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https://github.com/langbot-app/LangBot.git
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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.
72 lines
3.0 KiB
YAML
72 lines
3.0 KiB
YAML
id: local-agent-multimodal-debug-chat
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title: "Local Agent Debug Chat preserves uploaded image input"
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mode: agent-browser
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area: pipeline
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type: regression
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priority: p2
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risk: medium
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ci_eligible: false
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tags:
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- local-agent
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- multimodal
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- pipeline
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skills:
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- langbot-env-setup
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- langbot-testing
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env:
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- LANGBOT_FRONTEND_URL
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- LANGBOT_BACKEND_URL
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- LANGBOT_LOCAL_AGENT_PIPELINE_URL
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- LANGBOT_LOCAL_AGENT_PIPELINE_NAME
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automation: scripts/e2e/pipeline-debug-chat.mjs
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automation_env:
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- LANGBOT_FRONTEND_URL
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- LANGBOT_BROWSER_PROFILE
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- LANGBOT_CHROMIUM_EXECUTABLE
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- LANGBOT_LOCAL_AGENT_PIPELINE_URL
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- LANGBOT_LOCAL_AGENT_PIPELINE_NAME
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automation_pipeline_url_env: LANGBOT_LOCAL_AGENT_PIPELINE_URL
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automation_pipeline_name_env: LANGBOT_LOCAL_AGENT_PIPELINE_NAME
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automation_prompt: "I attached an image. Reply only IMAGE_OK if you received the image."
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automation_expected_text: "IMAGE_OK"
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automation_image_base64_fixture: "skills/langbot-testing/fixtures/multimodal/red-square.png.base64"
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setup_automation:
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- "node:scripts/e2e/ensure-local-agent-pipeline.mjs --write-env"
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setup_provides_env:
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- LANGBOT_LOCAL_AGENT_PIPELINE_URL
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- LANGBOT_LOCAL_AGENT_PIPELINE_NAME
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preconditions:
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- "The selected model route accepts image input, or the case is intentionally checking graceful provider rejection."
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steps:
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- "Prepare a small PNG file for upload. The bundled fixture base64 is at skills/langbot-testing/fixtures/multimodal/red-square.png.base64 if a temporary file is needed."
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- "Open LANGBOT_FRONTEND_URL."
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- "Navigate to Pipelines and open the target local-agent pipeline."
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- "Open Configuration > AI."
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- "Use runner Default or the pluginized langbot/local-agent runner."
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- "Select a model that supports image input in the current environment, or use a known model that at least accepts the uploaded image payload."
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- "Save the pipeline."
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- "Open Debug Chat."
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- "Attach the PNG through the image/file upload control. Prefer the bundled 64x64 red-square fixture; 1x1 images may be rejected by some model providers before runner behavior is exercised."
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- "Confirm the user compose area or sent message shows the image attachment."
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- "Send: I attached an image. Reply only IMAGE_OK if you received the image."
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checks:
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- "UI: The sent User message shows an image attachment, not just text."
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- "UI: The Bot message contains IMAGE_OK."
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- "Network or logs: The browser sends an image upload request, or backend logs show the local-agent input contains an image."
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- "Console: No unexpected frontend runtime errors appear during upload or Debug Chat."
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evidence_required:
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- ui
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- screenshot
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- console
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- network
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- backend_log
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diagnostics:
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- "If the model cannot process image input, repeat with a multimodal-capable model before diagnosing local-agent."
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- "For RAG plus multimodal coverage, keep a KB bound and verify the image remains visible while the answer uses the KB sentinel."
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troubleshooting:
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- local-agent-model-route-unavailable
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- plugin-runtime-timeout
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- proxy-env-mismatch
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- provider-image-parse-error
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- survey-widget-blocks-debug-chat
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