{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_clevis","slug":"clevis","name":"Clevis","type":"product","url":"https://clevis.app","page_url":"https://unfragile.ai/clevis","categories":["app-builders"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_clevis__cap_0","uri":"capability://automation.workflow.visual.workflow.builder.for.ai.app.composition","name":"visual workflow builder for ai app composition","description":"Clevis provides a drag-and-drop interface that chains AI model calls, data transformations, and conditional logic without code. Users connect nodes representing API calls, prompt templates, and data flows into directed acyclic graphs (DAGs) that execute sequentially or in parallel. The builder abstracts away HTTP request construction, authentication, and response parsing by exposing model-agnostic input/output ports that automatically serialize/deserialize between UI forms and API payloads.","intents":["I want to chain multiple AI model calls together without writing API integration code","I need to build a workflow that takes user input, processes it through an LLM, and returns structured output","I want to visualize the logic flow of my AI app before deploying it"],"best_for":["Non-technical founders and solo entrepreneurs prototyping AI tools","Product managers validating AI-powered feature ideas without engineering resources","Small teams building simple chatbots or content generation pipelines"],"limitations":["No support for complex control flow (loops, recursion, dynamic branching based on runtime conditions)","Limited ability to debug workflow execution — error messages lack stack traces or node-level execution logs","Visual DAG representation becomes unwieldy with >20 nodes, making large workflows difficult to maintain","No version control or collaborative editing — single-user workflows only"],"requires":["Web browser with modern JavaScript support (Chrome 90+, Firefox 88+, Safari 14+)","API keys for integrated AI providers (OpenAI, Anthropic, etc.) if using cloud models","Basic understanding of API request/response structures and JSON"],"input_types":["text (user input, prompts)","structured data (JSON objects from previous workflow steps)","file uploads (for document processing workflows)"],"output_types":["text (LLM responses)","structured data (JSON extracted from model outputs)","file downloads (generated documents, images)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clevis__cap_1","uri":"capability://tool.use.integration.multi.provider.ai.model.integration.with.unified.prompt.interface","name":"multi-provider ai model integration with unified prompt interface","description":"Clevis abstracts differences between OpenAI, Anthropic, and local model APIs through a unified prompt node that accepts template variables, system messages, and model parameters (temperature, max_tokens, top_p). The platform handles provider-specific authentication, request formatting, and response parsing internally. Users define prompts once and can swap between providers (e.g., GPT-4 to Claude) by changing a dropdown without rewriting the workflow.","intents":["I want to use multiple AI models in the same workflow without managing separate API clients","I need to switch between cloud and local models for cost optimization or privacy","I want to compare outputs from different models on the same prompt without manual testing"],"best_for":["Teams evaluating multiple LLM providers for production use","Cost-conscious builders wanting to route requests to cheaper models based on task complexity","Organizations with privacy requirements needing on-premise model deployment alongside cloud APIs"],"limitations":["No automatic prompt optimization across providers — users must manually tune temperature/tokens for each model's quirks","Limited support for provider-specific features (e.g., OpenAI's function calling, Anthropic's extended thinking) — workflows must use lowest-common-denominator feature set","Latency varies significantly by provider; no built-in load balancing or failover to faster alternatives","Local model support requires manual setup of inference server; no bundled runtime"],"requires":["API keys for cloud providers (OpenAI, Anthropic, etc.)","For local models: running inference server (Ollama, vLLM, or similar) accessible via HTTP","Understanding of model-specific parameter ranges and output formats"],"input_types":["text (prompts with template variables like {{user_input}})","structured data (JSON context passed to system message)"],"output_types":["text (raw model response)","structured data (JSON extracted from response via post-processing)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clevis__cap_10","uri":"capability://automation.workflow.workflow.versioning.and.deployment.management","name":"workflow versioning and deployment management","description":"Clevis allows creators to save workflow versions and deploy specific versions to production. Users can revert to previous versions if a deployment breaks, and maintain separate draft and published versions. The platform tracks version history with timestamps and creator information, but does not support branching or collaborative editing.","intents":["I want to test changes to my workflow before deploying to production","I need to roll back to a previous version if something breaks","I want to maintain a history of changes to my workflow"],"best_for":["Solo creators iterating on workflows with safety","Teams managing production AI apps with change control","Builders A/B testing workflow variations"],"limitations":["No branching — only linear version history","No collaborative editing — only one user can edit a workflow at a time","No diff view — cannot see what changed between versions","Version history is tied to workflow — cannot compare versions across different workflows","No automated testing or validation before deployment"],"requires":["Clevis account with workflow ownership"],"input_types":["workflow state (nodes, connections, configurations)"],"output_types":["version history (list of versions with timestamps)","version snapshot (complete workflow state at specific version)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clevis__cap_11","uri":"capability://automation.workflow.marketplace.for.discovering.and.sharing.workflows","name":"marketplace for discovering and sharing workflows","description":"Clevis provides a marketplace where creators can publish workflows for other users to discover, clone, and use. Published workflows can be monetized (paid) or free. The marketplace includes search, filtering by category/rating, and one-click cloning. However, the marketplace is nascent with limited content and discoverability.","intents":["I want to find pre-built AI workflows to use or customize","I want to share my workflow with the community and earn revenue","I need to discover best practices and examples from other creators"],"best_for":["Creators looking to monetize AI workflows at scale","Users seeking pre-built solutions to avoid building from scratch","Community members sharing knowledge and templates"],"limitations":["Marketplace has minimal content — few workflows available compared to alternatives","Discoverability is poor — no algorithmic recommendations or trending section","Quality control is absent — no review process or rating system for workflows","Revenue potential is low — minimal user base means limited sales","No workflow licensing or usage restrictions — cloned workflows can be modified and resold"],"requires":["Published workflow (for publishing to marketplace)","Clevis account (for browsing and cloning)"],"input_types":["workflow metadata (title, description, category, price)","workflow definition (nodes, connections, configurations)"],"output_types":["marketplace listing (public URL, clone link)","cloned workflow (copy in user's account)","revenue reports (sales, payouts)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clevis__cap_2","uri":"capability://automation.workflow.integrated.payment.processing.and.app.monetization","name":"integrated payment processing and app monetization","description":"Clevis embeds Stripe payment processing directly into published apps, allowing creators to charge users per API call, per subscription tier, or per-use basis without external payment infrastructure. The platform handles billing logic, invoice generation, and payout management. Creators define pricing rules in the workflow (e.g., 'charge $0.10 per request'), and Clevis automatically gates access and deducts credits from user accounts before executing the workflow.","intents":["I want to monetize my AI app without setting up Stripe, payment processing, or billing infrastructure","I need to charge users based on usage (per API call) rather than fixed subscriptions","I want to offer tiered pricing (free tier with limits, paid tier with higher quotas)"],"best_for":["Solo entrepreneurs launching their first monetized AI product","Indie developers avoiding payment infrastructure complexity","Teams prototyping pricing models before committing to custom billing systems"],"limitations":["Pricing logic is workflow-level only — no cross-app usage aggregation or enterprise contracts","Limited payment methods — Stripe only, no PayPal, crypto, or regional payment processors","No advanced billing features (usage-based metering, seat-based licensing, custom invoicing)","Payout delays and fees are Stripe-standard (2-3 days, 2.2% + $0.30 per transaction); no negotiation","Marketplace discovery is nascent — monetized apps have minimal organic traffic"],"requires":["Stripe account (requires business registration and tax ID in most jurisdictions)","Clevis account with verified email","Published workflow (not in draft state)"],"input_types":["pricing configuration (per-call rate, subscription tier definitions)","user account data (credit balance, subscription status)"],"output_types":["payment receipts (email to user)","payout reports (to creator dashboard)","usage analytics (API calls, revenue per workflow)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clevis__cap_3","uri":"capability://text.generation.language.prompt.template.management.with.variable.substitution","name":"prompt template management with variable substitution","description":"Clevis provides a template system for AI prompts that supports variable interpolation (e.g., {{user_input}}, {{context}}) and conditional text blocks. Templates are stored in the workflow and rendered at runtime by substituting variables from user input, previous workflow steps, or external data sources. The system supports Handlebars-style syntax for basic logic (if/else, loops) within prompts.","intents":["I want to reuse prompt templates across multiple workflows without duplicating text","I need to dynamically inject user data or context into prompts at runtime","I want to test different prompt variations without rebuilding the workflow"],"best_for":["Content creators iterating on prompt engineering without code changes","Teams managing multiple similar workflows (e.g., different content types with same structure)","Builders A/B testing prompt variations for quality or cost optimization"],"limitations":["No version control for prompt templates — changes overwrite previous versions","Limited debugging visibility — no way to inspect rendered prompt before sending to model","Conditional logic is basic (if/else only) — no complex branching or nested conditions","No prompt optimization tooling (e.g., automatic parameter tuning, cost estimation)"],"requires":["Understanding of Handlebars template syntax","Knowledge of available variables in the workflow context"],"input_types":["text (template with {{variable}} placeholders)","structured data (JSON objects to inject into template)"],"output_types":["text (rendered prompt ready for model API)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clevis__cap_4","uri":"capability://data.processing.analysis.data.transformation.and.extraction.from.model.outputs","name":"data transformation and extraction from model outputs","description":"Clevis includes transformation nodes that parse, filter, and restructure AI model outputs into structured data. Users can extract JSON fields from text responses, split responses into arrays, apply regex patterns, or map responses to predefined schemas. The platform supports chaining transformations (e.g., extract JSON → filter by field → format as CSV) without writing code.","intents":["I want to extract structured data from unstructured LLM responses","I need to validate that model output matches my expected schema before passing it downstream","I want to transform model output into a format my database or API expects"],"best_for":["Builders integrating AI outputs with downstream systems (databases, APIs, webhooks)","Teams processing high-volume AI-generated content that needs standardization","Non-technical users needing data cleanup without writing Python/JavaScript"],"limitations":["Transformation logic is limited to simple operations (JSON extraction, regex, field mapping) — no complex data wrangling","No built-in error handling for malformed outputs — failed transformations halt workflow execution","Performance degrades with large datasets (>10MB) — no streaming or batch processing","Limited schema validation — no JSON Schema or TypeScript type support"],"requires":["Understanding of JSON structure and basic regex patterns","Knowledge of expected output format from upstream model"],"input_types":["text (raw model response)","structured data (JSON from previous steps)"],"output_types":["structured data (JSON, CSV, or custom format)","text (formatted strings)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clevis__cap_5","uri":"capability://tool.use.integration.workflow.publishing.and.public.api.endpoint.generation","name":"workflow publishing and public api endpoint generation","description":"Clevis automatically exposes published workflows as HTTP REST APIs with auto-generated OpenAPI schemas. Users can publish a workflow and immediately get a public URL that accepts JSON requests and returns responses. The platform handles API authentication (API keys), rate limiting, request validation, and response formatting. No manual API server setup or deployment is required.","intents":["I want to expose my AI workflow as an API without building a backend server","I need to integrate my workflow with external applications via HTTP requests","I want to share my workflow with other users who can call it programmatically"],"best_for":["Solo developers building AI microservices without backend infrastructure","Teams integrating Clevis workflows with existing applications via REST","Non-technical creators wanting to expose workflows to developers without coding"],"limitations":["API endpoints are synchronous only — no async/webhook support for long-running workflows","Rate limiting is platform-wide (not per-user) — no fine-grained quota management","No built-in API versioning — breaking changes to workflows affect all consumers","Limited API documentation generation — OpenAPI schema is auto-generated but lacks custom descriptions","No API analytics or monitoring — users cannot see request/response logs or error rates"],"requires":["Published workflow (not in draft state)","Clevis account to manage API keys","HTTP client (curl, Postman, SDK) to call the endpoint"],"input_types":["JSON (request body matching workflow input schema)"],"output_types":["JSON (response matching workflow output schema)","HTTP status codes (200 for success, 4xx for validation errors, 5xx for execution errors)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clevis__cap_6","uri":"capability://safety.moderation.user.authentication.and.access.control.for.published.apps","name":"user authentication and access control for published apps","description":"Clevis provides built-in user authentication for published workflows, supporting email/password signup, API key authentication, and optional OAuth integration. Creators can restrict workflow access to authenticated users only, track usage per user, and manage user quotas (e.g., 100 API calls/month per free user). The platform handles session management, password hashing, and token generation internally.","intents":["I want to require users to sign up before using my AI app","I need to track which users are calling my API and how much they're using it","I want to enforce usage limits (e.g., free users get 100 calls/month, paid users get unlimited)"],"best_for":["Creators monetizing AI apps and needing user identity for billing","Teams managing multi-tenant workflows with per-user quotas","Builders protecting proprietary AI workflows from unauthorized access"],"limitations":["No advanced authentication (SAML, LDAP, custom OAuth providers) — email/password and API keys only","No fine-grained permissions — users are either authenticated or not; no role-based access control","User data is stored on Clevis servers — no option for self-hosted authentication","No audit logging for user actions — creators cannot see who accessed what and when","Password reset flow is basic — no custom email templates or multi-factor authentication"],"requires":["Published workflow","Clevis account with email verification"],"input_types":["user credentials (email, password)","API key (for programmatic access)"],"output_types":["authentication token (JWT or session cookie)","user profile data (email, signup date, usage stats)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clevis__cap_7","uri":"capability://automation.workflow.workflow.execution.logging.and.error.tracking","name":"workflow execution logging and error tracking","description":"Clevis logs workflow executions with timestamps, input/output data, and execution duration. When workflows fail, the platform captures error messages and the node where failure occurred. Creators can view execution history in a dashboard, filter by date/status, and export logs for debugging. However, logs lack granular node-level tracing and stack traces.","intents":["I want to see what happened when my workflow failed","I need to debug why a user's request returned an error","I want to monitor workflow performance and identify bottlenecks"],"best_for":["Builders troubleshooting workflow failures in production","Teams monitoring AI app reliability and performance","Creators analyzing user behavior and common error patterns"],"limitations":["Logs are retained for limited time (unclear retention policy) — no long-term audit trail","No structured logging format — logs are human-readable but not machine-parseable","No real-time log streaming — logs appear in dashboard with delay","Limited filtering options — cannot filter by specific node or variable value","No integration with external logging services (Datadog, Splunk, CloudWatch)"],"requires":["Published workflow with execution history","Clevis dashboard access"],"input_types":["workflow execution data (inputs, outputs, timestamps)"],"output_types":["execution logs (text, JSON export)","error reports (node name, error message, timestamp)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clevis__cap_8","uri":"capability://planning.reasoning.conditional.branching.and.dynamic.workflow.routing","name":"conditional branching and dynamic workflow routing","description":"Clevis supports conditional nodes that evaluate expressions (e.g., 'if user_input contains keyword') and route workflow execution to different branches. Users can chain conditions to create complex decision trees without code. The platform evaluates conditions at runtime and executes only the relevant branch, skipping unnecessary API calls and reducing costs.","intents":["I want my workflow to behave differently based on user input or previous results","I need to route requests to different AI models based on task complexity or cost","I want to skip expensive API calls if certain conditions are met"],"best_for":["Builders creating adaptive workflows that respond to different input types","Teams optimizing costs by routing simple tasks to cheaper models","Creators implementing multi-step approval or validation workflows"],"limitations":["Condition syntax is limited to simple comparisons (equals, contains, regex) — no complex boolean logic","No loop support — cannot iterate over arrays or repeat steps","Branching becomes visually complex with >5 conditions — difficult to maintain","No dynamic branch creation — all branches must be defined upfront"],"requires":["Understanding of condition syntax and available variables","Knowledge of expected data types for comparison"],"input_types":["variables from workflow context (user input, previous step outputs)","literal values (strings, numbers, booleans)"],"output_types":["branch selection (which path to execute next)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clevis__cap_9","uri":"capability://tool.use.integration.integration.with.external.apis.and.webhooks","name":"integration with external apis and webhooks","description":"Clevis includes HTTP request nodes that allow workflows to call external APIs (REST endpoints, webhooks) and process responses. Users configure request method (GET, POST, etc.), headers, authentication (API keys, OAuth), and request body without writing code. Responses are automatically parsed and available to downstream workflow steps.","intents":["I want to fetch data from an external API and pass it to my AI model","I need to send workflow results to a webhook or external service","I want to integrate my workflow with third-party tools (Slack, Discord, etc.)"],"best_for":["Builders connecting AI workflows to existing business systems","Teams automating cross-platform workflows (e.g., AI analysis → Slack notification)","Creators integrating with SaaS tools without custom code"],"limitations":["Limited to REST APIs — no GraphQL, gRPC, or WebSocket support","Authentication is basic (API keys, Bearer tokens) — no advanced OAuth flows or mTLS","No built-in retry logic or circuit breaker for unreliable APIs","Request/response size limits are undocumented — large payloads may fail silently","No request/response transformation — must manually map fields between APIs"],"requires":["External API endpoint (HTTP accessible)","API credentials (if required by endpoint)","Understanding of API request/response format"],"input_types":["HTTP method (GET, POST, PUT, DELETE, etc.)","URL (with optional path parameters)","headers (Content-Type, Authorization, etc.)","request body (JSON, form data)"],"output_types":["HTTP response (status code, headers, body)","parsed response data (JSON, text)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support (Chrome 90+, Firefox 88+, Safari 14+)","API keys for integrated AI providers (OpenAI, Anthropic, etc.) if using cloud models","Basic understanding of API request/response structures and JSON","API keys for cloud providers (OpenAI, Anthropic, etc.)","For local models: running inference server (Ollama, vLLM, or similar) accessible via HTTP","Understanding of model-specific parameter ranges and output formats","Clevis account with workflow ownership","Published workflow (for publishing to marketplace)","Clevis account (for browsing and cloning)","Stripe account (requires business registration and tax ID in most jurisdictions)"],"failure_modes":["No support for complex control flow (loops, recursion, dynamic branching based on runtime conditions)","Limited ability to debug workflow execution — error messages lack stack traces or node-level execution logs","Visual DAG representation becomes unwieldy with >20 nodes, making large workflows difficult to maintain","No version control or collaborative editing — single-user workflows only","No automatic prompt optimization across providers — users must manually tune temperature/tokens for each model's quirks","Limited support for provider-specific features (e.g., OpenAI's function calling, Anthropic's extended thinking) — workflows must use lowest-common-denominator feature set","Latency varies significantly by provider; no built-in load balancing or failover to faster alternatives","Local model support requires manual setup of inference server; no bundled runtime","No branching — only linear version history","No collaborative editing — only one user can edit a workflow at a time","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.25,"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":"2026-05-24T12:16:29.717Z","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=clevis","compare_url":"https://unfragile.ai/compare?artifact=clevis"}},"signature":"+RcmMWuRDUlj3RWSAGQWoM55Tj713Fh8coNS6WIEJN/XpCLGotGNncrofm8KDdi8Fix09O64yzWkx/jcdgfYBQ==","signedAt":"2026-06-23T03:38:38.604Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/clevis","artifact":"https://unfragile.ai/clevis","verify":"https://unfragile.ai/api/v1/verify?slug=clevis","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"}}