{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_riku-ai","slug":"riku-ai","name":"Riku.ai","type":"product","url":"https://riku.ai","page_url":"https://unfragile.ai/riku-ai","categories":["app-builders"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_riku-ai__cap_0","uri":"capability://automation.workflow.visual.workflow.builder.for.ai.logic.chaining","name":"visual workflow builder for ai logic chaining","description":"Riku.ai provides a drag-and-drop interface that allows non-technical users to visually compose multi-step AI workflows by connecting nodes representing API calls, LLM prompts, conditional logic, and data transformations. The builder abstracts away JSON/API complexity by exposing input/output mapping through a graphical interface, enabling users to chain together complex sequences without writing code. Under the hood, workflows are likely compiled into a DAG (directed acyclic graph) structure that executes sequentially or in parallel based on node dependencies.","intents":["Build a multi-step chatbot that calls external APIs without writing backend code","Chain together multiple LLM calls with conditional branching based on previous outputs","Create a content generation pipeline that transforms user input through multiple AI models","Orchestrate a customer support workflow that routes queries to different AI agents based on intent"],"best_for":["Non-technical founders and product managers prototyping AI features","Small teams without dedicated backend engineers","Startups iterating rapidly on customer-facing AI workflows"],"limitations":["Visual abstraction adds cognitive overhead for complex branching logic with 10+ nodes","Limited ability to express advanced control flow patterns (loops, recursive calls, state machines)","No version control or collaborative editing for workflows across team members","Debugging complex workflows requires manual inspection of execution logs rather than step-through debugging"],"requires":["Web browser with modern JavaScript support","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Basic understanding of API request/response structure"],"input_types":["text prompts","structured JSON from API responses","user form inputs","webhook payloads"],"output_types":["text (LLM responses)","structured JSON","API call results","webhook triggers"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_riku-ai__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.model.switching.with.unified.interface","name":"multi-provider llm model switching with unified interface","description":"Riku.ai abstracts away provider-specific API differences (OpenAI, Anthropic, Cohere, etc.) by exposing a unified model selection interface where users can swap between providers without changing prompt structure or workflow logic. This is implemented through a provider adapter layer that normalizes request/response formats, parameter mappings (temperature, max_tokens, etc.), and error handling across different LLM APIs. Users can A/B test models or switch providers based on cost/performance without rebuilding workflows.","intents":["Switch from GPT-4 to Claude mid-project to reduce API costs","A/B test different models on the same prompt to compare output quality","Avoid vendor lock-in by maintaining flexibility to migrate to cheaper or better-performing models","Use specialized models for specific tasks (e.g., Cohere for classification, GPT-4 for reasoning)"],"best_for":["Cost-conscious startups optimizing LLM spend across multiple models","Teams evaluating different LLM providers before committing to one","Builders wanting to hedge against provider outages or API changes"],"limitations":["Unified interface may not expose advanced provider-specific features (e.g., OpenAI's function calling, Anthropic's extended thinking)","Parameter normalization can mask subtle behavioral differences between models (e.g., temperature interpretation varies)","Switching models mid-production requires re-testing outputs for quality/consistency","No built-in cost tracking or optimization recommendations across providers"],"requires":["API keys for desired LLM providers (OpenAI, Anthropic, Cohere, etc.)","Understanding of model capabilities and pricing differences"],"input_types":["text prompts","system instructions","model parameters (temperature, max_tokens, top_p)"],"output_types":["text completions","structured JSON (if model supports it)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_riku-ai__cap_10","uri":"capability://automation.workflow.collaborative.workflow.editing.and.team.management","name":"collaborative workflow editing and team management","description":"Riku.ai likely provides team collaboration features that allow multiple users to work on the same workflows, though the editorial summary suggests this may be underdeveloped. This would include shared access to workflows, role-based permissions (viewer, editor, admin), and possibly version control or audit logs. The implementation likely uses a centralized workspace model where teams can organize workflows into projects or folders and manage access at the team level.","intents":["Allow multiple team members to build and edit workflows together","Control who can view, edit, or deploy workflows using role-based permissions","Track changes to workflows and understand who made modifications","Organize workflows into projects or folders for team collaboration"],"best_for":["Teams building AI workflows collaboratively","Organizations needing access control and audit trails","Startups scaling from solo builders to team-based development"],"limitations":["Collaborative editing features are not explicitly documented","No mention of real-time collaboration or conflict resolution for simultaneous edits","Version control and rollback capabilities are unclear","Audit logs and change tracking may be limited or unavailable","Role-based access control granularity is unknown"],"requires":["Team account with multiple users"],"input_types":["workflow definitions","user permissions"],"output_types":["shared workflows","access control lists"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_riku-ai__cap_11","uri":"capability://automation.workflow.error.handling.and.fallback.logic.in.workflows","name":"error handling and fallback logic in workflows","description":"Riku.ai allows workflows to include error handling nodes that catch failures from API calls or LLM requests and execute fallback logic. This might include retry logic, default values, or alternative workflow paths when steps fail. The implementation likely uses try-catch patterns at the workflow step level, allowing users to define what happens when an API call times out, an LLM request fails, or a webhook returns an error. This prevents entire workflows from failing due to a single step's error.","intents":["Retry failed API calls automatically before escalating to manual intervention","Use a cached or default response if an LLM API call fails","Route to a fallback workflow if a primary step fails","Gracefully handle timeouts and rate limiting from external APIs"],"best_for":["Teams running production workflows that need resilience","Builders creating workflows that depend on external APIs","Startups automating critical business processes"],"limitations":["Error handling configuration is likely basic (unclear if exponential backoff or circuit breakers are supported)","No built-in dead letter queues or error tracking for failed workflows","Fallback logic is limited to simple alternatives (no complex error recovery strategies)","Unclear if error handling is per-step or workflow-level","No integration with external error tracking platforms (Sentry, Rollbar, etc.)"],"requires":["Understanding of error types and recovery strategies"],"input_types":["error conditions","fallback logic"],"output_types":["error handling results","fallback responses"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_riku-ai__cap_12","uri":"capability://automation.workflow.workflow.deployment.and.versioning","name":"workflow deployment and versioning","description":"Riku.ai allows users to deploy workflows to production and manage multiple versions. This likely includes the ability to publish a workflow, create new versions, and potentially roll back to previous versions if issues arise. The platform probably maintains a version history and allows users to compare versions or promote versions from staging to production. Deployment is likely one-click or automatic, without requiring manual infrastructure setup.","intents":["Deploy a tested workflow to production without downtime","Create a new version of a workflow while keeping the old version running","Roll back to a previous workflow version if a new version causes issues","Compare changes between workflow versions to understand what changed"],"best_for":["Teams managing multiple workflow versions in production","Builders iterating on workflows without disrupting users","Startups needing quick deployment without DevOps infrastructure"],"limitations":["Version control is likely basic (no branching or merge capabilities mentioned)","No built-in testing or staging environment before production deployment","Rollback process and time to rollback are unclear","No canary deployments or gradual rollout strategies","Unclear if version history is retained indefinitely or pruned after a certain period"],"requires":["Workflow tested and ready for deployment"],"input_types":["workflow definitions"],"output_types":["deployed workflows","version history"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_riku-ai__cap_2","uri":"capability://tool.use.integration.real.time.api.and.webhook.integration.for.workflow.triggers","name":"real-time api and webhook integration for workflow triggers","description":"Riku.ai enables workflows to be triggered by incoming webhooks and to call external APIs as workflow steps, with real-time request/response handling. The platform exposes webhook URLs that can receive POST requests from external systems, parse the payload, and execute workflows with that data as input. Workflows can also make HTTP calls to third-party APIs (Slack, Stripe, Salesforce, etc.) as intermediate steps, with response data flowing into subsequent nodes. This is implemented through a webhook listener service and HTTP client abstraction that handles authentication (API keys, OAuth), retries, and timeout management.","intents":["Trigger an AI workflow when a customer submits a form or sends a message to Slack","Call Stripe API to fetch customer data, pass it to an LLM for personalized content generation, then post results back to Slack","Build a customer support bot that receives tickets via webhook, analyzes them with an LLM, and creates Jira issues automatically","Integrate AI-generated content into existing business tools (CRM, email platform, knowledge base) without custom backend code"],"best_for":["Teams integrating AI into existing SaaS stacks (Slack, Salesforce, Stripe, etc.)","Builders creating event-driven AI workflows triggered by customer actions","Non-technical users automating business processes without backend infrastructure"],"limitations":["Webhook URLs are public and require authentication/validation to prevent abuse (unclear if Riku.ai enforces this by default)","No built-in request signing or HMAC validation for webhook security","API call latency adds to overall workflow execution time (no parallel API calls mentioned)","Limited retry logic and error handling for failed API calls (unclear if exponential backoff is implemented)","No built-in rate limiting or throttling for high-volume webhook traffic"],"requires":["Webhook URL accessible from external systems","API keys or OAuth credentials for third-party services","Understanding of HTTP request/response structure"],"input_types":["JSON webhook payloads","form submissions","API responses"],"output_types":["HTTP POST requests to external APIs","webhook responses","API response data for downstream workflow steps"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_riku-ai__cap_3","uri":"capability://text.generation.language.prompt.engineering.and.template.management","name":"prompt engineering and template management","description":"Riku.ai provides a prompt editor interface where users can write and test LLM prompts with variable substitution, system instructions, and example-based few-shot learning. The platform likely stores prompts as templates with named variables (e.g., {{customer_name}}, {{product_type}}) that are populated at runtime from workflow inputs or previous step outputs. Users can test prompts interactively before deploying them to production workflows, with version history and rollback capabilities (unclear if explicitly stated). This abstracts away raw API calls and enables non-technical users to iterate on prompt quality without understanding JSON request formatting.","intents":["Write and test a customer support prompt with system instructions and few-shot examples","Create reusable prompt templates for common tasks (summarization, classification, content generation)","Iterate on prompt quality by testing variations against sample inputs","Version control prompts and roll back to previous versions if quality degrades"],"best_for":["Non-technical content creators and product managers optimizing prompts","Teams building multiple AI workflows that share similar prompt patterns","Builders experimenting with prompt engineering techniques without coding"],"limitations":["No advanced prompt engineering features like chain-of-thought templates or structured output schemas","Variable substitution is basic string replacement (no conditional logic or transformations)","Limited visibility into token usage and cost per prompt variation","No built-in prompt testing framework or A/B testing for prompt variants","Version history and rollback capabilities not explicitly documented"],"requires":["Basic understanding of LLM prompting principles","Access to at least one LLM provider API"],"input_types":["text prompts","system instructions","few-shot examples","variable placeholders"],"output_types":["LLM completions","prompt templates"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_riku-ai__cap_4","uri":"capability://automation.workflow.conditional.logic.and.branching.in.workflows","name":"conditional logic and branching in workflows","description":"Riku.ai allows workflows to include conditional branches based on LLM outputs, API responses, or user inputs. This is implemented through if/then/else nodes that evaluate conditions (e.g., 'if sentiment is negative, route to escalation workflow') and route execution to different workflow paths. The platform likely supports basic comparison operators (equals, contains, greater than) and boolean logic (AND, OR). Conditions can reference outputs from previous workflow steps, enabling data-driven branching without hardcoding logic.","intents":["Route customer support tickets to different AI agents based on sentiment analysis","Skip workflow steps if certain conditions are met (e.g., skip payment if customer is VIP)","Implement fallback logic (if API call fails, use cached response or default value)","Create multi-path workflows where different customers see different AI-generated content based on their profile"],"best_for":["Teams building intelligent routing and decision-making workflows","Builders creating personalized AI experiences based on user attributes","Startups automating complex business logic without backend code"],"limitations":["Limited to simple comparison operators (unclear if regex or complex pattern matching is supported)","No support for loops or recursive branching (workflows are DAGs, not Turing-complete)","Debugging branching logic requires manual inspection of execution logs","No built-in testing framework for validating branching conditions across test cases","Complex nested conditions become difficult to visualize in the workflow builder"],"requires":["Understanding of conditional logic and boolean operators"],"input_types":["LLM outputs","API response data","user inputs","workflow variables"],"output_types":["routed workflow paths","conditional execution results"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_riku-ai__cap_5","uri":"capability://data.processing.analysis.data.transformation.and.variable.mapping.between.workflow.steps","name":"data transformation and variable mapping between workflow steps","description":"Riku.ai provides a data mapping interface that allows users to transform and route data between workflow steps without writing code. This includes extracting fields from JSON responses, formatting text, and mapping outputs from one step to inputs of the next. The platform likely uses a visual JSON path selector or simple field mapping UI where users can click to select which data to pass forward. This abstracts away the need to write JavaScript or Python for data transformation, making workflows accessible to non-technical users.","intents":["Extract the 'sentiment' field from an LLM classification response and use it to route to different workflow paths","Transform an API response (e.g., customer data from Salesforce) into a format suitable for an LLM prompt","Map LLM output to fields in a downstream API call (e.g., generated email body to Slack message)","Combine data from multiple sources (user input + API response + LLM output) into a single structured object"],"best_for":["Non-technical users building workflows that integrate multiple APIs and LLMs","Teams avoiding custom backend code for data transformation","Builders prototyping complex data pipelines without engineering resources"],"limitations":["Limited to simple field extraction and mapping (no complex transformations like aggregation, filtering, or grouping)","No support for array operations or nested object manipulation","Unclear if custom transformation functions or expressions are supported","Data type conversions (string to number, date parsing) may require manual handling","No built-in validation or schema enforcement for transformed data"],"requires":["Understanding of JSON structure and field paths"],"input_types":["JSON objects","API responses","LLM outputs","user inputs"],"output_types":["transformed JSON","mapped workflow variables"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_riku-ai__cap_6","uri":"capability://automation.workflow.workflow.execution.monitoring.and.logging","name":"workflow execution monitoring and logging","description":"Riku.ai provides execution logs and monitoring dashboards that show the status of workflow runs, including which steps succeeded/failed, execution time per step, and error messages. Users can inspect individual workflow executions to debug issues, view input/output data at each step, and understand where failures occurred. This is likely implemented through a centralized logging service that captures execution traces and exposes them via a dashboard UI. Logs may be retained for a limited time period (unclear if there's long-term log storage or archival).","intents":["Debug why a workflow failed by inspecting execution logs and error messages","Monitor workflow performance and identify bottleneck steps","Audit workflow executions for compliance or troubleshooting customer issues","Understand data flow through a complex workflow by viewing intermediate step outputs"],"best_for":["Teams running production AI workflows that need visibility into failures","Builders debugging complex multi-step workflows","Startups monitoring workflow performance and reliability"],"limitations":["Log retention period unclear (may be limited to recent executions only)","No built-in alerting or notifications for failed workflows","Limited filtering and search capabilities for finding specific executions","No integration with external monitoring/observability platforms (DataDog, New Relic, etc.)","Unclear if logs include full request/response payloads or are truncated for privacy"],"requires":["Access to Riku.ai dashboard"],"input_types":["workflow execution traces"],"output_types":["execution logs","error messages","performance metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_riku-ai__cap_7","uri":"capability://automation.workflow.scheduled.workflow.execution.and.cron.based.triggers","name":"scheduled workflow execution and cron-based triggers","description":"Riku.ai allows workflows to be executed on a schedule (e.g., daily, weekly, or custom cron expressions) without requiring external scheduling infrastructure. This is implemented through a scheduler service that triggers workflows at specified intervals and passes any configured parameters as input. Scheduled workflows can be used for batch processing, periodic data synchronization, or recurring AI tasks. The platform likely stores schedule configurations and manages execution history for scheduled runs.","intents":["Generate daily AI-powered reports and email them to stakeholders","Sync data between systems on a schedule (e.g., pull customer data from Salesforce daily and process with AI)","Run batch content generation jobs (e.g., generate product descriptions for 1000 items nightly)","Monitor external systems periodically and alert if anomalies are detected"],"best_for":["Teams automating recurring business processes without cron servers","Builders creating batch AI processing pipelines","Startups running scheduled reports or data synchronization"],"limitations":["Unclear if cron expressions are fully supported or limited to preset intervals (daily, weekly, etc.)","No built-in retry logic for failed scheduled executions","Timezone handling may be limited (unclear if user timezone is respected)","No visibility into scheduled execution history or missed runs","Scaling limitations for high-frequency schedules (e.g., every minute)"],"requires":["Workflow configured and tested before scheduling"],"input_types":["schedule configuration (interval or cron expression)","optional workflow parameters"],"output_types":["scheduled workflow executions","execution results"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_riku-ai__cap_8","uri":"capability://text.generation.language.multi.step.prompt.chaining.with.context.preservation","name":"multi-step prompt chaining with context preservation","description":"Riku.ai enables workflows to chain multiple LLM calls together, with outputs from one prompt fed as inputs to the next. The platform preserves context across steps, allowing later prompts to reference earlier outputs and build on previous reasoning. This is implemented through a context management layer that maintains state across workflow steps and passes relevant data forward. For example, a workflow might first classify a customer message, then generate a response based on the classification, then summarize the interaction. Each step has access to outputs from previous steps.","intents":["Build a multi-turn reasoning workflow where each LLM call builds on previous outputs","Implement a classification → generation pipeline (classify intent, then generate appropriate response)","Create a summarization → analysis workflow (summarize customer feedback, then analyze sentiment)","Chain multiple specialized models for different tasks in a single workflow"],"best_for":["Teams building complex AI reasoning workflows","Builders implementing multi-step content generation pipelines","Startups creating sophisticated customer-facing AI features"],"limitations":["Context grows with each step, increasing token usage and latency","No built-in optimization for context window management (unclear if irrelevant context is pruned)","Debugging multi-step chains requires inspecting outputs at each step","No built-in cost tracking for cumulative token usage across steps","Limited visibility into how context is passed between steps"],"requires":["Understanding of how to structure prompts for multi-step reasoning","API keys for LLM providers"],"input_types":["initial user input","LLM outputs from previous steps"],"output_types":["final LLM output","intermediate step outputs"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_riku-ai__cap_9","uri":"capability://automation.workflow.freemium.usage.based.pricing.with.transparent.cost.tracking","name":"freemium usage-based pricing with transparent cost tracking","description":"Riku.ai offers a freemium model where users can build and test workflows for free, with paid tiers based on API usage (LLM calls, API integrations, workflow executions). The platform likely tracks usage metrics (number of LLM calls, tokens consumed, API calls made) and displays them in a dashboard. However, the editorial summary notes that pricing opacity and unclear scaling costs are a weakness, suggesting that cost tracking may not be transparent enough for users to predict expenses as usage grows.","intents":["Experiment with AI workflows without upfront cost commitment","Understand how much each workflow step costs in terms of API usage","Scale from free tier to paid tier as usage grows","Monitor and optimize spending on LLM API calls"],"best_for":["Startups and small teams with limited budgets","Builders experimenting with AI workflows before committing resources","Teams wanting to avoid upfront licensing costs"],"limitations":["Pricing tiers and cost structure are not clearly documented (per editorial summary)","Unclear how costs scale as usage grows (per-call pricing, tiered pricing, etc.)","No built-in cost optimization recommendations or alerts for high spending","Free tier limits may be restrictive for production use","Switching from free to paid tier may require manual intervention or approval"],"requires":["Credit card for paid tier (if usage exceeds free limits)"],"input_types":["usage metrics (LLM calls, API calls, workflow executions)"],"output_types":["cost estimates","usage reports"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Basic understanding of API request/response structure","API keys for desired LLM providers (OpenAI, Anthropic, Cohere, etc.)","Understanding of model capabilities and pricing differences","Team account with multiple users","Understanding of error types and recovery strategies","Workflow tested and ready for deployment","Webhook URL accessible from external systems","API keys or OAuth credentials for third-party services"],"failure_modes":["Visual abstraction adds cognitive overhead for complex branching logic with 10+ nodes","Limited ability to express advanced control flow patterns (loops, recursive calls, state machines)","No version control or collaborative editing for workflows across team members","Debugging complex workflows requires manual inspection of execution logs rather than step-through debugging","Unified interface may not expose advanced provider-specific features (e.g., OpenAI's function calling, Anthropic's extended thinking)","Parameter normalization can mask subtle behavioral differences between models (e.g., temperature interpretation varies)","Switching models mid-production requires re-testing outputs for quality/consistency","No built-in cost tracking or optimization recommendations across providers","Collaborative editing features are not explicitly documented","No mention of real-time collaboration or conflict resolution for simultaneous edits","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"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":"2026-04-05T13:23:42.551Z","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=riku-ai","compare_url":"https://unfragile.ai/compare?artifact=riku-ai"}},"signature":"YnHB+81fOLHQv4bWETb4D73AIdEJlUcy458UiOFwelM4qe7sXBoY3mKKW+bEHszn6LuVSLIYhG/CUW5bF16fAw==","signedAt":"2026-06-22T04:24:44.570Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/riku-ai","artifact":"https://unfragile.ai/riku-ai","verify":"https://unfragile.ai/api/v1/verify?slug=riku-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"}}