{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-chainlit","slug":"pypi-chainlit","name":"chainlit","type":"framework","url":"https://pypi.org/project/chainlit/","page_url":"https://unfragile.ai/pypi-chainlit","categories":["app-builders"],"tags":["Agents","LLM","MCP","chat","ui","chatbot","ui","conversational","ai","copilot","gen","ai","langchain","openai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-chainlit__cap_0","uri":"capability://text.generation.language.decorator.based.conversational.callback.system.with.real.time.message.streaming","name":"decorator-based conversational callback system with real-time message streaming","description":"Chainlit provides a Python decorator-based callback system (@cl.on_message, @cl.on_chat_start, @cl.on_action) that hooks into a FastAPI + Socket.IO backend to enable real-time bidirectional message streaming between client and server. Developers define conversational logic as async Python functions that receive Message objects and emit responses via the cl.Message API, with automatic WebSocket serialization and session-scoped state management. The system handles connection lifecycle, message queuing, and concurrent request handling through FastAPI's async runtime.","intents":["Build a chatbot backend without managing WebSocket plumbing or connection state","Stream LLM responses token-by-token to the frontend in real-time","Handle multi-turn conversations with session-scoped context persistence","Trigger side effects (logging, analytics, file uploads) on specific conversation events"],"best_for":["Python developers building conversational AI prototypes and production apps","Teams integrating LLM frameworks (LangChain, LlamaIndex) into chat interfaces","Builders who want to avoid WebSocket/HTTP complexity and focus on conversation logic"],"limitations":["Async-only design — synchronous callbacks are not supported, requiring developers to wrap blocking I/O","Session state is in-memory by default — requires explicit data layer configuration for persistence across server restarts","Single-threaded per session — concurrent message handling within a session requires manual locking or queue management","No built-in rate limiting or backpressure handling — high-volume message streams may cause memory pressure"],"requires":["Python 3.10+","FastAPI (included as dependency)","python-socketio (included as dependency)","Async event loop (uvicorn or similar ASGI server)"],"input_types":["Message objects (text, metadata, user ID)","File uploads (multipart form data)","Action payloads (JSON from UI interactions)"],"output_types":["Message objects (streamed via WebSocket)","Step objects (for tracing LLM calls)","Element objects (interactive UI components)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-chainlit__cap_1","uri":"capability://memory.knowledge.langchain.and.llamaindex.callback.instrumentation.with.automatic.step.tracing","name":"langchain and llamaindex callback instrumentation with automatic step tracing","description":"Chainlit provides native callback handlers for LangChain (ChainlitCallbackHandler) and LlamaIndex (LlamaIndexCallbackHandler) that automatically instrument LLM calls, tool invocations, and retrieval operations into a hierarchical Step system. Each step captures input/output, model metadata, token counts, and latency, creating a visual trace in the UI. The callbacks hook into the frameworks' event systems (LangChain's BaseCallbackHandler, LlamaIndex's BaseCallbackHandler) and emit Step objects via the Chainlit emitter, with no code changes required beyond adding the callback to the chain/agent initialization.","intents":["Visualize LLM call chains and tool invocations in the chat UI without manual instrumentation","Capture token usage and latency metrics for cost and performance analysis","Debug multi-step agent reasoning by inspecting intermediate steps and tool outputs","Persist LLM traces to the database for audit and replay"],"best_for":["Teams using LangChain or LlamaIndex who want observability without adding tracing libraries","Developers building agents and need to visualize reasoning chains in production","Organizations requiring audit trails of LLM interactions for compliance"],"limitations":["Callback instrumentation adds ~50-100ms overhead per step due to event emission and serialization","Only captures events exposed by the framework's callback interface — custom chain logic not using callbacks is invisible","Token counting relies on framework-provided estimates — actual billing may differ from displayed counts","No automatic sampling or filtering — high-volume chains may create thousands of steps, impacting UI performance"],"requires":["LangChain 0.1.0+ OR LlamaIndex 0.9.0+","Python 3.10+","Chainlit callback handler imported and registered in chain initialization"],"input_types":["LangChain Chain/Agent objects","LlamaIndex QueryEngine/Agent objects","LLM call parameters (model, temperature, max_tokens)"],"output_types":["Step objects (hierarchical trace tree)","Metadata (token counts, latency, model name)","Tool call logs (input/output for each tool invocation)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-chainlit__cap_10","uri":"capability://automation.workflow.configuration.management.via.chainlit.toml.with.environment.variable.overrides","name":"configuration management via chainlit.toml with environment variable overrides","description":"Chainlit uses a declarative configuration system based on chainlit.toml (TOML format) for setting application metadata, UI customization, authentication, data persistence, and feature flags. Configuration is loaded at startup and can be overridden via environment variables (e.g., CHAINLIT_AUTH_SECRET). The system supports feature flags for enabling/disabling functionality (e.g., CHAINLIT_ENABLE_TELEMETRY), and provides a Config class for programmatic access to settings.","intents":["Configure authentication, database, and UI settings without code changes","Switch between development (SQLite) and production (PostgreSQL) configurations via environment variables","Enable/disable features (telemetry, auth) via feature flags","Customize UI branding (logo, title, theme colors) via configuration"],"best_for":["Teams deploying Chainlit to multiple environments (dev, staging, prod)","Organizations wanting to manage configuration via environment variables (12-factor app pattern)","Developers needing feature flags for gradual rollout or A/B testing"],"limitations":["Configuration is static at startup — changes require server restart","No built-in validation of configuration values — invalid settings may cause runtime errors","Environment variable overrides are limited to top-level keys — nested configuration requires TOML editing","No configuration versioning or rollback — changes are immediate and irreversible","Feature flags are binary (on/off) — no support for gradual rollout percentages"],"requires":["Python 3.10+","chainlit.toml file in the application root (optional, uses defaults if missing)"],"input_types":["chainlit.toml file (TOML format)","Environment variables (KEY=VALUE)"],"output_types":["Config object (in-memory configuration)","UI customization (applied to frontend)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-chainlit__cap_11","uri":"capability://automation.workflow.cli.interface.with.hot.reload.development.mode.and.headless.api.mode","name":"cli interface with hot-reload development mode and headless api mode","description":"Chainlit provides a command-line interface (chainlit run, chainlit deploy, chainlit create) for running applications. The run command supports hot-reload (--watch flag) for automatic server restart on file changes, debug mode (--debug flag) for detailed logging, and headless mode (--headless flag) for API-only operation without the UI. The CLI also provides options for specifying port, host, and other runtime parameters.","intents":["Start a Chainlit application in development mode with automatic reload on file changes","Run Chainlit as a headless API (no UI) for integration with external frontends","Deploy Chainlit applications to production with custom port/host configuration","Debug applications with detailed logging and error messages"],"best_for":["Developers building Chainlit applications and wanting fast iteration","Teams integrating Chainlit backend with custom frontends","DevOps engineers deploying Chainlit to production"],"limitations":["Hot-reload only watches Python files — changes to frontend code require manual refresh","Headless mode still requires the FastAPI server — no option to run callbacks without HTTP","CLI options are limited — complex configurations require chainlit.toml","No built-in process management — requires external tools (systemd, Docker) for production deployment","Debug mode adds significant overhead — not suitable for production use"],"requires":["Python 3.10+","Chainlit installed via pip","Application file (e.g., app.py) in the current directory"],"input_types":["CLI arguments (--watch, --debug, --port, etc.)","Application file (Python module)"],"output_types":["Running server (listening on specified port)","Log output (to stdout/stderr)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-chainlit__cap_12","uri":"capability://tool.use.integration.platform.integrations.for.slack.discord.and.microsoft.teams.with.message.routing","name":"platform integrations for slack, discord, and microsoft teams with message routing","description":"Chainlit provides integrations with messaging platforms (Slack, Discord, Microsoft Teams) that route platform-specific messages to Chainlit callbacks and send responses back to the platform. Each platform integration uses the platform's API (Slack Bolt, Discord.py, Microsoft Bot Framework) to receive messages, convert them to Chainlit Message objects, and emit them to the appropriate callback. Responses are converted back to platform-specific format and sent to the user.","intents":["Deploy a Chainlit chatbot to Slack, Discord, or Teams without building separate bots","Handle messages from multiple platforms with a single Chainlit application","Maintain conversation context across platform-specific sessions","Leverage Chainlit's LLM integration and tracing in platform-native chat interfaces"],"best_for":["Teams wanting to deploy chatbots to Slack/Teams without building custom integrations","Organizations with existing Slack/Teams workspaces wanting to add AI capabilities","Developers building multi-platform chatbots"],"limitations":["Platform integrations are limited to Slack, Discord, Teams — other platforms require custom implementation","Message formatting is platform-specific — rich elements (buttons, forms) may not render correctly on all platforms","Conversation context is limited to platform-native sessions — no cross-platform conversation history","Authentication is delegated to the platform — Chainlit's auth system is bypassed","Rate limiting is platform-specific — Chainlit has no built-in rate limiting for platform messages"],"requires":["Python 3.10+","Platform API credentials (Slack bot token, Discord token, Teams app ID)","Platform-specific SDK (slack-bolt, discord.py, botbuilder)"],"input_types":["Platform-specific messages (Slack events, Discord messages, Teams activities)","Platform user context (user ID, channel ID)"],"output_types":["Platform-specific responses (Slack messages, Discord embeds, Teams cards)","Conversation history (in platform-native format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-chainlit__cap_2","uri":"capability://data.processing.analysis.multi.backend.data.persistence.layer.with.pluggable.storage.providers","name":"multi-backend data persistence layer with pluggable storage providers","description":"Chainlit abstracts data persistence through a DataLayer interface supporting multiple backends: SQLAlchemy (PostgreSQL, MySQL, SQLite), DynamoDB, and cloud storage (AWS S3, Azure Blob, GCP Cloud Storage). The system uses a repository pattern with concrete implementations (SQLAlchemyDataLayer, DynamoDBDataLayer) that handle CRUD operations for conversations, messages, steps, and user data. Configuration is declarative via chainlit.toml or environment variables, allowing runtime backend switching without code changes. The data model uses SQLAlchemy ORM for relational backends and custom serialization for NoSQL, with automatic schema migration support.","intents":["Persist conversations and traces to a production database without writing SQL or ORM code","Switch between SQLite (development) and PostgreSQL (production) via configuration only","Store conversation history for analytics, compliance, or user replay","Scale to multi-region deployments using DynamoDB or cloud storage"],"best_for":["Teams deploying Chainlit to production and needing persistent conversation storage","Organizations with existing database infrastructure (PostgreSQL, DynamoDB) wanting to integrate Chainlit","Developers building multi-tenant SaaS platforms requiring isolated conversation storage per user"],"limitations":["SQLAlchemy backend requires asyncpg or aiomysql for async support — synchronous drivers block the event loop","DynamoDB backend requires AWS credentials and incurs per-request billing — not suitable for high-volume, low-latency queries","No built-in sharding or partitioning — single database becomes a bottleneck at scale (>10k concurrent users)","Schema migrations are manual for custom data models — Chainlit provides no automatic migration tooling","Cloud storage backends (S3, Blob) are append-only — updating existing records requires full object rewrites"],"requires":["Python 3.10+","SQLAlchemy 2.0+ (for SQL backends)","asyncpg 0.27+ (for PostgreSQL async support)","boto3 (for DynamoDB/S3)","azure-storage-blob or google-cloud-storage (for cloud storage)","Database credentials or cloud service account"],"input_types":["Conversation objects (messages, steps, metadata)","User data (ID, name, email)","File uploads (binary data for storage)"],"output_types":["Persisted conversation records (queryable via ORM or cloud APIs)","Conversation history (for UI replay or analytics)","User session data (for multi-turn context)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-chainlit__cap_3","uri":"capability://automation.workflow.real.time.bidirectional.websocket.communication.with.automatic.session.management","name":"real-time bidirectional websocket communication with automatic session management","description":"Chainlit uses python-socketio (Socket.IO 4.x protocol) to establish persistent WebSocket connections between browser clients and the FastAPI backend, with automatic reconnection, message queuing, and session lifecycle management. Each client connection is assigned a session ID, and all messages are routed through a session-scoped context (cl.user_session) that persists across message exchanges. The system handles connection drops, browser tab switching, and concurrent requests through Socket.IO's built-in acknowledgment and retry mechanisms, with configurable timeouts and heartbeat intervals.","intents":["Maintain persistent connection state across multiple message exchanges without polling","Stream LLM responses token-by-token with low latency (<100ms per token)","Handle client reconnections transparently without losing conversation context","Support multiple concurrent users with isolated session state"],"best_for":["Developers building real-time chat interfaces requiring sub-second latency","Teams deploying Chainlit behind load balancers or reverse proxies (Socket.IO handles connection upgrades)","Applications requiring persistent session state across network interruptions"],"limitations":["Socket.IO adds ~50-100ms latency overhead compared to raw WebSockets due to protocol framing and acknowledgment","Session state is in-memory by default — server restarts lose all active sessions unless explicitly persisted","Horizontal scaling requires Socket.IO adapter (Redis, RabbitMQ) for cross-server message routing — not included by default","Browser tab switching may create duplicate connections — requires client-side deduplication logic","Message ordering is guaranteed per session but not across sessions — distributed tracing becomes complex"],"requires":["python-socketio 5.9+ (included as dependency)","python-engineio 4.7+ (included as dependency)","ASGI server (uvicorn, hypercorn) with WebSocket support","Browser with WebSocket support (all modern browsers)"],"input_types":["WebSocket frames (binary or text)","Socket.IO events (JSON-serialized)","Session metadata (user ID, connection ID)"],"output_types":["Real-time message streams (streamed to client)","Session state updates (broadcast to client)","Acknowledgment frames (for message delivery confirmation)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-chainlit__cap_4","uri":"capability://image.visual.react.based.frontend.ui.with.real.time.message.composition.and.interactive.elements","name":"react-based frontend ui with real-time message composition and interactive elements","description":"Chainlit provides a React/TypeScript frontend (@chainlit/app) that renders messages, steps, and interactive elements (buttons, file uploads, forms) in real-time as they arrive via WebSocket. The frontend uses a state management system (likely Redux or Context API based on DeepWiki references) to maintain conversation history, user input, and UI state, with automatic re-rendering on message updates. Elements are composable components (Image, PDF, File, Plotly charts) that can be embedded in messages, and the UI supports markdown rendering, syntax highlighting for code blocks, and audio playback. The Copilot Widget provides an embeddable chat interface for third-party websites.","intents":["Display conversational AI responses with rich formatting (markdown, code blocks, embedded media)","Render interactive elements (buttons, forms, file uploads) that trigger backend actions","Show LLM reasoning chains as collapsible step trees with token counts and latency","Embed a chat widget on external websites without hosting a separate frontend"],"best_for":["Teams building consumer-facing chat applications with rich UI requirements","Developers wanting a pre-built chat UI without designing custom React components","Organizations embedding Chainlit chat into existing websites via the Copilot Widget"],"limitations":["Frontend is tightly coupled to Chainlit's backend — cannot be easily swapped for custom UI","Message rendering is limited to predefined element types — custom visualizations require extending the Element system","State management is client-side only — no automatic persistence across browser refreshes (relies on backend conversation history)","Copilot Widget requires iframe embedding — may have CORS or CSP policy conflicts on some websites","No built-in accessibility features (WCAG compliance) — requires manual testing and remediation"],"requires":["React 18+","TypeScript 4.9+","Node.js 18+ (for building frontend)","Chainlit backend running on accessible URL (for WebSocket connection)"],"input_types":["Message objects (text, metadata, author)","Element objects (images, files, charts)","Step objects (for trace visualization)","User interactions (button clicks, form submissions, file uploads)"],"output_types":["Rendered HTML/CSS (displayed in browser)","WebSocket events (user input sent to backend)","DOM state (conversation history, UI state)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-chainlit__cap_5","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.integration.with.tool.schema.validation","name":"model context protocol (mcp) server integration with tool schema validation","description":"Chainlit supports the Model Context Protocol (MCP) standard for exposing tools and resources to LLMs through a standardized interface. Developers can register MCP servers (local or remote) that define tools with JSON schemas, and Chainlit automatically makes these tools available to LangChain agents or direct LLM calls. The system validates tool inputs against schemas, handles tool execution with error recovery, and streams tool results back to the LLM. MCP integration is declarative via configuration or programmatic via the cl.MCP API.","intents":["Connect LLM agents to external tools (APIs, databases, file systems) via standardized MCP protocol","Validate tool inputs against JSON schemas before execution, preventing malformed requests","Expose internal tools to multiple LLM providers without rewriting tool definitions","Enable tool discovery and documentation through MCP's introspection capabilities"],"best_for":["Teams building multi-provider LLM agents that need tool portability","Organizations standardizing on MCP for tool definition across multiple applications","Developers wanting schema-based tool validation without manual input checking"],"limitations":["MCP is a relatively new standard — not all LLM providers have native MCP support","Remote MCP servers add network latency — tool execution may be slower than local implementations","Schema validation is strict — tools with flexible input schemas may require wrapper functions","Error handling is basic — tool failures are logged but not automatically retried or escalated","No built-in rate limiting or quota management for tool usage"],"requires":["Python 3.10+","mcp package (if using MCP client)","MCP server running (local or remote)","JSON schema definitions for tools"],"input_types":["Tool definitions (JSON schema)","Tool invocation requests (JSON with input parameters)","MCP server responses (JSON)"],"output_types":["Tool execution results (JSON or text)","Error messages (if tool execution fails)","Tool metadata (name, description, schema)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-chainlit__cap_6","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, Microsoft), JWT tokens, password-based login, or custom header-based schemes (for reverse proxy integration). The system uses a User object to represent authenticated users, with optional role and permission attributes for authorization. Authentication is enforced at the WebSocket connection level — unauthenticated clients are rejected before reaching callback handlers. Custom authentication can be implemented by subclassing the Auth class and overriding the authenticate() method.","intents":["Secure Chainlit applications with OAuth login (Google, GitHub, Microsoft)","Integrate with existing authentication systems via custom header-based auth or JWT","Implement role-based access control (RBAC) to restrict features per user","Audit user interactions by capturing authenticated user IDs in conversation logs"],"best_for":["Teams deploying Chainlit to production requiring user authentication","Organizations with existing OAuth providers (Google Workspace, GitHub Enterprise)","SaaS platforms needing multi-tenant user isolation and RBAC"],"limitations":["OAuth implementation is limited to Google, GitHub, Microsoft — custom OAuth providers require custom Auth subclass","JWT validation is basic — no built-in support for token refresh or expiration handling","Custom header auth assumes reverse proxy handles authentication — no validation of header authenticity","RBAC is application-level only — no fine-grained permission system (e.g., per-conversation access control)","No built-in session revocation — logged-out users may retain WebSocket connections until timeout"],"requires":["Python 3.10+","OAuth provider credentials (client ID, secret) for OAuth auth","JWT secret key (for JWT auth)","Reverse proxy with authentication support (for custom header auth)"],"input_types":["OAuth authorization code (from OAuth provider)","JWT token (in Authorization header)","Custom headers (from reverse proxy)","Username/password (for password-based auth)"],"output_types":["User object (with ID, email, roles)","Authentication token (for session management)","Authorization decision (allow/deny WebSocket connection)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-chainlit__cap_7","uri":"capability://data.processing.analysis.file.upload.and.multipart.form.handling.with.automatic.storage.integration","name":"file upload and multipart form handling with automatic storage integration","description":"Chainlit provides built-in file upload support through the @cl.on_file_upload callback and the FileUploadMessage type. Files are received as multipart form data, automatically stored in the configured data layer (local filesystem, S3, Azure Blob, etc.), and made available to callback handlers as File objects with metadata (name, size, mime type, path). The system handles concurrent uploads, validates file types/sizes, and provides a file browser UI component for users to select and upload files.","intents":["Allow users to upload documents (PDFs, images, CSVs) for processing by LLM agents","Store uploaded files in cloud storage (S3, Azure) without manual configuration","Validate file types and sizes before processing to prevent abuse","Provide file history and management UI for users to view/delete uploaded files"],"best_for":["Teams building document analysis or RAG applications requiring user file uploads","Developers wanting automatic file storage without writing upload handlers","Organizations needing to store user uploads in cloud storage for compliance"],"limitations":["File validation is basic — only checks mime type and size, not content validation","No built-in virus scanning or malware detection — requires external service integration","File storage is tied to the data layer — switching storage backends requires reconfiguring all file paths","No automatic cleanup of old files — requires manual deletion or external garbage collection","Concurrent uploads may cause race conditions if multiple users upload to the same path"],"requires":["Python 3.10+","Multipart form data support (built into FastAPI)","Storage backend configured (local filesystem, S3, Azure, etc.)","Sufficient disk/cloud storage quota"],"input_types":["Multipart form data (file binary + metadata)","File metadata (name, size, mime type)"],"output_types":["File object (with path, size, mime type)","Stored file (in configured storage backend)","File metadata (for UI display)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-chainlit__cap_8","uri":"capability://planning.reasoning.step.and.message.lifecycle.management.with.hierarchical.tracing","name":"step and message lifecycle management with hierarchical tracing","description":"Chainlit provides a Step system for capturing intermediate results in multi-step processes (LLM calls, tool invocations, retrievals). Steps are hierarchical — parent steps can contain child steps, creating a tree structure that mirrors the execution flow. Each step captures input, output, metadata (model, tokens, latency), and status (running, succeeded, failed). Steps are emitted via the cl.Step API and automatically rendered in the UI as collapsible trees. The system also provides a Message type for user/assistant messages with optional metadata and elements.","intents":["Visualize multi-step agent reasoning in the UI without manual instrumentation","Capture intermediate results for debugging and audit purposes","Display token counts and latency per step for cost and performance analysis","Enable step replay and re-execution for testing and optimization"],"best_for":["Teams building agents and needing visibility into reasoning chains","Developers debugging complex multi-step processes","Organizations requiring audit trails of LLM interactions"],"limitations":["Step hierarchy is limited to 1-2 levels deep in the UI — deeply nested steps become hard to navigate","Step metadata (tokens, latency) relies on framework-provided estimates — actual values may differ","No automatic step cleanup — long-running conversations accumulate thousands of steps, impacting UI performance","Step replay is not implemented — users cannot re-run individual steps from the UI","No built-in step filtering or search — finding specific steps in large traces is difficult"],"requires":["Python 3.10+","cl.Step API imported and used in callback handlers"],"input_types":["Step input (text, JSON, or structured data)","Step metadata (model, tokens, latency)","Step status (running, succeeded, failed)"],"output_types":["Step objects (rendered in UI as tree)","Step metadata (displayed in UI)","Step history (persisted to database)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-chainlit__cap_9","uri":"capability://tool.use.integration.action.callback.system.for.interactive.ui.elements.with.payload.handling","name":"action callback system for interactive ui elements with payload handling","description":"Chainlit provides an @cl.action decorator for registering callbacks that respond to user interactions (button clicks, form submissions, select changes). Actions are defined with a name and optional payload schema, and the UI renders corresponding interactive elements. When a user triggers an action, the payload is sent to the backend via WebSocket, deserialized, and passed to the action callback. The system handles action validation, error handling, and response streaming back to the UI.","intents":["Create interactive buttons and forms that trigger backend logic without page reloads","Implement user feedback loops (thumbs up/down, rating) for conversation quality","Build multi-step workflows where each step is triggered by user action","Validate user input on the backend before processing"],"best_for":["Teams building interactive chat experiences with user feedback mechanisms","Developers implementing multi-step workflows or wizards","Applications requiring backend validation of user input"],"limitations":["Action payload validation is manual — no built-in schema validation like MCP","No built-in debouncing or rate limiting — rapid action clicks may cause duplicate processing","Action responses are limited to text/messages — cannot update arbitrary UI state","No transaction support — action failures may leave the system in an inconsistent state","Action history is not persisted — no audit trail of user interactions"],"requires":["Python 3.10+","@cl.action decorator imported and used in callback handlers"],"input_types":["Action name (string)","Action payload (JSON or structured data)","User context (user ID, session ID)"],"output_types":["Action response (text, message, or status)","Side effects (database updates, API calls)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","FastAPI (included as dependency)","python-socketio (included as dependency)","Async event loop (uvicorn or similar ASGI server)","LangChain 0.1.0+ OR LlamaIndex 0.9.0+","Chainlit callback handler imported and registered in chain initialization","chainlit.toml file in the application root (optional, uses defaults if missing)","Chainlit installed via pip","Application file (e.g., app.py) in the current directory","Platform API credentials (Slack bot token, Discord token, Teams app ID)"],"failure_modes":["Async-only design — synchronous callbacks are not supported, requiring developers to wrap blocking I/O","Session state is in-memory by default — requires explicit data layer configuration for persistence across server restarts","Single-threaded per session — concurrent message handling within a session requires manual locking or queue management","No built-in rate limiting or backpressure handling — high-volume message streams may cause memory pressure","Callback instrumentation adds ~50-100ms overhead per step due to event emission and serialization","Only captures events exposed by the framework's callback interface — custom chain logic not using callbacks is invisible","Token counting relies on framework-provided estimates — actual billing may differ from displayed counts","No automatic sampling or filtering — high-volume chains may create thousands of steps, impacting UI performance","Configuration is static at startup — changes require server restart","No built-in validation of configuration values — invalid settings may cause runtime errors","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:24.098Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-chainlit","compare_url":"https://unfragile.ai/compare?artifact=pypi-chainlit"}},"signature":"rH78k1Fjz5aPLlcCZHIA4MKg286RAeBdz20Xvu3RXM6D72WtkEspyZK4D5z2HDxXyBMWRrTmXkHtOJoXpomNAw==","signedAt":"2026-06-20T08:25:09.080Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-chainlit","artifact":"https://unfragile.ai/pypi-chainlit","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-chainlit","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}