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e9dd584792
* 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.
2.9 KiB
2.9 KiB
Test Environment Setup
Docker Compose (GitOps)
Create in server-deploy repo under servers/<hostname>/langbot-test/docker-compose.yaml:
version: "3"
services:
langbot_plugin_runtime:
image: rockchin/langbot:latest
container_name: langbot-test-runtime
volumes:
- /opt/docker-data/langbot-test/data/plugins:/app/data/plugins
ports:
- "5411:5401"
restart: on-failure
environment:
- TZ=Asia/Shanghai
command: ["uv", "run", "--no-sync", "-m", "langbot_plugin.cli.__init__", "rt"]
networks:
- langbot_test_network
langbot:
image: rockchin/langbot:latest
container_name: langbot-test
volumes:
- /opt/docker-data/langbot-test/data:/app/data
ports:
- "5310:5300"
restart: on-failure
depends_on:
- langbot_plugin_runtime
environment:
- TZ=Asia/Shanghai
networks:
- langbot_test_network
networks:
langbot_test_network:
driver: bridge
Post-Deploy Configuration
After first start, LangBot auto-generates data/config.yaml. You need to update plugin.runtime_ws_url to match the runtime container name:
# On the host, edit config
sed -i 's|ws://localhost:5400/control/ws|ws://langbot-test-runtime:5400/control/ws|' \
/opt/docker-data/langbot-test/data/config.yaml
docker restart langbot-test
Installing a Plugin
Copy plugin directory to data/plugins/ on the host:
scp -r MyPlugin/ user@host:/opt/docker-data/langbot-test/data/plugins/MyPlugin/
docker restart langbot-test-runtime # Runtime picks up new plugins on restart
Caddy Reverse Proxy (Optional)
If testing externally, add to Caddyfile on the same host:
langbot-test.example.com {
reverse_proxy langbot-test:5300
}
Then reload: docker exec caddy caddy reload --config /etc/caddy/Caddyfile
The WebSocket endpoint works through Caddy without special config.
WebSocket Test Script (Node.js)
const WebSocket = require('ws');
const PIPELINE_UUID = '<your-pipeline-uuid>';
const BASE = 'wss://langbot-test.example.com';
const URL = `${BASE}/api/v1/pipelines/${PIPELINE_UUID}/ws/connect?session_type=group`;
const ws = new WebSocket(URL, {
headers: { Origin: BASE }
});
const send = (text) => {
ws.send(JSON.stringify({
type: 'message',
message: [{ type: 'Plain', text }]
}));
console.log('[SENT]', text);
};
ws.on('message', (data) => {
const msg = JSON.parse(data.toString());
if (msg.type === 'connected') {
console.log('Connected!');
// Send test messages
send('Message 1');
setTimeout(() => send('Message 2'), 500);
setTimeout(() => send('!summary'), 2000);
} else if (msg.type === 'response' && msg.data?.is_final) {
console.log('[BOT]', msg.data.content);
}
});
ws.on('error', (e) => console.error('Error:', e.message));
setTimeout(() => { ws.close(); process.exit(); }, 60000);
Requires: npm install ws