{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_illusion-ai","slug":"illusion-ai","name":"Illusion AI","type":"product","url":"https://illusion.ws","page_url":"https://unfragile.ai/illusion-ai","categories":["app-builders"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_illusion-ai__cap_0","uri":"capability://text.generation.language.no.code.generative.ai.application.builder.with.visual.workflow.composition","name":"no-code generative ai application builder with visual workflow composition","description":"Illusion provides a visual, drag-and-drop interface for composing multi-step generative AI workflows without writing code. Users connect pre-built AI blocks (text generation, image generation, data processing) into directed acyclic graphs, with data flowing between nodes via implicit type coercion and JSON serialization. The platform abstracts away API authentication, prompt engineering, and model selection through templated blocks that expose only high-level parameters.","intents":["Build a custom AI tool in minutes without hiring a developer","Prototype a generative AI workflow to validate a business idea","Create internal automation tools that combine multiple AI models","Expose AI capabilities to non-technical team members via a simple interface"],"best_for":["Solo entrepreneurs and small teams without engineering resources","Non-technical founders prototyping AI-powered MVPs","Business analysts building internal automation workflows","Product managers validating AI feature concepts before engineering investment"],"limitations":["No-code abstraction limits fine-grained control over model parameters, prompt optimization, and error handling","Workflow complexity is constrained by visual canvas scalability — deeply nested or highly branching workflows become difficult to manage","No built-in version control or collaborative editing — concurrent modifications by multiple users may cause conflicts","Limited debugging capabilities — error messages are often generic and don't expose underlying API failures or model-specific issues"],"requires":["Web browser with modern JavaScript support (Chrome, Firefox, Safari, Edge)","API keys for connected generative AI providers (OpenAI, Anthropic, or other supported models)","Basic understanding of workflow logic and data flow concepts"],"input_types":["text (prompts, user input)","images (for vision-capable models)","structured data (JSON, CSV)","file uploads"],"output_types":["text (generated content, summaries, responses)","images (generated or processed)","structured data (JSON, CSV)","files (downloadable artifacts)"],"categories":["text-generation-language","image-visual","automation-workflow","no-code-platform"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_illusion-ai__cap_1","uri":"capability://tool.use.integration.multi.model.generative.ai.orchestration.with.provider.abstraction","name":"multi-model generative ai orchestration with provider abstraction","description":"Illusion abstracts away differences between generative AI providers (OpenAI, Anthropic, etc.) by exposing a unified interface for text and image generation. Users select a model from a dropdown without managing API endpoints, authentication headers, or provider-specific parameter mappings. The platform translates high-level parameters (temperature, max tokens, system prompt) into provider-specific API calls, handling rate limiting, retries, and fallback logic transparently.","intents":["Use multiple AI models in a single workflow without learning each provider's API","Switch between models (e.g., GPT-4 to Claude) without rebuilding the workflow","Avoid vendor lock-in by abstracting provider-specific implementation details","Combine outputs from different models (e.g., GPT-4 for reasoning, DALL-E for images) in one application"],"best_for":["Teams wanting to experiment with multiple AI models without engineering overhead","Builders prototyping model-agnostic AI applications","Organizations evaluating different providers before committing to one"],"limitations":["Provider abstraction may hide model-specific capabilities or quirks — advanced users cannot access provider-specific parameters like top-p sampling or custom stop sequences","Fallback and retry logic is opaque — users cannot customize retry strategies or implement circuit breakers","No cost optimization — the platform does not route requests to cheaper models or implement intelligent model selection based on task complexity","Supported models are limited to those Illusion has integrated — cutting-edge or niche models may not be available"],"requires":["API keys for at least one supported generative AI provider (OpenAI, Anthropic, etc.)","Active subscription or credits with the selected provider","Understanding of model capabilities and appropriate use cases"],"input_types":["text prompts","structured parameters (temperature, max tokens, system prompt)"],"output_types":["text (generated content)","images (generated or processed)","metadata (token usage, model name, latency)"],"categories":["tool-use-integration","text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_illusion-ai__cap_10","uri":"capability://automation.workflow.workflow.versioning.and.rollback.with.change.history","name":"workflow versioning and rollback with change history","description":"Illusion maintains a version history of workflow changes, allowing users to view previous versions, compare changes, and rollback to earlier versions if needed. Each version is timestamped and includes metadata about what changed (e.g., 'updated prompt', 'changed model'). Users can restore a previous version with a single click, and the platform prevents accidental overwrites by requiring confirmation before publishing breaking changes.","intents":["Revert a workflow to a previous version if a recent change broke functionality","Compare different versions to understand what changed and why","Maintain a history of prompt and parameter changes for auditing","Experiment with changes without fear of losing the working version"],"best_for":["Teams iterating on workflows and needing to track changes","Workflows in production that require audit trails","Builders experimenting with different configurations"],"limitations":["Version history is limited to Illusion's storage — very old versions may be deleted after a retention period","No branching or merging — users cannot work on multiple versions in parallel","No collaborative version control — concurrent edits by multiple users may create conflicts","Rollback is manual — there is no automatic rollback on errors or performance degradation","Version comparison is limited to visual diffs — understanding the impact of changes requires manual analysis"],"requires":["Active workflow with change history"],"input_types":["workflow definition changes"],"output_types":["version history with timestamps","change diffs","rollback capability"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_illusion-ai__cap_11","uri":"capability://automation.workflow.error.handling.and.retry.logic.with.fallback.workflows","name":"error handling and retry logic with fallback workflows","description":"Illusion allows users to define error handling strategies for workflow steps, including automatic retries with exponential backoff, fallback workflows, and error notifications. Users can configure which errors trigger retries (e.g., rate limits, timeouts) versus which errors should fail the workflow (e.g., authentication errors). Failed workflows can trigger alternative workflows or send alerts to users.","intents":["Handle transient API failures (rate limits, timeouts) by automatically retrying","Implement graceful degradation by falling back to alternative models or workflows on failure","Get notified when workflows fail so issues can be addressed quickly","Build resilient workflows that don't fail on temporary infrastructure issues"],"best_for":["Production workflows that need to handle failures gracefully","High-volume workflows that may hit API rate limits","Workflows that depend on external APIs that may be unreliable"],"limitations":["Retry logic is limited to simple exponential backoff — custom retry strategies are not supported","Fallback workflows are manual — the platform does not automatically select the best fallback based on error type","Error notifications are limited to email or webhooks — integration with monitoring tools (PagerDuty, Datadog) is not built-in","Error handling is configured per-step — global error handling policies are not supported","No circuit breaker pattern — workflows will continue retrying even if an API is down"],"requires":["Understanding of error types and appropriate handling strategies","Configuration of retry policies and fallback workflows"],"input_types":["error type and condition","retry policy (max attempts, backoff strategy)","fallback workflow or action"],"output_types":["retry execution","fallback workflow execution","error notifications"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_illusion-ai__cap_2","uri":"capability://text.generation.language.visual.prompt.engineering.and.parameter.tuning.interface","name":"visual prompt engineering and parameter tuning interface","description":"Illusion exposes a visual editor for crafting and iterating on prompts and model parameters (temperature, max tokens, system instructions) without touching code. Users can test prompts in real-time against live models, see token counts and estimated costs, and save prompt variations as templates. The interface provides guidance on prompt best practices and suggests parameter adjustments based on output quality.","intents":["Experiment with different prompts and parameters to optimize model output quality","Understand how temperature and token limits affect model behavior without reading API docs","Estimate costs before running workflows at scale","Save and reuse effective prompts across multiple workflows"],"best_for":["Non-technical users learning how to effectively prompt AI models","Product teams iterating on AI-powered features","Builders optimizing model outputs for specific use cases"],"limitations":["Visual parameter tuning interface may not expose advanced options like logit bias, function calling schemas, or custom token weights","No built-in A/B testing framework — users cannot systematically compare prompt variations across datasets","Cost estimation is approximate and may not account for streaming, retries, or provider-specific pricing tiers","Prompt templates are generic — domain-specific prompt engineering patterns are not provided"],"requires":["Active API key and credits with a supported generative AI provider","Basic understanding of how prompts influence model behavior"],"input_types":["text (prompts, system instructions)","numeric parameters (temperature, max tokens, top-p)"],"output_types":["text (model output, token count, cost estimate)","saved prompt templates"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_illusion-ai__cap_3","uri":"capability://automation.workflow.freemium.application.deployment.and.sharing.with.usage.based.scaling","name":"freemium application deployment and sharing with usage-based scaling","description":"Illusion allows users to deploy built workflows as standalone applications with a shareable URL, enabling non-technical users to distribute AI tools to colleagues or customers. The freemium model provides free tier deployments with usage limits (e.g., requests per month), and paid tiers scale based on actual API consumption. The platform handles hosting, scaling, and billing — users only pay for the underlying AI API calls, not infrastructure.","intents":["Deploy a custom AI tool and share it with team members or customers without managing servers","Start with free tier to validate demand before committing to paid infrastructure","Scale usage automatically without worrying about capacity planning or DevOps","Monetize AI applications by charging users per request or subscription"],"best_for":["Solo entrepreneurs launching AI-powered side projects with minimal upfront cost","Small teams deploying internal tools without IT infrastructure","Builders testing market demand before investing in custom development","Non-technical founders wanting to offer AI services to customers"],"limitations":["Free tier has strict usage limits (requests per month, execution time per request) — production workloads require paid plans","No built-in authentication or user management — deployed applications are public or require external auth layer","Scalability is limited by Illusion's infrastructure — high-traffic applications may experience rate limiting or latency","No SLA or uptime guarantees for free tier — reliability is not guaranteed for production use","Vendor lock-in — exporting deployed applications to self-hosted infrastructure is not supported"],"requires":["Illusion account with active freemium or paid subscription","API keys for underlying generative AI providers (costs are passed through to user)","Shareable URL (no custom domain support on free tier)"],"input_types":["workflow definition (created in visual builder)","user input (text, images, files) at runtime"],"output_types":["shareable URL","deployed application with web interface","usage analytics and billing data"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_illusion-ai__cap_4","uri":"capability://automation.workflow.template.library.and.workflow.marketplace.for.rapid.application.bootstrapping","name":"template library and workflow marketplace for rapid application bootstrapping","description":"Illusion provides a library of pre-built workflow templates (e.g., 'Email Writer', 'Image Background Remover', 'Customer Support Chatbot') that users can clone and customize. Templates include example prompts, parameter configurations, and integration patterns. A community marketplace allows users to publish and discover workflows created by other users, enabling rapid bootstrapping of new applications without starting from scratch.","intents":["Start building an AI application by cloning a similar template instead of building from scratch","Learn best practices by examining how experienced users structure workflows","Discover new use cases and applications by browsing community templates","Share a workflow with the community and gain visibility for custom tools"],"best_for":["Beginners learning how to structure AI workflows","Teams wanting to quickly launch variations of common AI applications","Community members sharing domain-specific workflow patterns"],"limitations":["Template quality is inconsistent — community templates may be poorly documented or use outdated model versions","No versioning or maintenance system — templates can become stale as AI models and APIs evolve","Limited customization guidance — cloning a template does not teach users how to modify it for their specific use case","No revenue sharing for template creators — community contributions are not incentivized","Templates are limited to Illusion's supported models and integrations"],"requires":["Illusion account","API keys for models used in the template"],"input_types":["template selection (from library or marketplace)"],"output_types":["cloned workflow ready for customization","published template available to community"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_illusion-ai__cap_5","uri":"capability://planning.reasoning.conditional.logic.and.branching.for.multi.path.workflow.execution","name":"conditional logic and branching for multi-path workflow execution","description":"Illusion supports conditional branching in workflows, allowing users to route execution based on model outputs or user inputs. Users can define if-then-else logic visually (e.g., 'if sentiment is negative, route to escalation workflow; otherwise, respond with generated message'). Conditions are evaluated at runtime against structured or unstructured data, and multiple branches can execute in parallel or sequence.","intents":["Route workflow execution based on AI model outputs (e.g., sentiment analysis, classification)","Implement multi-step decision trees without writing code","Handle edge cases and error conditions with fallback workflows","Create adaptive workflows that behave differently based on user input or context"],"best_for":["Building customer support workflows that route based on intent or sentiment","Implementing content moderation pipelines with escalation logic","Creating adaptive AI applications that respond differently to different inputs"],"limitations":["Conditional logic is limited to simple if-then-else patterns — complex boolean logic or nested conditions are difficult to express visually","No support for loops or iteration — workflows cannot repeat steps based on conditions","Condition evaluation is synchronous — asynchronous or event-driven branching is not supported","Debugging conditional logic is difficult — the platform does not provide visibility into which branch was taken or why"],"requires":["Understanding of workflow logic and conditional execution","Structured or semi-structured data to evaluate conditions against"],"input_types":["model outputs (text, classifications, scores)","user inputs (text, selections)","structured data (JSON)"],"output_types":["routed workflow execution","branch-specific outputs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_illusion-ai__cap_6","uri":"capability://data.processing.analysis.data.transformation.and.extraction.from.unstructured.ai.outputs","name":"data transformation and extraction from unstructured ai outputs","description":"Illusion provides blocks for extracting structured data from unstructured model outputs (e.g., parsing JSON from text generation, extracting entities from summaries, converting images to text via OCR). Users can define extraction schemas visually or via templates, and the platform uses regex, JSON parsing, or secondary AI calls to extract and validate data. Extracted data can be passed to downstream workflow steps or exported to external systems.","intents":["Extract structured data from AI-generated text without writing parsing code","Convert unstructured outputs (images, PDFs) into machine-readable formats","Validate and clean AI outputs before passing to downstream systems","Map extracted data to external APIs or databases"],"best_for":["Workflows that need to extract structured data from AI outputs","Integrations with downstream systems that require structured input","Data pipelines that combine AI generation with ETL"],"limitations":["Extraction logic is limited to simple patterns — complex or ambiguous extraction requires manual configuration","No built-in validation framework — extracted data may be incomplete or malformed","Extraction accuracy depends on model output consistency — if the model changes output format, extraction may fail silently","No error handling for failed extractions — workflows do not gracefully handle malformed outputs"],"requires":["Understanding of the expected output format","Schema definition (JSON, regex, or template)"],"input_types":["unstructured text (AI-generated content)","images (for OCR)","semi-structured data (JSON with inconsistent fields)"],"output_types":["structured data (JSON, CSV)","extracted fields (entities, values)","validation results"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_illusion-ai__cap_7","uri":"capability://tool.use.integration.integration.with.external.apis.and.data.sources.via.http.connectors","name":"integration with external apis and data sources via http connectors","description":"Illusion allows workflows to call external APIs (REST, webhooks) to fetch data, trigger actions, or send results to third-party systems. Users configure API endpoints, authentication (API keys, OAuth), request/response mapping, and error handling visually. The platform handles HTTP requests, retries, and response parsing, allowing workflows to integrate with CRMs, databases, messaging platforms, and custom backends without code.","intents":["Fetch data from external APIs to use as context in AI prompts","Send AI-generated outputs to external systems (CRM, email, Slack, etc.)","Trigger workflows based on webhooks from external services","Build end-to-end automation that combines AI with existing business systems"],"best_for":["Integrating AI workflows with existing business systems and tools","Building end-to-end automation that spans multiple platforms","Teams wanting to avoid custom backend development"],"limitations":["API integration is limited to HTTP-based services — non-REST APIs (gRPC, GraphQL) require custom adapters","Authentication is limited to API keys and basic OAuth — complex auth flows (SAML, multi-factor) are not supported","Request/response mapping is manual — complex data transformations require additional transformation blocks","Error handling is basic — retries and fallbacks must be configured manually for each API call","Rate limiting and throttling are not built-in — high-volume integrations may hit API rate limits"],"requires":["API endpoint URL and documentation","Authentication credentials (API key, OAuth token, etc.)","Understanding of request/response format"],"input_types":["API endpoint configuration","request parameters (headers, body, query params)","authentication credentials"],"output_types":["API response (JSON, XML, plain text)","parsed response data","error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_illusion-ai__cap_8","uri":"capability://automation.workflow.batch.processing.and.asynchronous.execution.for.high.volume.workflows","name":"batch processing and asynchronous execution for high-volume workflows","description":"Illusion supports batch processing of multiple inputs (e.g., processing 1000 images or generating summaries for 100 documents) by queuing requests and executing them asynchronously. Users upload batch data (CSV, JSON, file list) and the platform distributes execution across available resources, providing progress tracking and result aggregation. Batch jobs can be scheduled to run at specific times or triggered by webhooks.","intents":["Process large datasets through AI workflows without blocking the UI","Schedule batch jobs to run during off-peak hours to optimize costs","Generate reports or exports by processing many inputs in parallel","Integrate batch processing into data pipelines or ETL workflows"],"best_for":["Processing large datasets through AI models","Cost-sensitive applications that can batch requests to reduce API calls","Workflows that need to process data on a schedule"],"limitations":["Batch processing is limited by Illusion's infrastructure — very large batches (millions of items) may be throttled or rejected","No built-in cost optimization — batch jobs do not automatically select cheaper models or batch API endpoints","Result aggregation is basic — complex post-processing of batch results requires additional steps","No built-in retry logic for failed items — failed batch items must be manually reprocessed","Batch job monitoring is limited — users cannot see detailed progress or logs for individual items"],"requires":["Batch data in supported format (CSV, JSON, file list)","Sufficient API quota with underlying providers to process the batch"],"input_types":["batch data (CSV, JSON, file uploads)","schedule configuration (cron, webhook trigger)"],"output_types":["aggregated results (CSV, JSON)","progress tracking and logs","error reports"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_illusion-ai__cap_9","uri":"capability://automation.workflow.usage.monitoring.cost.tracking.and.analytics.dashboard","name":"usage monitoring, cost tracking, and analytics dashboard","description":"Illusion provides a dashboard showing API usage (requests, tokens, model calls), estimated costs, and performance metrics (latency, error rates). Users can set usage alerts and spending limits to prevent unexpected bills. Analytics break down usage by workflow, model, and time period, helping users identify optimization opportunities. The platform provides recommendations for cost reduction (e.g., switching to cheaper models, batching requests).","intents":["Monitor API costs and prevent unexpected bills from high-volume workflows","Identify which workflows or models are consuming the most resources","Optimize workflows based on performance and cost metrics","Forecast costs and budget for AI infrastructure"],"best_for":["Teams managing multiple AI workflows and wanting to control costs","Builders optimizing workflows for cost efficiency","Organizations forecasting AI infrastructure spending"],"limitations":["Cost tracking is approximate — actual charges from AI providers may differ due to rounding or pricing changes","Analytics are limited to Illusion's metrics — detailed provider-specific metrics (e.g., cache hits, fine-tuning costs) are not available","Cost optimization recommendations are generic — domain-specific optimization strategies are not provided","Spending limits are enforced at the Illusion level, not at the provider level — some providers may charge before Illusion blocks the request","No integration with external cost management tools (e.g., FinOps platforms)"],"requires":["Active workflows with API usage","Access to Illusion dashboard"],"input_types":["workflow execution data","API provider billing data"],"output_types":["usage metrics (requests, tokens, model calls)","cost estimates and actual charges","performance metrics (latency, error rates)","analytics reports and recommendations"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support (Chrome, Firefox, Safari, Edge)","API keys for connected generative AI providers (OpenAI, Anthropic, or other supported models)","Basic understanding of workflow logic and data flow concepts","API keys for at least one supported generative AI provider (OpenAI, Anthropic, etc.)","Active subscription or credits with the selected provider","Understanding of model capabilities and appropriate use cases","Active workflow with change history","Understanding of error types and appropriate handling strategies","Configuration of retry policies and fallback workflows","Active API key and credits with a supported generative AI provider"],"failure_modes":["No-code abstraction limits fine-grained control over model parameters, prompt optimization, and error handling","Workflow complexity is constrained by visual canvas scalability — deeply nested or highly branching workflows become difficult to manage","No built-in version control or collaborative editing — concurrent modifications by multiple users may cause conflicts","Limited debugging capabilities — error messages are often generic and don't expose underlying API failures or model-specific issues","Provider abstraction may hide model-specific capabilities or quirks — advanced users cannot access provider-specific parameters like top-p sampling or custom stop sequences","Fallback and retry logic is opaque — users cannot customize retry strategies or implement circuit breakers","No cost optimization — the platform does not route requests to cheaper models or implement intelligent model selection based on task complexity","Supported models are limited to those Illusion has integrated — cutting-edge or niche models may not be available","Version history is limited to Illusion's storage — very old versions may be deleted after a retention period","No branching or merging — users cannot work on multiple versions in parallel","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.15000000000000002,"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":"2026-05-24T12:16:31.445Z","last_scraped_at":"2026-04-05T13:23:42.560Z","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=illusion-ai","compare_url":"https://unfragile.ai/compare?artifact=illusion-ai"}},"signature":"OHhVetkXoalxq163b9U1U/JekNz5B8W9MY9aSyif4PaWga++/0SL3Ny+/LAfV+Rq9vmUQhFweOUkWfAPPtK3Bw==","signedAt":"2026-06-21T20:05:24.688Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/illusion-ai","artifact":"https://unfragile.ai/illusion-ai","verify":"https://unfragile.ai/api/v1/verify?slug=illusion-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"}}