{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"chainlit","slug":"chainlit","name":"Chainlit","type":"framework","url":"https://github.com/Chainlit/chainlit","page_url":"https://unfragile.ai/chainlit","categories":["app-builders","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"chainlit__cap_0","uri":"capability://tool.use.integration.decorator.based.conversational.callback.registration.with.websocket.lifecycle.management","name":"decorator-based conversational callback registration with websocket lifecycle management","description":"Chainlit uses Python decorators (@cl.on_message, @cl.on_chat_start, @cl.on_file_upload) to register callbacks that automatically bind to FastAPI/Socket.IO WebSocket lifecycle events. When a user sends a message, the framework routes it through the registered callback, manages session state across concurrent connections, and emits responses back to the frontend via Socket.IO in real-time. The callback system integrates with the Emitter pattern to enable streaming responses without blocking.","intents":["Define conversational logic in Python without managing WebSocket plumbing","Handle multi-turn conversations with automatic session persistence","Stream LLM responses token-by-token to the frontend as they arrive","Execute setup/teardown logic per conversation session"],"best_for":["Python developers building LLM chatbots who want rapid prototyping without frontend work","Teams migrating from REST APIs to real-time conversational interfaces","Developers familiar with Flask/FastAPI decorator patterns"],"limitations":["Callbacks are synchronous by default; async support requires explicit async/await syntax","No built-in request queuing — high concurrency may require external load balancing","Callback execution is single-threaded per session, blocking on long-running operations"],"requires":["Python 3.10+","FastAPI and python-socketio installed","Understanding of async/await for streaming operations"],"input_types":["text messages","file uploads","user actions/button clicks"],"output_types":["text responses","streamed token sequences","structured messages with metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit__cap_1","uri":"capability://text.generation.language.real.time.streaming.message.composition.with.multi.step.reasoning.visualization","name":"real-time streaming message composition with multi-step reasoning visualization","description":"Chainlit's Step and Message system enables developers to decompose conversational flows into discrete, visualizable steps (e.g., 'Retrieving context', 'Generating response', 'Formatting output'). Each step can stream content incrementally, and the frontend React component renders step hierarchies with collapsible UI, timing metadata, and status indicators. Steps are managed via the Emitter system, which batches updates and sends them to the frontend via Socket.IO, enabling smooth streaming without overwhelming the client.","intents":["Visualize multi-step LLM reasoning (retrieval → generation → formatting) in real-time","Show intermediate outputs and debugging information without blocking the final response","Track execution time and token counts per step for observability","Enable users to understand the 'why' behind AI responses"],"best_for":["Developers building RAG systems who want to show retrieval context","Teams building agent-based systems with multiple reasoning steps","Enterprises requiring explainability and audit trails for AI decisions"],"limitations":["Step hierarchy is limited to 2-3 levels deep before UI becomes cluttered","No built-in branching logic — conditional step execution requires manual control flow","Step metadata (timing, tokens) requires explicit instrumentation; no automatic profiling"],"requires":["Python 3.10+","Understanding of Step and Message APIs","Frontend React components to render step hierarchies (provided by Chainlit)"],"input_types":["step names and descriptions","streamed text content","metadata (timing, token counts)"],"output_types":["hierarchical step UI with collapsible sections","real-time streaming text","timing and performance metrics"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit__cap_10","uri":"capability://text.generation.language.react.based.frontend.with.real.time.message.streaming.and.responsive.ui","name":"react-based frontend with real-time message streaming and responsive ui","description":"Chainlit's frontend is a React/TypeScript application that renders messages, steps, elements, and actions in real-time. The frontend connects to the backend via Socket.IO, receives message updates as they stream, and renders them incrementally without page reloads. The UI is responsive, supports dark mode, and includes accessibility features (ARIA labels, keyboard navigation). The frontend is pre-built and deployed automatically; developers don't need to write React code.","intents":["Display conversational AI responses in a polished, ChatGPT-like interface","Stream LLM responses token-by-token for perceived responsiveness","Render multi-step reasoning with collapsible step hierarchies","Support mobile and desktop users with responsive design"],"best_for":["Developers who want a production-ready UI without frontend work","Teams deploying to web browsers (desktop, mobile, tablet)","Enterprises requiring accessible, responsive interfaces"],"limitations":["UI customization is limited to CSS variables and configuration; no component-level customization","Frontend is bundled with the backend; separate frontend deployments require custom setup","No offline support; requires active WebSocket connection"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","Active WebSocket connection to backend"],"input_types":["user text input","file uploads","button clicks","form submissions"],"output_types":["rendered messages","streamed text","media elements","interactive actions"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit__cap_11","uri":"capability://automation.workflow.configuration.driven.deployment.with.environment.variable.management","name":"configuration-driven deployment with environment variable management","description":"Chainlit uses environment variables and a chainlit.toml configuration file to manage deployment settings (database URL, OAuth credentials, storage provider, feature flags). The framework automatically loads configuration at startup and validates required variables. Developers can define custom configuration via the config object, and the CLI provides commands to manage settings without code changes. This enables seamless transitions from development (local SQLite) to production (PostgreSQL + S3).","intents":["Deploy the same codebase to multiple environments (dev, staging, prod) without code changes","Manage sensitive credentials (API keys, database passwords) securely","Enable feature flags for gradual rollouts and A/B testing","Configure storage, database, and authentication providers per environment"],"best_for":["Teams deploying to multiple environments with different configurations","Enterprises with strict credential management policies","Developers using CI/CD pipelines for automated deployments"],"limitations":["Configuration is environment-variable-based; no UI for runtime configuration changes","No built-in secrets management; requires external tools (AWS Secrets Manager, HashiCorp Vault)","Configuration validation is basic; complex validation logic requires custom code"],"requires":["Python 3.10+","Environment variables set in deployment environment","chainlit.toml file in project root (optional)"],"input_types":["environment variables","chainlit.toml configuration file"],"output_types":["loaded configuration","validated settings"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit__cap_12","uri":"capability://automation.workflow.cli.based.development.workflow.with.hot.reloading.and.debugging","name":"cli-based development workflow with hot-reloading and debugging","description":"Chainlit provides a CLI (chainlit run, chainlit deploy) that manages the development and deployment lifecycle. The chainlit run command starts a development server with hot-reloading, automatically restarting the backend when code changes are detected. The CLI also handles project initialization, dependency management, and deployment to cloud platforms. Developers can debug applications using standard Python debugging tools (pdb, debugpy) integrated with the CLI.","intents":["Start a development server with automatic hot-reloading","Initialize new Chainlit projects with boilerplate code","Deploy applications to cloud platforms without manual configuration","Debug conversational logic using standard Python debugging tools"],"best_for":["Python developers familiar with CLI tools (Flask, Django)","Teams using CI/CD pipelines for automated deployments","Developers iterating rapidly on conversational logic"],"limitations":["Hot-reloading may lose state between restarts; stateful operations require persistence","Deployment is limited to supported cloud platforms (Hugging Face Spaces, etc.); custom deployments require manual setup","Debugging requires running the server in development mode; production debugging is limited"],"requires":["Python 3.10+","Chainlit CLI installed (pip install chainlit)","Project structure with app.py or similar entry point"],"input_types":["CLI commands","Python source files"],"output_types":["development server","deployment artifacts"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit__cap_13","uri":"capability://text.generation.language.copilot.widget.for.embedding.chainlit.chatbots.in.external.websites","name":"copilot widget for embedding chainlit chatbots in external websites","description":"Chainlit provides a Copilot widget that can be embedded in external websites via a single script tag. The widget opens a chat interface in a floating window, connects to a Chainlit backend via WebSocket, and enables users to interact with the chatbot without leaving the host website. The widget is fully customizable (colors, position, initial message) via JavaScript configuration and supports pre-authentication via JWT tokens.","intents":["Embed a chatbot in a website without building a custom frontend","Provide customer support via a floating chat widget","Integrate Chainlit chatbots into existing web applications","Enable website visitors to interact with AI assistants without navigation"],"best_for":["Developers adding chatbot functionality to existing websites","Teams building customer support interfaces","Enterprises integrating AI assistants into web applications"],"limitations":["Widget customization is limited to CSS variables; no component-level customization","Widget requires active WebSocket connection; no offline support","Cross-origin requests require CORS configuration on the backend"],"requires":["Chainlit backend running and accessible via HTTPS","Website with ability to embed external scripts","CORS configuration on backend to allow widget domain"],"input_types":["user text input","file uploads","button clicks"],"output_types":["rendered chat interface","streamed responses"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit__cap_14","uri":"capability://text.generation.language.audio.input.output.support.with.streaming.speech.synthesis","name":"audio input/output support with streaming speech synthesis","description":"Chainlit supports audio input (user speech via microphone) and audio output (text-to-speech synthesis). The frontend captures audio from the user's microphone, sends it to the backend for processing (transcription, LLM response generation), and plays back synthesized speech. The framework integrates with speech-to-text and text-to-speech APIs (OpenAI Whisper, Google Cloud Speech-to-Text, etc.) and streams audio responses in real-time.","intents":["Enable voice-based interactions with chatbots","Build voice assistants that respond with synthesized speech","Support users who prefer voice over text input","Create hands-free conversational experiences"],"best_for":["Developers building voice assistants or voice-enabled chatbots","Teams creating accessibility features for text-based interfaces","Enterprises building voice-first customer support systems"],"limitations":["Audio processing requires external APIs (Whisper, Google Cloud Speech-to-Text); no local speech recognition","Audio quality depends on user's microphone and network latency","Text-to-speech synthesis adds latency; real-time streaming requires low-latency APIs"],"requires":["Python 3.10+","API keys for speech-to-text and text-to-speech providers","User browser with microphone access and WebAudio API support"],"input_types":["audio streams (user speech)","text (for synthesis)"],"output_types":["transcribed text","synthesized audio"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit__cap_2","uri":"capability://tool.use.integration.langchain.and.llamaindex.callback.instrumentation.with.automatic.llm.metadata.extraction","name":"langchain and llamaindex callback instrumentation with automatic llm metadata extraction","description":"Chainlit provides native callback classes (ChainlitCallbackHandler for LangChain, ChainlitCallbackManager for LlamaIndex) that hook into framework-specific event systems to automatically capture LLM calls, token counts, model names, and latency. These callbacks integrate with Chainlit's Step system, so LangChain chains and LlamaIndex query engines automatically emit step updates without developer intervention. The callbacks extract generation metadata (prompt tokens, completion tokens, model) and surface it in the UI.","intents":["Automatically instrument LangChain chains without modifying chain code","Capture LLM API calls and token usage from LlamaIndex query engines","Display model metadata and token counts in the UI for cost tracking","Integrate existing LangChain/LlamaIndex code with Chainlit without refactoring"],"best_for":["Teams with existing LangChain or LlamaIndex applications wanting to add UI","Developers building RAG systems who need token usage visibility","Enterprises requiring LLM cost tracking and observability"],"limitations":["Callbacks only capture events emitted by LangChain/LlamaIndex; custom LLM calls require manual instrumentation","Token counts depend on LLM provider APIs; some providers don't return accurate counts","No automatic cost calculation — requires external pricing data"],"requires":["Python 3.10+","LangChain 0.1+ or LlamaIndex 0.9+","API keys for LLM providers (OpenAI, Anthropic, etc.)"],"input_types":["LangChain Chain objects","LlamaIndex QueryEngine objects"],"output_types":["step updates with LLM metadata","token counts and model names","latency measurements"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit__cap_3","uri":"capability://data.processing.analysis.file.upload.handling.with.automatic.storage.provider.abstraction","name":"file upload handling with automatic storage provider abstraction","description":"Chainlit abstracts file storage behind a pluggable Data Layer that supports local filesystem, S3, GCS, and Azure Blob Storage. When a user uploads a file via the frontend, the framework routes it through the @cl.on_file_upload callback, persists it to the configured storage provider, and returns a File object with metadata (name, size, MIME type, storage path). Developers can process uploaded files (PDFs, images, CSVs) without worrying about storage backend implementation.","intents":["Accept file uploads from users and process them in conversational context","Store uploaded files durably without managing cloud storage SDKs","Build document-based RAG systems where users upload PDFs or CSVs","Support multi-file uploads in a single conversation"],"best_for":["Developers building document analysis chatbots","Teams deploying to cloud platforms (AWS, GCP, Azure) who need scalable storage","Enterprises with data governance requirements (on-prem storage, encryption)"],"limitations":["File size limits depend on storage provider; no built-in chunking for large files","No automatic virus scanning or malware detection","Storage provider credentials must be configured via environment variables; no UI-based configuration"],"requires":["Python 3.10+","Storage provider SDK (boto3 for S3, google-cloud-storage for GCS, etc.)","Cloud credentials (AWS_ACCESS_KEY_ID, GOOGLE_APPLICATION_CREDENTIALS, etc.)"],"input_types":["binary file data","file metadata (name, MIME type)"],"output_types":["File objects with storage paths","file metadata","processed file content (if parsed)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit__cap_4","uri":"capability://image.visual.multi.modal.element.rendering.with.inline.media.composition","name":"multi-modal element rendering with inline media composition","description":"Chainlit's Element system enables developers to embed images, audio, PDFs, and custom HTML into conversations. Elements are created via Python objects (cl.Image, cl.Audio, cl.File, cl.Pdf) and rendered inline in the message stream on the frontend. The React frontend automatically handles media display, lazy-loading, and responsive sizing. Elements can be referenced by messages, enabling rich content composition without frontend code.","intents":["Display generated images or charts inline in conversations","Embed audio responses or user-uploaded audio files","Show PDFs or documents referenced in the conversation","Create rich, media-heavy conversational experiences without frontend work"],"best_for":["Developers building image generation or analysis chatbots","Teams creating voice-enabled conversational interfaces","Enterprises building document-centric AI assistants"],"limitations":["Custom HTML elements are sandboxed; no access to parent window or external scripts","Large media files (videos, high-res images) may cause UI lag; no built-in compression","Element rendering is limited to predefined types; custom media types require workarounds"],"requires":["Python 3.10+","Media files accessible via URL or local filesystem","Frontend React components to render elements (provided by Chainlit)"],"input_types":["image files (PNG, JPEG, WebP)","audio files (MP3, WAV)","PDF files","custom HTML strings"],"output_types":["rendered media in message stream","lazy-loaded images and audio players","embedded PDF viewers"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit__cap_5","uri":"capability://data.processing.analysis.session.scoped.data.persistence.with.pluggable.database.backends","name":"session-scoped data persistence with pluggable database backends","description":"Chainlit's Data Layer provides a session-scoped persistence abstraction that supports PostgreSQL, SQLAlchemy, and cloud-native databases. Conversation history, user metadata, and file references are automatically persisted to the configured database. Developers can query conversation history via the Data Layer API without writing SQL. The framework handles connection pooling, transaction management, and schema migrations automatically.","intents":["Persist conversation history across sessions for user continuity","Query past conversations for analytics or user support","Store user preferences and metadata without managing database code","Enable multi-user deployments with isolated conversation histories"],"best_for":["Teams deploying production chatbots requiring conversation persistence","Enterprises with data governance requirements (on-prem databases, encryption)","Developers building multi-user platforms with per-user conversation isolation"],"limitations":["Schema is opinionated; custom data models require extending the Data Layer","No built-in query optimization; complex analytics queries may require direct database access","Database credentials must be configured via environment variables; no UI-based setup"],"requires":["Python 3.10+","PostgreSQL 12+ or SQLAlchemy-compatible database","Database connection string (DATABASE_URL environment variable)"],"input_types":["conversation messages","user metadata","file references"],"output_types":["conversation history","user profiles","analytics data"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit__cap_6","uri":"capability://safety.moderation.oauth.and.jwt.based.authentication.with.role.based.access.control","name":"oauth and jwt-based authentication with role-based access control","description":"Chainlit provides pluggable authentication via OAuth (Google, GitHub, Azure AD), password-based JWT, and custom authentication handlers. The framework manages session tokens, user identity verification, and role-based authorization (admin, user, viewer). Authentication is enforced at the WebSocket level, preventing unauthenticated users from accessing conversations. Developers can implement custom authentication by extending the AuthClient class.","intents":["Secure chatbot deployments with user authentication","Implement role-based access control (admin vs. regular users)","Integrate with enterprise identity providers (Azure AD, Okta)","Support both OAuth and password-based authentication"],"best_for":["Enterprises deploying chatbots to internal users","Teams integrating with existing identity infrastructure","Developers building multi-tenant SaaS platforms"],"limitations":["OAuth configuration requires provider-specific setup (client ID, secret); no automatic discovery","Role-based access control is limited to predefined roles; custom permission models require extension","Session tokens are stored in browser localStorage; no built-in CSRF protection"],"requires":["Python 3.10+","OAuth provider credentials (client ID, secret) or password database","HTTPS for production deployments"],"input_types":["OAuth authorization codes","username/password credentials","custom authentication tokens"],"output_types":["JWT tokens","user identity and roles","session metadata"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit__cap_7","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.integration.with.tool.calling","name":"model context protocol (mcp) server integration with tool calling","description":"Chainlit integrates with the Model Context Protocol, enabling LLMs to call external tools via a standardized interface. Developers define MCP servers that expose tools (functions), and Chainlit automatically converts MCP tool schemas to function-calling payloads compatible with OpenAI, Anthropic, and other LLM APIs. When an LLM requests a tool call, Chainlit routes it to the appropriate MCP server, executes the tool, and returns the result to the LLM for further reasoning.","intents":["Enable LLMs to call external APIs and tools (web search, database queries, etc.)","Implement agentic workflows where LLMs decide which tools to use","Standardize tool definitions across multiple LLM providers","Build complex multi-step reasoning with tool feedback loops"],"best_for":["Developers building LLM agents with external tool access","Teams standardizing tool definitions across multiple LLM providers","Enterprises integrating LLMs with legacy systems via tool calling"],"limitations":["MCP server setup requires additional infrastructure; no built-in server hosting","Tool execution is synchronous; long-running tools may block the LLM response loop","Error handling is basic; tool failures require explicit error messages to the LLM"],"requires":["Python 3.10+","MCP server implementation (custom or third-party)","LLM API key for provider supporting function calling (OpenAI, Anthropic, etc.)"],"input_types":["MCP tool schemas","LLM function-calling requests"],"output_types":["tool execution results","function-calling responses"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit__cap_8","uri":"capability://automation.workflow.action.based.user.interaction.with.button.callbacks.and.form.inputs","name":"action-based user interaction with button callbacks and form inputs","description":"Chainlit's Action system enables developers to embed interactive buttons and form inputs in messages. When a user clicks a button or submits a form, the framework triggers a registered @cl.action callback with the user's input. Actions are rendered inline in the message stream on the frontend, enabling conversational workflows where users make choices (e.g., 'Refine search', 'Show more details') without typing. Actions are fully typed and support validation.","intents":["Create conversational workflows with user choices (buttons, dropdowns)","Collect structured input (forms) without breaking conversation flow","Implement refinement loops where users guide AI reasoning","Build interactive tutorials or guided experiences"],"best_for":["Developers building conversational UIs with user guidance","Teams creating interactive tutorials or onboarding flows","Enterprises building customer support chatbots with predefined actions"],"limitations":["Actions are limited to buttons and simple form inputs; no complex UI components","Action callbacks are synchronous; long-running operations require async/await","No built-in validation framework; custom validation requires manual implementation"],"requires":["Python 3.10+","Understanding of Action API and callback registration"],"input_types":["button clicks","form submissions","dropdown selections"],"output_types":["action callback execution","form data","user choices"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit__cap_9","uri":"capability://automation.workflow.fastapi.based.backend.with.socket.io.real.time.communication","name":"fastapi-based backend with socket.io real-time communication","description":"Chainlit's backend is built on FastAPI with python-socketio for real-time WebSocket communication. The framework automatically creates a FastAPI application, configures Socket.IO namespaces for message routing, and manages concurrent WebSocket connections. Developers define conversational logic via decorators, and the framework handles HTTP routing, CORS, and WebSocket lifecycle management automatically. The backend supports hot-reloading during development via the CLI.","intents":["Deploy conversational AI applications without managing FastAPI boilerplate","Handle real-time bidirectional communication between frontend and backend","Support concurrent user sessions with isolated state","Enable rapid development with hot-reloading"],"best_for":["Python developers familiar with FastAPI who want rapid prototyping","Teams deploying to cloud platforms (AWS, GCP, Azure) with FastAPI support","Developers building real-time conversational interfaces"],"limitations":["FastAPI configuration is opinionated; advanced routing requires extending the framework","Socket.IO adds ~50-100ms latency per message compared to REST APIs","Concurrent connection limits depend on server resources; no built-in auto-scaling"],"requires":["Python 3.10+","FastAPI and python-socketio installed","Understanding of async/await for streaming operations"],"input_types":["HTTP requests","WebSocket messages"],"output_types":["HTTP responses","WebSocket events"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"chainlit__headline","uri":"capability://app.builders.python.framework.for.building.conversational.ai.applications","name":"python framework for building conversational ai applications","description":"Chainlit is a Python framework designed to create production-ready conversational AI UIs quickly, enabling developers to build ChatGPT-like interfaces with features like streaming and multi-step reasoning.","intents":["best Python framework for conversational AI","conversational AI framework for production","how to build ChatGPT-like interfaces","framework for interactive AI applications","best tools for developing AI chatbots"],"best_for":["developers building conversational UIs","teams integrating LLMs"],"limitations":["requires Python knowledge","may need additional integrations for specific use cases"],"requires":["Python 3.10+","Node.js 20+"],"input_types":["text input","file uploads"],"output_types":["interactive web interfaces","real-time messages"],"categories":["app-builders"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","FastAPI and python-socketio installed","Understanding of async/await for streaming operations","Understanding of Step and Message APIs","Frontend React components to render step hierarchies (provided by Chainlit)","Modern web browser (Chrome, Firefox, Safari, Edge)","Active WebSocket connection to backend","Environment variables set in deployment environment","chainlit.toml file in project root (optional)","Chainlit CLI installed (pip install chainlit)"],"failure_modes":["Callbacks are synchronous by default; async support requires explicit async/await syntax","No built-in request queuing — high concurrency may require external load balancing","Callback execution is single-threaded per session, blocking on long-running operations","Step hierarchy is limited to 2-3 levels deep before UI becomes cluttered","No built-in branching logic — conditional step execution requires manual control flow","Step metadata (timing, tokens) requires explicit instrumentation; no automatic profiling","UI customization is limited to CSS variables and configuration; no component-level customization","Frontend is bundled with the backend; separate frontend deployments require custom setup","No offline support; requires active WebSocket connection","Configuration is environment-variable-based; no UI for runtime configuration changes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"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":"2026-06-17T09:51:04.690Z","last_scraped_at":null,"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=chainlit","compare_url":"https://unfragile.ai/compare?artifact=chainlit"}},"signature":"kvl0VbLxIR2I8a3+iSlYqVVQG/wFevtTuxerMGKV1MpffuBsQGo2hvV/7+VIaI9SueL3vzsNQSEqiWV5IfNgDQ==","signedAt":"2026-06-20T00:12:12.546Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/chainlit","artifact":"https://unfragile.ai/chainlit","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}