4 compared
ML & AI app UI frameworks compared
Python UI frameworks for data and LLM apps, compared on focus, source, and setup.
Open in the interactive comparison tool| Field | Streamlit Apache-2.0 Python framework for turning scripts into interactive data apps, dashboards, reports, chat apps, multipage tools, and deployable analytical interfaces. Open dossier | Gradio Apache-2.0 Python framework for building and sharing machine-learning demos, AI web apps, model interfaces, chatbots, API front ends, and interactive evaluation tools. Open dossier | Chainlit Apache-2.0 Python framework for building production-ready conversational AI apps with chat lifecycles, messages, steps, actions, elements, authentication, persistence, and integrations. Open dossier | Marimo Apache-2.0 reactive Python notebook stored as pure Python for reproducible experiments, SQL-backed data workflows, script execution, app deployment, and AI-assisted editing. Open dossier |
|---|---|---|---|---|
| Trust | ||||
| Install risk | Review first | Review first | Review first | Review first |
| Notes | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ |
| Category | tools | tools | tools | tools |
| Source | source-backed | source-backed | source-backed | source-backed |
| Author | Streamlit | Gradio | Chainlit Maintainers | Marimo Team |
| Added | 2026-06-04 | 2026-06-04 | 2026-06-04 | 2026-06-04 |
| Platforms | CLI | CLI | CLI | CLI |
| Source repo | — | — | — | — |
| Safety notes | ✓Streamlit apps execute Python scripts and rerun code from top to bottom on user interaction or code changes, so destructive writes, API calls, database mutations, long jobs, and side effects need explicit guards. Cached data can be global across users, sessions, and reruns, while Session State is scoped to a browser session; teams should avoid accidentally sharing user-specific or sensitive data through global caches. The Streamlit security docs warn that `st.cache_data` and `st.session_state` use Python pickle internally, and that malicious pickle data can execute arbitrary code during unpickling. Cached resources such as database connections and ML models can be shared globally and must be thread-safe, or should be scoped per session when isolation is required. Secrets should not be committed to repositories; Streamlit supports environment variables, local or project secrets files, and platform-specific secret storage. OIDC authentication identifies users but does not provide authorization or delegated OAuth access by itself, so apps still need explicit permission checks for admin actions and user-specific data. Custom components, embedded HTML, iframes, JavaScript, media, file uploaders, and third-party packages should be treated as trusted app code and reviewed before public deployment. Chat elements and LLM integrations can call external APIs and stream long-running responses, so prompts, attachments, generated outputs, rate limits, and provider credentials need operational limits. | ✓Gradio apps wrap arbitrary Python functions and can run model inference, file operations, network calls, database queries, subprocesses, and side effects, so exposed functions should be treated as trusted server code. Setting `share=True` creates a publicly accessible share link through a tunnel, and the docs warn that anyone with the link can use the model while computation continues on the local computer. Public apps, API endpoints, and MCP servers need authentication, authorization, rate limits, queue limits, API visibility settings, resource quotas, and monitoring before being exposed to untrusted users. File upload components allow users to upload files to the computer or server running the app; apps should set maximum file sizes, validate content, clean temporary files, and avoid processing untrusted files unsafely. File access controls such as `allowed_paths`, `blocked_paths`, and static paths can expose local files if configured too broadly, so allowed paths should be minimal and app-specific. Queue and concurrency settings can protect scarce GPU or model resources, but unlimited concurrency or unbounded queues can exhaust memory, disk, network, API quota, or accelerator capacity. Custom CSS, JavaScript, HTML, iframes, components, client-side functions, and embedded analytics can run browser code or load third-party resources and should be reviewed before deployment. Strict CORS should normally stay enabled for localhost apps; disabling it can increase CSRF exposure when embedding locally running apps. | ✓Chainlit apps run Python code in response to user chat events, actions, lifecycle hooks, MCP tool calls, and integrations, so app functions should be treated as trusted server code. The docs say Chainlit applications are public by default; private apps require an authentication secret and at least one authentication callback. Authentication identifies users, but app code still needs explicit authorization checks for admin controls, private chat history, user-specific data, file access, and external tool execution. Steps can expose intermediate reasoning, tool inputs, tool outputs, and chain-of-thought-style traces depending on configuration, so production apps should decide whether to show full steps, only tool calls, or hide them. Actions trigger Python callbacks from clickable UI controls, and Ask APIs can block code while requesting text, files, actions, or forms; handlers should validate user input and guard side effects. MCP support can connect to SSE, streamable HTTP, and stdio tool providers, discover tools, and execute them, so tool permissions, command-line tools, credentials, and network reachability need review. Deployment docs note that production runs should use headless mode, host binding must be intentional, and Docker deployments commonly need `--host 0.0.0.0`; reverse proxies and websockets need explicit configuration. Environment variable docs warn against hardcoding API keys and recommend keeping `.env` out of version control; public apps should not ship the maintainer's own provider keys to broad audiences. | ✓Marimo notebooks execute Python and SQL, can write files, query databases, call APIs, access object storage, install packages, and start web servers, so notebooks should be treated as trusted project code. Reactive execution automatically tracks variable dependencies and can run downstream cells after upstream changes; expensive, destructive, or side-effectful cells need lazy runtime, disabled cells, startup autorun, and manual-run policies. The docs note that Marimo tracks variable definitions and references statically, not arbitrary mutations across cells, so mutable shared state should be designed carefully to avoid misleading results. App mode uses `marimo run` to serve notebooks as web apps with code hidden by default, but public deployments still need authentication, authorization, rate limiting, reverse proxy policy, and traceback disclosure review. Disabling token protection, passing access tokens in URLs, or exposing edit servers can give unauthorized users access to notebook execution and should be avoided outside controlled environments. SQL cells can interpolate Python values, query local files and remote databases, and use engines or extensions such as DuckDB, so SQL strings, credentials, object paths, and output destinations should be reviewed before automation. Built-in AI and copilot features may inspect notebook code, prompts, tool context, and referenced variable values; provider selection, API keys, local model behavior, and cost controls should be configured deliberately. Package-management features can serialize requirements and auto-install dependencies into notebook-specific environments, so teams should pin, review, and scan packages before sharing or deploying notebooks. |
| Privacy notes | ✓Streamlit apps can process Python source, widget inputs, uploaded files, session state, cached results, DataFrames, SQL queries, database rows, charts, maps, chat prompts, logs, and rendered outputs. Secrets can be loaded from `st.secrets`, TOML files, environment variables, deployment settings, or third-party secret managers; these values should stay out of source control, screenshots, app output, and logs. Community Cloud and other hosted deployment paths can involve GitHub account connection, source repository access, app metadata, runtime logs, dependency installation, secrets configuration, and platform-specific retention policies. OIDC login stores identity information and an identity cookie for the app session; user profile fields, login status, and authorization decisions should be treated as sensitive app data. Global caches, global resources, and persisted session data can expose values across users or reruns if the app does not separate public, shared, and user-specific data deliberately. Custom components, embedded iframes, external APIs, databases, warehouses, LLM providers, and analytics services may receive user inputs, prompts, query results, browser metadata, or credentials depending on app design. | ✓Gradio apps can process prompts, chat history, uploaded files, images, audio, video, labels, model inputs and outputs, flagging data, examples, API requests, queue metadata, logs, and generated artifacts. Public share links, Hugging Face Spaces, API clients, JavaScript clients, cURL access, MCP exposure, and hosted deployment platforms may make demos reachable beyond the original local environment. Authentication, user identity, IP addresses, request headers, API visibility, rate-limiting metadata, and queue status can become part of app logic or logs and should be handled as sensitive operational data. Uploaded files and generated outputs may be moved into Gradio cache directories or allowed static paths; teams should define retention, cleanup, and access-control policies. Custom telemetry, analytics, JavaScript, third-party components, model providers, databases, object stores, LLM APIs, and deployment hosts may receive user inputs or outputs depending on app design. Gradio exposes analytics configuration options, and teams with privacy requirements should review telemetry settings, environment variables, hosted-platform terms, and downstream service logging. | ✓Chainlit apps can process chat messages, prompts, chat history, steps, tool inputs and outputs, user sessions, uploaded files, elements, human feedback, tags, metadata, logs, API calls, and generated artifacts. Enabling data persistence stores and uses chat and element data that is otherwise not persisted by default; teams should define retention, deletion, access, export, and monitoring policies. Authentication can store user identity, unique identifiers, headers, OAuth profile data, login state, and auth tokens that should be scoped and protected as sensitive application data. The user session is unique to a user and a chat session; shared globals can leak state across users, as the docs warn in their user-session example. Environment variables, `.env` files, model provider keys, vector database keys, OAuth secrets, MCP credentials, and deployment settings should stay out of committed code, screenshots, logs, and persisted chat artifacts. Multi-platform deployments such as web app, Copilot embed, Teams, Slack, Discord, custom React frontends, and Chainlit integrations may pass chat content and metadata through additional platforms with separate privacy terms. | ✓Marimo workflows can process notebook source code, cell outputs, variable values, DataFrames, SQL queries, schemas, database rows, object-store paths, generated apps, CLI arguments, logs, and exported artifacts. User configuration can store runtime, server, completion, and AI-provider settings, while app configuration can live inside notebook files; secrets should stay in environment variables or secret stores rather than committed notebooks. AI-assisted editing can send prompts, notebook context, code, schemas, and referenced in-memory values to configured hosted providers, or to local model services when those are selected. Database and remote-storage workflows can expose connection strings, credentials, table names, bucket names, object keys, query text, sample rows, and download paths to notebooks, logs, cloud services, and deployed apps. Token login, query-parameter access tokens, Basic auth headers, reverse-proxy headers, and server logs should be handled as sensitive operational data. |
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