{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"dify-template-gallery","slug":"dify-template-gallery","name":"Dify Template Gallery","type":"repo","url":"https://github.com/langgenius/dify","page_url":"https://unfragile.ai/dify-template-gallery","categories":["app-builders","rag-knowledge","automation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"dify-template-gallery__cap_0","uri":"capability://planning.reasoning.visual.workflow.orchestration.with.node.based.dag.execution","name":"visual workflow orchestration with node-based dag execution","description":"Dify implements a drag-and-drop workflow builder that compiles visual node graphs into directed acyclic graphs (DAGs) executed via a Node Factory pattern with dependency injection. The workflow engine supports 8+ node types (LLM, HTTP, code execution, knowledge retrieval, human input, conditional branching) with a pause-resume mechanism for human-in-the-loop workflows. Node execution is serialized through a state machine that tracks context propagation between nodes, enabling complex multi-step orchestrations without code.","intents":["Build multi-step LLM pipelines without writing orchestration code","Create approval workflows that pause for human input and resume automatically","Chain API calls, database queries, and LLM calls in a single visual canvas","Test workflow logic with mock data before deployment"],"best_for":["Non-technical product managers building chatbot workflows","Teams prototyping RAG pipelines without backend engineering","Enterprises requiring audit trails and approval gates in AI workflows"],"limitations":["DAG execution is sequential by default — no native parallelization of independent branches","Context propagation between nodes requires explicit variable mapping; implicit data flow not supported","Workflow testing uses mock system that may not catch runtime provider failures","No built-in retry logic or circuit breaker patterns for failed nodes"],"requires":["Dify backend running (Python 3.9+, PostgreSQL for state storage)","At least one LLM provider configured (OpenAI, Anthropic, Ollama, etc.)","Web frontend access to visual builder (Node.js 18+ for dev builds)"],"input_types":["workflow JSON definition (node graph schema)","user input variables (text, files, structured data)","context from previous node outputs"],"output_types":["workflow execution trace (node-by-node logs)","final output from terminal node","conversation history with intermediate steps"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify-template-gallery__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.model.invocation.with.quota.management","name":"multi-provider llm model invocation with quota management","description":"Dify abstracts LLM provider differences through a Provider and Model architecture that normalizes API calls across OpenAI, Anthropic, Ollama, Azure, and 20+ other providers. The Model Invocation Pipeline applies quota management via credit pools, rate limiting, and cost tracking per tenant/workspace. Provider configurations are stored in a centralized registry with environment-based credential injection, enabling multi-tenant isolation where each workspace can use different provider credentials.","intents":["Switch between LLM providers (OpenAI to Anthropic) without changing workflow definitions","Track token usage and costs per model invocation across multiple providers","Enforce rate limits and quota caps per workspace to prevent runaway costs","Use local Ollama models alongside cloud providers in the same workflow"],"best_for":["Teams managing costs across multiple LLM providers","SaaS platforms offering white-label AI features with per-customer billing","Enterprises with provider lock-in concerns requiring multi-provider flexibility"],"limitations":["Provider abstraction adds ~50-100ms latency per invocation due to normalization layer","Not all provider-specific features (e.g., vision, function calling) are exposed uniformly","Quota enforcement is soft (advisory) — no hard blocking of requests at quota boundary","Credit pool system requires external billing integration for production use"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","PostgreSQL database for quota tracking and credit pool state","Celery worker for async quota updates (optional but recommended)"],"input_types":["provider configuration (API key, model name, temperature, max_tokens)","prompt text or structured messages","quota limits (tokens per day, cost cap, rate limit)"],"output_types":["LLM completion text","token usage metrics (input_tokens, output_tokens, cost)","quota status (remaining credits, rate limit headers)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify-template-gallery__cap_10","uri":"capability://automation.workflow.template.gallery.with.pre.built.workflow.examples","name":"template gallery with pre-built workflow examples","description":"Dify provides a Template Gallery with pre-built workflow templates for common use cases (customer support chatbot, content summarization, code review agent, email classifier). Templates are stored as JSON workflow definitions that users can import, customize, and deploy with minimal configuration. Templates include example prompts, tool configurations, and dataset references, enabling rapid prototyping without building workflows from scratch.","intents":["Start with a pre-built customer support chatbot template instead of building from scratch","Learn best practices by examining template workflows and prompt engineering","Customize a template for a specific use case (e.g., change industry-specific terminology)","Share custom workflows with team members or the community"],"best_for":["Non-technical users getting started with Dify","Teams prototyping AI applications quickly","Communities sharing workflows and best practices"],"limitations":["Templates are static — no dynamic customization based on user inputs","Template versioning is manual — no automatic updates when templates change","No template marketplace or community contribution system","Templates may require provider-specific configuration (API keys, model names)"],"requires":["Access to Dify instance with template gallery enabled","LLM provider credentials for template workflows"],"input_types":["template selection (template ID or name)","customization parameters (prompts, tools, datasets)"],"output_types":["imported workflow definition","customized workflow ready for deployment"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify-template-gallery__cap_11","uri":"capability://text.generation.language.chat.and.completion.api.with.streaming.response.support","name":"chat and completion api with streaming response support","description":"Dify exposes Chat and Completion APIs that accept user messages and return LLM responses with streaming support via Server-Sent Events (SSE). The API Architecture normalizes requests across different application types (chatbot, agent, workflow) with a unified request/response format. Streaming responses enable real-time display of LLM output as tokens arrive, improving perceived latency. The API supports conversation context injection, enabling stateless clients to maintain multi-turn conversations.","intents":["Build a web chat interface that streams LLM responses in real-time","Integrate Dify chatbots into external applications via REST API","Send a batch of messages and get completions without managing conversation state","Retrieve conversation history and resume conversations across sessions"],"best_for":["Frontend developers integrating Dify chatbots into web/mobile apps","Teams building multi-channel chatbots (web, Slack, Teams)","Developers requiring low-latency chat APIs"],"limitations":["Streaming is SSE-based — no WebSocket support for bidirectional communication","API authentication is token-based — no OAuth 2.0 support","Rate limiting is global — no per-user or per-API-key limits","Conversation context is passed in request — no server-side session management"],"requires":["Dify API key (generated in workspace settings)","HTTP client supporting SSE (for streaming responses)","Conversation ID (for multi-turn conversations)"],"input_types":["user message (text)","conversation ID (for context)","optional files (for RAG augmentation)"],"output_types":["LLM response (streamed via SSE or returned in full)","conversation metadata (ID, created_at, updated_at)","usage metrics (tokens, cost)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify-template-gallery__cap_12","uri":"capability://planning.reasoning.web.frontend.with.drag.and.drop.workflow.builder.ui","name":"web frontend with drag-and-drop workflow builder ui","description":"Dify provides a React-based web frontend with a visual workflow builder featuring drag-and-drop node composition, real-time preview, and inline prompt editing. The Frontend Build System uses Vite for fast development builds and supports dark mode, responsive design, and accessibility features. Workflow Node UI Components render different node types (LLM, HTTP, code, knowledge retrieval) with context-aware configuration panels. The Chat Interface supports message rendering, file uploads, and feedback collection.","intents":["Build workflows visually without writing code or YAML","Preview workflow outputs in real-time as you edit prompts","Test workflows with mock data before deployment","Collect user feedback on chatbot responses via thumbs up/down"],"best_for":["Non-technical product managers building chatbots","Teams prototyping workflows quickly","Enterprises requiring visual workflow design without code"],"limitations":["Drag-and-drop builder has limited keyboard navigation — mouse-heavy interaction","Real-time preview is limited to single-node testing — no full workflow preview","Responsive design is basic — limited mobile support for workflow editing","Accessibility features are incomplete — WCAG compliance not guaranteed"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","Node.js 18+ for development builds","Dify backend running (API endpoint)"],"input_types":["workflow definition (imported or created)","user input via chat interface"],"output_types":["rendered workflow visualization","chat response with formatting","feedback submission"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify-template-gallery__cap_13","uri":"capability://automation.workflow.configuration.management.with.environment.based.credential.injection","name":"configuration management with environment-based credential injection","description":"Dify implements a centralized Configuration Management system that reads settings from environment variables, YAML files, and database records with a priority hierarchy. Provider credentials (API keys, OAuth tokens) are injected at runtime from environment variables, preventing hardcoding of secrets. The configuration system supports feature flags for A/B testing and gradual rollouts, enabling teams to enable/disable features without redeployment.","intents":["Configure LLM providers without modifying code","Manage different configurations for dev, staging, and production environments","Enable feature flags to test new functionality with a subset of users","Rotate API credentials without restarting the application"],"best_for":["Teams deploying Dify across multiple environments","Enterprises requiring secrets management and credential rotation","Developers running A/B tests with feature flags"],"limitations":["Configuration priority is fixed (env vars > YAML > database) — no custom priority","Feature flags are simple boolean toggles — no percentage-based rollouts","Credential rotation requires manual environment variable updates","No built-in secrets encryption — relies on external secrets manager"],"requires":["Environment variables or YAML configuration file","PostgreSQL for database-backed configuration","External secrets manager (optional, for credential rotation)"],"input_types":["environment variables (KEY=VALUE)","YAML configuration file","database configuration records"],"output_types":["resolved configuration values","feature flag status (enabled/disabled)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify-template-gallery__cap_2","uri":"capability://memory.knowledge.rag.pipeline.with.vector.database.integration.and.retrieval.strategies","name":"rag pipeline with vector database integration and retrieval strategies","description":"Dify implements a complete RAG system with a Document Indexing Pipeline that chunks, embeds, and stores documents in pluggable vector databases (Weaviate, Pinecone, Milvus, Qdrant). The Retrieval Strategies layer supports hybrid search (keyword + semantic), metadata filtering, and summary index generation for large document collections. Knowledge Retrieval Nodes in workflows query these indices with configurable similarity thresholds and result ranking, enabling semantic search without writing database queries.","intents":["Upload PDFs, Word docs, or web pages and make them searchable via semantic queries","Retrieve relevant context from a knowledge base to augment LLM prompts","Filter search results by metadata (date, author, document type) before ranking","Use hybrid search combining keyword matching and semantic similarity for better recall"],"best_for":["Teams building customer support chatbots with company documentation","Enterprises with large document repositories requiring semantic search","Developers prototyping RAG applications without managing vector DB infrastructure"],"limitations":["Document indexing is asynchronous via Celery — newly uploaded docs may not be searchable for 10-30 seconds","Chunking strategy is fixed (recursive text splitter) — no support for custom chunking logic","Metadata filtering requires pre-defined schema; dynamic metadata not supported","Summary index generation requires additional LLM calls, increasing latency and cost"],"requires":["Vector database instance (Weaviate, Pinecone, Milvus, or Qdrant)","Embedding model configured (OpenAI, Hugging Face, or local)","Celery worker for async document indexing","PostgreSQL for knowledge base metadata storage"],"input_types":["documents (PDF, DOCX, TXT, Markdown, web URLs)","semantic query text","metadata filters (key-value pairs)"],"output_types":["ranked search results with similarity scores","retrieved document chunks with metadata","context string formatted for LLM prompt injection"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify-template-gallery__cap_3","uri":"capability://tool.use.integration.tool.and.plugin.ecosystem.with.mcp.protocol.support","name":"tool and plugin ecosystem with mcp protocol support","description":"Dify provides a Tool Provider architecture supporting three integration patterns: built-in tools (web search, file operations), API-based tools (REST endpoints with schema-driven function calling), and MCP (Model Context Protocol) plugins executed in isolated daemon processes. Tools are registered in a central registry with JSON schema definitions, enabling LLM agents to discover and invoke them via function calling. The Plugin Daemon manages lifecycle, sandboxing, and communication with external tool providers.","intents":["Extend workflows with custom tools (database queries, Slack notifications, Stripe API calls)","Enable LLM agents to autonomously call external APIs based on task requirements","Integrate MCP-compatible tools without modifying Dify core","Define tool schemas that LLMs can understand and invoke with proper argument validation"],"best_for":["Teams building AI agents that need to interact with external systems","SaaS platforms offering extensibility via tool plugins","Developers integrating Dify with existing tool ecosystems (Zapier, Make, etc.)"],"limitations":["Tool execution is synchronous — long-running tools block workflow execution","MCP plugin isolation adds ~200-500ms overhead per tool invocation","Tool schema validation is schema-only; no runtime type checking","No built-in retry or timeout handling for failed tool calls"],"requires":["Tool provider API credentials (for API-based tools)","MCP server implementation (for MCP plugins)","JSON schema definition for tool inputs/outputs","Plugin Daemon running (optional, for MCP support)"],"input_types":["tool schema (JSON schema for inputs)","tool invocation request (tool name + arguments)","API credentials (API key, OAuth token, etc.)"],"output_types":["tool execution result (JSON or text)","error message with failure reason","execution metadata (latency, tokens used)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify-template-gallery__cap_4","uri":"capability://safety.moderation.multi.tenant.workspace.and.role.based.access.control","name":"multi-tenant workspace and role-based access control","description":"Dify implements a Tenant Model with workspace-level resource isolation, enabling multiple teams to operate independently within a single deployment. Role-Based Access Control (RBAC) defines permissions at workspace, dataset, and app levels with roles including Admin, Editor, and Viewer. Authentication supports multiple flows (email/password, OAuth, SAML) with session management via Flask-Login. Account Lifecycle Management handles user provisioning, deprovisioning, and workspace invitations.","intents":["Host multiple customer teams in a single Dify instance with data isolation","Grant team members different permission levels (admin can modify workflows, viewer can only chat)","Invite external collaborators to specific workspaces without exposing other workspaces","Audit who accessed what resources and when via workspace activity logs"],"best_for":["SaaS platforms offering white-label AI app builders","Enterprises deploying Dify on-premise for multiple departments","Teams requiring fine-grained access control and audit trails"],"limitations":["RBAC is workspace-level only — no row-level security within datasets","Permission checks are enforced at API layer — no built-in data masking","Audit logs are basic (who, what, when) — no detailed change tracking","OAuth/SAML setup requires manual configuration per identity provider"],"requires":["PostgreSQL for tenant and RBAC state storage","Email service for workspace invitations (SMTP or SendGrid)","Identity provider for OAuth/SAML (optional, for enterprise auth)"],"input_types":["user credentials (email, password)","workspace invitation token","role assignment (Admin, Editor, Viewer)"],"output_types":["session token (JWT or session cookie)","workspace list with accessible resources","audit log entries (user, action, timestamp, resource)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify-template-gallery__cap_5","uri":"capability://memory.knowledge.conversation.and.feedback.management.with.message.persistence","name":"conversation and feedback management with message persistence","description":"Dify persists all conversations in PostgreSQL with message-level granularity, enabling retrieval of full chat histories, user feedback (thumbs up/down, ratings), and conversation analytics. The Conversation API supports streaming responses, message editing, and conversation branching (creating alternate paths from a message). Feedback is stored with optional annotations, enabling training data collection for model fine-tuning or RLHF workflows.","intents":["Retrieve full chat history for a user across sessions","Collect user feedback on LLM responses for quality monitoring","Export conversations as training data for model improvement","Analyze conversation patterns to identify common user intents"],"best_for":["Teams building customer-facing chatbots requiring conversation history","Enterprises collecting feedback for model fine-tuning","Developers analyzing chatbot performance via conversation analytics"],"limitations":["Conversation storage is unbounded — no automatic archival or pruning","Feedback collection is manual (requires explicit user action) — no implicit feedback","Message editing creates orphaned references in conversation tree","No built-in PII redaction — sensitive data in conversations is stored as-is"],"requires":["PostgreSQL for conversation and feedback storage","API endpoint for conversation retrieval (GET /conversations/{id}/messages)"],"input_types":["user message (text, file upload)","feedback signal (rating, annotation)","conversation ID"],"output_types":["message list with timestamps and sender","feedback aggregates (average rating, annotation text)","conversation export (JSON or CSV)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify-template-gallery__cap_6","uri":"capability://automation.workflow.batch.processing.and.async.task.execution.with.celery","name":"batch processing and async task execution with celery","description":"Dify uses Celery for background task processing, enabling long-running operations (document indexing, batch inference, report generation) to execute asynchronously without blocking the API. Tasks are queued in Redis or RabbitMQ with configurable retry logic, dead-letter handling, and task status tracking. Batch processing APIs allow users to submit multiple requests (e.g., 1000 documents for embedding) and poll for completion status.","intents":["Upload 10,000 documents and index them without waiting for completion","Run batch inference on a dataset and download results when ready","Schedule periodic tasks (daily report generation, model retraining) without manual intervention","Monitor long-running task progress and handle failures gracefully"],"best_for":["Teams processing large document collections for RAG","Enterprises running batch inference jobs on datasets","Developers building async workflows with progress tracking"],"limitations":["Task status polling adds latency — no real-time progress updates via WebSocket","Celery configuration is complex (broker, backend, worker scaling) — requires DevOps expertise","Dead-letter queue handling is manual — failed tasks require manual intervention","No built-in task prioritization — all tasks are processed FIFO"],"requires":["Redis or RabbitMQ for task queue","Celery worker processes running (separate from API)","PostgreSQL for task status storage","Monitoring tool (Flower) for task visibility (optional)"],"input_types":["batch request (list of items to process)","task configuration (retry count, timeout, priority)"],"output_types":["task ID for status polling","task status (pending, processing, completed, failed)","batch results (JSON or CSV)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify-template-gallery__cap_7","uri":"capability://text.generation.language.prompt.management.and.versioning.with.template.variables","name":"prompt management and versioning with template variables","description":"Dify provides a Prompt Management system that stores prompt templates with variable placeholders (e.g., {{user_input}}, {{context}}), version history, and A/B testing support. Prompts are compiled at runtime by substituting variables from workflow context, enabling non-technical users to edit prompts without modifying workflows. Prompt versioning allows rollback to previous versions and comparison of prompt changes.","intents":["Edit LLM prompts without redeploying workflows","Test multiple prompt variations (A/B testing) to optimize response quality","Track prompt changes over time and rollback to previous versions","Reuse prompt templates across multiple workflows"],"best_for":["Product teams iterating on chatbot behavior without engineering involvement","Teams running A/B tests on prompt variations","Enterprises requiring audit trails of prompt changes"],"limitations":["Variable substitution is simple string replacement — no conditional logic or loops","A/B testing requires manual traffic splitting — no built-in traffic allocation","Prompt versioning is manual — no automatic version creation on save","No prompt optimization suggestions — users must manually iterate"],"requires":["PostgreSQL for prompt storage and version history","Workflow context with variable values at runtime"],"input_types":["prompt template text with {{variable}} placeholders","variable values from workflow context","A/B test configuration (variant weights)"],"output_types":["compiled prompt with variables substituted","prompt version history (list of previous versions)","A/B test results (variant performance metrics)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify-template-gallery__cap_8","uri":"capability://data.processing.analysis.file.upload.and.document.processing.with.format.detection","name":"file upload and document processing with format detection","description":"Dify implements a File Upload API that accepts documents in multiple formats (PDF, DOCX, TXT, Markdown, CSV, JSON) with automatic format detection and parsing. Uploaded files are stored in configurable backends (local filesystem, S3, Azure Blob Storage) and indexed asynchronously via Celery. The Document Management system tracks file metadata (size, upload time, processing status) and enables deletion with cascading cleanup of indexed embeddings.","intents":["Upload a PDF and automatically extract text for RAG indexing","Process CSV files and convert rows to embeddings for semantic search","Store uploaded files in S3 for scalability and durability","Track document processing status and handle parsing errors gracefully"],"best_for":["Teams building document-centric chatbots (customer support, legal review)","Enterprises processing large document collections","Developers requiring flexible file storage backends"],"limitations":["Format detection is based on file extension — no content-based MIME type validation","PDF parsing uses simple text extraction — no support for scanned PDFs or OCR","File size limits are configurable but not enforced at upload time","Deleted files are soft-deleted from metadata but not purged from storage backends"],"requires":["File storage backend (local filesystem, S3, Azure Blob Storage, etc.)","Celery worker for async document parsing","PostgreSQL for file metadata storage"],"input_types":["file upload (multipart/form-data)","file metadata (name, description, tags)"],"output_types":["file ID and metadata","processing status (pending, processing, completed, failed)","extracted text content"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify-template-gallery__cap_9","uri":"capability://safety.moderation.observability.and.tracing.with.opentelemetry.integration","name":"observability and tracing with opentelemetry integration","description":"Dify integrates OpenTelemetry for distributed tracing, capturing execution traces across workflow nodes, LLM calls, and tool invocations. The Trace Manager exports traces to backends like Jaeger, Datadog, or Sentry, enabling visibility into latency bottlenecks and error propagation. Traces include metadata (model name, token usage, cost) and support sampling for high-volume applications. Integration with Sentry provides error tracking and alerting.","intents":["Debug slow workflows by identifying which nodes are bottlenecks","Track LLM token usage and costs across all invocations","Monitor error rates and get alerted on failures","Export traces to external observability platforms for analysis"],"best_for":["Teams running production AI applications requiring visibility","Enterprises with existing observability stacks (Datadog, Splunk)","Developers debugging complex workflows with multiple LLM calls"],"limitations":["Trace sampling is global — no per-workflow or per-user sampling","Trace export adds ~50-100ms latency per request","Sentry integration is basic (error tracking only) — no custom metrics","Trace retention is backend-dependent — Dify doesn't enforce retention policies"],"requires":["OpenTelemetry collector or backend (Jaeger, Datadog, Sentry, etc.)","Environment variables for trace exporter configuration","Network connectivity to trace backend"],"input_types":["workflow execution context","LLM invocation details (model, tokens, cost)","error information"],"output_types":["distributed trace with span hierarchy","latency metrics per node","error logs with stack traces"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify-template-gallery__headline","uri":"capability://automation.workflow.open.source.llm.app.development.platform","name":"open-source llm app development platform","description":"Dify is an open-source platform designed for developing LLM applications, featuring a visual workflow builder and a gallery of pre-built templates for chatbots, agents, and RAG pipelines, making it easy to orchestrate complex workflows without extensive coding.","intents":["best open-source LLM app development platform","LLM app builder for chatbots","RAG framework for automation","visual workflow builder for AI applications","template gallery for LLM development"],"best_for":["developers looking for open-source solutions","teams needing rapid prototyping"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Dify backend running (Python 3.9+, PostgreSQL for state storage)","At least one LLM provider configured (OpenAI, Anthropic, Ollama, etc.)","Web frontend access to visual builder (Node.js 18+ for dev builds)","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","PostgreSQL database for quota tracking and credit pool state","Celery worker for async quota updates (optional but recommended)","Access to Dify instance with template gallery enabled","LLM provider credentials for template workflows","Dify API key (generated in workspace settings)","HTTP client supporting SSE (for streaming responses)"],"failure_modes":["DAG execution is sequential by default — no native parallelization of independent branches","Context propagation between nodes requires explicit variable mapping; implicit data flow not supported","Workflow testing uses mock system that may not catch runtime provider failures","No built-in retry logic or circuit breaker patterns for failed nodes","Provider abstraction adds ~50-100ms latency per invocation due to normalization layer","Not all provider-specific features (e.g., vision, function calling) are exposed uniformly","Quota enforcement is soft (advisory) — no hard blocking of requests at quota boundary","Credit pool system requires external billing integration for production use","Templates are static — no dynamic customization based on user inputs","Template versioning is manual — no automatic updates when templates change","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.6,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","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=dify-template-gallery","compare_url":"https://unfragile.ai/compare?artifact=dify-template-gallery"}},"signature":"EdXXh0QUiS2hvsGky7ZhrYl6wR5R94YWOh8HPqU3kAhd1LNsmYamu2hjP8efwNxkuqqphQSm6TRl2l8I75PKCA==","signedAt":"2026-06-23T13:09:24.800Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dify-template-gallery","artifact":"https://unfragile.ai/dify-template-gallery","verify":"https://unfragile.ai/api/v1/verify?slug=dify-template-gallery","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"}}