{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_durable-ai","slug":"durable-ai","name":"Durable AI","type":"product","url":"https://durable.ai","page_url":"https://unfragile.ai/durable-ai","categories":["app-builders"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_durable-ai__cap_0","uri":"capability://code.generation.editing.natural.language.to.application.generation","name":"natural-language-to-application-generation","description":"Converts natural language descriptions of business logic and workflows into executable application code and UI layouts without manual coding. Uses generative AI to interpret user intent from plain English prompts, then synthesizes corresponding visual components, data models, and backend logic rules. The system appears to employ a multi-stage pipeline: intent parsing → component selection → code generation → UI assembly, though the exact neurosymbolic reasoning mechanism is undocumented.","intents":["I want to describe my business process in plain English and have an app built automatically","I need to prototype a workflow-based application without learning a programming language","I want to generate boilerplate application structure from a text description of requirements"],"best_for":["non-technical entrepreneurs building MVP applications","small business owners automating internal workflows","product managers prototyping ideas before engineering handoff"],"limitations":["Generative AI interpretation of intent is probabilistic — complex or ambiguous requirements may produce incorrect logic","No transparent mechanism to validate or correct generated code before deployment","Neurosymbolic reasoning claims lack architectural documentation; unclear if symbolic constraints actually guide generation or if it's standard LLM-based code synthesis","Generated applications likely require manual refinement for production use cases"],"requires":["Durable AI account with active subscription","Web browser with modern JavaScript support","Clear, structured natural language description of application requirements"],"input_types":["natural language text","workflow descriptions","business process narratives"],"output_types":["executable application code","visual UI layouts","data model schemas","workflow automation rules"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_durable-ai__cap_1","uri":"capability://automation.workflow.visual.workflow.builder.with.ai.suggestions","name":"visual-workflow-builder-with-ai-suggestions","description":"Provides a drag-and-drop visual interface for constructing application workflows, with AI-powered suggestions for next steps, component connections, and logic branches. The builder likely uses a graph-based workflow representation (nodes for actions/decisions, edges for transitions) and integrates an LLM to suggest contextually relevant next steps based on the current workflow state and user intent. Suggestions may be generated via prompt engineering that includes the current workflow graph as context.","intents":["I want to visually design a workflow and have AI suggest the next logical steps","I need to connect components and automate transitions without writing conditional logic","I want to build approval chains, data pipelines, or multi-step processes with minimal configuration"],"best_for":["business process designers without programming experience","teams building internal automation tools","non-technical users creating approval workflows or data pipelines"],"limitations":["AI suggestions are heuristic-based and may not align with domain-specific business logic","No explicit validation that suggested workflows are correct or complete","Visual builder complexity may grow with workflow size; scalability to 50+ node workflows unclear","Workflow execution semantics (error handling, retries, rollback) not documented"],"requires":["Durable AI account with active subscription","Web browser with drag-and-drop support","Basic understanding of workflow concepts (steps, conditions, branches)"],"input_types":["visual component selections","workflow node configurations","natural language descriptions of desired workflow behavior"],"output_types":["executable workflow definitions","visual workflow diagrams","workflow execution logs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_durable-ai__cap_10","uri":"capability://data.processing.analysis.application.analytics.and.monitoring","name":"application-analytics-and-monitoring","description":"Provides built-in analytics and monitoring for deployed applications, tracking user behavior, application performance, and error rates. The system likely collects telemetry data (page views, user actions, workflow executions) and performance metrics (response times, database queries, API latency), then presents insights through dashboards and alerts. Monitoring may include error tracking, performance profiling, and usage analytics to help users understand how their applications are being used and identify issues.","intents":["I want to understand how users are interacting with my application","I need to monitor application performance and catch errors in production","I want to track key metrics like workflow completion rates and user engagement"],"best_for":["teams operating applications in production","founders who need to understand user behavior and engagement","organizations with performance and reliability requirements"],"limitations":["Analytics and monitoring are likely limited to Durable AI's managed hosting; no support for custom metrics or external monitoring tools","Data retention and privacy practices not documented; unclear how long telemetry is stored","No explicit integration with external analytics platforms (Mixpanel, Amplitude, DataDog)","Alert configuration and notification mechanisms not documented"],"requires":["Durable AI account with active subscription","Deployed application with user traffic","Web browser with dashboard visualization support"],"input_types":["application telemetry","user interactions","performance metrics"],"output_types":["analytics dashboards","performance reports","error logs","usage insights"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_durable-ai__cap_2","uri":"capability://data.processing.analysis.ai.powered.data.model.inference","name":"ai-powered-data-model-inference","description":"Automatically infers data models and database schemas from natural language descriptions of entities and relationships. The system likely parses user descriptions to extract entity names, attributes, and relationships, then generates corresponding schema definitions (tables, fields, types, constraints). May use pattern matching or LLM-based entity extraction to identify common data structures (e.g., 'customer' → id, name, email, phone fields) and suggest appropriate field types and validations.","intents":["I want to describe my data structure in English and have the database schema generated automatically","I need to define entities and relationships without writing SQL or schema configuration","I want to generate CRUD operations and data validation rules from a description of my data model"],"best_for":["non-technical founders building data-driven applications","rapid prototyping teams that need quick schema iteration","users unfamiliar with relational database design"],"limitations":["Inferred schemas may not match optimal database design patterns; no normalization validation","Complex relationships (many-to-many, polymorphic associations) may be incorrectly inferred","No explicit mechanism to review or modify inferred schemas before application generation","Scalability to large schemas (100+ entities) unclear; performance degradation likely with LLM-based inference"],"requires":["Durable AI account with active subscription","Clear natural language description of data entities and relationships","Web browser with form input support"],"input_types":["natural language entity descriptions","relationship narratives","business domain descriptions"],"output_types":["database schema definitions","field type specifications","relationship constraints","validation rules"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_durable-ai__cap_3","uri":"capability://planning.reasoning.neurosymbolic.logic.synthesis","name":"neurosymbolic-logic-synthesis","description":"Combines neural (generative AI) and symbolic (rule-based) reasoning to synthesize application logic and business rules. The claimed approach suggests that symbolic constraints (e.g., 'approval must come before payment') guide neural code generation to produce logic that satisfies both learned patterns and explicit rules. However, the specific implementation — whether constraints are enforced via prompt engineering, post-generation validation, or integrated into the generation process — is undocumented. This capability is central to Durable AI's differentiation claim but lacks transparent architectural details.","intents":["I want to define business rules and have the system generate code that respects those constraints","I need to ensure generated logic satisfies specific domain requirements without manual verification","I want to combine learned patterns from examples with explicit rule constraints in code generation"],"best_for":["organizations with strict compliance or business rule requirements","teams building applications where logic correctness is critical","users seeking AI-assisted code generation with formal guarantees"],"limitations":["Neurosymbolic reasoning mechanism is not publicly documented; unclear how symbolic constraints are enforced","No evidence that symbolic reasoning provides meaningful advantages over standard LLM code generation with prompt-based constraints","Validation that generated code actually satisfies symbolic constraints is not transparent","Scalability to complex constraint systems (100+ rules) unknown; likely requires manual rule specification"],"requires":["Durable AI account with active subscription","Explicit specification of business rules or constraints","Understanding of domain-specific logic requirements"],"input_types":["natural language rule descriptions","constraint specifications","business logic narratives"],"output_types":["constraint-satisfying application code","business rule implementations","validation logic"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_durable-ai__cap_4","uri":"capability://code.generation.editing.ai.assisted.ui.component.generation","name":"ai-assisted-ui-component-generation","description":"Automatically generates visual UI components and layouts from natural language descriptions or workflow specifications. The system likely maintains a library of pre-built components (forms, tables, cards, modals) and uses LLM-based layout reasoning to select and arrange components based on user intent. May employ a constraint-based layout engine to ensure responsive design and accessibility compliance. Component generation likely includes automatic binding to underlying data models and workflow logic.","intents":["I want to describe the user interface I need and have it generated automatically","I need to create responsive layouts without designing CSS or HTML","I want to bind UI components to data models and workflows without manual wiring"],"best_for":["non-designers building functional prototypes","rapid prototyping teams prioritizing speed over design polish","users building internal tools where UI aesthetics are secondary"],"limitations":["Generated UI may not match professional design standards or brand guidelines","Limited customization of component styling and layout; difficult to override AI-generated designs","Responsive design quality depends on layout engine sophistication; mobile experience may be suboptimal","No explicit design review or approval workflow before UI deployment"],"requires":["Durable AI account with active subscription","Natural language description of desired UI or workflow context","Web browser with modern CSS and JavaScript support"],"input_types":["natural language UI descriptions","workflow specifications","data model definitions"],"output_types":["HTML/CSS component definitions","responsive layout specifications","component-to-data bindings","interactive UI prototypes"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_durable-ai__cap_5","uri":"capability://tool.use.integration.intelligent.api.integration.suggestion","name":"intelligent-api-integration-suggestion","description":"Suggests and configures API integrations based on application requirements and workflow context. The system likely analyzes the generated application logic and data models to identify external services that would be beneficial (e.g., payment processing for e-commerce, email for notifications), then suggests pre-built integrations and auto-configures connection parameters. May use a knowledge base of common API patterns and integration recipes to match application needs to available services.","intents":["I want the system to suggest which APIs my application needs based on its functionality","I need to integrate external services without manually researching and configuring API credentials","I want to add payment processing, email, SMS, or other third-party services with minimal setup"],"best_for":["non-technical founders building applications that require external services","rapid prototyping teams that need quick integration setup","users unfamiliar with API authentication and configuration"],"limitations":["Suggestions are heuristic-based and may not match actual application needs","Limited to pre-integrated services; custom or niche APIs require manual configuration","API credential management and security practices not documented; unclear how secrets are stored","No explicit validation that suggested integrations are correctly configured or functional"],"requires":["Durable AI account with active subscription","API credentials for external services (Stripe, SendGrid, Twilio, etc.)","Understanding of which external services are needed for the application"],"input_types":["application logic specifications","workflow definitions","data model descriptions"],"output_types":["API integration suggestions","integration configuration templates","credential binding specifications"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_durable-ai__cap_6","uri":"capability://code.generation.editing.context.aware.code.refinement","name":"context-aware-code-refinement","description":"Allows users to iteratively refine generated code and logic through natural language feedback and corrections. The system maintains context of the generated application (code, schema, workflows) and uses LLM-based reasoning to interpret user feedback and apply targeted modifications. Refinement likely operates at multiple levels: component-level (modify a single form), workflow-level (change a process step), or application-level (restructure the entire data model). The system must track changes and maintain consistency across dependent components.","intents":["I want to ask the AI to modify generated code without rewriting it from scratch","I need to fix logic errors or adjust workflows by describing the desired change in English","I want to iterate on the generated application through conversational feedback"],"best_for":["users building applications through iterative refinement","non-technical users who need to adjust generated code without learning to code","rapid prototyping teams that need quick iteration cycles"],"limitations":["Refinement is probabilistic; complex changes may be misinterpreted or produce unintended side effects","No explicit validation that refined code maintains consistency with existing components","Changes to data models may break dependent workflows or UI components; no automatic propagation of changes","Refinement history and rollback capabilities not documented"],"requires":["Durable AI account with active subscription","Generated application or code to refine","Clear natural language description of desired changes"],"input_types":["natural language feedback","change descriptions","correction requests"],"output_types":["modified application code","updated workflows","refined data models","change summaries"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_durable-ai__cap_7","uri":"capability://automation.workflow.application.deployment.and.hosting","name":"application-deployment-and-hosting","description":"Automatically deploys generated applications to a managed hosting environment without requiring manual DevOps configuration. The system likely handles infrastructure provisioning (servers, databases, load balancers), environment setup, and application deployment as part of the generation pipeline. Deployment may be one-click or automatic upon application completion. The hosting environment is likely proprietary (Durable AI-managed) rather than supporting deployment to arbitrary cloud providers, limiting portability.","intents":["I want my generated application to be live and accessible immediately without deployment configuration","I need to deploy applications without managing servers, databases, or DevOps infrastructure","I want automatic scaling and maintenance of my application without operational overhead"],"best_for":["non-technical founders who need applications live quickly","rapid prototyping teams that prioritize speed over infrastructure control","small teams without DevOps expertise"],"limitations":["Deployment is limited to Durable AI's managed hosting; no support for custom cloud providers (AWS, GCP, Azure)","Vendor lock-in; migrating applications to other platforms requires manual code extraction and reconfiguration","Scalability and performance characteristics of managed hosting not documented","No explicit SLA or uptime guarantees; reliability and disaster recovery practices unclear","Cost model for hosting and scaling not transparent; potential for unexpected bills at scale"],"requires":["Durable AI account with active subscription","Generated application ready for deployment","Internet connectivity for deployment and application access"],"input_types":["generated application code","data model definitions","workflow specifications"],"output_types":["live application URL","deployment status","application logs","performance metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_durable-ai__cap_8","uri":"capability://automation.workflow.multi.user.collaboration.and.version.control","name":"multi-user-collaboration-and-version-control","description":"Enables multiple users to collaborate on application development with built-in version control and conflict resolution. The system likely maintains a version history of application changes, allows concurrent editing with conflict detection, and provides mechanisms for reviewing and merging changes. Collaboration may be real-time (live editing with presence awareness) or asynchronous (change proposals and reviews). Version control likely operates at the application level rather than file-level, tracking changes to workflows, data models, and UI components as discrete units.","intents":["I want multiple team members to work on the same application simultaneously","I need to review changes before they're applied to the application","I want to track who made what changes and when, and be able to revert if needed"],"best_for":["small teams building applications collaboratively","organizations with change control and audit requirements","teams that need asynchronous collaboration across time zones"],"limitations":["Real-time collaboration may introduce latency and consistency issues; conflict resolution strategy not documented","Version control operates at application level; no fine-grained file-level history","No explicit branching or environment management (dev/staging/prod); unclear how to test changes before deployment","Concurrent editing of the same component may produce conflicts; resolution mechanism unclear"],"requires":["Durable AI account with active subscription","Multiple team members with accounts","Web browser with real-time collaboration support (WebSocket or similar)"],"input_types":["application changes","workflow modifications","data model updates"],"output_types":["version history","change logs","merged applications","conflict resolution results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_durable-ai__cap_9","uri":"capability://safety.moderation.application.testing.and.validation","name":"application-testing-and-validation","description":"Provides built-in testing and validation capabilities to verify generated applications before deployment. The system likely includes automated testing (unit tests for logic, integration tests for workflows, UI tests for components), validation rules (data type checking, constraint verification), and potentially manual testing tools (test data generation, user simulation). Testing may be triggered automatically or on-demand, and results are likely presented through a dashboard or report interface.","intents":["I want to automatically test my generated application to ensure it works correctly","I need to validate that workflows execute as expected and data models are correct","I want to catch errors before deploying to production"],"best_for":["teams building applications with critical logic that requires validation","organizations with quality assurance requirements","users who want confidence in generated code before deployment"],"limitations":["Automated testing coverage depends on test generation quality; complex logic may not be adequately tested","No explicit mechanism to write custom tests or define test scenarios","Test data generation may not cover edge cases or realistic scenarios","Testing results and coverage metrics not documented; unclear what constitutes 'passing' tests"],"requires":["Durable AI account with active subscription","Generated application with logic to test","Web browser with test result visualization support"],"input_types":["generated application code","workflow definitions","data model specifications"],"output_types":["test results","coverage reports","error logs","validation summaries"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Durable AI account with active subscription","Web browser with modern JavaScript support","Clear, structured natural language description of application requirements","Web browser with drag-and-drop support","Basic understanding of workflow concepts (steps, conditions, branches)","Deployed application with user traffic","Web browser with dashboard visualization support","Clear natural language description of data entities and relationships","Web browser with form input support","Explicit specification of business rules or constraints"],"failure_modes":["Generative AI interpretation of intent is probabilistic — complex or ambiguous requirements may produce incorrect logic","No transparent mechanism to validate or correct generated code before deployment","Neurosymbolic reasoning claims lack architectural documentation; unclear if symbolic constraints actually guide generation or if it's standard LLM-based code synthesis","Generated applications likely require manual refinement for production use cases","AI suggestions are heuristic-based and may not align with domain-specific business logic","No explicit validation that suggested workflows are correct or complete","Visual builder complexity may grow with workflow size; scalability to 50+ node workflows unclear","Workflow execution semantics (error handling, retries, rollback) not documented","Analytics and monitoring are likely limited to Durable AI's managed hosting; no support for custom metrics or external monitoring tools","Data retention and privacy practices not documented; unclear how long telemetry is stored","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.2,"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":"2026-05-24T12:16:30.283Z","last_scraped_at":"2026-04-05T13:23:42.561Z","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=durable-ai","compare_url":"https://unfragile.ai/compare?artifact=durable-ai"}},"signature":"Q/s3vHhA7a16VdQh+zyTi9zI4HZZPs3VkO2Pi2GFmy2s+82V3G0MJNuW5RRAOGmzF6hZgWCLBD5DB6UpEbRtCQ==","signedAt":"2026-06-22T18:14:44.849Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/durable-ai","artifact":"https://unfragile.ai/durable-ai","verify":"https://unfragile.ai/api/v1/verify?slug=durable-ai","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"}}