{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"dify","slug":"dify","name":"Dify","type":"framework","url":"https://github.com/langgenius/dify","page_url":"https://unfragile.ai/dify","categories":["app-builders","rag-knowledge","code-editors","automation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"dify__cap_0","uri":"capability://automation.workflow.visual.workflow.orchestration.with.node.based.dag.execution","name":"visual workflow orchestration with node-based dag execution","description":"Dify implements a node factory pattern with dependency injection to execute directed acyclic graphs (DAGs) where each node type (LLM, HTTP, code, knowledge retrieval, human input) is instantiated and executed in dependency order. The workflow engine manages state transitions, pause-resume mechanics via human input nodes, and error handling across multi-step pipelines. Nodes are defined declaratively in JSON/YAML and compiled into executable graphs at runtime.","intents":["I want to build multi-step AI workflows without writing orchestration code","I need to pause workflows for human approval and resume from that point","I want to visually compose LLM chains with conditional branching and loops","I need to test workflow logic with mock data before deploying to production"],"best_for":["teams building complex agent systems with multiple decision points","non-technical product managers designing AI workflows","developers migrating from hardcoded LLM chains to declarative pipelines"],"limitations":["No built-in distributed execution — all nodes execute sequentially or in-process on a single worker","Pause-resume state requires external persistence; no automatic checkpointing between nodes","Workflow testing uses mock system that may not capture all edge cases in production LLM behavior","DAG cycles are not supported; only acyclic workflows are valid"],"requires":["Python 3.9+","PostgreSQL or MySQL for workflow state storage","Redis for background task queuing (Celery)","At least one LLM provider API key (OpenAI, Anthropic, etc.)"],"input_types":["JSON workflow definition","structured parameters","file uploads (for knowledge nodes)","user input (via human input nodes)"],"output_types":["structured JSON response","streaming text output","execution trace with node-level logs","conversation history"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify__cap_1","uri":"capability://memory.knowledge.multi.strategy.rag.pipeline.with.vector.database.abstraction","name":"multi-strategy rag pipeline with vector database abstraction","description":"Dify implements a pluggable RAG system with a vector database factory pattern that abstracts over multiple backends (Weaviate, Pinecone, Milvus, Qdrant, etc.). The retrieval pipeline supports multiple strategies: dense vector similarity, BM25 hybrid search, metadata filtering, and summary index generation. Documents are chunked, embedded, and indexed asynchronously via Celery background tasks. The knowledge retrieval node in workflows can be configured with custom retrieval parameters and re-ranking strategies.","intents":["I want to build RAG applications without vendor lock-in to a specific vector database","I need to retrieve documents using both semantic similarity and keyword matching","I want to filter retrieved documents by metadata (date, source, category) before ranking","I need to index large document collections asynchronously without blocking the API"],"best_for":["enterprises with existing vector database infrastructure (Weaviate, Milvus)","teams building document-grounded chatbots with hybrid search requirements","developers needing fine-grained control over chunking, embedding, and retrieval strategies"],"limitations":["Vector database abstraction adds ~50-100ms latency per retrieval due to factory pattern indirection","No built-in re-ranking; custom re-rankers require external model integration","Metadata filtering is database-specific; complex filters may not translate across backends","Summary index generation requires additional LLM calls, increasing latency and cost","Chunking strategy is fixed (recursive character splitting); no adaptive chunking based on document structure"],"requires":["Python 3.9+","At least one vector database (Weaviate, Pinecone, Milvus, Qdrant, etc.)","Embedding model API (OpenAI, Hugging Face, local)","PostgreSQL for document metadata storage","Celery + Redis for async indexing"],"input_types":["PDF, DOCX, TXT, Markdown files","web URLs (for web scraping)","structured data (CSV, JSON)","external knowledge bases (via API)"],"output_types":["ranked document chunks with scores","metadata-filtered results","summary index entries","retrieval trace with strategy details"],"categories":["memory-knowledge","search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify__cap_10","uri":"capability://automation.workflow.observability.and.tracing.with.opentelemetry.and.sentry.integration","name":"observability and tracing with opentelemetry and sentry integration","description":"Dify instruments the entire application stack with OpenTelemetry (OTEL) for distributed tracing, metrics collection, and logging. Traces capture request flow through the API, workflow execution, LLM calls, and database queries. The system integrates with Sentry for error tracking and performance monitoring. Metrics include request latency, token usage, error rates, and queue depth. Logs are structured (JSON) and include trace context for correlation. The observability system is configurable to send data to external collectors (Jaeger, Datadog, etc.).","intents":["I want to understand the performance of my LLM applications with detailed tracing","I need to track errors and exceptions across the entire application stack","I want to monitor token usage and costs per workflow and user","I need to correlate logs, traces, and metrics for debugging production issues"],"best_for":["teams running Dify in production with SLO requirements","enterprises needing detailed observability for compliance","developers debugging performance issues in complex workflows"],"limitations":["OTEL instrumentation adds ~5-10% overhead to request latency","Trace sampling is required for high-traffic applications; sampled traces may miss rare issues","Sentry integration is optional; error tracking requires separate Sentry account","Metrics cardinality can explode with high-dimensional labels (e.g., per-user metrics); requires careful label design","Log volume can be high; requires external log aggregation (ELK, Datadog) for retention"],"requires":["OpenTelemetry collector (optional, for local collection)","Sentry account (optional, for error tracking)","External metrics backend (Prometheus, Datadog, etc., optional)","Log aggregation system (ELK, Datadog, etc., optional)"],"input_types":["OTEL configuration (endpoints, sampling rate)","Sentry DSN"],"output_types":["distributed traces","metrics (latency, errors, token usage)","structured logs","error reports"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify__cap_11","uri":"capability://memory.knowledge.knowledge.base.external.integration.with.api.based.retrieval","name":"knowledge base external integration with api-based retrieval","description":"Dify supports integrating external knowledge bases via API calls, enabling workflows to retrieve information from systems outside Dify (e.g., Confluence, Notion, custom databases). The knowledge retrieval node can be configured to call external APIs instead of querying local vector databases. The system handles API authentication, response parsing, and result ranking. External knowledge bases are treated as first-class citizens alongside local datasets, allowing seamless switching between local and external sources.","intents":["I want to use my existing knowledge base (Confluence, Notion) in Dify workflows without duplicating data","I need to query custom databases or APIs for information retrieval","I want to combine local RAG with external knowledge sources","I need to avoid re-indexing large external knowledge bases"],"best_for":["enterprises with existing knowledge management systems (Confluence, Notion)","teams wanting to avoid data duplication","applications needing real-time access to external data sources"],"limitations":["External API latency adds 200-1000ms per retrieval; slower than local vector database queries","API authentication must be configured manually; no automatic credential management","Response parsing is custom per API; no universal adapter for all knowledge bases","No caching of external API responses; repeated queries hit the API each time","Rate limiting is API-specific; no built-in protection against exceeding API quotas"],"requires":["External knowledge base API with authentication","API credentials (API key, OAuth token, etc.)","Network connectivity to external API"],"input_types":["query text","external knowledge base configuration","API credentials"],"output_types":["ranked results from external API","API response metadata","retrieval latency metrics"],"categories":["memory-knowledge","search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify__cap_12","uri":"capability://data.processing.analysis.annotation.and.feedback.system.for.model.improvement.and.dataset.curation","name":"annotation and feedback system for model improvement and dataset curation","description":"Dify provides an annotation interface where users can review workflow outputs, provide feedback (correct/incorrect, ratings, comments), and curate datasets. Annotations are stored with context (input, output, feedback, annotator) and can be exported for model fine-tuning or evaluation. The system supports batch annotation workflows and annotation templates for consistent feedback. Annotations are tracked with versioning, allowing rollback if needed. The annotation data feeds into model evaluation pipelines.","intents":["I want to collect human feedback on LLM outputs for model evaluation","I need to curate high-quality datasets from production conversations","I want to track annotation history and allow annotators to revise their feedback","I need to export annotated data for fine-tuning or evaluation"],"best_for":["teams building high-quality LLM applications with human evaluation","data annotation teams curating datasets","researchers evaluating LLM performance"],"limitations":["Annotation interface is basic; no advanced features like inter-annotator agreement metrics","No built-in quality control (e.g., gold standard examples, attention checks)","Annotation templates are limited to simple schemas; no complex conditional logic","Batch annotation requires manual workflow setup; no automated batching","No integration with external annotation platforms (Labelbox, Scale AI)"],"requires":["PostgreSQL for annotation storage","Web browser for annotation interface","User accounts for annotators"],"input_types":["workflow output to annotate","annotation template","annotator feedback"],"output_types":["annotated dataset","annotation statistics","exported data for fine-tuning"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify__cap_13","uri":"capability://automation.workflow.application.versioning.and.deployment.with.environment.management","name":"application versioning and deployment with environment management","description":"Dify supports versioning of applications (workflows, prompts, datasets) with automatic version tracking on each save. Applications can be deployed to different environments (development, staging, production) with environment-specific configurations (API keys, model selections, parameters). The system tracks deployment history and allows rollback to previous versions. Applications can be published as public APIs or embedded in websites. Version comparison shows changes between versions, enabling easy review of modifications.","intents":["I want to version my workflows and prompts to track changes over time","I need to deploy applications to different environments with different configurations","I want to rollback to a previous version if a deployment causes issues","I need to publish my application as a public API or embed it in my website"],"best_for":["teams managing multiple application versions","enterprises with strict deployment processes","developers needing to test changes before production deployment"],"limitations":["Versioning is automatic but not semantic; no version numbering scheme","Environment management is manual; no automated promotion between environments","Rollback is manual; no automatic rollback on deployment failure","Version comparison is UI-based; no programmatic access to version diffs","No branching support; versions are linear"],"requires":["PostgreSQL for version storage","Web browser for version management UI"],"input_types":["application configuration","environment-specific parameters"],"output_types":["version history","version comparison","deployment status","public API URL"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.model.invocation.with.quota.management.and.credit.pools","name":"multi-provider llm model invocation with quota management and credit pools","description":"Dify implements a provider and model architecture that abstracts over 20+ LLM providers (OpenAI, Anthropic, Ollama, Azure, etc.) through a unified invocation pipeline. The system manages API keys per provider, enforces quota limits via credit pools, tracks token usage per model, and supports streaming responses. Model invocation is instrumented with OpenTelemetry for observability. The architecture uses a provider registry pattern to dynamically load provider implementations at runtime.","intents":["I want to use multiple LLM providers in the same application without managing separate SDKs","I need to enforce usage quotas and billing limits across different models","I want to track token consumption per model and user for cost analysis","I need to switch between providers (e.g., OpenAI to Anthropic) without changing application code"],"best_for":["SaaS platforms offering multi-tenant LLM access with per-user quotas","enterprises evaluating multiple LLM providers and needing cost tracking","developers building cost-optimized applications that route requests to cheapest available model"],"limitations":["Quota enforcement is soft-limit only; no hard rate limiting at the API gateway level","Credit pool accounting is eventual-consistent; race conditions possible in high-concurrency scenarios","Provider implementations are tightly coupled to Dify's model abstraction; custom providers require forking","Token counting is approximate for some providers (e.g., Claude) due to lack of official tokenizer","No automatic provider failover; if primary provider is down, requests fail immediately"],"requires":["Python 3.9+","API keys for at least one LLM provider","PostgreSQL for quota and usage tracking","OpenTelemetry collector (optional, for observability)"],"input_types":["prompt text","structured messages (system, user, assistant roles)","function/tool definitions","streaming request parameters"],"output_types":["text completion","streaming token chunks","token usage metadata (input, output, total)","provider-specific response metadata"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify__cap_3","uri":"capability://tool.use.integration.mcp.protocol.integration.with.plugin.daemon.execution.environment","name":"mcp protocol integration with plugin daemon execution environment","description":"Dify integrates the Model Context Protocol (MCP) to enable external tools and services to be plugged into workflows via a standardized interface. The system runs a plugin daemon that manages MCP server lifecycle, handles tool discovery, and executes tool calls with sandboxed environments. Tools can be built-in (HTTP requests, code execution), API-based (external services), or MCP-compliant servers. The tool provider architecture uses a factory pattern to instantiate different tool types and manage their execution context.","intents":["I want to integrate external tools (APIs, databases, services) into my AI workflows without custom code","I need to expose my own services as MCP-compliant tools that other Dify instances can use","I want to sandbox tool execution to prevent malicious code from accessing sensitive resources","I need to discover and manage tools dynamically without restarting the application"],"best_for":["teams building extensible AI agents that need to integrate with diverse external services","enterprises with internal tool ecosystems wanting to expose them via MCP","developers building tool marketplaces or plugin systems on top of Dify"],"limitations":["Plugin daemon adds ~200-500ms overhead per tool invocation due to inter-process communication","Sandboxing is process-level only; no kernel-level isolation (use containers for stronger isolation)","MCP tool discovery is synchronous; large tool catalogs may cause UI lag","Error handling in plugin daemon is basic; tool crashes can leak resources if not properly cleaned up","No built-in tool versioning; updating a tool may break existing workflows"],"requires":["Python 3.9+","MCP server implementation (for custom tools)","Docker or container runtime (for sandboxed execution, optional)","Tool provider API keys (for API-based tools)"],"input_types":["tool schema (JSON)","tool parameters (structured data)","MCP server configuration","code (for code execution nodes)"],"output_types":["tool execution result","error messages","tool metadata and schema","execution trace"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify__cap_4","uri":"capability://data.processing.analysis.dataset.management.with.document.chunking.and.embedding.pipeline","name":"dataset management with document chunking and embedding pipeline","description":"Dify provides a dataset service that manages document lifecycle: upload, parsing, chunking, embedding, and indexing. Documents are parsed based on file type (PDF, DOCX, TXT, etc.), split using recursive character splitting with configurable chunk size and overlap, embedded using configurable embedding models, and indexed asynchronously via Celery. The system tracks document metadata (source, upload date, processing status) and supports incremental updates. Datasets can be used directly in knowledge retrieval nodes or exported as external knowledge bases.","intents":["I want to upload documents and automatically prepare them for RAG without manual preprocessing","I need to control chunking strategy (size, overlap) to optimize retrieval quality","I want to track which documents are indexed and re-index them if embedding model changes","I need to export my indexed documents as an external knowledge base for use in other systems"],"best_for":["teams building document-grounded chatbots with large document collections","non-technical users who need to upload documents without understanding embeddings","enterprises managing multiple datasets with different chunking and embedding strategies"],"limitations":["Chunking strategy is fixed (recursive character splitting); no semantic or structure-aware chunking","Embedding is synchronous per document; large batches may timeout or consume excessive memory","No deduplication; uploading the same document twice creates duplicate embeddings","Metadata extraction is minimal; no automatic extraction of title, author, date from documents","Incremental updates require manual re-indexing; no automatic change detection"],"requires":["Python 3.9+","PostgreSQL for document metadata","Vector database for embeddings","Embedding model API (OpenAI, Hugging Face, local)","Celery + Redis for async indexing","File storage backend (local filesystem, S3, etc.)"],"input_types":["PDF files","DOCX files","TXT files","Markdown files","CSV files","web URLs"],"output_types":["indexed document chunks","embedding vectors","document metadata","processing status and logs"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify__cap_5","uri":"capability://text.generation.language.prompt.engineering.ide.with.variable.interpolation.and.testing","name":"prompt engineering ide with variable interpolation and testing","description":"Dify provides a visual prompt editor that supports variable interpolation (using {{variable}} syntax), prompt templates with system/user/assistant roles, and built-in testing against multiple LLM providers. The IDE includes prompt versioning, A/B testing capabilities, and performance metrics (latency, token usage, cost). Prompts can be parameterized with input variables that are bound at runtime from workflow context or API parameters. The system tracks prompt history and allows rollback to previous versions.","intents":["I want to iterate on prompts visually without writing code","I need to test prompts against multiple LLM providers to compare quality and cost","I want to version prompts and track changes over time","I need to parameterize prompts with variables that are filled in at runtime"],"best_for":["prompt engineers and non-technical users optimizing LLM outputs","teams comparing prompt quality across providers (OpenAI, Anthropic, etc.)","developers building prompt-driven applications with frequent iterations"],"limitations":["Prompt testing is single-turn only; no multi-turn conversation testing in IDE","A/B testing requires manual setup; no automated statistical significance testing","Variable interpolation is simple string substitution; no conditional logic or loops in prompts","Prompt versioning is manual; no automatic versioning on each save","No prompt optimization suggestions; users must manually iterate"],"requires":["Web browser (Chrome, Firefox, Safari, Edge)","At least one LLM provider API key","Dify backend running (self-hosted or cloud)"],"input_types":["prompt text","variable definitions","test input data","LLM provider selection"],"output_types":["prompt output","token usage metrics","latency measurements","cost estimates","prompt version history"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify__cap_6","uri":"capability://safety.moderation.multi.tenant.workspace.isolation.with.role.based.access.control","name":"multi-tenant workspace isolation with role-based access control","description":"Dify implements a multi-tenant architecture where each workspace is a logical isolation boundary with separate datasets, workflows, API keys, and member permissions. The system uses role-based access control (RBAC) with predefined roles (admin, editor, member, guest) that control access to resources. Tenant isolation is enforced at the database query level using tenant context middleware. Authentication supports multiple methods: email/password, OAuth, SAML, and API keys. Member management includes invitations, role assignment, and audit logging.","intents":["I want to build a multi-tenant SaaS platform where each customer has isolated data and workflows","I need to control who can edit workflows, access datasets, and manage API keys within a workspace","I want to audit all changes made by team members for compliance","I need to support multiple authentication methods (email, OAuth, SAML) for enterprise customers"],"best_for":["SaaS platforms offering Dify as a service to multiple customers","enterprises with multiple teams needing workspace isolation","teams requiring audit trails and compliance logging"],"limitations":["Tenant isolation is logical only; no separate databases per tenant (shared database with tenant filtering)","RBAC is coarse-grained; no fine-grained permissions (e.g., read-only access to specific datasets)","Audit logging is basic; no detailed change tracking for individual fields","OAuth and SAML configuration requires manual setup; no self-service integration","Cross-workspace collaboration is not supported; users must switch workspaces manually"],"requires":["PostgreSQL with tenant context middleware","OAuth provider (for OAuth authentication)","SAML identity provider (for SAML authentication)","Email service (for invitations and notifications)"],"input_types":["user credentials","workspace configuration","member invitations","role assignments"],"output_types":["authentication tokens","workspace metadata","member list with roles","audit logs"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify__cap_7","uri":"capability://text.generation.language.streaming.chat.api.with.conversation.history.and.feedback.collection","name":"streaming chat api with conversation history and feedback collection","description":"Dify exposes a streaming chat API that accepts user messages, maintains conversation history, and returns LLM responses as server-sent events (SSE) for real-time streaming. The API supports multi-turn conversations with automatic context management (previous messages are included in the prompt). Users can provide feedback on responses (thumbs up/down, ratings, comments) which is stored for model improvement. The system tracks conversation metadata (start time, duration, model used, tokens consumed) and supports conversation export.","intents":["I want to build a chat interface that streams LLM responses in real-time","I need to maintain conversation history and automatically include it in subsequent requests","I want to collect user feedback on responses for model evaluation","I need to track conversation metrics (duration, tokens, cost) for analytics"],"best_for":["developers building chat applications with streaming UI","teams collecting feedback data for model fine-tuning","analytics teams tracking conversation metrics for product insights"],"limitations":["Conversation history is stored in-memory during session; no automatic persistence across server restarts","Streaming is SSE-based; no WebSocket support for lower latency","Feedback collection is basic (thumbs up/down); no structured feedback schema","Context management is naive (all previous messages included); no summarization for long conversations","No built-in rate limiting per user; requires external API gateway for protection"],"requires":["HTTP client supporting SSE (all modern browsers)","PostgreSQL for conversation storage","At least one LLM provider API key"],"input_types":["user message (text)","conversation ID","optional system prompt override"],"output_types":["streaming text chunks (SSE)","conversation metadata","feedback submission response"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify__cap_8","uri":"capability://automation.workflow.workflow.execution.api.with.async.job.processing.and.result.polling","name":"workflow execution api with async job processing and result polling","description":"Dify exposes a workflow execution API that accepts workflow input parameters, queues the workflow for execution via Celery, and returns a job ID for polling results. Workflows execute asynchronously in background workers, enabling long-running operations without blocking the API. The system supports streaming results via SSE for real-time progress updates. Execution traces are captured with node-level logs, token usage, and latency metrics. Results can be polled via the job ID or streamed via WebSocket.","intents":["I want to trigger long-running workflows from my application without blocking","I need to poll for workflow results or stream them in real-time","I want to see detailed execution traces with node-level logs and metrics","I need to integrate workflow execution into my existing application via REST API"],"best_for":["developers integrating Dify workflows into existing applications","teams running long-running workflows (document processing, batch analysis)","applications needing real-time progress updates during workflow execution"],"limitations":["Async execution adds latency (job queueing, worker pickup); not suitable for sub-100ms latency requirements","Polling requires client-side retry logic; no built-in exponential backoff","Streaming via SSE requires persistent connection; no automatic reconnection on network failure","Execution traces are verbose; filtering/searching traces requires external tools","No built-in result caching; identical workflow inputs may be re-executed"],"requires":["HTTP client (any language)","Celery + Redis for async job processing","PostgreSQL for job state storage","At least one LLM provider API key"],"input_types":["workflow ID","input parameters (JSON)","optional user ID for multi-tenant scenarios"],"output_types":["job ID","workflow result (JSON)","execution trace with node logs","token usage and cost metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify__cap_9","uri":"capability://data.processing.analysis.file.upload.and.management.with.virus.scanning.and.format.validation","name":"file upload and management with virus scanning and format validation","description":"Dify provides file upload endpoints that accept documents, images, and other files, validate file types and sizes, scan for viruses using ClamAV, and store files in configurable backends (local filesystem, S3, etc.). Uploaded files are tracked with metadata (filename, size, upload time, user) and can be referenced in workflows and datasets. The system supports resumable uploads for large files and automatic cleanup of temporary files. Files are served with access control to prevent unauthorized downloads.","intents":["I want to allow users to upload documents for RAG without worrying about malware","I need to store uploaded files in S3 or other cloud storage instead of local disk","I want to track which files were uploaded by which users for audit purposes","I need to support large file uploads with resumable upload capability"],"best_for":["applications accepting user-uploaded documents","teams with security requirements (virus scanning, access control)","platforms supporting large file uploads (>100MB)"],"limitations":["Virus scanning adds latency (~1-5 seconds per file); blocks upload until scan completes","ClamAV requires separate deployment and maintenance","File format validation is basic (MIME type checking); no deep content inspection","Resumable uploads require client-side implementation; no built-in browser support","Access control is binary (can access or cannot); no fine-grained file permissions"],"requires":["ClamAV daemon (for virus scanning)","S3 or local filesystem (for storage)","PostgreSQL for file metadata","HTTP client supporting multipart/form-data"],"input_types":["file (binary)","file metadata (filename, size)"],"output_types":["file ID","file metadata","virus scan result","storage location"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dify__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 applications using large language models, featuring a prompt IDE, RAG pipeline, and workflow orchestration for seamless integration and management.","intents":["best LLM app development platform","open-source framework for RAG applications","LLM development tools for workflow orchestration","best practices for building LLM applications","how to manage knowledge bases in LLM apps"],"best_for":["developers looking for open-source solutions","teams needing RAG capabilities"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["automation-workflow","app-builders","rag-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":60,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","PostgreSQL or MySQL for workflow state storage","Redis for background task queuing (Celery)","At least one LLM provider API key (OpenAI, Anthropic, etc.)","At least one vector database (Weaviate, Pinecone, Milvus, Qdrant, etc.)","Embedding model API (OpenAI, Hugging Face, local)","PostgreSQL for document metadata storage","Celery + Redis for async indexing","OpenTelemetry collector (optional, for local collection)","Sentry account (optional, for error tracking)"],"failure_modes":["No built-in distributed execution — all nodes execute sequentially or in-process on a single worker","Pause-resume state requires external persistence; no automatic checkpointing between nodes","Workflow testing uses mock system that may not capture all edge cases in production LLM behavior","DAG cycles are not supported; only acyclic workflows are valid","Vector database abstraction adds ~50-100ms latency per retrieval due to factory pattern indirection","No built-in re-ranking; custom re-rankers require external model integration","Metadata filtering is database-specific; complex filters may not translate across backends","Summary index generation requires additional LLM calls, increasing latency and cost","Chunking strategy is fixed (recursive character splitting); no adaptive chunking based on document structure","OTEL instrumentation adds ~5-10% overhead to request latency","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":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","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","compare_url":"https://unfragile.ai/compare?artifact=dify"}},"signature":"wlBrDnOh9BHqErtr/yL3xHIEqNf35O+4Kj/ULMdLV8yv6w40AQIzdiFyVkowkW0Zk7UwaiWM18CFikNxzviWBg==","signedAt":"2026-06-21T11:31:41.637Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dify","artifact":"https://unfragile.ai/dify","verify":"https://unfragile.ai/api/v1/verify?slug=dify","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"}}