{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-chainlit--chainlit","slug":"chainlit--chainlit","name":"chainlit","type":"product","url":"https://docs.chainlit.io","page_url":"https://unfragile.ai/chainlit--chainlit","categories":["app-builders"],"tags":["chatgpt","langchain","llm","openai","openai-chatgpt","python","ui"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-chainlit--chainlit__cap_0","uri":"capability://text.generation.language.decorator.based.conversational.callback.system.with.async.message.handling","name":"decorator-based conversational callback system with async message handling","description":"Chainlit implements a Python decorator-based callback system (@cl.on_message, @cl.on_chat_start, @cl.on_action) that automatically wires developer-defined functions into a FastAPI+Socket.IO backend. Each callback receives a Message object and can emit responses via the cl.Message API, which streams to the frontend in real-time through WebSocket connections. The system handles async/await natively, allowing blocking I/O operations to be non-blocking at the server level.","intents":["Define conversational logic without boilerplate server setup","Stream LLM responses token-by-token to the UI as they arrive","Handle user messages and trigger side effects (logging, API calls) in response","Manage conversation lifecycle events (session start, user disconnect)"],"best_for":["Python developers building LLM chatbots who want rapid prototyping without Flask/FastAPI boilerplate","Teams integrating LangChain or LlamaIndex chains into a UI without custom server code"],"limitations":["Python-only; no JavaScript/TypeScript backend support","Callbacks are single-threaded per session; CPU-bound operations block the event loop","No built-in request queuing or rate limiting at the callback level"],"requires":["Python 3.10+","FastAPI and python-socketio as transitive dependencies","Async-compatible LLM client (e.g., aiohttp for OpenAI)"],"input_types":["Message objects with text content and metadata","File uploads (multipart form data)","User actions (button clicks, form submissions)"],"output_types":["Streamed text messages","Structured elements (images, code blocks, tables)","Metadata (author, timestamp, generation tokens)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chainlit--chainlit__cap_1","uri":"capability://automation.workflow.real.time.bidirectional.websocket.message.streaming.with.step.tracking","name":"real-time bidirectional websocket message streaming with step tracking","description":"Chainlit maintains persistent WebSocket connections (via Socket.IO) between the React frontend and FastAPI backend, enabling real-time message streaming without polling. The Step and Message system tracks the lifecycle of each interaction: steps represent intermediate reasoning (e.g., LLM chain steps), while messages are user-visible outputs. Each step/message emits lifecycle events (created, updated, completed) that the frontend subscribes to, allowing progressive UI updates as tokens arrive or operations complete.","intents":["Display LLM tokens as they stream from the model in real-time","Show intermediate reasoning steps (e.g., tool calls, retrieval operations) as they execute","Maintain conversation state across browser refreshes or network interruptions","Broadcast updates to multiple connected clients in the same session"],"best_for":["Applications requiring low-latency user feedback (e.g., code generation, real-time analysis)","Teams building multi-user collaborative chat interfaces"],"limitations":["WebSocket connections require stateful server; horizontal scaling needs sticky sessions or a message broker","No built-in message persistence across server restarts without external database","Step tracking adds ~50-100ms overhead per step lifecycle event due to serialization and emission"],"requires":["Socket.IO client library on frontend (included in @chainlit/app)","Socket.IO server support in backend (FastAPI + python-socketio)","Network connectivity for WebSocket upgrade (may fail behind certain proxies)"],"input_types":["Message objects with text, metadata, and optional elements","Step objects with name, type, and output","Element objects (images, files, code blocks)"],"output_types":["Real-time streamed text","Step lifecycle events (created, updated, completed)","Rendered UI elements with progressive loading"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chainlit--chainlit__cap_10","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.integration.for.tool.use.and.resource.access","name":"model context protocol (mcp) server integration for tool-use and resource access","description":"Chainlit integrates with the Model Context Protocol (MCP), allowing LLMs to access external tools and resources via a standardized interface. MCP servers expose tools (functions) and resources (data) that the LLM can invoke or query. Chainlit's MCP integration automatically registers MCP servers and makes their tools available to LLM callbacks, enabling agents to call external APIs, query databases, or access files without hardcoding integrations.","intents":["Allow LLMs to call external APIs or tools via MCP servers","Provide LLMs with access to external resources (files, databases, web services)","Implement tool-use agents without hardcoding individual tool integrations","Enable standardized tool definitions that work across different LLM providers"],"best_for":["Teams building agent applications requiring access to external tools","Developers implementing tool-use without hardcoding integrations","Applications needing standardized tool definitions across multiple LLM providers"],"limitations":["MCP is a relatively new standard; not all LLM providers support MCP tool calling","MCP server availability is required at runtime; unavailable servers will cause tool calls to fail","Tool definitions must be manually registered with the MCP server; no automatic discovery","Error handling is basic; MCP server errors are propagated to the LLM without sanitization"],"requires":["MCP server running and accessible (local or remote)","MCP client library (mcp Python package)","LLM provider supporting MCP tool calling (e.g., Claude 3.5+)"],"input_types":["MCP server configuration (host, port, or stdio)","Tool definitions (name, description, parameters)","Tool invocation requests from the LLM"],"output_types":["Tool execution results","Resource data from MCP servers","Tool call traces in the UI"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chainlit--chainlit__cap_11","uri":"capability://text.generation.language.react.based.frontend.with.real.time.message.composition.and.state.management","name":"react-based frontend with real-time message composition and state management","description":"Chainlit's frontend (@chainlit/app) is a React/TypeScript application that renders the chat UI, manages WebSocket connections, and handles real-time message updates. The frontend uses React hooks for state management (messages, steps, user session) and Socket.IO for bidirectional communication with the backend. Messages are composed from text, elements, and metadata, with support for markdown rendering, syntax highlighting, and lazy loading of large content.","intents":["Render a professional chat UI without custom frontend development","Display real-time message streaming and step progress","Handle user input (text, file uploads, actions) and send to the backend","Manage conversation state and history on the client side"],"best_for":["Developers wanting a production-ready chat UI without building from scratch","Teams deploying Chainlit as a standalone web application","Applications requiring a responsive, mobile-friendly chat interface"],"limitations":["Frontend is React-based; customization requires React/TypeScript knowledge","UI customization is limited to configuration (colors, logos); deep customization requires forking the frontend","Large conversation histories (>1000 messages) may cause performance degradation due to DOM size","Mobile experience is optimized for portrait orientation; landscape mode may have layout issues"],"requires":["Modern web browser with WebSocket support","Node.js 18+ and npm/yarn for building custom frontend versions","Optional: custom CSS or React components for UI customization"],"input_types":["User text input","File uploads (drag-and-drop or file picker)","Action triggers (button clicks, slash commands)"],"output_types":["Rendered chat UI with messages, steps, and elements","WebSocket messages to the backend","Local state (messages, user session, UI state)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chainlit--chainlit__cap_12","uri":"capability://text.generation.language.audio.input.output.system.with.speech.to.text.and.text.to.speech.integration","name":"audio input/output system with speech-to-text and text-to-speech integration","description":"Chainlit provides an audio system that integrates speech-to-text (STT) and text-to-speech (TTS) capabilities. Users can record audio messages that are transcribed to text and sent to the backend, and the backend can generate audio responses that are played back in the UI. The system supports multiple STT/TTS providers (OpenAI Whisper, Azure Speech Services, Google Cloud Speech) via pluggable adapters.","intents":["Allow users to interact with the chatbot via voice instead of text","Generate audio responses for accessibility or hands-free interaction","Implement voice-based agents or virtual assistants","Support multiple languages for speech recognition and synthesis"],"best_for":["Accessibility-focused applications requiring voice interaction","Mobile or hands-free applications (e.g., voice assistants)","Applications serving non-English-speaking users with multilingual STT/TTS"],"limitations":["STT/TTS quality depends on the provider; background noise or accents may reduce accuracy","Audio processing adds latency (typically 1-5 seconds for transcription)","TTS voices are limited to provider offerings; custom voices require additional setup","Audio streaming requires additional bandwidth; not suitable for low-bandwidth environments"],"requires":["STT/TTS provider API key (OpenAI, Azure, Google Cloud)","Browser microphone permission for audio recording","Audio codec support in the browser (WebM, MP3, WAV)"],"input_types":["Audio recordings (WebM, MP3, WAV)","Language code for STT/TTS (e.g., 'en-US', 'fr-FR')"],"output_types":["Transcribed text from audio","Generated audio from text","Audio playback in the UI"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chainlit--chainlit__cap_13","uri":"capability://automation.workflow.configuration.system.with.environment.variables.yaml.files.and.runtime.overrides","name":"configuration system with environment variables, yaml files, and runtime overrides","description":"Chainlit uses a hierarchical configuration system that loads settings from environment variables, YAML files (chainlit.md), and runtime overrides. Configuration includes UI settings (theme, logo, title), feature flags, authentication settings, data persistence backends, and LLM provider credentials. The system validates configuration at startup and provides sensible defaults, allowing applications to be configured without code changes.","intents":["Configure Chainlit behavior (theme, authentication, storage) without code changes","Use different configurations for development, staging, and production environments","Enable/disable features via feature flags without redeploying","Manage secrets (API keys, database URLs) via environment variables"],"best_for":["Teams deploying Chainlit across multiple environments (dev, staging, prod)","Applications requiring feature flags or A/B testing","Deployments using containerization (Docker) or infrastructure-as-code (Terraform)"],"limitations":["YAML configuration is static; runtime changes require server restart","Environment variable names are not standardized; documentation is required for custom settings","Feature flags are boolean; no support for gradual rollouts or percentage-based flags","Configuration validation is basic; invalid settings may not be caught until runtime"],"requires":["Environment variables set in the deployment environment","Optional: chainlit.md file in the application directory","Optional: Python configuration objects for programmatic configuration"],"input_types":["Environment variables (KEY=VALUE)","YAML configuration file (chainlit.md)","Python configuration objects"],"output_types":["Loaded configuration accessible via cl.config","UI rendered with configured theme and settings","Feature flags evaluated at runtime"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chainlit--chainlit__cap_14","uri":"capability://automation.workflow.cli.interface.with.hot.reload.debug.mode.and.headless.operation","name":"cli interface with hot-reload, debug mode, and headless operation","description":"Chainlit provides a command-line interface (chainlit run) that starts the server with optional hot-reload, debug mode, and headless operation. The CLI supports watching for file changes and automatically reloading the application, enabling rapid development iteration. Debug mode enables detailed logging and data layer inspection. Headless mode runs the server without the UI, useful for API-only deployments or testing.","intents":["Start a Chainlit application with a single command","Develop iteratively with hot-reload on file changes","Debug applications with detailed logging and data layer inspection","Deploy as an API-only service without the web UI"],"best_for":["Developers building and testing Chainlit applications locally","Teams deploying Chainlit as a backend service (API only)","Debugging complex callback logic or data persistence issues"],"limitations":["Hot-reload may not work reliably with certain import patterns or circular dependencies","Debug mode adds significant overhead; not suitable for production","Headless mode disables the web UI; clients must use the API directly","CLI options are limited; complex deployments may require custom server setup"],"requires":["Python 3.10+ with Chainlit installed","Application file (e.g., app.py) with @cl.on_message decorated function"],"input_types":["CLI arguments (--watch, --debug, --headless, --port, --host)","Application file path"],"output_types":["Running Chainlit server","Web UI (unless --headless)","Debug logs (if --debug)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chainlit--chainlit__cap_2","uri":"capability://memory.knowledge.langchain.and.llamaindex.callback.instrumentation.with.automatic.chain.tracing","name":"langchain and llamaindex callback instrumentation with automatic chain tracing","description":"Chainlit provides native callback handlers for LangChain (ChainlitCallbackHandler) and LlamaIndex (LlamaIndexCallbackHandler) that automatically instrument chain execution without code changes. These handlers hook into the framework's internal event system, capturing LLM calls, tool invocations, and retrieval operations as Step objects. The callbacks extract metadata (tokens, latency, model name) and emit them to the frontend, enabling full chain visibility without manual logging.","intents":["Visualize LangChain agent execution flow (tool calls, reasoning steps) in the UI","Track token usage and latency across LLM calls within a chain","Debug retrieval-augmented generation (RAG) pipelines by seeing which documents were retrieved","Monitor LlamaIndex query engines without modifying application code"],"best_for":["Teams using LangChain agents or LlamaIndex query engines who want built-in observability","Developers building RAG applications needing visibility into retrieval and generation steps"],"limitations":["Callback coverage is limited to LangChain v0.1+ and LlamaIndex v0.9+; older versions may not emit all events","Custom LLM subclasses or tool implementations may not trigger callbacks if they bypass the framework's event system","Metadata extraction (e.g., token counts) depends on the LLM provider's response format; some providers don't return this data"],"requires":["langchain>=0.1.0 or llama-index>=0.9.0 (depending on which framework is used)","LLM API key (OpenAI, Anthropic, etc.) passed to the chain/engine","Chainlit callback handler imported and instantiated in the chain setup"],"input_types":["LangChain Chain or Agent objects","LlamaIndex QueryEngine or RAGQueryEngine objects","User queries (text)"],"output_types":["Step objects representing each LLM call, tool invocation, or retrieval operation","Metadata (tokens, latency, model name, tool name)","Rendered chain visualization in the UI"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chainlit--chainlit__cap_3","uri":"capability://data.processing.analysis.multi.backend.data.persistence.abstraction.with.sql.cloud.storage.and.vector.support","name":"multi-backend data persistence abstraction with sql, cloud storage, and vector support","description":"Chainlit abstracts data persistence through a pluggable DataLayer interface supporting PostgreSQL, SQLite, DynamoDB, Azure Blob Storage, Google Cloud Storage, and AWS S3. The system uses SQLAlchemy for ORM-based SQL persistence and boto3/cloud SDKs for object storage. Conversation history, user data, and file uploads are persisted to the configured backend, with optional vector storage integration for semantic search over conversation history. The abstraction allows switching backends via environment variables without code changes.","intents":["Persist conversation history across server restarts and user sessions","Store file uploads (images, documents) in cloud storage with metadata indexing","Query conversation history by user, timestamp, or semantic similarity","Implement multi-tenant applications with isolated data per user or organization"],"best_for":["Production deployments requiring durable conversation storage","Teams building multi-user platforms with per-user data isolation","Applications needing semantic search over conversation history (e.g., knowledge bases)"],"limitations":["SQLite is single-writer; concurrent writes will block or fail; not suitable for multi-process deployments","DynamoDB requires AWS credentials and incurs per-request charges; not cost-effective for low-traffic apps","Vector storage integration requires a separate vector database (Chroma, Pinecone, Weaviate); Chainlit does not provide built-in vector indexing","Schema migrations are manual; upgrading Chainlit may require database schema updates"],"requires":["Database URL or cloud credentials (e.g., DATABASE_URL=postgresql://..., AWS_ACCESS_KEY_ID)","Optional: asyncpg for PostgreSQL, boto3 for AWS, azure-storage-blob for Azure","Optional: vector database client (e.g., chromadb) if semantic search is needed"],"input_types":["Conversation messages (text, metadata)","File uploads (binary data, MIME type)","User metadata (ID, name, email)"],"output_types":["Persisted conversation records with timestamps","File references (URLs or object keys)","Query results (filtered conversations, user history)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chainlit--chainlit__cap_4","uri":"capability://safety.moderation.oauth.jwt.and.custom.header.based.authentication.with.role.based.access.control","name":"oauth, jwt, and custom header-based authentication with role-based access control","description":"Chainlit provides pluggable authentication via OAuth (Google, GitHub, Azure AD), JWT tokens, password-based login, and custom header-based schemes (for reverse proxy integration). The authentication system integrates with FastAPI's dependency injection, protecting all WebSocket and HTTP endpoints. Role-based access control (RBAC) allows restricting actions (e.g., file upload, message sending) to specific user roles. Custom authentication can be implemented by subclassing the Auth class and overriding the authenticate() method.","intents":["Require users to log in before accessing the chat interface","Integrate with existing identity providers (Google, GitHub, Azure AD) via OAuth","Restrict sensitive actions (e.g., file uploads, API calls) to specific user roles","Implement single sign-on (SSO) by extracting user identity from reverse proxy headers"],"best_for":["Enterprise deployments requiring OAuth or SAML integration","Multi-tenant SaaS platforms needing per-user data isolation and RBAC","Teams deploying behind reverse proxies (nginx, Cloudflare) with header-based auth"],"limitations":["OAuth requires registering the app with the identity provider and managing client secrets","JWT validation requires a shared secret or public key; key rotation is manual","Custom header auth is only secure if the reverse proxy is trusted; misconfiguration can allow header spoofing","RBAC is coarse-grained (role-based); fine-grained permissions (resource-level ACLs) are not built-in"],"requires":["OAuth provider credentials (client ID, client secret) for OAuth flows","JWT secret or public key for JWT validation","FastAPI dependency injection (included in Chainlit)","Optional: reverse proxy (nginx, Cloudflare) for header-based auth"],"input_types":["OAuth authorization code (from identity provider)","JWT token (in Authorization header)","Username and password (for password-based login)","Custom headers (from reverse proxy)"],"output_types":["Authenticated user object with ID, name, email, and roles","Session token (JWT or session cookie)","Access control decisions (allow/deny for actions)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chainlit--chainlit__cap_5","uri":"capability://image.visual.interactive.element.system.with.images.files.code.blocks.and.custom.html.rendering","name":"interactive element system with images, files, code blocks, and custom html rendering","description":"Chainlit's Element system allows embedding rich content (images, files, code blocks, tables, custom HTML) directly in messages. Elements are created via the cl.Image, cl.File, cl.Code, cl.Pdf classes and attached to messages. The frontend renders elements using React components, with support for lazy loading, syntax highlighting (for code), and PDF viewing. Custom HTML elements can be rendered via the cl.Html class, allowing arbitrary React component injection.","intents":["Display generated images or charts in the chat interface","Allow users to download generated files (PDFs, CSVs, code) from messages","Show code snippets with syntax highlighting and copy-to-clipboard functionality","Render custom visualizations or interactive components (e.g., tables, charts) in messages"],"best_for":["Applications generating code, images, or documents (e.g., code generation, data analysis)","Teams building rich chat UIs with embedded visualizations","Developers needing to display structured data (tables, JSON) in a readable format"],"limitations":["Custom HTML elements are rendered as React components; arbitrary JavaScript execution is not supported for security reasons","Large files (>100MB) may cause browser memory issues; no built-in streaming or chunked rendering","PDF rendering is client-side; very large PDFs may be slow to load","Element persistence depends on the configured DataLayer; without persistence, elements are lost on server restart"],"requires":["Chainlit frontend (@chainlit/app) to render elements","File paths or URLs for images and files (local paths are served via the Chainlit server)","Optional: syntax highlighting library (Prism.js, included in frontend)"],"input_types":["Image files (PNG, JPG, GIF, WebP)","Binary files (PDF, CSV, JSON, any MIME type)","Code strings with language specification","HTML strings or React component definitions"],"output_types":["Rendered images in the chat","Downloadable file links","Syntax-highlighted code blocks","Custom HTML/React components"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chainlit--chainlit__cap_6","uri":"capability://data.processing.analysis.file.upload.system.with.multipart.form.handling.and.cloud.storage.integration","name":"file upload system with multipart form handling and cloud storage integration","description":"Chainlit provides a file upload system that accepts multipart form data from the frontend, validates file types and sizes, and stores files in the configured backend (local filesystem, S3, Azure Blob, GCS). Uploaded files are associated with messages and users, with metadata (filename, MIME type, size) indexed in the database. The system supports drag-and-drop uploads and file type restrictions via configuration.","intents":["Allow users to upload documents, images, or data files for processing","Store uploaded files in cloud storage with automatic cleanup","Associate uploaded files with specific messages or users for context","Restrict file uploads to specific types (e.g., PDF, CSV) via configuration"],"best_for":["Applications processing user-provided documents (e.g., document analysis, data extraction)","Teams building collaborative tools with file sharing","Deployments using cloud storage (S3, Azure, GCS) for scalability"],"limitations":["File size limits are enforced at the application level; very large files (>1GB) may timeout or exhaust memory","Virus scanning is not built-in; applications handling untrusted files should integrate with a scanning service","File cleanup is manual; deleted messages do not automatically delete associated files from storage","Local filesystem storage is not suitable for multi-process or distributed deployments"],"requires":["Multipart form data support in the frontend (included in @chainlit/app)","Cloud storage credentials (AWS_ACCESS_KEY_ID, AZURE_STORAGE_CONNECTION_STRING, etc.) if using cloud backends","Sufficient disk space or cloud storage quota"],"input_types":["Multipart form data with file content and metadata","File type and size restrictions (via configuration)"],"output_types":["File references (local paths or cloud URLs)","File metadata (filename, MIME type, size, upload timestamp)","Persisted file records in the database"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chainlit--chainlit__cap_7","uri":"capability://memory.knowledge.session.management.with.user.context.and.conversation.isolation","name":"session management with user context and conversation isolation","description":"Chainlit manages user sessions via WebSocket connections, maintaining a session object (cl.user_session) that persists data across messages within a single conversation. Each session is tied to a user (authenticated or anonymous) and isolated from other sessions. The session object is a dictionary-like store for application state (e.g., conversation context, user preferences). Sessions are created on first connection and destroyed on disconnect, with optional persistence to the database.","intents":["Store conversation-specific state (e.g., retrieved documents, user preferences) without polluting global state","Isolate conversations between different users or browser tabs","Maintain context across multiple message exchanges without passing state through messages","Implement per-session rate limiting or quota tracking"],"best_for":["Multi-user applications requiring per-user or per-conversation state isolation","Applications with complex conversation context (e.g., RAG systems with retrieved documents)","Teams implementing per-session rate limiting or quota enforcement"],"limitations":["Session data is in-memory; not shared across multiple server instances without external state store (Redis)","Session data is lost on server restart unless explicitly persisted to the database","No built-in session expiration or cleanup; long-running sessions may accumulate memory","Session size is limited by available memory; storing large objects (e.g., embeddings) may cause memory issues"],"requires":["WebSocket connection (maintained by the frontend)","Optional: external state store (Redis) for multi-instance deployments","Optional: database persistence for session recovery across restarts"],"input_types":["User ID (from authentication)","Session data (arbitrary Python objects)"],"output_types":["Session object accessible in callbacks via cl.user_session","Persisted session records in the database (if enabled)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chainlit--chainlit__cap_8","uri":"capability://tool.use.integration.action.callback.system.for.user.triggered.interactions.buttons.forms.commands","name":"action callback system for user-triggered interactions (buttons, forms, commands)","description":"Chainlit provides an action system (@cl.action) that allows defining custom actions triggered by user interactions (button clicks, form submissions, slash commands). Actions are defined as decorated functions that receive an ActionCallback object with the action name and optional parameters. The frontend renders action buttons or command suggestions, and when triggered, the backend executes the corresponding callback. Actions can update the UI, send messages, or trigger side effects.","intents":["Add interactive buttons to messages (e.g., 'Regenerate', 'Copy', 'Feedback')","Implement slash commands (e.g., '/help', '/settings') for user navigation","Collect user input via forms and process submissions in callbacks","Trigger backend operations without sending a message (e.g., 'Like', 'Dislike')"],"best_for":["Applications needing rich interactivity beyond text messages","Teams building chatbots with command-based interfaces","Developers implementing feedback loops or user preferences"],"limitations":["Actions are callback-based; no built-in form validation or schema enforcement","Action parameters are passed as strings; complex data types require manual serialization","No built-in rate limiting on actions; malicious clients can spam action triggers","Action state is not persisted; actions are lost on server restart"],"requires":["Frontend support for action rendering (included in @chainlit/app)","Action callback function decorated with @cl.action"],"input_types":["Action name (string)","Action parameters (optional, passed as strings)"],"output_types":["Message updates or new messages","UI state changes","Side effects (API calls, database updates)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-chainlit--chainlit__cap_9","uri":"capability://tool.use.integration.platform.integration.with.slack.discord.and.microsoft.teams.via.webhooks","name":"platform integration with slack, discord, and microsoft teams via webhooks","description":"Chainlit provides integrations with messaging platforms (Slack, Discord, Microsoft Teams) that allow deploying the same conversational logic to multiple platforms via webhooks. Messages sent to the platform are forwarded to the Chainlit backend, processed by the same callbacks, and responses are sent back to the platform. 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