{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"streamlit","slug":"streamlit","name":"Streamlit","type":"framework","url":"https://github.com/streamlit/streamlit","page_url":"https://unfragile.ai/streamlit","categories":["app-builders","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"streamlit__cap_0","uri":"capability://automation.workflow.declarative.python.to.react.ui.compilation.with.automatic.re.execution","name":"declarative python-to-react ui compilation with automatic re-execution","description":"Transforms imperative Python scripts into reactive web UIs by executing the entire script on each state change, capturing all st.* API calls into a DeltaGenerator that builds a Protocol Buffer message stream (ForwardMsg), which is serialized and sent via WebSocket to a React frontend that reconstructs the UI. Uses a singleton Runtime managing AppSession instances per user, with script re-execution triggered by widget interactions or file changes, enabling developers to write linear Python code without explicit event handlers.","intents":["I want to turn my Python data analysis script into an interactive web app without learning HTML/CSS/JavaScript","I need to build a quick ML model demo where users can upload data and see results instantly","I want to create a dashboard that updates automatically when my Python code changes"],"best_for":["data scientists and ML engineers building internal tools and demos","solo developers prototyping data applications quickly","teams migrating from Jupyter notebooks to shareable web apps"],"limitations":["Full script re-execution on every interaction can be slow for long-running computations (mitigated by @st.cache_data but requires explicit annotation)","Stateless execution model means all state must be stored in st.session_state or external persistence; no implicit variable persistence across reruns","Limited to Python backend; frontend customization requires custom React components via Components v2 API"],"requires":["Python 3.8+","Streamlit package installed via pip","WebSocket-capable browser for real-time communication"],"input_types":["Python script (.py file)","User interactions (button clicks, text input, slider changes)"],"output_types":["HTML/CSS/JavaScript rendered in browser","React component tree via Protocol Buffer serialization"],"categories":["automation-workflow","web-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit__cap_1","uri":"capability://memory.knowledge.widget.state.management.with.automatic.session.persistence","name":"widget state management with automatic session persistence","description":"Manages widget state across script re-executions using st.session_state, a dictionary-like object that persists for the duration of a user session (WebSocket connection). Widget values are automatically keyed and stored; developers can also manually manage state by assigning to session_state[key]. State is maintained in memory per AppSession instance and survives script reruns but is lost on page refresh unless explicitly persisted to external storage (database, file, etc.).","intents":["I want form inputs to retain their values when my script reruns after a button click","I need to track user interactions across multiple script executions without writing database code","I want to build a multi-step form where each step's data persists until submission"],"best_for":["developers building interactive forms and multi-step workflows","teams building internal tools with user input validation","prototypers who need quick state management without backend complexity"],"limitations":["Session state is in-memory only; lost on server restart or page refresh unless explicitly persisted","No built-in distributed session storage; scaling to multiple server instances requires external state store (Redis, database)","State is per-user per-session; no cross-session or cross-user state sharing without external persistence layer"],"requires":["Python 3.8+","Streamlit 1.0+ (session_state API stabilized in 1.0)"],"input_types":["Widget interactions (st.button, st.text_input, st.slider, etc.)","Manual assignments to st.session_state dictionary"],"output_types":["Persisted state dictionary accessible across script reruns","Widget values automatically synced to session_state"],"categories":["memory-knowledge","state-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit__cap_10","uri":"capability://tool.use.integration.connection.api.for.database.and.api.integrations.with.built.in.connectors","name":"connection api for database and api integrations with built-in connectors","description":"Provides st.connection() API for managing connections to databases (SQL, MongoDB, Snowflake) and external services (HTTP APIs, Hugging Face, etc.). Built-in connectors handle authentication, connection pooling, and query execution. Developers call st.connection('connection_name') to get a connection object, then use methods like .query() or .execute() to interact with the service. Connections are cached per session and reused across script reruns, reducing connection overhead. Secrets are automatically injected into connection strings.","intents":["I want to query a database without writing connection boilerplate","I need to call external APIs from my app with automatic credential management","I want to cache database connections across script reruns"],"best_for":["developers building data apps that query databases","teams integrating with external APIs and services","builders creating dashboards with live data sources"],"limitations":["Built-in connectors are limited to popular services; custom integrations require implementing the Connection interface","Connection pooling is per-session; not shared across multiple users or server instances","No built-in query result caching; developers must use @st.cache_data for caching query results","Error handling is library-specific; developers must handle different exception types per connector"],"requires":["Python 3.8+","Streamlit 1.18+ (Connection API introduced)","Database driver or API client library (psycopg2 for PostgreSQL, pymongo for MongoDB, etc.)","Connection credentials in st.secrets"],"input_types":["Connection name and configuration","SQL queries or API requests","Credentials from st.secrets"],"output_types":["Query results as Pandas DataFrame or raw response","Connection object for manual query execution"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit__cap_11","uri":"capability://safety.moderation.oauth.and.oidc.authentication.with.st.experimental.user.for.user.info","name":"oauth and oidc authentication with st.experimental_user for user info","description":"Provides OAuth and OIDC integration for authenticating users via third-party providers (Google, GitHub, Azure AD, etc.). Streamlit Cloud handles OAuth flow automatically; self-hosted deployments require manual OAuth configuration. st.experimental_user provides access to authenticated user information (email, name, etc.). Authentication state is stored in session and persists across script reruns. Developers can gate app functionality behind authentication checks.","intents":["I want to restrict my app to authenticated users only","I need to personalize the app experience based on user identity","I want to use Google or GitHub login without building OAuth flow"],"best_for":["teams deploying to Streamlit Cloud with user authentication requirements","developers building internal tools with user access control","builders creating multi-tenant applications"],"limitations":["OAuth configuration is complex for self-hosted deployments; Streamlit Cloud handles it automatically","st.experimental_user is experimental API; may change in future versions","No built-in role-based access control (RBAC); developers must implement authorization logic manually","User information is limited to what OAuth provider returns; no custom user attributes"],"requires":["Python 3.8+","Streamlit 1.18+ (st.experimental_user introduced)","Streamlit Cloud account or self-hosted deployment with OAuth provider configuration","OAuth credentials from provider (Google, GitHub, etc.)"],"input_types":["OAuth provider configuration","User credentials from OAuth provider"],"output_types":["Authenticated user information (email, name, picture)","Session state indicating authentication status"],"categories":["safety-moderation","authentication"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit__cap_12","uri":"capability://automation.workflow.streamlit.community.cloud.deployment.with.automatic.git.integration","name":"streamlit community cloud deployment with automatic git integration","description":"Streamlit Community Cloud is a free hosting platform that automatically deploys Streamlit apps from GitHub repositories. Developers push code to GitHub, connect the repo to Streamlit Cloud, and the app is deployed automatically with a public URL. Cloud handles server infrastructure, SSL certificates, and app scaling. Supports environment variable injection via web UI, automatic app reloading on Git pushes, and integrated secrets management. No Docker or server configuration required.","intents":["I want to share my Streamlit app with others without managing servers","I need to deploy my app automatically when I push to GitHub","I want free hosting for my data app or ML demo"],"best_for":["individual developers and students sharing projects","teams prototyping and demoing applications","open-source projects needing free hosting"],"limitations":["Free tier has resource limits (CPU, memory, storage); heavy computation may timeout","No guaranteed uptime or SLA; suitable for demos and prototypes, not production","Secrets are stored in Streamlit Cloud's database; no option for external secret management","Custom domain requires Streamlit Cloud Pro subscription","Limited to GitHub integration; GitLab, Bitbucket not supported"],"requires":["GitHub account and repository with Streamlit app","Streamlit Community Cloud account (free)","Python 3.8+ and Streamlit installed in repo"],"input_types":["GitHub repository with Streamlit app","Environment variables and secrets via Cloud UI"],"output_types":["Public URL for deployed app","Automatic app reloading on Git pushes"],"categories":["automation-workflow","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit__cap_13","uri":"capability://automation.workflow.apptest.framework.for.unit.testing.streamlit.apps.with.script.simulation","name":"apptest framework for unit testing streamlit apps with script simulation","description":"Provides AppTest class for programmatically testing Streamlit apps by simulating script execution and widget interactions. Tests instantiate AppTest with app script path, call methods like .run() to execute the script, and interact with widgets via .button[0].click(), .text_input[0].set_value(), etc. AppTest captures script output, widget state, and exceptions, enabling assertions on app behavior without running a browser. Tests run in Python and integrate with pytest.","intents":["I want to unit test my Streamlit app without manual browser testing","I need to verify widget interactions and state changes programmatically","I want to catch regressions in my app with automated tests"],"best_for":["developers building production Streamlit apps with quality requirements","teams implementing CI/CD pipelines for Streamlit apps","builders creating reusable Streamlit components"],"limitations":["AppTest simulates script execution but doesn't render frontend; no visual regression testing","Widget interaction is limited to programmatic methods; no complex user interaction sequences","Custom components (Components v2) are not fully testable via AppTest; requires Playwright for E2E testing","Async operations and streaming responses are not fully supported in AppTest"],"requires":["Python 3.8+","Streamlit 1.27+ (AppTest introduced)","pytest for test execution","App script must be importable as a Python module"],"input_types":["Streamlit app script path","Widget interaction methods (.click(), .set_value(), etc.)","Session state initialization"],"output_types":["Script output captured as text or structured data","Widget state after interactions","Exceptions and errors raised during execution"],"categories":["automation-workflow","testing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit__cap_14","uri":"capability://automation.workflow.configuration.management.with.st.set.page.config.and.streamlit.config.files","name":"configuration management with st.set_page_config() and streamlit config files","description":"Provides st.set_page_config() for setting app metadata (title, icon, layout, theme) and .streamlit/config.toml for global configuration (server settings, logging, caching behavior). The Configuration System reads config files at startup and applies settings to the app, with st.set_page_config() allowing per-page overrides. Supports theme customization (light/dark mode, color schemes) and layout modes (wide, centered), with configuration changes requiring app restart.","intents":["I want to set my app's title, icon, and layout without writing HTML","I need to customize the theme (colors, fonts) to match my brand","I want to configure server settings (port, timeout) for my Streamlit deployment"],"best_for":["developers customizing app appearance and behavior","teams deploying Streamlit apps with specific server requirements","data scientists branding internal tools"],"limitations":["Configuration changes require app restart; no hot-reload for config updates","Theme customization is limited to predefined color schemes; no fine-grained CSS customization","Config file format (TOML) is not as flexible as programmatic configuration","No environment-specific configuration (dev, staging, prod); requires manual file switching"],"requires":["Python 3.8+","Streamlit 1.0+",".streamlit/config.toml file (optional)"],"input_types":["page config parameters (title, icon, layout, theme)","TOML configuration file"],"output_types":["configured app with custom title, icon, layout, theme"],"categories":["automation-workflow","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit__cap_2","uri":"capability://automation.workflow.intelligent.caching.with.st.cache.data.and.st.cache.resource.decorators","name":"intelligent caching with @st.cache_data and @st.cache_resource decorators","description":"Provides two-tier caching system: @st.cache_data caches function outputs (serialized to disk) and reuses them if inputs haven't changed (detected via hash of function arguments), while @st.cache_resource caches expensive objects like database connections or ML models (stored in memory, not serialized). Both decorators intercept function calls, compute a hash of inputs, check an in-memory cache, and skip execution if cache hit occurs. 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Each page is a separate Python file that executes independently with its own session_state namespace, but shares global session_state across pages. Navigation is handled client-side via URL routing (e.g., ?page=page_name) with server-side script selection based on the current page.","intents":["I want to build a multi-page dashboard with separate pages for data upload, analysis, and reporting","I need to organize my app into logical sections without building a complex routing system","I want users to bookmark and share links to specific pages in my app"],"best_for":["teams building complex data applications with multiple workflows","developers creating admin dashboards with separate sections","builders prototyping multi-step applications quickly"],"limitations":["Each page is a separate script execution; no shared local variables between pages (must use st.session_state)","Page discovery is file-system based; no programmatic route registration or dynamic routing","Sidebar navigation is auto-generated and not easily customizable; complex navigation patterns require custom components"],"requires":["Python 3.8+","Streamlit 1.18+ (multi-page apps feature)","Directory structure: app.py in root, pages/*.py for additional pages"],"input_types":["Python files in pages/ directory","User navigation clicks in sidebar"],"output_types":["Rendered page content","URL with page parameter for bookmarking/sharing"],"categories":["automation-workflow","navigation-routing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit__cap_4","uri":"capability://image.visual.built.in.data.visualization.with.plotly.matplotlib.and.altair.integration","name":"built-in data visualization with plotly, matplotlib, and altair integration","description":"Provides native rendering of popular Python visualization libraries through st.plotly_chart(), st.matplotlib_figure(), st.altair_chart(), and st.vega_lite_chart(). These functions serialize chart objects to JSON (for Plotly/Altair) or PNG (for Matplotlib) and embed them in the React frontend. Charts are interactive (Plotly, Altair) or static (Matplotlib), and support features like selection, hover tooltips, and responsive sizing. Streamlit also provides high-level charting functions (st.line_chart, st.bar_chart, st.area_chart) that wrap Altair for quick prototyping.","intents":["I want to display interactive charts without writing HTML/JavaScript","I need to show Matplotlib plots in my web app without converting to images","I want to create dashboards with Plotly charts that support hover tooltips and selection"],"best_for":["data scientists building dashboards and exploratory analysis apps","ML engineers visualizing model outputs and metrics","analysts creating interactive reports"],"limitations":["Chart interactivity is limited to library-native features (Plotly selections, Altair brushing); no custom interaction handlers","Matplotlib charts are rendered as static images; no interactivity or responsiveness","Large datasets (>10k points) can cause performance issues with Plotly/Altair due to JSON serialization overhead"],"requires":["Python 3.8+","Plotly, Matplotlib, Altair, or Vega-Lite installed (optional, depending on which charting library is used)","Streamlit 1.0+"],"input_types":["Plotly Figure objects","Matplotlib Figure objects","Altair Chart objects","Pandas DataFrames (for high-level st.line_chart, st.bar_chart)"],"output_types":["Interactive HTML/SVG charts (Plotly, Altair)","Static PNG images (Matplotlib)"],"categories":["image-visual","data-visualization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit__cap_5","uri":"capability://data.processing.analysis.interactive.data.editor.with.st.data.editor.for.in.place.dataframe.manipulation","name":"interactive data editor with st.data_editor for in-place dataframe manipulation","description":"Renders a spreadsheet-like UI for editing Pandas DataFrames or lists of dictionaries in-place, with support for column type specification, validation, and cell-level editing. The st.data_editor component captures user edits, returns the modified data structure, and allows developers to react to changes via callbacks or by checking if the data changed. Supports column types (int, float, string, datetime, checkbox), column configuration (width, disabled, hidden), and validation rules. Edited data is returned as a new DataFrame/dict, not mutated in-place.","intents":["I want users to edit data directly in my app without building a custom form","I need to validate user input as they edit cells in a spreadsheet","I want to allow bulk data entry or correction in a table format"],"best_for":["developers building data entry applications","teams creating internal tools for data correction and validation","analysts building interactive data cleaning workflows"],"limitations":["Large datasets (>1000 rows) can cause performance issues due to client-side rendering of all rows","No built-in pagination or virtualization; all rows must be rendered in the browser","Validation is client-side only; server-side validation requires manual checking of returned data","No built-in undo/redo or multi-user conflict resolution"],"requires":["Python 3.8+","Streamlit 1.23+ (st.data_editor introduced)","Pandas for DataFrame support"],"input_types":["Pandas DataFrame","List of dictionaries","Column configuration (type, width, disabled, hidden)"],"output_types":["Modified DataFrame or list of dictionaries","Boolean indicating if data was edited"],"categories":["data-processing-analysis","interactive-forms"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit__cap_6","uri":"capability://tool.use.integration.file.upload.and.download.with.automatic.temporary.storage","name":"file upload and download with automatic temporary storage","description":"Provides st.file_uploader() for accepting file uploads from users and st.download_button() for serving files to download. Uploaded files are stored in temporary directories (cleaned up after session ends) and accessible as BytesIO objects or file paths. Download button generates a client-side download link with specified filename and MIME type. Both functions handle file I/O automatically without requiring developers to manage temporary files or HTTP headers.","intents":["I want users to upload CSV files and process them in my app","I need to let users download processed data or generated reports","I want to handle file uploads without managing temporary directories"],"best_for":["developers building data processing applications","teams creating file conversion or analysis tools","analysts building report generation apps"],"limitations":["Uploaded files are stored in temporary directories; not persisted across server restarts","File size limits depend on server configuration and available disk space; no built-in chunked upload for large files","No progress indication for large file uploads or downloads","Temporary files are cleaned up after session ends; no built-in archival or audit logging"],"requires":["Python 3.8+","Streamlit 1.0+","Sufficient disk space for temporary file storage"],"input_types":["Binary file data from user upload","File path or BytesIO object for download"],"output_types":["BytesIO object or file path for uploaded file","Client-side download link with specified filename"],"categories":["tool-use-integration","file-handling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit__cap_7","uri":"capability://text.generation.language.chat.interface.with.st.chat.message.and.st.chat.input.for.conversational.apps","name":"chat interface with st.chat_message and st.chat_input for conversational apps","description":"Provides st.chat_message() context manager for rendering chat messages with role-based styling (user, assistant, etc.) and st.chat_input() for accepting user text input in a chat-like interface. Messages are rendered in a scrollable container with automatic styling and avatar support. Developers manage conversation history manually via st.session_state and re-render messages on each script rerun. Integrates with LLM APIs (OpenAI, Anthropic, etc.) via manual API calls within the chat loop.","intents":["I want to build a chatbot interface without custom HTML/CSS","I need to create a conversational app that maintains message history","I want to integrate an LLM API into my app with a chat UI"],"best_for":["developers building chatbot demos and conversational AI apps","teams creating customer support or Q&A interfaces","builders prototyping LLM-powered applications"],"limitations":["Message history must be managed manually via st.session_state; no built-in persistence","No built-in streaming support for LLM responses; requires manual implementation with st.write_stream() or similar","Chat input is single-line text only; no file attachments or rich media input","No built-in rate limiting, authentication, or abuse prevention"],"requires":["Python 3.8+","Streamlit 1.23+ (st.chat_message and st.chat_input introduced)","LLM API key (OpenAI, Anthropic, etc.) for actual chatbot functionality"],"input_types":["User text input via st.chat_input()","Message role (user, assistant, info, warning, error)","Message content (text, code, markdown)"],"output_types":["Rendered chat message with role-based styling","Conversation history stored in st.session_state"],"categories":["text-generation-language","interactive-ui"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit__cap_8","uri":"capability://tool.use.integration.custom.react.components.via.components.v2.api.with.bidirectional.communication","name":"custom react components via components v2 api with bidirectional communication","description":"Enables developers to build custom React components that communicate with Python via a BidiComponent (bidirectional component) pattern using Protocol Buffers. Components v2 uses a TypeScript/React SDK that wraps component logic and handles serialization of data between Python and JavaScript. Developers define component props in Python, render React components on the frontend, and receive user interactions back as Python objects. Communication is event-driven via WebSocket, allowing real-time updates without page reloads.","intents":["I want to build a custom interactive visualization that Streamlit doesn't provide","I need to integrate a third-party JavaScript library into my Streamlit app","I want to create a reusable component that other developers can use in their apps"],"best_for":["frontend developers extending Streamlit with custom components","teams building specialized visualizations or interactions","developers creating reusable component libraries"],"limitations":["Requires TypeScript/React knowledge; steeper learning curve than st.* functions","Components v2 is newer and less mature than v1; fewer community examples and libraries","Debugging bidirectional communication requires understanding Protocol Buffers and WebSocket communication","Component state is not automatically persisted; developers must manage state manually"],"requires":["Python 3.8+","Streamlit 1.16+ (Components v2 API)","Node.js 14+ and npm/yarn for building React components","TypeScript and React knowledge"],"input_types":["Python props (serialized to JSON via Protocol Buffers)","React component state and user interactions"],"output_types":["Rendered React component in browser","Python objects representing user interactions"],"categories":["tool-use-integration","custom-components"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit__cap_9","uri":"capability://safety.moderation.secrets.management.with.environment.variable.injection.and.streamlit.cloud.integration","name":"secrets management with environment variable injection and streamlit cloud integration","description":"Provides st.secrets for accessing secrets stored in .streamlit/secrets.toml (local development) or Streamlit Cloud's secrets manager (production). Secrets are injected as environment variables and accessible via st.secrets[key] or st.secrets.get(key). Supports TOML syntax for nested secrets and automatic reloading on file changes. Streamlit Cloud integrates with its web UI for managing secrets without exposing them in code or version control.","intents":["I want to store API keys and database credentials without hardcoding them","I need to use different secrets in development and production","I want to manage secrets securely in Streamlit Cloud without version control"],"best_for":["developers building apps that require API keys or database credentials","teams deploying to Streamlit Cloud and needing secure secret management","builders protecting sensitive configuration from version control"],"limitations":["Secrets are stored in plaintext in .streamlit/secrets.toml; requires .gitignore to prevent accidental commits","No built-in secret rotation or expiration; developers must manually update secrets","Secrets are loaded at app startup; changes require app restart (or manual reload in development)","No audit logging of secret access; no fine-grained access control per secret"],"requires":["Python 3.8+","Streamlit 1.0+",".streamlit/secrets.toml file in local development","Streamlit Cloud account for production secret management"],"input_types":["TOML-formatted secrets in .streamlit/secrets.toml","Secrets entered via Streamlit Cloud web UI"],"output_types":["Secret values accessible via st.secrets[key]","Environment variables injected into Python process"],"categories":["safety-moderation","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"streamlit__headline","uri":"capability://app.builders.interactive.web.app.framework.for.python","name":"interactive web app framework for python","description":"Streamlit is an open-source framework that enables developers to easily turn Python scripts into interactive web applications, simplifying the process of building data-driven apps and machine learning demos with minimal coding effort.","intents":["best interactive web app framework for Python","interactive web app framework for data visualization","how to create a web app from Python script","top frameworks for building ML demos","free tools for deploying Python web apps"],"best_for":["data scientists","machine learning engineers","Python developers"],"limitations":["primarily focused on Python","less suitable for complex front-end requirements"],"requires":["basic knowledge of Python"],"input_types":["Python scripts"],"output_types":["interactive web applications"],"categories":["app-builders"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Streamlit package installed via pip","WebSocket-capable browser for real-time communication","Streamlit 1.0+ (session_state API stabilized in 1.0)","Streamlit 1.18+ (Connection API introduced)","Database driver or API client library (psycopg2 for PostgreSQL, pymongo for MongoDB, etc.)","Connection credentials in st.secrets","Streamlit 1.18+ (st.experimental_user introduced)","Streamlit Cloud account or self-hosted deployment with OAuth provider configuration","OAuth credentials from provider (Google, GitHub, etc.)"],"failure_modes":["Full script re-execution on every interaction can be slow for long-running computations (mitigated by @st.cache_data but requires explicit annotation)","Stateless execution model means all state must be stored in st.session_state or external persistence; 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