{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_retune","slug":"retune","name":"Retune","type":"product","url":"https://retune.so","page_url":"https://unfragile.ai/retune","categories":["app-builders"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_retune__cap_0","uri":"capability://automation.workflow.visual.workflow.orchestration.with.drag.and.drop.node.composition","name":"visual workflow orchestration with drag-and-drop node composition","description":"Retune provides a canvas-based workflow builder where users connect pre-built nodes (AI models, data sources, conditional logic, API calls) through visual connections without writing code. The system likely uses a directed acyclic graph (DAG) execution model to parse node dependencies, validate connections, and execute workflows sequentially or in parallel based on node configuration. Each node encapsulates a discrete operation (LLM call, API request, data transformation) with configurable inputs/outputs that flow between connected nodes.","intents":["I want to chain multiple AI operations together without touching code","I need to build a customer service workflow that routes queries to different AI models based on intent","I want to create a data pipeline that fetches external data, processes it with an LLM, and returns results"],"best_for":["non-technical product managers building AI workflows","business analysts prototyping automation without engineering support","small teams needing rapid iteration on AI application logic"],"limitations":["No-code abstraction likely hides advanced control flow patterns; complex conditional logic may require workarounds","Visual canvas performance may degrade with 50+ nodes; no reported optimization for large workflows","Limited ability to debug intermediate node outputs without built-in inspection tools"],"requires":["Web browser with modern JavaScript support","Retune account with active project","API keys for any external services (OpenAI, Anthropic, custom APIs)"],"input_types":["text prompts","structured data (JSON)","API responses","file uploads"],"output_types":["text responses","structured JSON","API call results","formatted reports"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_retune__cap_1","uri":"capability://tool.use.integration.multi.model.llm.orchestration.with.provider.abstraction","name":"multi-model llm orchestration with provider abstraction","description":"Retune abstracts away provider-specific API differences (OpenAI, Anthropic, Cohere, etc.) through a unified node interface, allowing users to swap models or providers without reconfiguring downstream logic. The platform likely maintains a provider adapter layer that translates common parameters (temperature, max_tokens, system prompts) into provider-specific API calls and normalizes response formats back to a standard schema. This enables A/B testing across models and graceful fallback handling.","intents":["I want to test whether GPT-4 or Claude produces better outputs for my use case without rewriting workflows","I need to switch from OpenAI to a cheaper provider if costs spike","I want to route different request types to different models based on complexity"],"best_for":["teams evaluating multiple LLM providers for cost/quality tradeoffs","product managers A/B testing model performance on real user queries","cost-conscious builders wanting to use cheaper open-source models as fallbacks"],"limitations":["Provider abstraction may not expose advanced model-specific features (e.g., OpenAI's vision capabilities, Anthropic's extended thinking); users lose access to cutting-edge model features","Latency overhead from abstraction layer adds ~50-100ms per call","No built-in cost tracking or usage analytics per model; difficult to optimize spend across providers"],"requires":["API keys for at least one supported LLM provider","Understanding of model differences (context length, pricing, latency)","Retune account with model integration permissions"],"input_types":["text prompts","system instructions","structured parameters (temperature, max_tokens)"],"output_types":["normalized LLM responses","token usage metadata","provider-agnostic structured output"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_retune__cap_10","uri":"capability://automation.workflow.error.handling.and.retry.logic.without.code","name":"error handling and retry logic without code","description":"Retune allows users to configure error handling strategies (retry, fallback, skip) for workflow nodes through visual configuration, without writing code. The system likely supports exponential backoff retry strategies, fallback nodes that execute if primary nodes fail, and error propagation rules. This enables robust workflows that gracefully handle transient failures and API errors.","intents":["I want to retry an API call if it fails due to rate limiting","I need to use a fallback LLM provider if the primary provider is unavailable","I want to skip a workflow step if it fails and continue with the rest of the workflow"],"best_for":["teams building production workflows that require reliability","product managers ensuring AI applications handle failures gracefully","businesses integrating with unreliable external APIs"],"limitations":["Retry strategies limited to simple exponential backoff; no support for custom retry logic","Fallback handling may not support complex scenarios (e.g., fallback to fallback)","Error messages may not provide sufficient context for debugging","No built-in circuit breaker pattern; repeated failures may continue retrying indefinitely"],"requires":["Retune workflow with nodes that may fail","Configuration of retry and fallback strategies","Understanding of error handling patterns"],"input_types":["error types","retry configuration","fallback node selection"],"output_types":["retry attempts","fallback results","error logs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_retune__cap_11","uri":"capability://automation.workflow.team.collaboration.and.workflow.sharing","name":"team collaboration and workflow sharing","description":"Retune enables teams to collaborate on workflows through shared workspaces, role-based access control, and workflow sharing. The system likely manages permissions (view, edit, deploy) at the workflow level and tracks who made changes. This enables non-technical team members to contribute to workflow development while maintaining governance.","intents":["I want to share a workflow with my team so they can iterate on it","I need to restrict who can deploy workflows to production","I want to track who made changes to a workflow for audit purposes"],"best_for":["teams collaborating on AI workflow development","organizations requiring access control and audit trails","businesses with non-technical team members contributing to workflows"],"limitations":["Collaboration features likely limited to basic sharing and permissions; no real-time co-editing","No built-in approval workflow for changes; difficult to enforce review processes","Audit trail may not be comprehensive; difficult to track all changes","Role-based access control may be limited to basic roles (viewer, editor, admin)"],"requires":["Retune team account with multiple users","Appropriate permissions to share workflows","Understanding of role-based access control"],"input_types":["user email addresses","permission levels"],"output_types":["shared workflows","access logs","change history"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_retune__cap_2","uri":"capability://text.generation.language.prompt.engineering.and.a.b.testing.without.code","name":"prompt engineering and a/b testing without code","description":"Retune provides a built-in prompt editor with version control and A/B testing capabilities, allowing users to iterate on prompts and measure which variants produce better outputs. The system likely stores prompt versions, routes incoming requests to different prompt variants based on a split strategy (random, user ID, time-based), and aggregates metrics (response quality, user feedback, latency) to identify winning variants. This enables data-driven prompt optimization without requiring ML expertise.","intents":["I want to test two different system prompts to see which produces more accurate customer service responses","I need to measure whether adding few-shot examples improves output quality for my use case","I want to gradually roll out a new prompt version to 10% of users before full deployment"],"best_for":["product managers optimizing AI response quality iteratively","non-technical teams running prompt experiments without data science support","businesses wanting to measure prompt impact on user satisfaction metrics"],"limitations":["A/B testing requires sufficient traffic to reach statistical significance; low-volume use cases may not generate reliable results","No built-in statistical significance testing; users must manually interpret results","Limited ability to test complex prompt strategies (chain-of-thought, role-playing) without custom node logic"],"requires":["Active Retune project with at least one LLM node","Mechanism to capture user feedback or quality metrics (manual rating, downstream conversion tracking)","Sufficient traffic volume to generate meaningful A/B test results"],"input_types":["text prompts","system instructions","few-shot examples"],"output_types":["prompt variants","A/B test results (response quality, user feedback)","performance metrics (latency, cost per variant)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_retune__cap_3","uri":"capability://tool.use.integration.flexible.data.source.integration.with.custom.api.connections","name":"flexible data source integration with custom api connections","description":"Retune allows users to connect custom data sources (REST APIs, databases, file uploads) through a configuration interface that abstracts authentication, pagination, and response parsing. The platform likely provides a generic HTTP node or data connector that accepts endpoint URLs, headers, authentication credentials, and response mapping rules, enabling users to fetch external data without writing API client code. This supports both synchronous data fetching and asynchronous batch operations.","intents":["I want to fetch customer data from our internal API and pass it to an LLM for personalized responses","I need to query a database for context before generating a response","I want to integrate with third-party services (Stripe, Salesforce, Slack) without custom code"],"best_for":["teams building AI applications that require real-time data from internal systems","product managers integrating AI into existing business workflows","small businesses connecting multiple SaaS tools without engineering overhead"],"limitations":["No built-in support for complex authentication schemes (OAuth 2.0 with refresh tokens, mutual TLS); may require workarounds for enterprise APIs","Limited error handling and retry logic; failed API calls may not gracefully degrade","No built-in caching or request deduplication; high-volume workflows may hit rate limits","Response parsing limited to simple JSON/XML mapping; complex transformations require custom logic"],"requires":["API endpoint URL and authentication credentials (API key, basic auth, bearer token)","Understanding of target API schema and response format","Network access from Retune infrastructure to external APIs"],"input_types":["API endpoint URLs","authentication credentials","request parameters (query strings, request bodies)","file uploads"],"output_types":["parsed JSON/XML responses","structured data for downstream nodes","error messages and status codes"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_retune__cap_4","uri":"capability://automation.workflow.conditional.logic.and.branching.without.code","name":"conditional logic and branching without code","description":"Retune includes conditional nodes that allow users to branch workflow execution based on LLM outputs, data values, or user inputs without writing code. The system likely evaluates conditions (if-then-else, switch statements) against node outputs and routes execution to different downstream branches. This enables workflows to adapt behavior based on dynamic data, such as routing customer queries to different response templates based on detected intent.","intents":["I want to route customer inquiries to different response templates based on detected sentiment","I need to skip certain workflow steps if a condition isn't met","I want to retry an API call if the first attempt fails"],"best_for":["non-technical users building adaptive AI workflows","teams implementing conditional logic without developer support","product managers prototyping complex decision trees"],"limitations":["Limited to simple boolean and comparison operators; complex logical expressions may require workarounds","No built-in support for nested conditions; deeply nested logic becomes difficult to visualize","Condition evaluation happens synchronously; no support for async branching or parallel execution paths"],"requires":["Output from a previous node to evaluate against","Understanding of condition syntax (likely simple comparison operators)","Retune workflow with at least one upstream node"],"input_types":["text values","numeric values","boolean flags","structured data"],"output_types":["branched execution paths","conditional routing decisions"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_retune__cap_5","uri":"capability://tool.use.integration.deployment.and.api.exposure.for.custom.applications","name":"deployment and api exposure for custom applications","description":"Retune allows users to deploy workflows as callable APIs or embed them in custom applications through generated endpoints. The platform likely generates REST API endpoints that accept input parameters, execute the workflow, and return results, enabling developers to integrate Retune workflows into external applications without rebuilding logic. This may include webhook support for asynchronous execution and response formatting options.","intents":["I want to expose my Retune workflow as an API that my web application can call","I need to trigger a workflow from a Slack bot or custom integration","I want to embed an AI assistant into my product without rebuilding the logic"],"best_for":["developers integrating Retune workflows into existing applications","teams building AI-powered features without rebuilding logic","product managers deploying AI workflows to production quickly"],"limitations":["API rate limiting and quota management may require paid tier; free tier likely has restrictive limits","No built-in authentication beyond API keys; enterprise SSO or OAuth integration may not be available","Latency overhead from Retune infrastructure adds ~200-500ms per request; not suitable for sub-second response requirements","Limited customization of API response format; users may need to transform responses in their application"],"requires":["Deployed Retune workflow","API key for authentication","HTTP client library in target application","Understanding of workflow input/output schema"],"input_types":["JSON request bodies","query parameters","headers"],"output_types":["JSON responses","HTTP status codes","webhook payloads"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_retune__cap_6","uri":"capability://automation.workflow.user.input.collection.and.form.based.interaction","name":"user input collection and form-based interaction","description":"Retune provides form nodes that collect user input (text, file uploads, selections) within workflows, enabling interactive AI applications without custom frontend code. The system likely renders form fields based on node configuration, validates input, and passes collected data to downstream nodes. This enables building chatbot-like interfaces or multi-step workflows that gather information before processing.","intents":["I want to build a chatbot that asks follow-up questions based on user responses","I need to collect file uploads and process them with an LLM","I want to create a multi-step form that gathers context before generating a response"],"best_for":["non-technical teams building conversational AI interfaces","product managers prototyping user-facing AI features","businesses creating customer-facing AI tools without frontend development"],"limitations":["Limited form customization; complex UI requirements may require custom frontend code","No built-in validation beyond basic type checking; complex validation logic requires workarounds","File upload handling may have size limits; large file processing may require external storage","No built-in session management; multi-turn conversations require custom state handling"],"requires":["Retune workflow with form nodes","Web browser for user interaction","Understanding of form field types and validation rules"],"input_types":["text input","file uploads","dropdown selections","radio buttons","checkboxes"],"output_types":["collected user data","validated input","file references"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_retune__cap_7","uri":"capability://text.generation.language.response.formatting.and.template.based.output.generation","name":"response formatting and template-based output generation","description":"Retune provides template nodes that format LLM outputs into structured responses (JSON, HTML, markdown, plain text) without code. The system likely uses simple templating syntax (variable substitution, conditional blocks) to transform raw LLM outputs into application-specific formats. This enables consistent response formatting across workflows and easier integration with downstream systems.","intents":["I want to format LLM responses as JSON for my API consumers","I need to generate HTML emails from LLM outputs","I want to ensure responses follow a consistent structure regardless of model variation"],"best_for":["teams needing consistent response formatting across workflows","developers integrating Retune outputs into systems expecting specific formats","product managers ensuring brand-consistent AI responses"],"limitations":["Limited templating capabilities; complex transformations require custom code","No built-in validation that output matches expected schema; downstream systems may receive malformed data","Template syntax likely simple (variable substitution); no support for loops or complex logic"],"requires":["LLM output from upstream node","Template definition with variable placeholders","Understanding of target output format"],"input_types":["text responses","structured data","LLM outputs"],"output_types":["formatted JSON","HTML","markdown","plain text","structured responses"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_retune__cap_8","uri":"capability://automation.workflow.workflow.versioning.and.deployment.management","name":"workflow versioning and deployment management","description":"Retune tracks workflow versions and enables users to manage deployments (staging, production) without manual version control. The system likely maintains a version history of workflow changes, allows rollback to previous versions, and supports promoting workflows between environments. This enables safe iteration and production deployment without downtime.","intents":["I want to test a workflow change in staging before deploying to production","I need to quickly rollback to a previous workflow version if something breaks","I want to maintain separate workflows for different environments"],"best_for":["teams deploying AI workflows to production","product managers managing workflow changes safely","businesses requiring audit trails and change history"],"limitations":["Version control likely limited to workflow structure; no support for branching or merging complex changes","Rollback may not be instantaneous; production impact during rollback period","No built-in approval workflow; changes may deploy without review"],"requires":["Retune account with deployment permissions","Understanding of staging vs production environments","Access to workflow version history"],"input_types":["workflow changes","version selection"],"output_types":["deployed workflows","version history","deployment status"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_retune__cap_9","uri":"capability://automation.workflow.monitoring.and.usage.analytics.for.deployed.workflows","name":"monitoring and usage analytics for deployed workflows","description":"Retune provides dashboards that track workflow execution metrics (success rate, latency, cost, error rates) and usage patterns. The system likely logs each workflow execution, aggregates metrics over time, and surfaces insights through dashboards. This enables users to identify performance bottlenecks, optimize costs, and monitor production health without external monitoring tools.","intents":["I want to see how many times my workflow was called and what the average response time is","I need to track costs across different LLM providers to optimize spending","I want to identify which workflow steps are failing most frequently"],"best_for":["product managers monitoring AI application performance","teams optimizing workflow costs and latency","businesses tracking usage for billing and capacity planning"],"limitations":["Analytics likely limited to basic metrics; no support for custom events or business metrics","Data retention may be limited on free tier; historical analysis requires paid plan","No built-in alerting; users must manually check dashboards for issues","Limited drill-down capability; difficult to debug specific failed executions"],"requires":["Deployed Retune workflow with sufficient traffic","Access to analytics dashboard","Understanding of metrics (latency, success rate, cost)"],"input_types":["workflow execution logs"],"output_types":["usage metrics","performance dashboards","cost breakdowns","error reports"],"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","Retune account with active project","API keys for any external services (OpenAI, Anthropic, custom APIs)","API keys for at least one supported LLM provider","Understanding of model differences (context length, pricing, latency)","Retune account with model integration permissions","Retune workflow with nodes that may fail","Configuration of retry and fallback strategies","Understanding of error handling patterns","Retune team account with multiple users"],"failure_modes":["No-code abstraction likely hides advanced control flow patterns; complex conditional logic may require workarounds","Visual canvas performance may degrade with 50+ nodes; no reported optimization for large workflows","Limited ability to debug intermediate node outputs without built-in inspection tools","Provider abstraction may not expose advanced model-specific features (e.g., OpenAI's vision capabilities, Anthropic's extended thinking); users lose access to cutting-edge model features","Latency overhead from abstraction layer adds ~50-100ms per call","No built-in cost tracking or usage analytics per model; difficult to optimize spend across providers","Retry strategies limited to simple exponential backoff; no support for custom retry logic","Fallback handling may not support complex scenarios (e.g., fallback to fallback)","Error messages may not provide sufficient context for debugging","No built-in circuit breaker pattern; repeated failures may continue retrying indefinitely","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":"2026-05-24T12:16:33.095Z","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=retune","compare_url":"https://unfragile.ai/compare?artifact=retune"}},"signature":"JxAsNYVmpF7RjGdzIi1qR7j3wSpzZZOyVetL86NI3qvinb9XTPcNB78/R61m5m01k5HvQZ/xCBqxi9n7F96mBg==","signedAt":"2026-06-21T06:47:01.272Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/retune","artifact":"https://unfragile.ai/retune","verify":"https://unfragile.ai/api/v1/verify?slug=retune","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"}}