* feat(api): support global API key from config.yaml (api.global_api_key) Accept a config-defined global API key anywhere a web-UI key is accepted (X-API-Key / Bearer), with no login session and no DB record. Useful for automated deployments and AI agents (HTTP API + MCP). Defaults to empty (disabled); does not require the lbk_ prefix. - templates/config.yaml: add api.global_api_key with security notes - service/apikey.py: verify_api_key checks global key first (constant-time) - docs/API_KEY_AUTH.md: document the global key + security guidance - tests: cover global-key match, prefix-free, fallback-to-db, disabled * feat(mcp): expose LangBot management as an MCP server at /mcp Add an MCP (Model Context Protocol) server so external AI agents can manage a LangBot instance. Reuses the same API-key auth as the HTTP API (including the config.yaml global API key). - pkg/api/mcp/server.py: FastMCP server wrapping the service layer; 21 curated tools across system/bots/pipelines/models/knowledge/mcp-servers/skills - pkg/api/mcp/mount.py: ASGI dispatcher fronting Quart; authenticates /mcp requests with an API key, runs the streamable-HTTP session manager lifespan - controller/main.py: serve the wrapped ASGI app via hypercorn (was run_task) - web: new 'MCP' tab in the API integration dialog showing endpoint, auth, and client config; i18n for 8 locales - tests/manual/mcp_smoke.py: e2e check (401 unauth, list tools, call tools) Tool surface is intentionally curated (not all ~25 route groups) to keep the agent surface small, safe, and maintainable. Extend deliberately. * feat(skills): add in-repo skills/ as the single source of truth Migrate the agent skills + QA/e2e test harness from the (now archived) langbot-app/langbot-skills repo into LangBot/skills/, and add four new skills. Migrated: - langbot-plugin-dev, langbot-testing (e2e), langbot-env-setup, langbot-skills-maintenance, langbot-eba-adapter-dev - the bin/lbs CLI (src/, test/, scripts/, schemas/, qa-agent-docs/) New: - langbot-dev core backend + web development - langbot-deploy Docker/K8s deployment + config.yaml + global API key - langbot-mcp-ops operating the LangBot MCP server (/mcp) - langbot-space-ops operating the Space marketplace MCP server - src/cli.ts repoRoot(): recognize the skills assets root (skills.index.json + bin/lbs) so the CLI works when nested inside the LangBot repo - README.md: unified skill catalog; skills.index.json regenerated Parity with source verified: bin/lbs validate + node test suite match the source repo (only the uncommitted .lbpkg build-artifact fixture differs). * docs(agents): document agent-facing surfaces + API/MCP/skills sync rule * docs(readme): add 'Built for AI Agents' section across all locales Highlight MCP server, in-repo skills (single source of truth), AGENTS.md sync rule, and llms.txt. Cross-link LangBot Space MCP marketplace. * style(mcp): fix ruff format + prettier lint in MCP server and API panel * style(web): prettier format MCP i18n locale entries * docs(skills): note MCP instance control in dev/testing skills All development-guidance skills now point to the LangBot instance MCP server (/mcp) and the Space marketplace MCP server, reusing API keys.
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LangBot Skills 测试资产库规划
状态
这是早期测试资产库规划文档,保留用于解释 langbot-skills 的分层来源。
当前路线已经收敛为黑盒 E2E QA:开发者用 agent 通过浏览器测试 LangBot,
稳定路径沉淀为 case,失败知识沉淀为 troubleshooting。lbs test report 和
日志守卫已有 MVP,后续重点是报告证据、case 元数据和少量稳定路径自动化。当前优先级见:
docs/qa-agent/04-black-box-e2e-roadmap.md
本文中关于 case list/show、trouble show/search、test plan 的“计划实现”
内容已经部分过时,因为这些能力已经落地。
目标
让开发者 clone langbot-skills 后,可以把测试意图交给 agent,由 agent 复用已有环境配置、测试路径和故障知识完成 LangBot 功能验证。
典型场景:
- 冒烟测试:验证 pipeline Debug Chat、provider、常见页面是否正常。
- Provider 测试:添加 DeepSeek/OpenAI/Claude 等供应商并验证模型可用。
- 新 feature 测试:探索新 UI 路径,并在稳定后沉淀成 case/reference。
- 回归测试:复用旧路径,避免每个窗口重新探索登录、模型配置、pipeline 调试。
- 故障沉淀:把 runtime 超时、代理不一致、WebSocket 问题记录为可搜索资产。
核心方向见 03-agent-browser-qa-principles.md:agent 必须以浏览器/UI 为主路径,API/curl 只能作为诊断手段。
当前仓库结构
skills/
.env # 共享默认变量
langbot-env-setup/ # 环境准备、浏览器控制路径、代理、登录态
langbot-testing/ # WebUI / provider / pipeline 测试入口
langbot-plugin-dev/ # 插件开发测试
langbot-eba-adapter-dev/ # 平台适配器开发测试
src/
lbs.ts # CLI 源码
bin/
lbs # CLI 入口
docs/
qa-agent/ # 规划文档,历史目录名保留
设计分层
1. Skill 层
SKILL.md 只做触发和路由,不承载大段流程。
例子:
langbot-env-setup -> 选择 Computer Use / Playwright MCP / OAuth profile / proxy
langbot-testing -> 选择 WebUI / pipeline / provider / troubleshooting
2. Reference 层
Markdown 记录人和 agent 都能读的流程说明。
适合内容:
- 如何选择浏览器控制方式
- 如何启动/检查服务
- 如何执行 pipeline Debug Chat
- 如何处理 OAuth 登录态
3. Case 层
使用 YAML 记录可重复测试路径。
建议结构:
skills/langbot-testing/cases/
pipeline-debug-chat.yaml
provider-deepseek.yaml
建议格式:
id: pipeline-debug-chat
title: Pipeline Debug Chat returns a bot response
mode: agent-browser
area: pipeline
type: smoke
skills:
- langbot-env-setup
- langbot-testing
env:
- LANGBOT_FRONTEND_URL
- LANGBOT_BACKEND_URL
steps:
- Open LANGBOT_FRONTEND_URL
- Navigate to Pipelines
- Open target pipeline
- Select Debug Chat
- Send deterministic prompt
checks:
- "UI: User message appears"
- "UI: Bot message appears"
- "Console: No unexpected frontend errors"
- "Logs: Backend log includes Conversation(0) Streaming completed"
diagnostics:
- "Use API/curl only after the UI path is attempted, to distinguish frontend display failure from backend/runtime failure."
troubleshooting:
- plugin-runtime-timeout
- proxy-env-mismatch
4. Troubleshooting 层
故障资产会逐渐变大,适合结构化记录。
历史 Markdown 入口保留在:
skills/langbot-testing/references/troubleshooting.md
当前 canonical 结构化故障资产在:
skills/langbot-testing/troubleshooting/
plugin-runtime-timeout.yaml
proxy-env-mismatch.yaml
5. CLI 层
lbs 是统一入口,不再引入独立 qa 命令。
已实现或当前可用:
bin/lbs list
bin/lbs validate
bin/lbs index
bin/lbs new-skill <name>
bin/lbs new-ref <skill> <name>
bin/lbs case new pipeline-debug-chat --title "Pipeline Debug Chat"
bin/lbs case list
bin/lbs case show pipeline-debug-chat
bin/lbs trouble list <skill>
bin/lbs trouble show plugin-runtime-timeout
bin/lbs trouble search runtime
bin/lbs trouble add <skill> --title ... --symptom ... --cause ... --fix ...
bin/lbs test plan pipeline-debug-chat
bin/lbs test start pipeline-debug-chat
bin/lbs test run pipeline-debug-chat --dry-run
bin/lbs test report pipeline-debug-chat
bin/lbs test report pipeline-debug-chat --backend-log /path/to/backend.log
测试库位置
不要使用隐藏 .qa/ 作为主测试库。测试资产应该和 skill 放在一起,便于触发和维护:
skills/langbot-testing/
references/
cases/
troubleshooting/
reports/ # 可选,本地运行产物可按需忽略或输出到外部目录
如果未来需要项目本地测试库,可以允许 lbs 支持 --workspace 或项目根目录配置,但 canonical 资产仍保存在 langbot-skills。
阶段规划
阶段一:环境和测试路径沉淀
状态:基本完成,持续维护。
skills/.env管共享默认变量。langbot-env-setup拆出 Computer Use、Playwright MCP、OAuth profile、proxy、service startup。langbot-testing记录 WebUI、pipeline、provider 测试路径。lbs validate/index维护结构。
完成标准:
- agent 可以从
skills/.env和 references 中找到当前测试入口。 - pipeline Debug Chat 这类路径不再需要从头探索。
阶段二:结构化 case/troubleshooting
状态:主体已完成,继续补齐元数据和资产质量。
目标:
lbs case new/list/showlbs trouble show/search- case id 去重、字段校验、索引生成
完成标准:
- 冒烟测试路径可以用结构化 case 表示。
- 下一个 agent 窗口可以直接读取 case 执行。
阶段三:计划和报告
状态:已有 MVP,继续完善。
目标:
lbs test plan <case>- agent 按 plan 使用浏览器执行 UI QA
lbs test report- 日志守卫集成
- 报告产物和 evidence 约定
完成标准:
- agent 可以按 case plan 执行浏览器测试。
- 结果报告包含 UI 结果、后端日志、console 错误和 troubleshooting 建议。
执行规则
- agent 可以直接编辑 Markdown reference。
- 新增结构化 case/troubleshooting 时,优先使用
lbs。 - 每次结构变更后运行
bin/lbs validate。 - 每次索引相关变更后运行
bin/lbs index。 - 测试文档不写死端口,使用
skills/.env中的 URL 变量。 - 测试 case 的
mode固定为agent-browser。 - API/curl 只能写入
diagnostics,不能替代 UI 步骤和 UI 检查。