{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"activepieces","slug":"activepieces","name":"Activepieces","type":"repo","url":"https://github.com/activepieces/activepieces","page_url":"https://unfragile.ai/activepieces","categories":["automation","app-builders"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"activepieces__cap_0","uri":"capability://automation.workflow.visual.flow.builder.with.drag.and.drop.step.composition","name":"visual flow builder with drag-and-drop step composition","description":"Provides a web-based UI for constructing automation workflows by dragging action/trigger pieces onto a canvas and connecting them with data flow edges. The builder uses a state management layer (likely Redux or similar) to track the flow definition as a directed acyclic graph (DAG), with real-time validation of step connections and data type compatibility. Each step is rendered as a configurable card with input/output port definitions derived from the piece schema.","intents":["I want to build a multi-step automation without writing code","I need to visually see how data flows between different API calls and actions","I want to test individual steps in my workflow before running the full automation"],"best_for":["non-technical business users automating repetitive tasks","teams building internal integrations without dedicated engineers","rapid prototyping of multi-service workflows"],"limitations":["Complex conditional logic beyond simple if/else branching requires custom code pieces","Large workflows (50+ steps) may experience UI lag due to canvas rendering overhead","No collaborative real-time editing — only single-user flow editing per instance"],"requires":["Modern web browser with ES6+ support","Active Activepieces server instance","User authentication and workspace access"],"input_types":["piece definitions (JSON schema)","connection credentials","trigger configuration"],"output_types":["flow definition (DAG structure)","step configuration objects","validation errors"],"categories":["automation-workflow","ui-builder"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"activepieces__cap_1","uri":"capability://tool.use.integration.extensible.piece.framework.with.custom.action.trigger.development","name":"extensible piece framework with custom action/trigger development","description":"Implements a plugin architecture where integrations (pieces) are self-contained npm packages exporting action and trigger definitions with TypeScript types. The framework provides base classes and decorators for defining piece metadata, authentication handlers, and step logic. Pieces are loaded dynamically at runtime via a piece-loader service that resolves dependencies and validates schema compatibility before execution.","intents":["I want to create a custom integration for my proprietary API","I need to extend Activepieces with domain-specific actions for my industry","I want to contribute new integrations to the community ecosystem"],"best_for":["developers building custom integrations for internal APIs","open-source contributors extending the 200+ piece ecosystem","enterprises with proprietary systems requiring bespoke connectors"],"limitations":["Pieces must be written in TypeScript — no Python or other language support","Authentication handlers are limited to OAuth2, API keys, and basic auth patterns","No built-in rate limiting or retry logic at the piece level — must be implemented per-action"],"requires":["Node.js 18+","TypeScript 4.9+","Understanding of Activepieces piece SDK and decorators","npm or yarn for dependency management"],"input_types":["TypeScript source code","JSON schema definitions","authentication credentials"],"output_types":["compiled npm package","piece manifest with metadata","action/trigger implementations"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"activepieces__cap_10","uri":"capability://automation.workflow.flow.execution.monitoring.and.observability.with.run.history.and.logs","name":"flow execution monitoring and observability with run history and logs","description":"Tracks all flow executions with detailed logs including step inputs/outputs, execution duration, and error messages. Provides a UI for viewing run history, filtering by status/date/trigger type, and drilling down into individual step execution details. Logs are stored in the database with configurable retention policies. The system exposes metrics for monitoring (execution count, success rate, average duration) suitable for integration with observability platforms.","intents":["I want to see why a workflow failed and debug the issue","I need to monitor the success rate and performance of my workflows","I want to audit which workflows ran and when for compliance purposes"],"best_for":["teams running mission-critical workflows requiring visibility","debugging complex multi-step workflows","compliance and audit requirements"],"limitations":["Log retention is configurable but requires manual cleanup — no automatic archival","Large workflows (50+ steps) generate large log volumes — can impact database performance","No built-in log aggregation or centralized logging — logs are stored locally","Sensitive data (API responses, credentials) may be logged — requires careful configuration","Real-time log streaming is not supported — logs are available only after execution completes"],"requires":["Database for log storage","Sufficient disk space for log retention","User authentication for accessing logs"],"input_types":["flow execution ID","filter criteria (status, date range, trigger type)","step name or index"],"output_types":["execution history (list of runs with metadata)","step execution logs (inputs, outputs, duration, errors)","execution metrics (count, success rate, average duration)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"activepieces__cap_11","uri":"capability://automation.workflow.billing.and.quota.management.with.usage.tracking.and.rate.limiting","name":"billing and quota management with usage tracking and rate limiting","description":"Implements quota enforcement at the flow execution level, tracking metrics like number of runs, API calls, and data processed. Quotas are configured per workspace or user and enforced by the execution engine before starting a flow. The system tracks usage in real-time and provides dashboards for monitoring consumption. Rate limiting is applied at the API and webhook ingestion layers to prevent abuse.","intents":["I want to limit how many workflows my team can run per month","I need to track API usage and costs across my workflows","I want to prevent users from overwhelming the system with too many concurrent executions"],"best_for":["multi-tenant SaaS deployments of Activepieces","teams with cost control requirements","platforms requiring fair resource allocation across users"],"limitations":["Quota enforcement is coarse-grained — limited to flow runs and API calls, not per-piece metrics","No built-in cost allocation or chargeback — requires external billing system integration","Rate limiting is basic — no token bucket or sliding window algorithms","Quota overages are hard-blocked — no graceful degradation or burst capacity","No quota forecasting or alerts — users discover limits only when exceeded"],"requires":["Database for quota configuration and usage tracking","Billing system integration (for SaaS deployments)","Monitoring and alerting infrastructure"],"input_types":["quota configuration (max runs, max API calls, etc.)","usage metrics (run count, API call count, data processed)","rate limit parameters (requests per second, etc.)"],"output_types":["quota status (used, remaining, percentage)","usage reports","rate limit headers (X-RateLimit-Remaining, etc.)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"activepieces__cap_12","uri":"capability://automation.workflow.flow.testing.and.dry.run.execution.with.sample.data","name":"flow testing and dry-run execution with sample data","description":"Allows users to test workflows before deploying them to production by executing them with sample trigger data. The dry-run mode executes the flow through the same engine as production but with isolated state and no side effects (e.g., API calls are logged but not executed, or executed against test endpoints). Users can inspect step outputs and validate the flow logic without affecting external systems.","intents":["I want to test my workflow with sample data before enabling it","I need to verify that my data transformations are correct","I want to see what API calls my workflow will make without actually executing them"],"best_for":["developers building complex workflows with multiple integrations","teams requiring workflow validation before production deployment","workflows with side effects (sending emails, creating records) that need careful testing"],"limitations":["Dry-run mode requires manual sample data creation — no automatic test data generation","API calls in dry-run are logged but not executed — cannot validate API responses","Dry-run state is not persisted — cannot test pause/resume workflows","No support for testing error branches or retry logic","Dry-run results may differ from production due to timing or external state changes"],"requires":["Sample trigger data in JSON format","Flow definition with all required connections configured","Understanding of expected outputs for validation"],"input_types":["flow definition","sample trigger payload","dry-run configuration (mock API calls, etc.)"],"output_types":["step execution results","logged API calls (without execution)","validation errors"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"activepieces__cap_13","uri":"capability://automation.workflow.multi.tenant.workspace.isolation.with.role.based.access.control","name":"multi-tenant workspace isolation with role-based access control","description":"Implements workspace-level isolation where each workspace is a separate logical environment with its own flows, connections, and users. Access control is enforced through roles (admin, editor, viewer) with granular permissions for creating, editing, and executing flows. Workspaces are isolated at the database level with row-level security policies. Users can belong to multiple workspaces with different roles.","intents":["I want to separate workflows for different teams or projects","I need to grant different permissions to different users (some can edit, some can only view)","I want to ensure that one team's workflows and credentials are not visible to another team"],"best_for":["multi-tenant SaaS deployments of Activepieces","enterprises with multiple teams or departments","organizations requiring strict data isolation and access control"],"limitations":["Workspace isolation is logical, not physical — all workspaces share the same database","Row-level security adds database query overhead — can impact performance at scale","No built-in workspace quotas — one workspace can consume all resources","Cross-workspace workflows are not supported — automation must stay within a workspace","No audit logging of access control changes"],"requires":["Database with row-level security support (PostgreSQL recommended)","User authentication and authorization system","Role definitions and permission matrix"],"input_types":["workspace configuration","user role assignment","access control rules"],"output_types":["workspace membership","user permissions","access control violations (audit logs)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"activepieces__cap_14","uri":"capability://automation.workflow.flow.versioning.and.deployment.with.version.history","name":"flow versioning and deployment with version history","description":"Maintains version history for each flow, allowing users to view, compare, and revert to previous versions. Each published version is immutable and can be deployed independently. The engine tracks which version is currently active and can roll back to a previous version if needed. Supports draft and published states, enabling testing before deployment.","intents":["Track changes to workflows over time","Revert to a previous workflow version if a deployment breaks","Test workflow changes in draft mode before publishing","Compare workflow versions to understand what changed"],"best_for":["Teams managing critical workflows requiring change control","Organizations with deployment and rollback requirements","Developers iterating on workflow logic"],"limitations":["Version history is stored in the database; no native Git integration","Rollback is manual; no automatic rollback on failure","Version comparison is limited to visual diff; no semantic comparison","No support for branching or merging workflows"],"requires":["Database support for storing version history","UI access to manage versions"],"input_types":["flow definition","version metadata"],"output_types":["version history","version comparison","rollback events"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"activepieces__cap_15","uri":"capability://automation.workflow.billing.and.quota.management.with.usage.tracking","name":"billing and quota management with usage tracking","description":"Tracks workflow execution counts, API calls, and other usage metrics per workspace. Enforces quotas based on subscription tier, preventing workflows from executing if quotas are exceeded. Provides usage dashboards and billing reports. Supports multiple billing models (per-execution, per-user, etc.).","intents":["Monitor workflow usage and costs","Enforce execution limits based on subscription tier","Generate billing reports for cost allocation","Alert users when approaching quota limits"],"best_for":["Cloud deployments with multiple subscription tiers","Organizations tracking automation costs","Teams managing usage budgets"],"limitations":["Quota enforcement is at the workspace level; no per-user or per-workflow limits","No native integration with payment processors; billing must be handled externally","Usage metrics are approximate; real-time accuracy is not guaranteed","No support for custom billing models; limited to predefined tiers"],"requires":["Database support for tracking usage metrics","Subscription tier configuration","Billing system integration (external)"],"input_types":["usage events","quota configuration","subscription tier"],"output_types":["usage reports","quota enforcement","billing data"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"activepieces__cap_2","uri":"capability://automation.workflow.flow.execution.engine.with.step.by.step.dag.traversal.and.error.handling","name":"flow execution engine with step-by-step dag traversal and error handling","description":"Executes flows by traversing a directed acyclic graph (DAG) of steps, evaluating each step's inputs by resolving variable references and expressions, executing the step's logic (piece action or code), and propagating outputs to dependent steps. The engine maintains execution context including step results, flow variables, and connection credentials. Error handling includes configurable retry logic, error branches, and pause/resume capabilities for manual intervention.","intents":["I want to run my automation workflow and see results for each step","I need to retry failed steps without re-running the entire workflow","I want to pause a workflow for manual approval and resume it later"],"best_for":["teams running mission-critical automations requiring reliability","workflows with approval gates or manual intervention steps","high-volume automation scenarios requiring efficient resource usage"],"limitations":["No distributed execution across multiple machines — single worker processes flows sequentially","Pause/resume state is stored in database — requires external persistence layer","Circular dependencies in flows are not detected until runtime","Maximum execution time is configurable but long-running steps can timeout"],"requires":["Flow definition in Activepieces DAG format","Worker process with access to database and piece dependencies","Connection credentials for all pieces used in flow","PostgreSQL or compatible database for state persistence"],"input_types":["flow definition (DAG)","trigger payload","step configuration","connection credentials"],"output_types":["step execution results","flow completion status","error logs and stack traces","execution timeline"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"activepieces__cap_3","uri":"capability://automation.workflow.queue.based.worker.pool.for.distributed.flow.execution","name":"queue-based worker pool for distributed flow execution","description":"Uses a message queue (likely Redis or similar) to decouple flow triggering from execution. When a flow is triggered, a job is enqueued with the flow definition and trigger payload. Worker processes poll the queue, dequeue jobs, and execute flows in isolation. Multiple workers can process jobs concurrently, enabling horizontal scaling. The queue system handles job persistence, retry logic, and dead-letter queues for failed jobs.","intents":["I want to run multiple workflows concurrently without blocking each other","I need to scale my automation platform to handle thousands of daily triggers","I want to ensure failed workflows are retried automatically"],"best_for":["high-volume automation platforms processing thousands of flows daily","teams requiring horizontal scaling of workflow execution","deployments needing job persistence and failure recovery"],"limitations":["Requires external queue service (Redis, RabbitMQ, etc.) — adds operational complexity","Job ordering is not guaranteed across multiple workers — workflows may execute out of order","Queue latency adds 100-500ms overhead per job before execution starts","Dead-letter queue requires manual intervention to retry failed jobs"],"requires":["Redis 6.0+ or compatible message queue","Multiple worker processes (Node.js)","Database for job persistence and state","Monitoring tools for queue depth and worker health"],"input_types":["flow definition","trigger payload","job metadata"],"output_types":["job status (queued, processing, completed, failed)","execution results","error logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"activepieces__cap_4","uri":"capability://automation.workflow.webhook.trigger.ingestion.with.signature.verification.and.deduplication","name":"webhook trigger ingestion with signature verification and deduplication","description":"Exposes HTTP endpoints that accept webhook payloads from external services. Each webhook trigger has a unique URL and optional signature verification (HMAC-SHA256). Incoming webhooks are validated, deduplicated based on configurable keys, and enqueued as flow execution jobs. The system tracks webhook delivery status and provides retry mechanisms for failed deliveries.","intents":["I want to trigger workflows when external services send webhooks (GitHub, Stripe, etc.)","I need to verify webhook authenticity to prevent unauthorized flow execution","I want to deduplicate duplicate webhook deliveries from external services"],"best_for":["event-driven automation triggered by SaaS webhooks","teams integrating with GitHub, Stripe, Shopify, and similar webhook-enabled services","workflows requiring high reliability and duplicate prevention"],"limitations":["Webhook signature verification is optional — requires manual configuration per trigger","Deduplication is based on configurable keys — complex deduplication logic requires custom code","Webhook delivery is fire-and-forget — no guaranteed delivery if worker crashes","Rate limiting is not built-in — external services can overwhelm the webhook endpoint"],"requires":["Public-facing HTTP endpoint (webhook URL)","Webhook secret for signature verification (optional but recommended)","Flow definition with webhook trigger configured","Queue system for job persistence"],"input_types":["HTTP POST payload (JSON, form-encoded, or raw)","HTTP headers (for signature verification)","webhook metadata (timestamp, delivery ID)"],"output_types":["webhook delivery status (accepted, rejected, duplicate)","flow execution job","error response"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"activepieces__cap_5","uri":"capability://code.generation.editing.code.execution.sandbox.for.custom.javascript.typescript.logic","name":"code execution sandbox for custom javascript/typescript logic","description":"Allows users to write custom code steps that execute arbitrary JavaScript/TypeScript within a sandboxed environment. The sandbox is implemented using Node.js VM module or similar isolation mechanism, with access to step inputs, flow variables, and utility functions. Code is executed with a timeout and memory limit to prevent resource exhaustion. The sandbox provides a safe execution context without access to the host filesystem or network (unless explicitly configured).","intents":["I want to transform data between steps using custom logic","I need to implement conditional branching based on complex business rules","I want to call APIs or perform calculations not available in pre-built pieces"],"best_for":["developers comfortable with JavaScript/TypeScript","workflows requiring custom data transformation or validation","teams needing flexibility beyond pre-built piece capabilities"],"limitations":["Sandbox isolation adds 50-200ms overhead per code step execution","No access to Node.js filesystem APIs — cannot read/write files directly","Limited to synchronous code — async/await requires special handling","Debugging is difficult — stack traces may not map to original source code","Code is stored as plain text — no encryption or obfuscation"],"requires":["Node.js 18+ with VM module support","JavaScript/TypeScript knowledge","Understanding of Activepieces context object and available utilities","Timeout and memory limits configured in engine"],"input_types":["JavaScript/TypeScript source code","step inputs (any JSON-serializable type)","flow variables"],"output_types":["code execution result (any JSON-serializable type)","error logs and stack traces"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"activepieces__cap_6","uri":"capability://tool.use.integration.connection.credential.management.with.oauth2.and.api.key.support","name":"connection credential management with oauth2 and api key support","description":"Manages authentication credentials for external services through a secure credential store. Supports multiple auth patterns: OAuth2 (with authorization code flow), API keys, basic auth, and custom headers. Credentials are encrypted at rest and decrypted only when needed for piece execution. The system handles OAuth2 token refresh automatically and provides a UI for users to authorize and manage connections.","intents":["I want to securely store API keys and OAuth tokens for my integrations","I need to authorize my Activepieces instance to access my Google/GitHub/Slack accounts","I want to rotate credentials without updating all my workflows"],"best_for":["teams requiring secure credential storage and rotation","workflows using multiple SaaS integrations with different auth methods","enterprises with compliance requirements for credential management"],"limitations":["Encryption key management is the responsibility of the operator — requires secure key storage","OAuth2 token refresh is automatic but requires the original refresh token","No built-in credential rotation or expiration policies","Credentials are decrypted in memory during execution — vulnerable to memory dumps","No audit logging of credential access by default"],"requires":["Encryption key (stored securely, e.g., in environment variables)","Database for credential storage","OAuth2 client credentials for services requiring OAuth","HTTPS for secure OAuth2 callback URLs"],"input_types":["auth type (oauth2, api_key, basic_auth, custom)","auth parameters (client_id, client_secret, api_key, etc.)","OAuth2 authorization code"],"output_types":["encrypted credential object","OAuth2 access token","connection status (valid, expired, revoked)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"activepieces__cap_7","uri":"capability://automation.workflow.flow.versioning.and.deployment.with.rollback.capability","name":"flow versioning and deployment with rollback capability","description":"Maintains version history of flow definitions, allowing users to create, publish, and rollback to previous versions. Each version is immutable and tagged with metadata (creator, timestamp, change description). The system supports promoting versions from development to production environments. Rollback is implemented by creating a new version that references the previous version's definition.","intents":["I want to test changes to my workflow without affecting the live version","I need to rollback to a previous workflow version if something breaks","I want to track who made changes to my workflows and when"],"best_for":["teams managing multiple workflow versions and environments","workflows requiring change tracking and audit trails","deployments with staging and production environments"],"limitations":["No branching or merging — only linear version history","Rollback creates a new version — does not restore the exact previous state","No conflict resolution if multiple users edit the same flow simultaneously","Version history is stored in database — requires backup strategy"],"requires":["Database for version storage","User authentication for tracking version creators","Flow definition in Activepieces format"],"input_types":["flow definition","version metadata (description, tags)","version number or timestamp"],"output_types":["versioned flow definition","version history (list of versions with metadata)","diff between versions"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"activepieces__cap_8","uri":"capability://text.generation.language.ai.piece.integration.for.llm.powered.actions.and.data.extraction","name":"ai piece integration for llm-powered actions and data extraction","description":"Provides pre-built pieces for interacting with large language models (Claude, GPT-4, etc.) within workflows. Includes actions for prompt execution, structured data extraction, and content generation. The pieces handle model selection, parameter configuration (temperature, max tokens), and response parsing. Supports streaming responses for long-running operations and integrates with the credential management system for API key storage.","intents":["I want to use Claude or GPT-4 to analyze or transform data in my workflow","I need to extract structured data from unstructured text using an LLM","I want to generate content (emails, summaries, etc.) using an AI model"],"best_for":["workflows requiring natural language processing or content generation","teams using Claude, GPT-4, or other LLM APIs","automation scenarios where traditional logic is insufficient"],"limitations":["LLM API costs are passed through to the user — no cost optimization or caching","Response quality depends on prompt engineering — requires expertise to get good results","No built-in prompt versioning or A/B testing","Streaming responses add complexity to error handling and retry logic","Token limits may cause failures on large inputs — requires manual chunking"],"requires":["API key for LLM service (OpenAI, Anthropic, etc.)","Understanding of prompt engineering and LLM capabilities","Budget for LLM API calls"],"input_types":["prompt template (with variable interpolation)","model parameters (temperature, max_tokens, etc.)","input text or data to process"],"output_types":["LLM response (text or structured JSON)","token usage statistics","error messages"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"activepieces__cap_9","uri":"capability://automation.workflow.flow.trigger.scheduling.with.cron.expressions.and.interval.based.execution","name":"flow trigger scheduling with cron expressions and interval-based execution","description":"Enables workflows to be triggered on a schedule using cron expressions or simple interval-based triggers (every N minutes/hours/days). The scheduler runs as a background service that evaluates trigger conditions at specified intervals and enqueues flow execution jobs. Supports timezone-aware scheduling and allows users to configure execution windows (e.g., only run during business hours).","intents":["I want to run a workflow every day at a specific time","I need to execute a workflow on a cron schedule (e.g., every Monday at 9 AM)","I want to run a workflow every N minutes to check for updates"],"best_for":["scheduled data synchronization tasks","periodic reporting and data aggregation workflows","maintenance tasks that need to run on a fixed schedule"],"limitations":["Cron scheduling is limited to minute-level precision — no sub-minute triggers","Timezone handling requires careful configuration — DST transitions can cause issues","No built-in backoff or jitter — thundering herd problem if many workflows trigger simultaneously","Missed triggers are not automatically retried — if scheduler is down, triggers are skipped","No support for complex scheduling logic (e.g., 'every business day except holidays')"],"requires":["Scheduler service running continuously","Database for trigger configuration and execution history","Queue system for job enqueuing","Timezone database (e.g., IANA timezone data)"],"input_types":["cron expression (e.g., '0 9 * * MON')","interval (e.g., 'every 5 minutes')","timezone (e.g., 'America/New_York')"],"output_types":["scheduled execution job","trigger execution history","next scheduled execution time"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"activepieces__headline","uri":"capability://automation.workflow.no.code.business.automation.tool","name":"no-code business automation tool","description":"Activepieces is an open-source no-code business automation tool that allows users to create automated workflows using a visual flow builder and over 200 integrations, making it accessible for non-developers to streamline their processes.","intents":["best no-code automation tool","no-code solution for business workflows","top visual flow builders","no-code platforms with AI integration","self-hosted automation tools"],"best_for":["small to medium businesses","non-technical users"],"limitations":["may require technical setup for self-hosting"],"requires":[],"input_types":[],"output_types":[],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Modern web browser with ES6+ support","Active Activepieces server instance","User authentication and workspace access","Node.js 18+","TypeScript 4.9+","Understanding of Activepieces piece SDK and decorators","npm or yarn for dependency management","Database for log storage","Sufficient disk space for log retention","User authentication for accessing logs"],"failure_modes":["Complex conditional logic beyond simple if/else branching requires custom code pieces","Large workflows (50+ steps) may experience UI lag due to canvas rendering overhead","No collaborative real-time editing — only single-user flow editing per instance","Pieces must be written in TypeScript — no Python or other language support","Authentication handlers are limited to OAuth2, API keys, and basic auth patterns","No built-in rate limiting or retry logic at the piece level — must be implemented per-action","Log retention is configurable but requires manual cleanup — no automatic archival","Large workflows (50+ steps) generate large log volumes — can impact database performance","No built-in log aggregation or centralized logging — logs are stored locally","Sensitive data (API responses, credentials) may be logged — requires careful configuration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.370Z","last_scraped_at":null,"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=activepieces","compare_url":"https://unfragile.ai/compare?artifact=activepieces"}},"signature":"i7ibRVCl7ik/YqjD+M7s4EHAJexNwJ7UnEWzMn7BV5ojeeC3U2KmaxVKRceOEDBLZFohkmbkJ2EBsru11w7fDQ==","signedAt":"2026-06-21T18:18:50.450Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/activepieces","artifact":"https://unfragile.ai/activepieces","verify":"https://unfragile.ai/api/v1/verify?slug=activepieces","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"}}