0,
-                            "delta": {
-                                "role": "assistant",
-                                "tool_calls": [{"index": 0, **call}],
-                            },
-                            "finish_reason": None,
-                        }
-                    ],
-                }
-            )
-        else:
-            chunks.append(
-                {
-                    "id": completion["id"],
-                    "object": "chat.completion.chunk",
-                    "created": completion["created"],
-                    "model": completion["model"],
-                    "choices": [
-                        {
-                            "index": 0,
-                            "delta": {"role": "assistant", "content": message.get("content", "")},
-                            "finish_reason": None,
-                        }
-                    ],
-                }
-            )
-        chunks.append(
-            {
-                "id": completion["id"],
-                "object": "chat.completion.chunk",
-                "created": completion["created"],
-                "model": completion["model"],
-                "choices": [{"index": 0, "delta": {}, "finish_reason": finish_reason}],
-            }
-        )
-
-        self.send_response(200)
-        self.send_header("Content-Type", "text/event-stream")
-        self.send_header("Cache-Control", "no-cache")
-        self.end_headers()
-        for chunk in chunks:
-            self.wfile.write(f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n".encode())
-        self.wfile.write(b"data: [DONE]\n\n")
-        self.wfile.flush()
-
-
-if __name__ == "__main__":
-    ThreadingHTTPServer(("0.0.0.0", 9000), StubHandler).serve_forever()  # noqa: S104

e2e/verify_stack.py

@@ -1,285 +0,0 @@
-from __future__ import annotations
-
-import atexit
-import io
-import os
-import tarfile
-import time
-import uuid
-from typing import Any
-
-import httpx
-import jwt
-
-import docker
-from agent_platform.gateway.provider import workspace_ref
-
-BASE_URL = os.getenv("E2E_BASE_URL", "http://127.0.0.1:3000").rstrip("/")
-ADMIN_EMAIL = os.getenv("E2E_ADMIN_EMAIL", "admin-e2e@example.invalid")
-ADMIN_PASSWORD = os.getenv("E2E_ADMIN_PASSWORD", "e2e-admin-password")
-FORWARD_JWT_SECRET = os.getenv(
-    "E2E_FORWARD_JWT_SECRET",
-    "e2e-forward-jwt-secret-at-least-32-bytes",
-)
-INTERNAL_PROVIDER_KEY = os.getenv(
-    "E2E_INTERNAL_PROVIDER_KEY",
-    "e2e-provider-secret-at-least-32-bytes",
-)
-EXPECTED_MODELS = {
-    "luna",
-    "terra",
-    "sol",
-    "deepseek-v4-pro",
-}
-
-
-def _auth(token: str) -> dict[str, str]:
-    return {"Authorization": f"Bearer {token}"}
-
-
-def _signin(client: httpx.Client, email: str, password: str) -> dict[str, Any]:
-    response = client.post("/api/v1/auths/signin", json={"email": email, "password": password})
-    response.raise_for_status()
-    data = response.json()
-    assert data.get("token")
-    return data
-
-
-def _stream_chat(
-    client: httpx.Client,
-    token: str,
-    *,
-    model: str,
-    chat_id: str,
-    prompt: str,
-) -> tuple[int, str]:
-    user_message_id = uuid.uuid4().hex
-    assistant_message_id = uuid.uuid4().hex
-    payload: dict[str, Any] = {
-        "model": model,
-        "messages": [{"role": "user", "content": prompt}],
-        "stream": True,
-        "chat_id": chat_id,
-        "id": assistant_message_id,
-        "assistant_message_id": assistant_message_id,
-        "user_message": {
-            "id": user_message_id,
-            "parentId": None,
-            "childrenIds": [assistant_message_id],
-            "role": "user",
-            "content": prompt,
-            "timestamp": int(time.time()),
-        },
-        "params": {},
-        "features": {},
-    }
-    with client.stream(
-        "POST",
-        "/api/chat/completions",
-        headers=_auth(token),
-        json=payload,
-        timeout=90,
-    ) as response:
-        return response.status_code, "".join(response.iter_text())
-
-
-def _workspace_file(client: docker.DockerClient, user_id: str, path: str) -> str:
-    container = client.containers.get(workspace_ref(user_id).container_name)
-    result = container.exec_run(["cat", f"/workspace/{path}"], user="1000:1000")
-    assert result.exit_code == 0, result.output.decode(errors="replace")
-    return result.output.decode()
-
-
-def _cleanup_workspaces(users: list[dict[str, Any]]) -> None:
-    client = docker.from_env()
-    for user in users:
-        ref = workspace_ref(user["id"])
-        try:
-            client.containers.get(ref.container_name).remove(force=True)
-        except docker.errors.NotFound:
-            pass
-        try:
-            client.volumes.get(ref.volume_name).remove(force=True)
-        except docker.errors.NotFound:
-            pass
-        try:
-            client.networks.get(ref.network_name).remove()
-        except docker.errors.NotFound:
-            pass
-    client.close()
-
-
-def _runtime_url(client: docker.DockerClient) -> str:
-    container = client.containers.get("k1412-agent-e2e-runtime-1")
-    networks = container.attrs["NetworkSettings"]["Networks"]
-    internal = next(value for name, value in networks.items() if name.endswith("_internal"))
-    return f"http://{internal['IPAddress']}:8000"
-
-
-def _runtime_headers(user: dict[str, Any], chat_id: str) -> dict[str, str]:
-    now = int(time.time())
-    identity = jwt.encode(
-        {
-            "sub": user["id"],
-            "email": user["email"],
-            "name": user["name"],
-            "role": "user",
-            "iss": "open-webui",
-            "iat": now,
-            "exp": now + 60,
-        },
-        FORWARD_JWT_SECRET,
-        algorithm="HS256",
-    )
-    return {
-        "Authorization": f"Bearer {INTERNAL_PROVIDER_KEY}",
-        "X-OpenWebUI-User-Jwt": identity,
-        "X-OpenWebUI-Chat-Id": chat_id,
-    }
-
-
-def main() -> None:
-    suffix = uuid.uuid4().hex[:10]
-    user_specs = [
-        (f"E2E User A {suffix}", f"e2e-a-{suffix}@example.invalid", "e2e-user-password-a"),
-        (f"E2E User B {suffix}", f"e2e-b-{suffix}@example.invalid", "e2e-user-password-b"),
-    ]
-    with httpx.Client(base_url=BASE_URL, timeout=30) as client:
-        health = client.get("/health")
-        health.raise_for_status()
-        assert health.json().get("status") is True
-
-        admin = _signin(client, ADMIN_EMAIL, ADMIN_PASSWORD)
-        admin_token = admin["token"]
-
-        config = client.get("/api/v1/auths/admin/config", headers=_auth(admin_token))
-        config.raise_for_status()
-        admin_config = config.json()
-        assert admin_config["ENABLE_SIGNUP"] is True
-        assert admin_config["DEFAULT_USER_ROLE"] == "pending"
-        assert admin_config["ENABLE_API_KEYS"] is False
-        assert admin_config["ENABLE_AUTOMATIONS"] is False
-
-        model_response = client.get("/api/models?refresh=true", headers=_auth(admin_token))
-        model_response.raise_for_status()
-        model_payload = model_response.json()
-        model_ids = {item["id"] for item in model_payload["data"]}
-        assert model_ids == EXPECTED_MODELS, model_ids
-        assert "ChatGPT-5.6" not in model_response.text
-
-        users: list[dict[str, Any]] = []
-        atexit.register(_cleanup_workspaces, users)
-        for name, email, password in user_specs:
-            signup = client.post(
-                "/api/v1/auths/signup",
-                json={"name": name, "email": email, "password": password},
-            )
-            signup.raise_for_status()
-            pending = signup.json()
-            assert pending["role"] == "pending"
-
-            denied = client.get("/api/models", headers=_auth(pending["token"]))
-            assert denied.status_code in {401, 403}
-
-            approved = client.post(
-                f"/api/v1/users/{pending['id']}/update",
-                headers=_auth(admin_token),
-                json={"role": "user"},
-            )
-            approved.raise_for_status()
-            assert approved.json()["role"] == "user"
-            users.append(_signin(client, email, password))
-
-        for user in users:
-            models = client.get("/api/models", headers=_auth(user["token"]))
-            models.raise_for_status()
-            user_model_ids = {item["id"] for item in models.json()["data"]}
-            assert user_model_ids == EXPECTED_MODELS, user_model_ids
-            assert "ChatGPT-5.6" not in models.text
-            forbidden = client.get("/api/v1/auths/admin/config", headers=_auth(user["token"]))
-            assert forbidden.status_code in {401, 403}
-
-        chat_ids = [f"local:e2e-{suffix}-a", f"local:e2e-{suffix}-b"]
-        for index, user in enumerate(users):
-            marker = f"E2E_AGENT_USER_{index}_{suffix}"
-            status_code, body = _stream_chat(
-                client,
-                user["token"],
-                model="luna" if index == 0 else "terra",
-                chat_id=chat_ids[index],
-                prompt=marker,
-            )
-            assert status_code == 200, body
-
-        docker_client = docker.from_env()
-        assert _workspace_file(docker_client, users[0]["id"], "work-proof.txt") == f"E2E_AGENT_USER_0_{suffix}"
-        assert _workspace_file(docker_client, users[1]["id"], "work-proof.txt") == f"E2E_AGENT_USER_1_{suffix}"
-        for user in users:
-            ref = workspace_ref(user["id"])
-            workspace = docker_client.containers.get(ref.container_name)
-            workspace.reload()
-            assert set(workspace.attrs["NetworkSettings"]["Networks"]) == {ref.network_name}
-
-        listing = client.get(
-            "/api/v1/k1412/workspace/files",
-            headers=_auth(users[0]["token"]),
-            params={"path": "."},
-        )
-        listing.raise_for_status()
-        assert "work-proof.txt" in {item["name"] for item in listing.json()["entries"]}
-        download = client.get(
-            "/api/v1/k1412/workspace/download",
-            headers=_auth(users[0]["token"]),
-            params={"path": "work-proof.txt"},
-        )
-        download.raise_for_status()
-        assert download.text == f"E2E_AGENT_USER_0_{suffix}"
-        isolated_listing = client.get(
-            "/api/v1/k1412/workspace/files",
-            headers=_auth(users[1]["token"]),
-            params={"path": "."},
-        )
-        isolated_listing.raise_for_status()
-        assert "work-proof.txt" in {item["name"] for item in isolated_listing.json()["entries"]}
-        isolated_download = client.get(
-            "/api/v1/k1412/workspace/download",
-            headers=_auth(users[1]["token"]),
-            params={"path": "work-proof.txt"},
-        )
-        isolated_download.raise_for_status()
-        assert isolated_download.text == f"E2E_AGENT_USER_1_{suffix}"
-        archive = client.get(
-            "/api/v1/k1412/workspace/archive",
-            headers=_auth(users[0]["token"]),
-            params={"path": "."},
-        )
-        archive.raise_for_status()
-        with tarfile.open(fileobj=io.BytesIO(archive.content), mode="r:gz") as workspace_tar:
-            assert any(name.endswith("work-proof.txt") for name in workspace_tar.getnames())
-
-        with httpx.Client(base_url=_runtime_url(docker_client), timeout=30, trust_env=False) as runtime:
-            runtime_headers = _runtime_headers(users[0], chat_ids[0])
-            events = runtime.get(f"/v1/runs/{chat_ids[0]}", headers=runtime_headers)
-            events.raise_for_status()
-            event_types = {event["type"] for event in events.json()["items"]}
-            assert {"run.created", "tool.completed", "run.completed"} <= event_types
-
-            legacy = runtime.post(
-                "/v1/chat/completions",
-                headers=runtime_headers,
-                json={
-                    "model": "work-high",
-                    "messages": [{"role": "user", "content": "LEGACY_MODEL_MUST_FAIL"}],
-                    "stream": False,
-                },
-            )
-            assert legacy.status_code == 404, legacy.text
-
-    print(
-        "E2E passed: auth approval, four Agent models, custom loop evidence, file downloads, "
-        "and per-user workspace isolation."
-    )
-
-
-if __name__ == "__main__":
-    main()

frontend/app/.env.example

@@ -0,0 +1,4 @@
+OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+
+# chat persistence -- sign up on cloud.assistant-ui.com
+# NEXT_PUBLIC_ASSISTANT_BASE_URL=

frontend/app/.gitignore

@@ -0,0 +1,45 @@
+# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
+
+# dependencies
+/node_modules
+/.pnp
+.pnp.*
+.yarn/*
+!.yarn/patches
+!.yarn/plugins
+!.yarn/releases
+!.yarn/versions
+
+# testing
+/coverage
+
+# next.js
+/.next/
+/out/
+
+# production
+/build
+
+# misc
+.DS_Store
+*.pem
+
+# debug
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+.pnpm-debug.log*
+
+# env files (can opt-in for committing if needed)
+.env*
+!.env.example
+
+# vercel
+.vercel
+
+# typescript
+*.tsbuildinfo
+next-env.d.ts
+
+# Turborepo
+.turbo

frontend/app/README.md

@@ -0,0 +1,27 @@
+# ZK Data Agent Frontend
+
+中控数据开发平台前端，基于 [assistant-ui](https://github.com/assistant-ui/assistant-ui) starter project 改造。
+
+## Getting Started
+
+First, add your OpenAI API key to `.env.local` file:
+
+```
+OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+```
+
+Then, run the development server:
+
+```bash
+npm run dev
+# or
+yarn dev
+# or
+pnpm dev
+# or
+bun dev
+```
+
+Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
+
+You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.