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* 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.
117 lines
2.9 KiB
Markdown
117 lines
2.9 KiB
Markdown
# Test Environment Setup
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## Docker Compose (GitOps)
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Create in `server-deploy` repo under `servers/<hostname>/langbot-test/docker-compose.yaml`:
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```yaml
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version: "3"
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services:
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langbot_plugin_runtime:
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image: rockchin/langbot:latest
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container_name: langbot-test-runtime
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volumes:
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- /opt/docker-data/langbot-test/data/plugins:/app/data/plugins
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ports:
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- "5411:5401"
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restart: on-failure
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environment:
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- TZ=Asia/Shanghai
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command: ["uv", "run", "--no-sync", "-m", "langbot_plugin.cli.__init__", "rt"]
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networks:
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- langbot_test_network
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langbot:
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image: rockchin/langbot:latest
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container_name: langbot-test
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volumes:
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- /opt/docker-data/langbot-test/data:/app/data
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ports:
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- "5310:5300"
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restart: on-failure
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depends_on:
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- langbot_plugin_runtime
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environment:
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- TZ=Asia/Shanghai
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networks:
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- langbot_test_network
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networks:
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langbot_test_network:
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driver: bridge
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```
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## Post-Deploy Configuration
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After first start, LangBot auto-generates `data/config.yaml`. You need to update `plugin.runtime_ws_url` to match the runtime container name:
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```bash
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# On the host, edit config
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sed -i 's|ws://localhost:5400/control/ws|ws://langbot-test-runtime:5400/control/ws|' \
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/opt/docker-data/langbot-test/data/config.yaml
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docker restart langbot-test
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```
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## Installing a Plugin
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Copy plugin directory to `data/plugins/` on the host:
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```bash
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scp -r MyPlugin/ user@host:/opt/docker-data/langbot-test/data/plugins/MyPlugin/
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docker restart langbot-test-runtime # Runtime picks up new plugins on restart
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```
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## Caddy Reverse Proxy (Optional)
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If testing externally, add to Caddyfile on the same host:
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```
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langbot-test.example.com {
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reverse_proxy langbot-test:5300
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}
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```
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Then reload: `docker exec caddy caddy reload --config /etc/caddy/Caddyfile`
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The WebSocket endpoint works through Caddy without special config.
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## WebSocket Test Script (Node.js)
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```javascript
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const WebSocket = require('ws');
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const PIPELINE_UUID = '<your-pipeline-uuid>';
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const BASE = 'wss://langbot-test.example.com';
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const URL = `${BASE}/api/v1/pipelines/${PIPELINE_UUID}/ws/connect?session_type=group`;
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const ws = new WebSocket(URL, {
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headers: { Origin: BASE }
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});
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const send = (text) => {
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ws.send(JSON.stringify({
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type: 'message',
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message: [{ type: 'Plain', text }]
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}));
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console.log('[SENT]', text);
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};
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ws.on('message', (data) => {
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const msg = JSON.parse(data.toString());
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if (msg.type === 'connected') {
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console.log('Connected!');
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// Send test messages
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send('Message 1');
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setTimeout(() => send('Message 2'), 500);
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setTimeout(() => send('!summary'), 2000);
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} else if (msg.type === 'response' && msg.data?.is_final) {
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console.log('[BOT]', msg.data.content);
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}
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});
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ws.on('error', (e) => console.error('Error:', e.message));
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setTimeout(() => { ws.close(); process.exit(); }, 60000);
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```
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Requires: `npm install ws`
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