{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-open-webui--open-webui","slug":"open-webui--open-webui","name":"open-webui","type":"webapp","url":"https://openwebui.com","page_url":"https://unfragile.ai/open-webui--open-webui","categories":["chatbots-assistants"],"tags":["ai","llm","llm-ui","llm-webui","llms","mcp","ollama","ollama-webui","open-webui","openai","openapi","rag","self-hosted","ui","webui"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-open-webui--open-webui__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.model.aggregation.and.discovery","name":"multi-provider llm model aggregation and discovery","description":"Open WebUI implements a unified model discovery and aggregation layer that abstracts over heterogeneous LLM providers (Ollama, OpenAI, Anthropic, etc.) through a FastAPI backend with provider-specific adapter patterns. The system maintains a dynamic model registry that polls each configured provider's API endpoints, normalizes model metadata (context windows, capabilities, pricing), and exposes a unified model list to the frontend via REST endpoints. This enables users to seamlessly switch between local Ollama instances and cloud providers without reconfiguring the UI.","intents":["Switch between local and cloud LLM providers without UI reconfiguration","Discover available models across multiple provider accounts in one interface","Compare model capabilities and select the best fit for a task","Aggregate models from Ollama, OpenAI, Anthropic, and custom API endpoints"],"best_for":["Teams managing hybrid local/cloud LLM deployments","Developers building multi-model AI applications","Organizations evaluating different LLM providers"],"limitations":["Model discovery latency depends on provider API response times; no caching layer for model lists","Custom provider adapters require manual implementation for non-standard APIs","No automatic model capability inference — requires manual metadata configuration per provider"],"requires":["Python 3.9+","FastAPI backend running","At least one configured LLM provider (Ollama, OpenAI API key, etc.)","Network connectivity to provider endpoints"],"input_types":["provider configuration (API keys, endpoints, model names)"],"output_types":["JSON model registry with normalized metadata","UI model selector dropdown"],"categories":["tool-use-integration","model-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-webui--open-webui__cap_1","uri":"capability://memory.knowledge.rag.powered.document.ingestion.with.multi.format.extraction","name":"rag-powered document ingestion with multi-format extraction","description":"Open WebUI implements a document ingestion pipeline that accepts multiple file formats (PDF, DOCX, TXT, Markdown, images with OCR) and processes them through a content extraction engine that splits documents into semantic chunks, generates embeddings via configurable embedding models, and stores vectors in a pluggable vector database (Chroma, Weaviate, Milvus). The system maintains a knowledge base per workspace, enabling users to augment LLM context with domain-specific documents. Retrieval uses semantic similarity search with optional reranking to surface the most relevant chunks during chat.","intents":["Upload company documents and use them as context for LLM responses","Build a searchable knowledge base from mixed document formats","Augment LLM responses with retrieved document excerpts","Manage multiple knowledge bases per workspace or team"],"best_for":["Teams building internal knowledge assistants","Organizations with document-heavy workflows (legal, medical, technical)","Developers prototyping RAG applications without external infrastructure"],"limitations":["Embedding quality depends on chosen embedding model; no automatic model selection","Vector database must be separately deployed (Chroma, Weaviate, etc.) — no built-in persistence","Chunk size and overlap are configurable but not automatically optimized for document type","OCR quality for scanned PDFs depends on image quality; no built-in document preprocessing"],"requires":["Python 3.9+","Vector database instance (Chroma, Weaviate, Milvus, or compatible)","Embedding model API access (OpenAI, local Ollama embedding model, etc.)","File upload storage (local filesystem or S3-compatible)"],"input_types":["PDF, DOCX, TXT, Markdown, images (with OCR)","document metadata (title, tags, source)"],"output_types":["vector embeddings stored in vector database","retrieved document chunks with similarity scores","augmented LLM context with source attribution"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-webui--open-webui__cap_10","uri":"capability://text.generation.language.prompt.and.tool.management.with.versioning.and.sharing","name":"prompt and tool management with versioning and sharing","description":"Open WebUI provides a management interface for creating, versioning, and sharing reusable prompts and tools. Prompts are templates with variable substitution that users can save and reuse across conversations. Tools are custom functions with schema definitions that can be registered in the tool registry. Both prompts and tools support versioning, enabling users to track changes and revert to previous versions. Users can share prompts and tools with other workspace members or make them public for community use. The system maintains a prompt library and tool marketplace for discovery.","intents":["Create reusable prompt templates with variable substitution","Build custom tools and share them with team members","Version prompts and tools to track changes","Discover and reuse community prompts and tools"],"best_for":["Teams standardizing on prompt templates","Developers building custom tools for their organization","Communities sharing AI workflows and integrations"],"limitations":["Variable substitution is basic; no conditional logic or loops","Tool implementation requires Python code; no visual tool builder","Sharing is manual; no automatic distribution or updates","Version history is not compressed; storage grows with each version"],"requires":["FastAPI backend with prompt/tool management endpoints","Database for storing prompt and tool definitions","User authentication for access control"],"input_types":["prompt template text with variable placeholders","tool schema and implementation code","version metadata (author, timestamp, description)"],"output_types":["prompt library (JSON)","tool registry (JSON schemas)","version history (snapshots)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-webui--open-webui__cap_11","uri":"capability://automation.workflow.scheduled.automations.and.calendar.based.workflows","name":"scheduled automations and calendar-based workflows","description":"Open WebUI includes a scheduling system that allows users to define automated workflows triggered by time-based events or calendar entries. Automations can execute predefined prompts, invoke tools, or run custom scripts on a schedule (daily, weekly, monthly, or custom cron expressions). The system maintains a calendar view of scheduled automations and provides execution logs for monitoring. Automations can be triggered by calendar events (e.g., run a report generation workflow at the start of each month) or external webhooks. Results of automated workflows can be stored, emailed, or posted to channels.","intents":["Automate recurring tasks like report generation or data collection","Schedule LLM-powered workflows to run at specific times","Trigger automations based on calendar events","Monitor automation execution and view results"],"best_for":["Teams automating routine tasks","Organizations generating periodic reports with AI","Developers building scheduled LLM workflows"],"limitations":["Scheduling is time-based only; no event-driven triggers (e.g., file uploads)","Automation execution latency depends on task complexity; no guaranteed execution time","Execution logs are not compressed; storage grows with each execution","No built-in error handling or retry logic; failed automations require manual intervention"],"requires":["FastAPI backend with scheduling service (APScheduler or similar)","Cron expression support for custom schedules","LLM provider for executing automated prompts"],"input_types":["automation definition (prompt, tools, schedule)","calendar event metadata","webhook payload (optional)"],"output_types":["automation execution result","execution log entry","email or channel notification (optional)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-webui--open-webui__cap_12","uri":"capability://safety.moderation.admin.panel.with.user.management.analytics.and.evaluations","name":"admin panel with user management, analytics, and evaluations","description":"Open WebUI includes an admin panel for managing users, monitoring usage, and evaluating model performance. The admin interface provides user management (create, edit, delete, reset passwords), usage analytics (tokens consumed, API calls, model usage), and a leaderboard for comparing model performance on evaluation tasks. Admins can view detailed logs of user interactions, monitor system health, and configure global settings. The system tracks metrics like token usage per user/model, API costs, and response latency. Evaluations allow admins to define benchmark tasks and compare model outputs.","intents":["Monitor user activity and API usage","Manage user accounts and permissions","Compare model performance on benchmark tasks","Track costs and optimize resource allocation"],"best_for":["Administrators managing multi-user Open WebUI deployments","Organizations tracking AI costs and usage","Teams evaluating model performance"],"limitations":["Analytics are real-time only; no historical trend analysis","Evaluation leaderboard is manual; no automatic benchmark execution","User activity logs are not anonymized; privacy concerns for sensitive deployments","No built-in cost allocation or chargeback system"],"requires":["Admin user role","FastAPI backend with admin endpoints","Database for storing usage metrics and logs"],"input_types":["user management requests (create, edit, delete)","evaluation task definitions","analytics query parameters"],"output_types":["user list and permissions","usage analytics (JSON)","evaluation leaderboard","system health metrics"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-webui--open-webui__cap_13","uri":"capability://text.generation.language.internationalization.with.dynamic.translation.and.locale.support","name":"internationalization with dynamic translation and locale support","description":"Open WebUI implements a translation system that supports multiple languages with dynamic locale switching. The frontend uses a translation library that loads locale-specific strings from JSON files, enabling users to switch languages without page reload. The system supports variable interpolation in translations (e.g., 'Hello {name}'), enabling dynamic content in multiple languages. Backend responses are localized based on user locale preference. The system maintains a list of supported locales and provides a UI for selecting language.","intents":["Use Open WebUI in multiple languages","Switch languages without page reload","Contribute translations for new languages","Localize backend responses based on user preference"],"best_for":["Global teams using Open WebUI","Organizations deploying to non-English-speaking regions","Communities contributing translations"],"limitations":["Translation quality depends on community contributions; no professional translation service","Variable interpolation is basic; no pluralization or gender-specific translations","Backend localization is limited; only UI strings are translated","No automatic translation; all translations are manual"],"requires":["Translation library (i18n) in frontend","JSON translation files for each locale","User locale preference storage"],"input_types":["locale code (e.g., 'en', 'fr', 'de')","translation strings with variable placeholders"],"output_types":["localized UI strings","localized backend responses"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-webui--open-webui__cap_14","uri":"capability://text.generation.language.markdown.rendering.with.syntax.highlighting.and.interactive.code.blocks","name":"markdown rendering with syntax highlighting and interactive code blocks","description":"Open WebUI implements a markdown rendering pipeline that parses streamed markdown content progressively as it arrives from LLMs. The system uses a markdown parser to convert markdown to HTML, applies syntax highlighting to code blocks using a syntax highlighter library (e.g., Highlight.js), and renders interactive components for code blocks (copy button, language indicator). Code blocks can be executed directly in the browser (for JavaScript) or sent to the backend for execution (for Python, shell commands). The rendering pipeline also handles LaTeX math expressions, tables, and other markdown extensions.","intents":["Display formatted LLM responses with proper syntax highlighting","Execute code blocks directly from chat responses","Copy code snippets with a single click","Render mathematical expressions and tables"],"best_for":["Developers receiving code from LLMs","Researchers working with mathematical content","Teams sharing formatted documentation"],"limitations":["Progressive rendering adds complexity; malformed markdown can break rendering","Code execution is sandboxed; no access to user filesystem or environment variables","Syntax highlighting is client-side only; no server-side rendering","LaTeX rendering requires additional library; increases bundle size"],"requires":["Markdown parser library (e.g., markdown-it)","Syntax highlighter library (e.g., Highlight.js)","Optional: LaTeX renderer (e.g., KaTeX)"],"input_types":["markdown text (streamed or batch)","code block language identifier"],"output_types":["rendered HTML with syntax highlighting","interactive code block components","execution results (for executable code)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-webui--open-webui__cap_15","uri":"capability://automation.workflow.sidebar.navigation.with.drag.and.drop.folder.organization","name":"sidebar navigation with drag-and-drop folder organization","description":"Open WebUI implements a sidebar navigation component that displays chats, notes, and other content organized in a hierarchical folder structure. The sidebar supports drag-and-drop operations for moving items between folders, creating new folders, and reorganizing content. The system maintains folder state in the database, enabling persistence across sessions. Users can collapse/expand folders, search for items, and pin frequently-used chats or notes to the top. The sidebar also displays workspace switcher, user menu, and settings access.","intents":["Organize chats and notes in folders for easy navigation","Quickly switch between frequently-used conversations","Search for past conversations by name or content","Manage workspace and user settings from sidebar"],"best_for":["Users with many conversations and notes","Teams organizing content by project or topic","Power users needing quick navigation"],"limitations":["Drag-and-drop is client-side only; no conflict resolution for concurrent moves","Search is by name only; no full-text search of conversation content","Folder nesting is unlimited; no depth limit can lead to deep hierarchies","Sidebar state is not synced across tabs; opening in new tab shows default state"],"requires":["Svelte frontend with drag-and-drop library","Database for storing folder structure","FastAPI backend with folder management endpoints"],"input_types":["drag-and-drop events","folder creation/deletion requests","search queries"],"output_types":["updated folder structure (JSON)","search results (chat/note list)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-webui--open-webui__cap_16","uri":"capability://text.generation.language.model.editor.with.custom.system.prompts.and.parameter.tuning","name":"model editor with custom system prompts and parameter tuning","description":"Open WebUI provides a model editor interface that allows users to create custom model variants by defining system prompts, adjusting generation parameters (temperature, top_p, max_tokens, etc.), and configuring model-specific settings. Custom models are saved with a name and description, and can be used in conversations like built-in models. The system maintains a model registry that includes both built-in models and user-created variants. Model parameters are validated against provider constraints (e.g., temperature range 0-2 for OpenAI). Users can share custom models with other workspace members.","intents":["Create custom model variants with specific system prompts","Tune generation parameters for different use cases","Save and reuse model configurations across conversations","Share model configurations with team members"],"best_for":["Teams standardizing on model configurations","Developers fine-tuning models for specific tasks","Organizations creating role-specific model variants"],"limitations":["Parameter validation is provider-specific; custom providers may not validate correctly","Model variants are not versioned; no rollback to previous configurations","No A/B testing framework; comparing variants requires manual testing","Parameter tuning is manual; no automatic optimization"],"requires":["FastAPI backend with model editor endpoints","Database for storing custom model definitions","LLM provider with parameter support"],"input_types":["system prompt text","generation parameters (temperature, top_p, etc.)","model name and description"],"output_types":["custom model definition (JSON)","model registry with variants"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-webui--open-webui__cap_2","uri":"capability://text.generation.language.real.time.websocket.based.chat.streaming.with.multi.model.response.display","name":"real-time websocket-based chat streaming with multi-model response display","description":"Open WebUI uses a WebSocket architecture for bidirectional real-time communication between frontend and FastAPI backend, enabling streaming LLM responses character-by-character as they arrive from providers. The system implements a message history tree structure that supports branching conversations (multiple responses to the same prompt), and a response message component that renders streamed content with progressive markdown parsing, code block syntax highlighting, and interactive text actions. Multi-model responses allow users to generate responses from multiple LLMs in parallel and compare them side-by-side.","intents":["Stream LLM responses in real-time without waiting for full completion","Generate multiple responses from different models and compare them","Branch conversations to explore alternative response paths","Interact with code blocks and formatted content in responses"],"best_for":["Users expecting low-latency, interactive chat experiences","Developers comparing model outputs across providers","Teams exploring multiple solution paths in a single conversation"],"limitations":["WebSocket connections require persistent network — not suitable for offline-first applications","Streaming adds ~50-100ms latency per chunk compared to batch responses","Message history tree can grow unbounded; no automatic pruning or archival","Multi-model responses consume resources proportional to number of models queried"],"requires":["WebSocket support in browser and server","FastAPI backend with WebSocket endpoint configured","LLM provider with streaming API support (OpenAI, Ollama, Anthropic, etc.)"],"input_types":["user message text","selected model(s)","conversation context (previous messages)"],"output_types":["streamed text chunks","rendered markdown with syntax highlighting","interactive response components (code blocks, links, buttons)"],"categories":["text-generation-language","real-time-communication"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-webui--open-webui__cap_3","uri":"capability://tool.use.integration.tool.execution.system.with.schema.based.function.calling","name":"tool execution system with schema-based function calling","description":"Open WebUI implements a tool execution system that allows LLMs to invoke external functions through a schema-based function registry. Tools are defined with JSON schemas describing inputs/outputs, and the backend maintains a registry of available tools that can be exposed to LLMs via function-calling APIs (OpenAI, Anthropic, Ollama). When an LLM requests tool execution, the backend validates the function call against the schema, executes the tool (which may be a built-in integration like web search or image generation, or a custom user-defined function), and returns results back to the LLM for further processing.","intents":["Enable LLMs to search the web and retrieve current information","Allow LLMs to generate images and incorporate them into responses","Create custom tools that LLMs can invoke autonomously","Build agentic workflows where LLMs decide which tools to use"],"best_for":["Developers building LLM agents with external tool access","Teams implementing autonomous workflows","Organizations extending LLM capabilities with custom integrations"],"limitations":["Tool execution latency depends on external service response times; no built-in caching","Schema validation adds ~20-50ms per tool call","Custom tool implementation requires Python backend code; no visual tool builder","Tool output must be serializable to JSON; binary outputs require base64 encoding"],"requires":["Python 3.9+","LLM provider with function-calling support (OpenAI, Anthropic, Ollama)","External service credentials for web search, image generation, etc.","FastAPI backend with tool registry endpoint"],"input_types":["tool schema (JSON schema format)","function call request from LLM","tool parameters (validated against schema)"],"output_types":["tool execution result (JSON-serializable)","error messages if validation fails","tool output returned to LLM"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-webui--open-webui__cap_4","uri":"capability://search.retrieval.web.search.integration.with.result.ranking.and.attribution","name":"web search integration with result ranking and attribution","description":"Open WebUI integrates web search capabilities (via Brave Search, Google Search, or other providers) as a tool that LLMs can invoke during chat. When an LLM requests web search, the backend queries the search provider, retrieves ranked results with snippets and URLs, and returns them to the LLM with source attribution. The system maintains search result caching to avoid duplicate queries and provides users with visibility into which search results informed the LLM's response through inline source citations.","intents":["Augment LLM responses with current information from the web","Provide source attribution for facts retrieved from web search","Enable LLMs to autonomously decide when web search is needed","Cache search results to reduce API calls and latency"],"best_for":["Users needing current information (news, prices, events)","Teams building fact-checking or research assistants","Developers implementing agentic systems with information retrieval"],"limitations":["Search result quality depends on provider; no automatic result validation","Search latency adds 1-3 seconds per query; no streaming search results","Search result caching is time-based; no intelligent cache invalidation","Source attribution requires LLM to cite sources; no automatic citation enforcement"],"requires":["Web search provider API key (Brave Search, Google Search, etc.)","Network connectivity to search provider","LLM with function-calling support to invoke search tool"],"input_types":["search query (text)","search parameters (number of results, language, etc.)"],"output_types":["ranked search results with snippets and URLs","source attribution in LLM response","cached results for duplicate queries"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-webui--open-webui__cap_5","uri":"capability://image.visual.image.generation.integration.with.multiple.provider.support","name":"image generation integration with multiple provider support","description":"Open WebUI integrates image generation capabilities through a pluggable provider system supporting DALL-E, Stable Diffusion, and other image generation APIs. When an LLM requests image generation (via function calling), the backend routes the request to the configured provider, handles authentication, and returns generated images with metadata. The system stores generated images in the chat history and allows users to regenerate images with different prompts or parameters. A dedicated image playground provides a UI for direct image generation without chat context.","intents":["Generate images from text descriptions within chat conversations","Create visual content without leaving the chat interface","Experiment with different image generation models and parameters","Store and retrieve generated images with conversation history"],"best_for":["Teams creating visual content alongside text","Developers building creative AI applications","Users exploring image generation models"],"limitations":["Image generation latency is high (10-60 seconds depending on provider)","Generated images consume storage; no automatic cleanup or archival","Image quality varies significantly between providers; no automatic provider selection","Cost per image can be substantial; no built-in usage tracking or limits"],"requires":["Image generation provider API key (DALL-E, Stable Diffusion, etc.)","Image storage backend (local filesystem or S3-compatible)","Network connectivity to image generation provider"],"input_types":["image prompt (text description)","generation parameters (size, style, quality, etc.)"],"output_types":["generated image (PNG, JPEG)","image metadata (prompt, model, generation time)","image URL for embedding in chat"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-webui--open-webui__cap_6","uri":"capability://text.generation.language.collaborative.note.taking.with.tiptap.editor.and.ai.integration","name":"collaborative note-taking with tiptap editor and ai integration","description":"Open WebUI includes a TipTap-based rich text editor for note-taking that supports collaborative editing, version history, and AI-powered content generation. Users can create notes with formatted text, file attachments, and embedded AI-generated content. The system maintains version history for each note, enabling users to view and restore previous versions. AI integration allows users to invoke LLMs directly within the editor to generate, edit, or expand note content. Notes are organized in a workspace hierarchy and can be shared with other users.","intents":["Create and organize notes with rich formatting and AI assistance","Collaborate on notes with version history and conflict resolution","Generate note content using LLMs without leaving the editor","Attach files and embed AI-generated content in notes"],"best_for":["Teams collaborating on documentation and knowledge","Researchers organizing findings with AI assistance","Organizations building internal wikis with AI augmentation"],"limitations":["Collaborative editing requires real-time sync; no offline-first support","Version history grows unbounded; no automatic pruning or compression","AI content generation within editor adds latency to user interactions","File attachments require separate storage backend; no built-in file versioning"],"requires":["TipTap editor library (included in frontend)","WebSocket support for real-time collaboration","LLM provider for AI content generation","File storage backend for attachments"],"input_types":["rich text content (via TipTap editor)","file attachments","AI generation prompts"],"output_types":["formatted note content (HTML, Markdown)","version history snapshots","AI-generated content insertions"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-webui--open-webui__cap_7","uri":"capability://text.generation.language.channel.based.messaging.with.real.time.synchronization","name":"channel-based messaging with real-time synchronization","description":"Open WebUI implements a channel system for team communication that mirrors chat functionality but with multi-user support. Channels are persistent conversation spaces where users can post messages, share files, and invoke tools. The system uses WebSocket-based real-time synchronization to broadcast messages and events to all channel members, maintaining message history and enabling threaded conversations. Channels can be organized hierarchically and have configurable access controls.","intents":["Create team communication spaces with AI assistance","Share files and collaborate on tasks within channels","Maintain persistent conversation history for team reference","Organize channels hierarchically by project or topic"],"best_for":["Teams using Open WebUI as a collaborative AI platform","Organizations replacing Slack-like tools with AI-native communication","Distributed teams needing persistent, searchable conversation history"],"limitations":["Message history can grow unbounded; no automatic archival or pruning","Real-time sync requires persistent WebSocket connections; no offline message queueing","Channel access control is basic; no fine-grained permission management","No built-in message search or filtering; relies on browser find functionality"],"requires":["WebSocket support for real-time synchronization","FastAPI backend with channel management endpoints","User authentication and authorization system"],"input_types":["message text","file attachments","tool invocations"],"output_types":["message history (JSON)","real-time message events (WebSocket)","channel metadata and access controls"],"categories":["text-generation-language","real-time-communication"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-webui--open-webui__cap_8","uri":"capability://safety.moderation.multi.method.authentication.with.oauth.ldap.and.scim.provisioning","name":"multi-method authentication with oauth, ldap, and scim provisioning","description":"Open WebUI supports multiple authentication methods including OAuth (GitHub, Google, etc.), LDAP directory integration, and SCIM-based user provisioning. The system maintains a token and session management layer that handles authentication state, token refresh, and logout. LDAP integration enables organizations to authenticate users against existing directory services. SCIM provisioning allows automated user and group management from identity providers. Access control is enforced through role-based access control (RBAC) with configurable permissions per user and group.","intents":["Integrate Open WebUI with existing enterprise authentication systems","Enable single sign-on (SSO) via OAuth or LDAP","Automate user provisioning and deprovisioning via SCIM","Manage user permissions and roles at scale"],"best_for":["Enterprise organizations with existing identity infrastructure","Teams requiring SSO and centralized user management","Organizations needing automated user provisioning"],"limitations":["LDAP integration requires network connectivity to directory server; no offline authentication","SCIM provisioning is one-way (provider to Open WebUI); no reverse sync","Token refresh requires active session; no automatic background refresh","RBAC is basic; no attribute-based access control (ABAC)"],"requires":["OAuth provider (GitHub, Google, etc.) or LDAP server","SCIM identity provider (optional, for automated provisioning)","FastAPI backend with authentication middleware","Token storage (session store or JWT)"],"input_types":["OAuth authorization code or LDAP credentials","SCIM user/group provisioning requests"],"output_types":["authentication token (JWT or session ID)","user profile and permissions","SCIM provisioning acknowledgment"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-open-webui--open-webui__cap_9","uri":"capability://automation.workflow.workspace.and.knowledge.base.management.with.hierarchical.organization","name":"workspace and knowledge base management with hierarchical organization","description":"Open WebUI organizes content into workspaces that serve as isolated environments for teams or projects. Each workspace maintains its own set of chats, notes, knowledge bases, models, and tools. The system supports hierarchical folder structures for organizing chats and notes within a workspace. Knowledge bases are workspace-scoped, enabling teams to maintain separate document collections. Users can switch between workspaces and have role-based access to each workspace. Workspace settings allow configuration of default models, tools, and integrations.","intents":["Organize AI interactions by project or team","Maintain separate knowledge bases for different domains","Configure workspace-specific models and tools","Control access to workspace content via RBAC"],"best_for":["Organizations with multiple teams or projects","Teams managing domain-specific knowledge bases","Enterprises requiring workspace-level access control"],"limitations":["Workspace switching requires page reload; no seamless context switching","Knowledge bases are workspace-scoped; no cross-workspace search","Workspace settings are not versioned; no rollback capability","No workspace templates; each workspace must be configured manually"],"requires":["FastAPI backend with workspace management endpoints","User authentication and authorization system","Database for workspace metadata and access control"],"input_types":["workspace name and configuration","user role and permissions","knowledge base and tool assignments"],"output_types":["workspace metadata (JSON)","workspace access control list","workspace-scoped content (chats, notes, knowledge bases)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","FastAPI backend running","At least one configured LLM provider (Ollama, OpenAI API key, etc.)","Network connectivity to provider endpoints","Vector database instance (Chroma, Weaviate, Milvus, or compatible)","Embedding model API access (OpenAI, local Ollama embedding model, etc.)","File upload storage (local filesystem or S3-compatible)","FastAPI backend with prompt/tool management endpoints","Database for storing prompt and tool definitions","User authentication for access control"],"failure_modes":["Model discovery latency depends on provider API response times; no caching layer for model lists","Custom provider adapters require manual implementation for non-standard APIs","No automatic model capability inference — requires manual metadata configuration per provider","Embedding quality depends on chosen embedding model; no automatic model selection","Vector database must be separately deployed (Chroma, Weaviate, etc.) — no built-in persistence","Chunk size and overlap are configurable but not automatically optimized for document type","OCR quality for scanned PDFs depends on image quality; no built-in document preprocessing","Variable substitution is basic; no conditional logic or loops","Tool implementation requires Python code; no visual tool builder","Sharing is manual; no automatic distribution or updates","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.480273648000786,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:57:01.479Z","last_commit":"2026-05-03T11:12:12Z"},"community":{"stars":135314,"forks":19248,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=open-webui--open-webui","compare_url":"https://unfragile.ai/compare?artifact=open-webui--open-webui"}},"signature":"myjpOJ6Qla5CgeSbFcezkQFqJqJkzWxfkBZQONKJ++Sq/G/fa2Lb3xq1BgII4aTG9LVynmjjQvJFuUNfa7E1Dg==","signedAt":"2026-06-21T11:43:50.522Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/open-webui--open-webui","artifact":"https://unfragile.ai/open-webui--open-webui","verify":"https://unfragile.ai/api/v1/verify?slug=open-webui--open-webui","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"}}