{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-wavezync--durable","slug":"wavezync--durable","name":"durable","type":"workflow","url":"https://wavezync.com","page_url":"https://unfragile.ai/wavezync--durable","categories":["automation"],"tags":["ai-workflow","automation","durable-execution","durable-objects","elixir","postgresql","queue","workflow"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-wavezync--durable__cap_0","uri":"capability://automation.workflow.declarative.workflow.definition.via.elixir.dsl.macros","name":"declarative workflow definition via elixir dsl macros","description":"Defines complex multi-step workflows using Elixir macros (workflow, step, branch, parallel, foreach) that compile to an AST-based execution plan persisted in PostgreSQL. The DSL abstracts control flow, state management, and resumability into composable building blocks, eliminating boilerplate for long-running processes. Workflows are defined as pure Elixir code with compile-time validation of step dependencies and control flow structure.","intents":["Define a multi-step approval workflow that pauses for human input without holding resources","Build a distributed transaction with automatic rollback (saga pattern) across multiple services","Create a scheduled batch job that retries failed steps with exponential backoff","Compose hierarchical workflows where parent workflows spawn and coordinate child workflows"],"best_for":["Elixir/Phoenix teams building long-running business processes","Teams replacing scattered background job systems with unified workflow orchestration","Developers needing human-in-the-loop approval workflows without custom state machines"],"limitations":["Elixir-only; no Python, Go, or Node.js SDKs available","DSL compilation happens at module load time; dynamic workflow generation requires runtime macro evaluation","Step logic must be pure Elixir functions; external language integration requires wrapper modules","PostgreSQL required for state persistence; no support for other databases"],"requires":["Elixir 1.14+","PostgreSQL 12+ for workflow state storage","Ecto for database schema management","Phoenix (optional, for web UI integration)"],"input_types":["Elixir module definitions with workflow/step macros","Configuration maps for step options (retries, timeouts, compensation)"],"output_types":["Compiled workflow execution plan stored as PostgreSQL records","Workflow instance state with step execution history and context"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-wavezync--durable__cap_1","uri":"capability://automation.workflow.postgresql.backed.durable.state.persistence.with.automatic.resumability","name":"postgresql-backed durable state persistence with automatic resumability","description":"Persists complete workflow execution state (step results, context, execution history) to PostgreSQL after each step completes, enabling workflows to resume from the exact point of interruption after crashes, restarts, or arbitrary delays. Uses Ecto schemas (WorkflowExecution, StepExecution) to model workflow state as relational data with transactional consistency guarantees. Resumability is automatic—the execution engine queries persisted state and continues from the last completed step without explicit checkpointing logic.","intents":["Ensure a workflow survives application crashes and resumes without data loss","Pause a workflow for days or months and resume it on-demand without holding memory","Query historical execution state and step results for auditing and debugging","Coordinate multi-instance deployments where any instance can resume workflows started by another"],"best_for":["Production systems requiring 99.9%+ uptime for long-running processes","Teams with existing PostgreSQL infrastructure and Ecto expertise","Workflows spanning hours to months (e.g., approval chains, batch processing)"],"limitations":["PostgreSQL becomes a critical dependency; no fallback to in-memory state","Step context must be serializable to JSON; complex Elixir terms (PIDs, references) require custom serialization","Persistence adds ~10-50ms latency per step completion (database round-trip)","No built-in encryption for sensitive data in context; requires application-level encryption before persistence"],"requires":["PostgreSQL 12+ with Ecto adapter configured","Database migrations for WorkflowExecution and StepExecution schemas","Network connectivity to PostgreSQL during workflow execution"],"input_types":["Workflow context (maps with serializable values)","Step execution results (any JSON-serializable Elixir term)"],"output_types":["PostgreSQL rows in workflow_executions and step_executions tables","Queryable execution history with timestamps and error traces"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-wavezync--durable__cap_10","uri":"capability://automation.workflow.multi.instance.deployment.with.distributed.concurrency.control","name":"multi-instance deployment with distributed concurrency control","description":"Supports deploying Durable across multiple application instances with automatic concurrency control via database-level locking. When multiple instances attempt to execute the same workflow, the execution engine uses PostgreSQL row-level locks to ensure only one instance executes a given workflow step at a time. This enables horizontal scaling without a central coordinator. The execution engine polls for available work (steps ready to execute) and acquires locks before execution, ensuring distributed safety.","intents":["Scale workflow execution across multiple application instances without a central coordinator","Ensure only one instance executes a given workflow step, preventing duplicate execution","Implement load balancing where any instance can pick up and execute available workflow steps","Enable zero-downtime deployments where new instances gradually take over workflow execution"],"best_for":["Production deployments requiring high availability and horizontal scaling","Teams with existing PostgreSQL infrastructure and multi-instance deployment patterns","Workflows with high throughput that benefit from distributed execution"],"limitations":["Database becomes a bottleneck; lock contention increases with number of instances","Polling for available work adds latency (typically 1-5 seconds) compared to push-based scheduling","No built-in load balancing; all instances compete equally for work","Lock timeouts can cause cascading failures if an instance crashes while holding a lock"],"requires":["PostgreSQL with row-level locking support","Multiple application instances with Durable configured","Network connectivity between instances and PostgreSQL"],"input_types":["Workflow instance ID","Step execution request"],"output_types":["Lock acquired on workflow step","Step execution result persisted to database"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-wavezync--durable__cap_11","uri":"capability://automation.workflow.workflow.execution.observability.via.log.capture.and.state.querying","name":"workflow execution observability via log capture and state querying","description":"Provides comprehensive observability into workflow execution via two mechanisms: (1) automatic log capture that records all step execution logs to the database, and (2) queryable workflow state that enables inspection of execution history, step results, and context at any point in time. Logs are captured from Elixir's Logger and associated with specific step executions. Workflow state can be queried via Ecto queries or API endpoints, enabling real-time monitoring and debugging of running workflows.","intents":["Debug a failed workflow by inspecting step execution logs and context","Monitor workflow progress in real-time via API queries","Audit workflow execution history for compliance and debugging","Build dashboards showing workflow status, step results, and error rates"],"best_for":["Production workflows requiring detailed execution visibility","Teams building workflow monitoring dashboards","Compliance-heavy workflows requiring audit trails"],"limitations":["Log capture adds overhead (~5-10ms per step) due to database writes","Querying large execution histories can be slow without proper database indexing","No built-in log retention policy; logs accumulate indefinitely in the database","Sensitive data in logs (passwords, API keys) is not automatically redacted"],"requires":["PostgreSQL for storing logs and execution state","Ecto queries or API endpoints for state inspection","Proper database indexing on workflow_executions and step_executions tables"],"input_types":["Step execution logs (from Elixir Logger)","Workflow context and step results"],"output_types":["Queryable log records associated with step executions","Workflow execution state including step history and context"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-wavezync--durable__cap_12","uri":"capability://tool.use.integration.pluggable.queue.and.message.bus.adapters.for.custom.integration","name":"pluggable queue and message bus adapters for custom integration","description":"Provides extensible queue and message bus adapter interfaces, enabling custom implementations for step execution scheduling and event delivery. The default implementation uses PostgreSQL polling, but adapters can implement push-based scheduling (e.g., via RabbitMQ, Kafka) or custom event delivery mechanisms. Adapters implement a standard interface (enqueue, dequeue, publish, subscribe) and are plugged into the Durable supervision tree via configuration. This enables integration with existing message infrastructure without modifying core workflow logic.","intents":["Integrate Durable with existing RabbitMQ or Kafka infrastructure for step scheduling","Implement custom event delivery via webhooks or message brokers","Reduce database polling overhead by using push-based scheduling","Implement custom queue strategies (e.g., priority queues, fair scheduling)"],"best_for":["Teams with existing message infrastructure (RabbitMQ, Kafka) wanting to integrate Durable","Workflows requiring low-latency step scheduling (push-based vs polling)","Custom queue strategies or event delivery mechanisms"],"limitations":["Adapter implementation requires Elixir expertise; no pre-built adapters for common message brokers","Adapter interface is not stable; changes to core Durable may require adapter updates","No built-in adapter for popular message brokers (RabbitMQ, Kafka); must implement custom adapters","Mixing adapters (e.g., PostgreSQL for some workflows, RabbitMQ for others) is not supported"],"requires":["Adapter module implementing queue/message bus interface","Configuration to plug adapter into Durable supervision tree","External message infrastructure (if using non-PostgreSQL adapter)"],"input_types":["Step execution request (enqueue)","Event data (publish)"],"output_types":["Step execution scheduled via adapter","Event delivered via adapter"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-wavezync--durable__cap_13","uri":"capability://automation.workflow.workflow.cancellation.with.cascading.cleanup","name":"workflow cancellation with cascading cleanup","description":"Enables cancellation of running workflows via the cancel API, which marks the workflow as cancelled and triggers cleanup of associated resources. When a workflow is cancelled, the execution engine stops executing new steps, executes compensations for completed steps (in reverse order), and marks the workflow as cancelled in the database. Cancellation is asynchronous and resumable—if the application crashes during cancellation, the process resumes from the last completed compensation.","intents":["Cancel a long-running approval workflow if the request is withdrawn","Stop a batch processing workflow and clean up partial results","Implement user-initiated workflow cancellation with automatic resource cleanup","Handle workflow timeouts by cancelling and rolling back"],"best_for":["Workflows that can be cancelled by users or external systems","Long-running workflows requiring graceful shutdown","Workflows with significant resource allocation that must be cleaned up on cancellation"],"limitations":["Cancellation is not instantaneous; currently executing steps complete before cancellation takes effect","Compensations are executed sequentially; cancellation can take significant time if many steps have compensations","No built-in timeout for cancellation; if a compensation hangs, cancellation can block indefinitely","Cancelled workflows cannot be resumed; cancellation is final"],"requires":["Workflow instance ID to cancel","Compensation functions defined for steps that need cleanup"],"input_types":["Workflow instance ID","Cancellation reason (optional)"],"output_types":["Workflow marked as cancelled in database","Compensation execution results"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-wavezync--durable__cap_2","uri":"capability://automation.workflow.configurable.retry.logic.with.multiple.backoff.strategies","name":"configurable retry logic with multiple backoff strategies","description":"Provides per-step retry configuration with exponential, linear, constant, and custom backoff strategies. When a step fails, the execution engine automatically reschedules it based on the configured backoff function, max retry count, and jitter settings. Retries are persisted to the database, allowing workflows to survive transient failures (network timeouts, rate limits) without manual intervention. Backoff state is tracked in StepExecution records, enabling observability into retry attempts and failure patterns.","intents":["Automatically retry a step that calls an external API with exponential backoff","Configure different retry strategies for different steps (e.g., aggressive retries for network calls, no retries for validation failures)","Observe retry history and failure patterns for debugging and alerting","Implement circuit-breaker-like behavior by exhausting retries and triggering compensation"],"best_for":["Workflows integrating with unreliable external services (APIs, webhooks, third-party SaaS)","Distributed systems where transient failures are common","Teams needing fine-grained control over failure handling per step"],"limitations":["Retry logic is step-level only; no workflow-level retry (entire workflow cannot be retried as a unit)","Backoff strategies are deterministic; jitter is optional and must be configured explicitly","Max retry count is static per step; cannot adjust retries dynamically based on failure type","No built-in circuit breaker; must implement via compensation or external monitoring"],"requires":["Step definition with retry options (max_retries, backoff_strategy, jitter)","PostgreSQL for tracking retry state across restarts"],"input_types":["Step failure (exception or error result)","Retry configuration (max_retries: integer, backoff: :exponential | :linear | :constant | custom function)"],"output_types":["Rescheduled step execution with updated attempt count","StepExecution record with retry metadata (attempt_number, next_retry_at)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-wavezync--durable__cap_3","uri":"capability://automation.workflow.human.in.the.loop.workflow.pausing.with.event.and.input.resumption","name":"human-in-the-loop workflow pausing with event and input resumption","description":"Enables workflows to pause execution and wait for external events (webhooks, user input, approvals) or time-based delays without holding system resources. Implements three wait primitives: wait_for_event (pause until external event arrives), wait_for_input (pause until user provides data), and wait_for_approval (pause until approval is granted). Paused workflows are stored in PostgreSQL with a WaitState record indicating the resume condition. The execution engine polls or subscribes to resume events and automatically continues the workflow when the condition is met.","intents":["Pause an approval workflow until a manager approves a request via webhook or API call","Wait for a user to upload a document before proceeding to the next step","Implement a multi-day approval chain where each step waits for a different approver","Pause a workflow for a specific duration (e.g., wait 7 days before sending a reminder)"],"best_for":["Business process automation (approvals, onboarding, compliance workflows)","Multi-party workflows requiring human decision-making","Workflows with time-based gates (e.g., wait before escalating)"],"limitations":["No built-in UI for approvals; requires custom web interface or webhook integration","Event-based resumption requires external system to call resume API; no automatic event subscription","Paused workflows consume database rows but not memory; scaling to millions of paused workflows requires database optimization","No timeout for wait conditions; workflows can remain paused indefinitely if resume event never arrives"],"requires":["wait_for_event, wait_for_input, or wait_for_approval call in step definition","External system to trigger resume (via Durable.resume_workflow API)","PostgreSQL for storing WaitState records"],"input_types":["Wait condition (event_type, input_schema, approval_rules)","Resume payload (event data, user input, approval decision)"],"output_types":["WaitState record in PostgreSQL with resume condition","Resumed workflow execution with provided input/event data in context"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-wavezync--durable__cap_4","uri":"capability://automation.workflow.parallel.step.execution.with.join.semantics","name":"parallel step execution with join semantics","description":"Executes multiple steps concurrently using the parallel macro, with configurable join semantics (wait for all, wait for first, wait for N). The execution engine spawns Elixir processes for each parallel step, persists their results independently to PostgreSQL, and coordinates joining based on the specified strategy. Parallel execution is transparent to step logic—steps execute as normal Elixir functions, and the framework handles process spawning, result aggregation, and error propagation.","intents":["Fetch data from multiple external APIs in parallel before proceeding","Run independent validation checks concurrently and fail fast if any check fails","Execute multiple compensations in parallel when rolling back a distributed transaction","Implement fan-out/fan-in patterns for batch processing"],"best_for":["Workflows with independent steps that can run concurrently","I/O-bound operations (API calls, database queries) that benefit from parallelism","Teams needing fan-out/fan-in orchestration without custom process management"],"limitations":["Parallel steps must be independent; no shared mutable state between parallel branches","Join semantics are fixed at workflow definition time; cannot dynamically choose join strategy","Process spawning adds overhead (~1-5ms per parallel step); not suitable for fine-grained parallelism","Error handling in parallel steps requires explicit error propagation; partial failures must be handled in join logic"],"requires":["parallel macro in workflow definition","Join strategy specification (all, first, or custom count)","Elixir process supervision for spawned parallel steps"],"input_types":["List of steps to execute in parallel","Join strategy (all | first | {count, N})"],"output_types":["Aggregated results from all parallel steps","Error if any step fails (depending on join strategy)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-wavezync--durable__cap_5","uri":"capability://automation.workflow.conditional.branching.with.dynamic.path.selection","name":"conditional branching with dynamic path selection","description":"Implements conditional branching via the branch macro, which evaluates a condition function and selects one of multiple execution paths based on the result. Each branch is a separate workflow sub-graph with its own steps, retries, and error handling. The execution engine evaluates the condition at runtime, persists the selected branch to the database, and executes the chosen path. Branches can be nested and combined with other control flow primitives (parallel, foreach).","intents":["Route a workflow to different approval chains based on request amount (e.g., <$1000 vs >$1000)","Implement conditional retry logic (e.g., retry on network error, fail immediately on validation error)","Execute different compensation paths based on which step failed","Implement multi-tenant workflows where behavior differs by tenant type"],"best_for":["Workflows with conditional logic based on step results or context","Business rules engines that need to route workflows dynamically","Teams building complex approval chains with different rules per branch"],"limitations":["Condition function must be pure and deterministic; side effects in condition evaluation are not recommended","Branch paths are static at workflow definition time; cannot add new branches dynamically","No built-in support for switch/case with many branches; multiple nested branches become hard to read","Condition evaluation happens at runtime; no static analysis of reachability"],"requires":["branch macro in workflow definition","Condition function (context -> boolean or atom)","Step definitions for each branch"],"input_types":["Workflow context (used by condition function)","Condition result (boolean or atom)"],"output_types":["Selected branch path persisted to database","Execution results from the chosen branch"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-wavezync--durable__cap_6","uri":"capability://automation.workflow.iterative.execution.over.collections.with.foreach","name":"iterative execution over collections with foreach","description":"Implements collection iteration via the forEach macro, which executes a step or sub-workflow for each element in a collection. The execution engine iterates over the collection, spawning a step execution for each element with the element value in the context. Iteration state is persisted to the database, enabling workflows to resume from the last completed iteration after interruption. Supports both sequential and parallel iteration strategies.","intents":["Process a batch of items (e.g., send emails to 1000 users) without holding all items in memory","Iterate over API paginated results and process each page","Execute the same step for each element in a dynamic list (e.g., approve each line item in an order)","Implement map/reduce patterns where each iteration produces a result that feeds into aggregation"],"best_for":["Batch processing workflows with dynamic collection sizes","Workflows processing paginated API results","Teams needing resumable iteration without custom loop logic"],"limitations":["Collection must fit in memory at iteration start; no streaming/lazy evaluation","Iteration order is deterministic but not guaranteed to be in input order if parallel iteration is used","No built-in aggregation; results from each iteration must be manually collected in context","Modifying the collection during iteration is not supported; collection is immutable"],"requires":["forEach macro in workflow definition","Collection (list or enumerable) in workflow context","Step or sub-workflow to execute for each element"],"input_types":["Collection (list of items)","Step definition to execute per item"],"output_types":["List of step execution results (one per collection element)","Iteration state persisted to database with completion tracking"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-wavezync--durable__cap_7","uri":"capability://automation.workflow.compensation.and.saga.pattern.for.distributed.transaction.rollback","name":"compensation and saga pattern for distributed transaction rollback","description":"Implements the saga pattern via compensation functions, which are executed in reverse order when a workflow fails to rollback distributed transactions. Each step can define a compensate function that undoes the step's effects (e.g., refund a payment, delete a created resource). When a step fails and retries are exhausted, the execution engine executes compensations for all previously completed steps in reverse order. Compensations are persisted and resumable, ensuring rollback completes even if the application crashes.","intents":["Rollback a multi-step payment workflow if any step fails (e.g., charge card, create order, send confirmation)","Implement distributed transactions across multiple microservices without a central coordinator","Ensure cleanup of resources (temporary files, database records) when a workflow fails","Implement undo functionality for user-initiated workflows"],"best_for":["Workflows coordinating multiple external systems (payment processors, inventory, shipping)","Teams implementing saga pattern without a distributed transaction coordinator","Financial or e-commerce workflows requiring strong consistency guarantees"],"limitations":["Compensations must be idempotent; if a compensation fails and is retried, it must not double-undo","Compensations are executed sequentially in reverse order; no parallel compensation","No built-in compensation for external systems; must implement via API calls in compensation functions","Compensation logic must be maintained alongside step logic; drift between step and compensation can cause inconsistency"],"requires":["compensate function defined for each step that modifies external state","Compensation logic must be idempotent and handle partial failures","PostgreSQL for tracking compensation state"],"input_types":["Step result (used by compensation function)","Workflow context (available to compensation)"],"output_types":["Compensation execution results persisted to database","Workflow marked as compensated/rolled back"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-wavezync--durable__cap_8","uri":"capability://automation.workflow.scheduled.workflow.execution.via.cron.expressions","name":"scheduled workflow execution via cron expressions","description":"Enables workflows to run on schedules using cron expressions via the Scheduler API. The execution engine maintains a schedule table in PostgreSQL and periodically checks for due schedules, spawning new workflow instances at the scheduled times. Schedules can be created, updated, and deleted via API calls. Scheduled workflows are treated as normal workflow instances with full durability and observability. The scheduler uses a polling mechanism (configurable interval) to detect due schedules.","intents":["Run a daily batch job to process pending orders","Send weekly reminder emails to inactive users","Execute a monthly reconciliation workflow","Implement recurring tasks without external cron jobs or schedulers"],"best_for":["Applications needing recurring workflows without external cron infrastructure","Teams wanting to manage schedules via API rather than cron configuration","Workflows that need to be scheduled dynamically based on application state"],"limitations":["Polling-based scheduling has latency (typically 1-5 minutes) compared to event-driven scheduling","Cron expressions are limited to standard cron syntax; no custom scheduling logic","No built-in timezone support; cron times are interpreted in server timezone","Scheduler polling adds database load; scaling to thousands of schedules requires optimization"],"requires":["Scheduler module configured in Durable supervision tree","PostgreSQL for storing schedule definitions","Cron expression for schedule definition"],"input_types":["Cron expression (e.g., '0 9 * * MON' for 9 AM Mondays)","Workflow name and initial context"],"output_types":["Schedule record in PostgreSQL","New workflow instance spawned at scheduled time"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-wavezync--durable__cap_9","uri":"capability://automation.workflow.hierarchical.workflow.composition.with.parent.child.relationships","name":"hierarchical workflow composition with parent-child relationships","description":"Enables workflows to spawn and coordinate child workflows via parent-child relationships. A parent workflow can spawn one or more child workflows, wait for their completion, and aggregate their results. Child workflows are independent workflow instances with their own execution state, retries, and error handling, but their lifecycle is tied to the parent. The execution engine tracks parent-child relationships in the database and handles cascading cancellation and error propagation.","intents":["Decompose a complex workflow into reusable sub-workflows (e.g., approval workflow, payment workflow, notification workflow)","Implement hierarchical approval chains where each level spawns sub-workflows for parallel approvals","Coordinate multiple independent workflows and aggregate their results","Enable workflow reuse across multiple parent workflows"],"best_for":["Large workflows that benefit from decomposition into reusable sub-workflows","Teams building workflow libraries with composable building blocks","Workflows with hierarchical structure (e.g., multi-level approvals)"],"limitations":["Parent-child relationships are static at workflow definition time; cannot dynamically spawn arbitrary child workflows","Child workflow results must be aggregated manually in parent logic; no built-in result aggregation","Cascading cancellation is not atomic; if parent cancellation fails, child workflows may be left in inconsistent state","No built-in timeout for child workflow completion; parent can wait indefinitely"],"requires":["Parent workflow definition with child workflow spawning logic","Child workflow definitions that can be spawned independently","PostgreSQL for tracking parent-child relationships"],"input_types":["Child workflow name and initial context","Parent workflow context (passed to child)"],"output_types":["Child workflow instance ID","Child workflow execution results aggregated in parent context"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Elixir 1.14+","PostgreSQL 12+ for workflow state storage","Ecto for database schema management","Phoenix (optional, for web UI integration)","PostgreSQL 12+ with Ecto adapter configured","Database migrations for WorkflowExecution and StepExecution schemas","Network connectivity to PostgreSQL during workflow execution","PostgreSQL with row-level locking support","Multiple application instances with Durable configured","Network connectivity between instances and PostgreSQL"],"failure_modes":["Elixir-only; no Python, Go, or Node.js SDKs available","DSL compilation happens at module load time; dynamic workflow generation requires runtime macro evaluation","Step logic must be pure Elixir functions; external language integration requires wrapper modules","PostgreSQL required for state persistence; no support for other databases","PostgreSQL becomes a critical dependency; no fallback to in-memory state","Step context must be serializable to JSON; complex Elixir terms (PIDs, references) require custom serialization","Persistence adds ~10-50ms latency per step completion (database round-trip)","No built-in encryption for sensitive data in context; requires application-level encryption before persistence","Database becomes a bottleneck; lock contention increases with number of instances","Polling for available work adds latency (typically 1-5 seconds) compared to push-based scheduling","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.14592696474667582,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.2,"quality":0.25,"ecosystem":0.1,"match_graph":0.4,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:59:55.150Z","last_commit":"2026-05-02T11:27:18Z"},"community":{"stars":180,"forks":9,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=wavezync--durable","compare_url":"https://unfragile.ai/compare?artifact=wavezync--durable"}},"signature":"WEqKSph3lE5NZv+GydXj5mKr6ha3Dki0/E6nwtUwXtZSHrZggBCQOtLHzu4vc3arWeqSErCNyS4sBSMy7pAyAw==","signedAt":"2026-06-21T22:04:00.192Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/wavezync--durable","artifact":"https://unfragile.ai/wavezync--durable","verify":"https://unfragile.ai/api/v1/verify?slug=wavezync--durable","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"}}