{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_aigur-dev","slug":"aigur-dev","name":"Aigur.dev","type":"product","url":"https://client.aigur.dev","page_url":"https://unfragile.ai/aigur-dev","categories":["app-builders","automation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_aigur-dev__cap_0","uri":"capability://automation.workflow.visual.workflow.builder.with.drag.and.drop.node.composition","name":"visual workflow builder with drag-and-drop node composition","description":"Provides a canvas-based interface where users drag AI operation nodes (LLM calls, data transformations, conditionals, loops) and connect them via edges to define execution flow. The builder likely uses a graph-based data model (DAG) to represent workflows, with real-time validation of node connections and type compatibility. Workflows are stored as JSON/YAML configurations that can be versioned and deployed without code generation.","intents":["I want to design an AI workflow without writing code or learning a programming language","I need to quickly prototype a multi-step process that chains LLM calls with data transformations","I want to see the entire workflow structure visually before executing it"],"best_for":["non-technical product managers building AI automation","operations teams prototyping workflows before engineering handoff","citizen developers in enterprises without AI/ML background"],"limitations":["Visual abstraction hides complexity — advanced conditional logic or error handling may require custom node types","Large workflows (50+ nodes) become difficult to navigate and debug in a single canvas view","No built-in version control branching — workflow history is linear"],"requires":["Web browser with modern JavaScript support (Chrome, Firefox, Safari, Edge)","Account creation on Aigur.dev platform"],"input_types":["node configuration (JSON/YAML)","user input via form fields","API responses from connected services"],"output_types":["workflow definition (DAG representation)","execution logs and results","shareable workflow templates"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aigur-dev__cap_1","uri":"capability://automation.workflow.real.time.collaborative.workflow.editing.with.presence.awareness","name":"real-time collaborative workflow editing with presence awareness","description":"Enables multiple team members to edit the same workflow concurrently using operational transformation or CRDT-based conflict resolution. The platform tracks cursor positions, node selections, and edits in real-time, showing which team member is working on which part of the workflow. Changes are synchronized across all connected clients without requiring manual merges or version conflict resolution.","intents":["I want my team to design workflows together without waiting for each person to finish their turn","I need to see what my colleague is currently editing in the workflow to avoid duplicate work","I want to iterate on a workflow with real-time feedback from multiple stakeholders"],"best_for":["distributed teams collaborating asynchronously on AI automation","product teams with non-technical and technical members co-designing workflows","organizations doing rapid workflow iteration with stakeholder feedback"],"limitations":["Real-time sync adds 100-500ms latency depending on network conditions and server load","Conflict resolution for simultaneous node deletions or property changes may produce unexpected results","No built-in audit trail of who made which changes — only activity logs"],"requires":["Stable internet connection with WebSocket support","Team members with Aigur.dev accounts and workspace access","Browser with ES6+ support for real-time sync client"],"input_types":["node/edge modifications (add, delete, update)","property changes (node config, labels)","cursor/selection events"],"output_types":["synchronized workflow state across clients","activity feed showing edits","presence indicators (who is online)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aigur-dev__cap_10","uri":"capability://tool.use.integration.integration.with.external.apis.and.services.via.pre.built.connectors","name":"integration with external apis and services via pre-built connectors","description":"Provides pre-built connector nodes for popular services (Slack, Google Sheets, Salesforce, HubSpot, etc.) that handle authentication, request formatting, and response parsing. Users select a connector, authenticate with the service, and configure the operation (e.g., 'send Slack message', 'append row to Google Sheet'). The platform manages API credentials securely and abstracts away service-specific API details.","intents":["I want to send workflow results to Slack or email without managing API authentication","I need to read data from Google Sheets or Salesforce and use it in my workflow","I want to update external systems (CRM, project management tools) based on workflow output"],"best_for":["teams integrating AI workflows with existing business tools and services","non-technical users connecting workflows to external systems without API knowledge","organizations using popular SaaS tools (Slack, Google Workspace, Salesforce, etc.)"],"limitations":["Limited connector library — niche or newer services may not be supported","Connector features may lag behind official service APIs — advanced operations may require custom code nodes","Authentication requires users to grant Aigur.dev access to their service accounts (potential security concern)"],"requires":["Account with the external service (Slack workspace, Google account, Salesforce org, etc.)","Appropriate permissions to authenticate and perform operations in that service"],"input_types":["connector selection","authentication credentials (OAuth token, API key, etc.)","operation parameters (message text, sheet name, record fields, etc.)"],"output_types":["operation result (success/failure)","response data from service (message ID, row number, record ID, etc.)","error messages if operation fails"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aigur-dev__cap_11","uri":"capability://automation.workflow.workflow.scheduling.and.cron.based.automation","name":"workflow scheduling and cron-based automation","description":"Allows workflows to be executed on a schedule (daily, weekly, monthly, or custom cron expressions) without manual triggering. Users configure the schedule in the workflow settings, and the platform's scheduler triggers executions at the specified times. Scheduled executions are treated like any other execution, with full logging and monitoring available.","intents":["I want my workflow to run automatically every day at a specific time (e.g., daily report generation)","I need to process data on a recurring schedule without manual intervention","I want to set up a cron job for complex AI tasks without managing infrastructure"],"best_for":["teams automating recurring tasks (daily reports, weekly summaries, etc.)","organizations running batch AI processing on a schedule","systems requiring reliable, repeatable execution without manual triggers"],"limitations":["Scheduler granularity may be limited (e.g., minute-level precision may not be supported)","No built-in support for timezone-aware scheduling — may execute at unexpected times for global teams","Missed executions (e.g., due to platform downtime) may not be retried automatically"],"requires":["Workflow definition with schedule configuration","Understanding of cron syntax (if using custom expressions)"],"input_types":["schedule definition (daily, weekly, monthly, or cron expression)","timezone (if supported)","input parameters for scheduled execution"],"output_types":["scheduled execution confirmation","execution logs and results","next scheduled execution time"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aigur-dev__cap_12","uri":"capability://automation.workflow.team.workspace.management.with.role.based.access.control","name":"team workspace management with role-based access control","description":"Organizes workflows, templates, and team members into workspaces with role-based permissions. Workspace admins can invite team members, assign roles (admin, editor, viewer, executor), and control access to workflows and resources. The platform enforces permissions at the workflow level, preventing unauthorized users from viewing, editing, or executing workflows.","intents":["I want to organize my team's workflows into separate projects or departments","I need to control who can edit, execute, or view specific workflows","I want to invite new team members and assign them appropriate permissions"],"best_for":["organizations with multiple teams or departments using Aigur.dev","enterprises requiring governance and access control for AI workflows","teams with mixed skill levels (admins, editors, viewers)"],"limitations":["Role granularity may be limited — no mention of custom roles or fine-grained permissions","No built-in audit logging of permission changes or access attempts","Workspace isolation may not prevent cross-workspace data leakage if not properly implemented"],"requires":["Aigur.dev account with admin or owner permissions","Team members' email addresses to invite"],"input_types":["workspace name and description","team member email addresses","role assignments (admin, editor, viewer, executor)"],"output_types":["workspace creation confirmation","team member list with roles","access control list (ACL) for workflows"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aigur-dev__cap_2","uri":"capability://text.generation.language.llm.node.abstraction.with.multi.provider.support.and.prompt.templating","name":"llm node abstraction with multi-provider support and prompt templating","description":"Provides a standardized node type for LLM calls that abstracts away provider-specific APIs (OpenAI, Anthropic, Cohere, local models). Users configure the node with a prompt template (supporting variable interpolation from upstream nodes), model selection, temperature, max tokens, and other hyperparameters. The platform handles authentication, request formatting, and response parsing transparently, allowing non-technical users to chain LLM calls without managing API keys or request/response schemas.","intents":["I want to use different LLM providers in the same workflow without learning each API","I need to pass data from one LLM call's output into the next LLM's prompt dynamically","I want to adjust model parameters (temperature, max tokens) without rewriting code"],"best_for":["teams experimenting with multiple LLM providers to find cost/quality tradeoffs","non-technical users building multi-step reasoning workflows","organizations with existing relationships with multiple LLM vendors"],"limitations":["Abstraction layer adds 50-150ms overhead per LLM call due to request marshaling and response parsing","Limited support for advanced features like function calling, vision models, or streaming responses","No built-in prompt optimization or A/B testing — users must manually create separate nodes to compare prompts"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Understanding of prompt engineering basics (variable syntax, output format specification)"],"input_types":["prompt template (text with {{variable}} placeholders)","model identifier (string)","hyperparameters (temperature, max_tokens, top_p, etc.)","upstream node outputs (any type)"],"output_types":["LLM response text","token usage metadata","structured output (if prompt specifies JSON format)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aigur-dev__cap_3","uri":"capability://data.processing.analysis.data.transformation.and.extraction.nodes.with.schema.mapping","name":"data transformation and extraction nodes with schema mapping","description":"Provides pre-built node types for common data operations: JSON path extraction, field mapping, filtering, aggregation, and format conversion (CSV to JSON, etc.). Users define transformations declaratively (e.g., 'extract field X from input, rename to Y, filter where Z > 10') without writing code. The platform likely uses a schema-based approach where users specify input/output shapes, enabling type checking and validation across the workflow.","intents":["I need to extract specific fields from an API response or LLM output to pass to the next step","I want to transform data format (CSV to JSON, flatten nested objects) without coding","I need to filter or aggregate results based on conditions before passing downstream"],"best_for":["teams building data pipelines that mix AI and traditional ETL operations","non-technical users handling structured data transformations","workflows that need to normalize data from multiple sources before LLM processing"],"limitations":["Limited to declarative transformations — complex custom logic requires external code nodes or webhooks","No support for advanced operations like joins, window functions, or recursive transformations","Schema inference may fail on deeply nested or heterogeneous data structures"],"requires":["Understanding of JSON/CSV structure and basic data transformation concepts","Input data in structured format (JSON, CSV, XML)"],"input_types":["JSON objects","CSV/TSV data","XML documents","arrays of objects"],"output_types":["transformed JSON","flattened/nested structures","filtered arrays","aggregated results"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aigur-dev__cap_4","uri":"capability://planning.reasoning.conditional.branching.and.loop.control.flow.nodes","name":"conditional branching and loop control flow nodes","description":"Allows workflows to include decision points (if/else based on upstream data), loops (iterate over arrays with per-item processing), and error handling branches. Users define conditions using a visual rule builder (e.g., 'if field X equals Y, go to node A, else go to node B'). The platform executes branches conditionally and manages loop state, enabling complex multi-path workflows without explicit code.","intents":["I want my workflow to take different paths based on LLM output or data conditions","I need to process each item in a list through the same workflow steps","I want to handle errors gracefully by routing failed executions to a fallback step"],"best_for":["teams building decision-tree-like AI workflows (e.g., customer support routing)","workflows processing variable-length lists or batches","systems requiring error recovery and retry logic"],"limitations":["Visual rule builder may become unwieldy for complex boolean logic (many AND/OR conditions)","Loop performance degrades with large datasets (1000+ items) due to sequential execution","No built-in support for parallel branch execution — all paths execute serially"],"requires":["Understanding of conditional logic and control flow concepts","Data from upstream nodes to evaluate conditions against"],"input_types":["boolean expressions","comparison operators (==, !=, >, <, contains, etc.)","arrays for iteration","error objects from failed nodes"],"output_types":["branch selection (which path to execute)","loop iteration state","error handling results"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aigur-dev__cap_5","uri":"capability://automation.workflow.workflow.deployment.and.execution.with.version.management","name":"workflow deployment and execution with version management","description":"Converts visual workflows into executable configurations that can be deployed to a serverless execution environment. The platform manages versioning (each save creates a snapshot), allowing users to roll back to previous versions or run multiple versions in parallel. Deployments are triggered manually or via webhooks/API calls, with execution tracked and logged for debugging and audit purposes.","intents":["I want to deploy my workflow so it runs automatically when triggered by an event","I need to keep a history of workflow versions and revert to a previous version if something breaks","I want to monitor workflow executions and see logs to debug failures"],"best_for":["teams moving workflows from prototype to production","organizations requiring audit trails and version control for compliance","systems needing reliable, repeatable workflow execution"],"limitations":["Serverless execution model may have cold start latency (1-5 seconds) for infrequently-used workflows","No built-in support for long-running workflows (>15 minutes) — may require async job queues","Execution logs are retained for limited time (unclear retention policy from product description)"],"requires":["Workflow definition in Aigur.dev format","Deployment permissions in the workspace","API keys for external services called by the workflow"],"input_types":["workflow definition (DAG)","trigger configuration (webhook URL, schedule, manual)","input parameters for workflow execution"],"output_types":["execution ID and status","execution logs and timestamps","workflow output data","error messages and stack traces"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aigur-dev__cap_6","uri":"capability://tool.use.integration.webhook.and.api.trigger.configuration.for.workflow.invocation","name":"webhook and api trigger configuration for workflow invocation","description":"Allows workflows to be triggered by incoming HTTP requests (webhooks) or called directly via REST API. Users configure the trigger endpoint, specify expected input parameters, and map incoming request data to workflow variables. The platform generates a unique webhook URL for each workflow, handles authentication (API keys or basic auth), and routes requests to the appropriate workflow version.","intents":["I want to trigger my workflow when an external service sends a webhook (e.g., form submission, Slack message)","I need to call my workflow from my application via API without managing infrastructure","I want to expose my workflow as a reusable service that other teams can integrate with"],"best_for":["teams integrating AI workflows into existing applications","organizations building event-driven architectures with AI components","systems requiring synchronous workflow execution with immediate response"],"limitations":["Webhook delivery is not guaranteed (no built-in retry mechanism mentioned) — may require external reliability layer","Synchronous API calls block until workflow completes, limiting scalability for long-running workflows","No built-in rate limiting or quota management — potential for abuse or unexpected costs"],"requires":["HTTP client or webhook sender (Zapier, Make, custom code, etc.)","API key for authentication (if required by Aigur.dev)","Understanding of HTTP request/response format"],"input_types":["HTTP POST/GET requests","JSON request body","URL query parameters","HTTP headers"],"output_types":["HTTP response with workflow output","status code (200, 400, 500, etc.)","JSON response body","execution metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aigur-dev__cap_7","uri":"capability://automation.workflow.workflow.template.library.and.sharing.with.team.access.control","name":"workflow template library and sharing with team access control","description":"Provides a repository of pre-built workflow templates for common use cases (customer support, content generation, data processing, etc.). Users can browse templates, fork them into their workspace, and customize for their needs. The platform implements role-based access control (RBAC) for sharing workflows within teams, with granular permissions (view, edit, execute, delete). Templates can be marked as public (organization-wide) or private (individual).","intents":["I want to start with a template instead of building a workflow from scratch","I need to share a workflow with my team so they can use or modify it","I want to enforce that certain workflows can only be executed by specific team members"],"best_for":["organizations standardizing on common workflow patterns","teams with mixed technical skill levels (templates help non-technical users get started)","enterprises requiring governance and access control for AI workflows"],"limitations":["Template library quality and coverage unknown — may have limited templates for niche use cases","RBAC implementation details unclear — may lack fine-grained permissions (e.g., execute-only without edit)","No built-in template versioning — updates to shared templates may break dependent workflows"],"requires":["Aigur.dev account with workspace access","Appropriate permissions to create/share workflows"],"input_types":["template selection","customization parameters","access control settings (who can view/edit/execute)"],"output_types":["forked workflow instance","shared workflow link","access control list (ACL)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aigur-dev__cap_8","uri":"capability://automation.workflow.execution.monitoring.and.logging.with.performance.metrics","name":"execution monitoring and logging with performance metrics","description":"Tracks workflow executions in real-time, displaying execution status (running, completed, failed), step-by-step logs with timestamps, and performance metrics (execution time per node, total duration, token usage for LLM calls). Users can drill into individual executions to debug failures, inspect intermediate data, and understand bottlenecks. Logs are searchable and filterable by status, date range, or node.","intents":["I need to debug why a workflow execution failed and see what data caused the error","I want to understand which workflow steps are slow and optimize them","I need to track LLM token usage and costs across executions for billing purposes"],"best_for":["teams operating AI workflows in production and requiring observability","organizations tracking AI costs and optimizing for efficiency","developers debugging complex multi-step workflows"],"limitations":["Log retention period unclear — may not support long-term historical analysis","No built-in alerting or anomaly detection — users must manually monitor logs","Performance metrics may not include external API latency (only Aigur.dev execution time)"],"requires":["Workflow deployed and executed at least once","Access to execution logs (may require specific permissions)"],"input_types":["execution ID or date range filter","search queries (by status, node name, error message)"],"output_types":["execution timeline and status","step-by-step logs with timestamps","performance metrics (duration, token count)","error messages and stack traces","intermediate data snapshots"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_aigur-dev__cap_9","uri":"capability://code.generation.editing.custom.code.node.execution.with.sandboxed.runtime","name":"custom code node execution with sandboxed runtime","description":"Allows advanced users to write custom JavaScript or Python code within a workflow node, executed in a sandboxed environment with access to upstream workflow data. The code node provides a standard interface for input/output, with libraries available for common tasks (HTTP requests, JSON processing, etc.). The sandbox isolates custom code from the platform and other workflows, preventing malicious or buggy code from affecting system stability.","intents":["I need to perform a custom transformation or calculation that the built-in nodes don't support","I want to call a proprietary API or use a specialized library not available in standard nodes","I need to add complex business logic to my workflow without leaving the platform"],"best_for":["advanced users with programming skills who need flexibility beyond visual nodes","teams with custom business logic that can't be expressed declaratively","organizations integrating proprietary or niche APIs"],"limitations":["Sandboxed execution adds latency (100-500ms) compared to native nodes","Limited library availability — users may not have access to all npm/PyPI packages","Debugging custom code is harder than visual nodes — error messages may be cryptic","Security review may be required for code nodes in enterprise environments"],"requires":["JavaScript or Python programming knowledge","Understanding of the code node input/output interface","Familiarity with available libraries and APIs"],"input_types":["JavaScript or Python code (as text)","upstream workflow data (as function parameters)","environment variables or secrets"],"output_types":["return value from code execution","console logs for debugging","error messages if code throws exception"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support (Chrome, Firefox, Safari, Edge)","Account creation on Aigur.dev platform","Stable internet connection with WebSocket support","Team members with Aigur.dev accounts and workspace access","Browser with ES6+ support for real-time sync client","Account with the external service (Slack workspace, Google account, Salesforce org, etc.)","Appropriate permissions to authenticate and perform operations in that service","Workflow definition with schedule configuration","Understanding of cron syntax (if using custom expressions)","Aigur.dev account with admin or owner permissions"],"failure_modes":["Visual abstraction hides complexity — advanced conditional logic or error handling may require custom node types","Large workflows (50+ nodes) become difficult to navigate and debug in a single canvas view","No built-in version control branching — workflow history is linear","Real-time sync adds 100-500ms latency depending on network conditions and server load","Conflict resolution for simultaneous node deletions or property changes may produce unexpected results","No built-in audit trail of who made which changes — only activity logs","Limited connector library — niche or newer services may not be supported","Connector features may lag behind official service APIs — advanced operations may require custom code nodes","Authentication requires users to grant Aigur.dev access to their service accounts (potential security concern)","Scheduler granularity may be limited (e.g., minute-level precision may not be supported)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"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":"2026-05-24T12:16:29.132Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=aigur-dev","compare_url":"https://unfragile.ai/compare?artifact=aigur-dev"}},"signature":"aWr/qWLYtouS9ld+zZYsCRCEko0sIy6nxIsJh4JdWaZ7d6bjKjRNT6N6VZquaJGTCLf7vsveRLXsfPDZnxc9Aw==","signedAt":"2026-06-22T12:07:08.171Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/aigur-dev","artifact":"https://unfragile.ai/aigur-dev","verify":"https://unfragile.ai/api/v1/verify?slug=aigur-dev","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"}}