{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"inngest","slug":"inngest","name":"Inngest","type":"framework","url":"https://github.com/inngest/inngest","page_url":"https://unfragile.ai/inngest","categories":["automation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"inngest__cap_0","uri":"capability://automation.workflow.durable.step.based.workflow.execution.with.automatic.checkpointing","name":"durable step-based workflow execution with automatic checkpointing","description":"Executes multi-step workflows with automatic state checkpointing after each step, enabling resumption from failure points without re-executing completed steps. Uses Redis-backed state management with Lua scripts for atomic queue operations and checkpoint persistence. Steps are defined declaratively and executed sequentially with full execution history tracking via CQRS event storage.","intents":["Build reliable background jobs that survive process crashes and infrastructure failures","Execute multi-step LLM pipelines where each step (API call, processing, etc.) must be retryable independently","Track execution progress and replay workflows from any checkpoint for debugging"],"best_for":["Teams building AI agents with multiple LLM calls and external API dependencies","Backend engineers implementing reliable job processing without managing distributed state manually","Developers migrating from simple queue systems to durable workflow engines"],"limitations":["Checkpoint overhead adds ~50-100ms per step due to Redis persistence and state serialization","Step execution is sequential by default; parallel steps require explicit fan-out patterns","State size limited by Redis memory constraints; large intermediate results must be externalized","Requires Redis instance for production deployments; in-memory state not suitable for distributed systems"],"requires":["Redis 6.0+ for Lua script support and atomic operations","Node.js 18+ or Python 3.9+ (depending on SDK choice)","Network connectivity between execution environment and Redis/Inngest backend"],"input_types":["JSON event payloads","Function configuration (CUE-based schema)","Step definitions with input/output schemas"],"output_types":["Execution state snapshots (JSON)","Step results with metadata","Execution traces with timing and error information"],"categories":["automation-workflow","distributed-systems"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"inngest__cap_1","uri":"capability://automation.workflow.event.driven.workflow.triggering.with.pattern.matching","name":"event-driven workflow triggering with pattern matching","description":"Triggers workflow execution based on incoming events matched against declarative trigger patterns defined in CUE configuration. Events are ingested via REST API or SDK, normalized, and matched against registered function triggers using pattern matching logic. Supports event filtering, batching, and fan-out to multiple workflows from a single event.","intents":["Trigger background jobs automatically when specific events occur (user signup, payment received, etc.)","Fan out a single event to multiple workflows for parallel processing","Build event-driven architectures without managing pub/sub infrastructure directly"],"best_for":["SaaS platforms with event-driven architectures (webhooks, internal events)","AI applications that need to trigger LLM pipelines on specific user actions","Teams building reactive systems without maintaining Kafka/RabbitMQ infrastructure"],"limitations":["Pattern matching is synchronous; complex conditional logic requires custom code in trigger handlers","Event deduplication relies on event ID; duplicate events within the same millisecond may trigger multiple executions","No built-in event replay mechanism; historical events cannot be re-triggered without manual intervention","Event payload size limited to ~1MB; large payloads must be stored externally and referenced by ID"],"requires":["Event schema definition in CUE format","API endpoint or SDK client for event ingestion","Function definition with trigger configuration"],"input_types":["JSON event payloads","Event metadata (timestamp, source, ID)"],"output_types":["Workflow execution initiation","Execution ID for tracking"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"inngest__cap_10","uri":"capability://tool.use.integration.graphql.api.for.workflow.querying.and.mutation","name":"graphql api for workflow querying and mutation","description":"Exposes workflow execution data and operations via GraphQL API, enabling flexible querying of executions, functions, and traces. Supports filtering, pagination, and aggregation. Mutations enable triggering executions, canceling runs, and managing function configurations. Uses DataLoader pattern for efficient batch loading to prevent N+1 queries.","intents":["Query workflow execution history and status programmatically","Build custom dashboards and monitoring tools","Integrate workflow data into external systems"],"best_for":["Teams building custom dashboards and monitoring tools","Integration with external systems and analytics platforms","Developers familiar with GraphQL wanting flexible querying"],"limitations":["GraphQL queries can be expensive if not properly constrained; requires query depth limits","DataLoader batching adds latency for single-item queries","Subscription support is limited; real-time updates require polling","Schema changes require API versioning; breaking changes affect all clients"],"requires":["GraphQL client library","API authentication (API key or OAuth)"],"input_types":["GraphQL queries and mutations"],"output_types":["Execution data (JSON)","Function configurations","Trace information"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"inngest__cap_11","uri":"capability://automation.workflow.dashboard.ui.for.execution.monitoring.and.debugging","name":"dashboard ui for execution monitoring and debugging","description":"Web-based dashboard for monitoring workflow executions, viewing execution traces, and debugging failures. Displays execution timeline, step results, logs, and error information. Supports filtering by function, status, and date range. Includes real-time updates via WebSocket and detailed trace visualization with timing information.","intents":["Monitor workflow execution status in real-time","Debug workflow failures by inspecting execution traces","Identify performance bottlenecks and slow steps"],"best_for":["Operations teams monitoring workflow health","Developers debugging workflow failures","Teams wanting visibility into workflow execution without custom tooling"],"limitations":["Dashboard is read-only; cannot manually trigger executions or modify state","Real-time updates are limited to recent executions; historical data requires manual refresh","Trace visualization can be slow for workflows with thousands of steps","Dashboard does not support custom metrics or aggregations"],"requires":["Web browser with JavaScript support","Network access to Inngest backend"],"input_types":["Execution data from backend"],"output_types":["Visual representation of executions and traces"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"inngest__cap_12","uri":"capability://automation.workflow.fan.out.and.fan.in.patterns.for.parallel.step.execution","name":"fan-out and fan-in patterns for parallel step execution","description":"Enables parallel execution of multiple steps using fan-out pattern, where a single step spawns multiple child executions. Fan-in collects results from all child executions and passes them to subsequent steps. Implemented via step functions that return arrays of execution requests. Supports dynamic fan-out based on runtime data.","intents":["Process multiple items in parallel (e.g., send emails to multiple users)","Implement map-reduce patterns for data processing","Scale workflows horizontally by distributing work across multiple executions"],"best_for":["Batch processing workflows with variable parallelism","Data processing pipelines requiring map-reduce patterns","Workflows that need to process multiple items independently"],"limitations":["Fan-out creates multiple executions; each execution incurs overhead and consumes concurrency quota","Fan-in requires waiting for all child executions to complete; slow children block the workflow","No built-in timeout for fan-in; hung child executions will block indefinitely","Error handling for fan-in is complex; partial failures require custom logic"],"requires":["Step function that returns array of execution requests","Fan-in step to collect results"],"input_types":["Input data for fan-out","Array of execution requests"],"output_types":["Array of child execution results"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"inngest__cap_13","uri":"capability://code.generation.editing.cli.tools.for.function.initialization.deployment.and.management","name":"cli tools for function initialization, deployment, and management","description":"Command-line interface for initializing new functions, deploying to Inngest, managing function configurations, and running local development server. Supports scaffolding function templates, validating CUE schemas, and generating SDK code. Integrates with version control for tracking function changes.","intents":["Quickly scaffold new workflow functions with templates","Deploy function changes to production","Manage function configurations and versions"],"best_for":["Developers building workflows from command line","CI/CD pipelines deploying functions automatically","Teams with infrastructure-as-code practices"],"limitations":["CLI requires local installation; not suitable for web-based development environments","Deployment requires authentication; API keys must be managed securely","Schema validation is local; production validation may differ","No built-in rollback mechanism; previous versions must be managed manually"],"requires":["Node.js 18+ or Python 3.9+","Inngest CLI installed","API key for authentication"],"input_types":["Function template selection","Function configuration (CUE)","Deployment target"],"output_types":["Generated function scaffold","Deployment confirmation","Function metadata"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"inngest__cap_14","uri":"capability://code.generation.editing.multi.language.sdk.support.with.type.generation","name":"multi-language sdk support with type generation","description":"Provides SDKs for multiple languages (Node.js, Python, Go, etc.) with automatic type generation from CUE schemas. SDKs handle HTTP communication with execution engine, request signing, response validation, and error handling. Generated types ensure type safety across language boundaries.","intents":["Write workflow steps in preferred language","Ensure type safety between workflow definitions and step implementations","Integrate with existing codebases in multiple languages"],"best_for":["Polyglot teams wanting unified workflow orchestration","Organizations with existing codebases in multiple languages","Developers preferring type-safe SDKs"],"limitations":["SDK implementations may have feature parity issues; not all features available in all languages","Type generation requires schema changes to propagate to all SDKs","SDK versions must be kept in sync; mismatched versions may cause compatibility issues","Language-specific idioms may not translate well across SDKs"],"requires":["SDK for target language (Node.js, Python, Go, etc.)","CUE schema for type generation"],"input_types":["Function definition (code)","Step input data"],"output_types":["Step result","Generated types"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"inngest__cap_2","uri":"capability://automation.workflow.automatic.retry.with.exponential.backoff.and.jitter","name":"automatic retry with exponential backoff and jitter","description":"Automatically retries failed steps using configurable exponential backoff with jitter to prevent thundering herd. Retry logic is built into the execution engine and applied transparently across all step types. Supports max retry counts, custom backoff curves, and retry-specific error classification (retryable vs permanent failures).","intents":["Handle transient failures in external API calls without manual retry logic","Prevent cascading failures when downstream services are temporarily unavailable","Configure different retry strategies for different step types (LLM calls vs database operations)"],"best_for":["AI applications calling unreliable external LLM APIs","Microservice architectures with network-dependent operations","Teams that want resilience without implementing retry logic in every step"],"limitations":["Exponential backoff can cause long delays for frequently-failing steps; max retry duration may exceed workflow timeout","Jitter is pseudo-random; deterministic retry behavior cannot be guaranteed for testing","Permanent failure classification is heuristic-based; some transient errors may be misclassified as permanent","No built-in circuit breaker; repeated failures to the same service will continue retrying until max attempts"],"requires":["Step definition with retry configuration (maxAttempts, initialDelay, etc.)","Error classification logic (custom or built-in)"],"input_types":["Step execution result with error information","Retry configuration (backoff curve, max attempts)"],"output_types":["Retry decision (retry or fail)","Scheduled retry timestamp"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"inngest__cap_3","uri":"capability://automation.workflow.concurrency.control.with.per.function.and.per.key.limits","name":"concurrency control with per-function and per-key limits","description":"Enforces concurrency constraints at multiple levels: global per-function limits (max concurrent executions), and per-key limits (max concurrent executions for a specific entity like user ID or order ID). Uses Redis-backed distributed locking with Lua scripts to enforce limits atomically across multiple execution nodes. Queues excess executions and processes them as capacity becomes available.","intents":["Prevent rate limit violations when calling external APIs with per-user quotas","Serialize operations on shared resources (e.g., only one workflow per user at a time)","Implement backpressure to avoid overwhelming downstream services"],"best_for":["Multi-tenant SaaS platforms with per-customer rate limits","AI applications calling rate-limited LLM APIs","Systems with shared resource constraints (database connections, API quotas)"],"limitations":["Per-key concurrency limits require explicit key definition in workflow; no automatic key inference","Lock contention on high-concurrency keys can cause Redis bottlenecks; horizontal scaling requires Redis cluster","Queued executions have unpredictable latency; no SLA guarantees on queue processing time","Concurrency limits are soft; burst traffic may exceed limits briefly before being enforced"],"requires":["Redis instance with Lua script support","Concurrency configuration (global limit, per-key limit)","Key extraction logic if using per-key limits"],"input_types":["Workflow execution request","Concurrency key (optional, for per-key limits)"],"output_types":["Execution allowed or queued","Queue position if queued"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"inngest__cap_4","uri":"capability://automation.workflow.pause.and.resume.with.event.driven.continuations","name":"pause and resume with event-driven continuations","description":"Pauses workflow execution at any step and resumes when a specific event is received. Pause state is persisted in Redis with event matching logic; when a matching event arrives, the workflow automatically resumes from the pause point with the event data available to subsequent steps. Supports timeout-based auto-resumption and conditional pause logic.","intents":["Wait for external events (user approval, webhook callback, scheduled time) without blocking resources","Implement approval workflows where human review must occur before proceeding","Build workflows that depend on asynchronous external processes (payment confirmation, file processing)"],"best_for":["Approval-based workflows with human-in-the-loop steps","Workflows waiting for external webhooks or callbacks","Long-running processes that should not consume resources while waiting"],"limitations":["Pause state is stored in Redis; very long pauses (months) may cause memory pressure","Event matching for resume is exact; complex conditional resume logic requires custom code","Timeout-based resumption requires background job processing; no guaranteed resume time","Paused workflows are not visible in standard execution traces; requires special UI/API to inspect pause state"],"requires":["Event definition for resume trigger","Pause configuration (timeout, event matching logic)","Mechanism to send resume event (API, webhook, etc.)"],"input_types":["Pause trigger condition","Resume event definition","Timeout duration (optional)"],"output_types":["Pause state persisted","Resume event received","Workflow resumed with event data"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"inngest__cap_5","uri":"capability://tool.use.integration.http.driver.with.sdk.communication.and.response.handling","name":"http driver with sdk communication and response handling","description":"Executes workflow steps by making HTTP requests to SDK endpoints (user-defined functions), handling request/response serialization, timeout management, and error classification. Uses HTTP/2 with connection pooling for efficiency. Supports multiple SDK implementations (Node.js, Python, Go) via a standardized protocol. Handles response validation, type coercion, and automatic retry on network errors.","intents":["Execute user-defined workflow steps written in any language via HTTP","Integrate with existing microservices by wrapping them as workflow steps","Support polyglot workflows where different steps run in different languages/runtimes"],"best_for":["Teams with polyglot codebases wanting unified workflow orchestration","Workflows that need to call existing microservices as steps","Developers who prefer writing steps in their native language"],"limitations":["HTTP round-trip latency adds 50-200ms per step; not suitable for sub-millisecond operations","SDK endpoint must be network-accessible; no support for in-process function execution","Request/response serialization overhead for large payloads; binary data must be base64-encoded","Timeout management is client-side only; hung SDK endpoints will block execution until timeout expires","No built-in request signing for untrusted networks; requires TLS for security"],"requires":["HTTP-accessible SDK endpoint","SDK implementation for target language (Node.js, Python, Go, etc.)","Network connectivity between executor and SDK endpoint"],"input_types":["Step input data (JSON)","Step configuration","Execution context (run ID, step ID, etc.)"],"output_types":["Step result (JSON)","Error information if step fails","Execution metadata (duration, retries)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"inngest__cap_6","uri":"capability://tool.use.integration.websocket.based.connect.gateway.for.long.lived.sdk.connections","name":"websocket-based connect gateway for long-lived sdk connections","description":"Provides a WebSocket gateway that allows SDKs to maintain long-lived connections to the Inngest backend, enabling server-initiated execution requests without polling. SDKs register themselves via WebSocket and receive step execution requests in real-time. Supports automatic reconnection, connection pooling, and load balancing across multiple SDK instances.","intents":["Reduce latency for step execution by eliminating polling overhead","Support SDK deployments behind NAT/firewalls that cannot accept inbound HTTP connections","Enable server-initiated execution for real-time workflows"],"best_for":["High-frequency workflows where polling latency is unacceptable","Edge deployments or SDK instances behind restrictive firewalls","Real-time applications requiring sub-second execution latency"],"limitations":["WebSocket connections consume server resources; not suitable for millions of concurrent SDKs","Connection state is not persisted; SDK reconnection may lose in-flight requests","Load balancing across multiple SDK instances requires explicit configuration","Debugging WebSocket issues is more complex than HTTP request/response debugging"],"requires":["WebSocket-capable SDK implementation","Network connectivity supporting WebSocket protocol (may require proxy configuration)","Inngest backend with Connect gateway enabled"],"input_types":["SDK registration message","Step execution request"],"output_types":["Step result","Error information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"inngest__cap_7","uri":"capability://code.generation.editing.function.schema.definition.and.validation.with.cue","name":"function schema definition and validation with cue","description":"Defines workflow functions using CUE configuration language, which provides type-safe schema definition with validation, documentation, and code generation. Function schemas specify triggers, steps, concurrency limits, retry policies, and input/output types. CUE schemas are compiled into efficient validators and used for runtime type checking and IDE autocomplete.","intents":["Define workflow structure with type safety and validation","Generate SDK code and type definitions from schema","Document workflows with embedded schema information"],"best_for":["Teams that value type safety and schema-driven development","Polyglot teams wanting a language-agnostic workflow definition format","Organizations with strict schema governance requirements"],"limitations":["CUE has a learning curve; developers unfamiliar with the language may find it verbose","Schema changes require redeployment; no hot-reload for schema updates","CUE validation is strict; permissive schemas require explicit configuration","IDE support for CUE is limited compared to mainstream languages"],"requires":["CUE compiler (included in Inngest CLI)","Understanding of CUE syntax and semantics"],"input_types":["CUE schema definition"],"output_types":["Compiled schema validator","Generated SDK types","Schema documentation"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"inngest__cap_8","uri":"capability://automation.workflow.development.server.with.local.workflow.execution.and.hot.reload","name":"development server with local workflow execution and hot reload","description":"Provides a local development server that executes workflows against a local Redis instance, enabling rapid iteration without deploying to production. Supports hot reload of function definitions and automatic re-execution of failed workflows. Includes a web UI for inspecting execution traces, logs, and step results in real-time.","intents":["Develop and test workflows locally before deploying to production","Debug workflow execution with detailed traces and logs","Iterate rapidly on workflow logic without manual redeployment"],"best_for":["Individual developers and small teams building workflows","Rapid prototyping and experimentation","Debugging complex workflow logic"],"limitations":["Local Redis instance may not accurately simulate production concurrency and performance","Hot reload only works for function definitions; infrastructure changes require server restart","Web UI is read-only; cannot manually trigger executions or modify state from UI","Development server is single-threaded; cannot test concurrent execution patterns"],"requires":["Redis instance (local or remote)","Node.js 18+ or Python 3.9+","Inngest CLI"],"input_types":["Function definitions (code or CUE)","Event payloads for testing"],"output_types":["Execution traces","Step results and logs","Error information"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"inngest__cap_9","uri":"capability://automation.workflow.execution.tracing.and.observability.with.cqrs.event.sourcing","name":"execution tracing and observability with cqrs event sourcing","description":"Records all workflow executions as immutable event streams using CQRS (Command Query Responsibility Segregation) pattern. Every step execution, pause, resume, and state change is recorded as an event. Events are stored in append-only log and used to reconstruct execution state, generate traces, and enable audit trails. Traces include timing, error information, and full execution context.","intents":["Debug workflow failures by replaying execution history","Generate audit trails for compliance and forensics","Monitor workflow performance and identify bottlenecks","Reconstruct execution state without querying live system"],"best_for":["Regulated industries requiring audit trails (finance, healthcare)","Teams debugging complex workflow failures","Organizations monitoring workflow performance at scale"],"limitations":["Event storage grows linearly with execution volume; requires periodic archival for long-term retention","Event reconstruction is slower than direct state queries; not suitable for real-time dashboards","Event schema changes require migration logic; backward compatibility must be maintained","Trace visualization can be slow for workflows with thousands of steps"],"requires":["Event storage backend (Redis, database, etc.)","Event schema definitions"],"input_types":["Execution events (step started, step completed, pause, resume, etc.)"],"output_types":["Execution traces (JSON)","Audit logs","Performance metrics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"inngest__headline","uri":"capability://automation.workflow.event.driven.workflow.orchestration.framework","name":"event-driven workflow orchestration framework","description":"Inngest is an event-driven framework for building durable workflows and background jobs, featuring automatic retries, concurrency control, and integration for LLM pipelines, making it ideal for developers looking to manage complex event-driven processes.","intents":["best event-driven workflow engine","event-driven framework for AI jobs","durable workflow orchestration tools","how to build reliable background jobs","event orchestration solutions for LLM pipelines"],"best_for":["AI applications","background job management"],"limitations":[],"requires":[],"input_types":["events","functions"],"output_types":["executed workflows","job statuses"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Redis 6.0+ for Lua script support and atomic operations","Node.js 18+ or Python 3.9+ (depending on SDK choice)","Network connectivity between execution environment and Redis/Inngest backend","Event schema definition in CUE format","API endpoint or SDK client for event ingestion","Function definition with trigger configuration","GraphQL client library","API authentication (API key or OAuth)","Web browser with JavaScript support","Network access to Inngest backend"],"failure_modes":["Checkpoint overhead adds ~50-100ms per step due to Redis persistence and state serialization","Step execution is sequential by default; parallel steps require explicit fan-out patterns","State size limited by Redis memory constraints; large intermediate results must be externalized","Requires Redis instance for production deployments; in-memory state not suitable for distributed systems","Pattern matching is synchronous; complex conditional logic requires custom code in trigger handlers","Event deduplication relies on event ID; duplicate events within the same millisecond may trigger multiple executions","No built-in event replay mechanism; historical events cannot be re-triggered without manual intervention","Event payload size limited to ~1MB; large payloads must be stored externally and referenced by ID","GraphQL queries can be expensive if not properly constrained; requires query depth limits","DataLoader batching adds latency for single-item queries","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","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=inngest","compare_url":"https://unfragile.ai/compare?artifact=inngest"}},"signature":"lsR6MqAsQROJfKUxb3qDRbE5gTFth5JfftBy2t/maiCQfou9OXLxuzWBLhPWVeo9CsZrNlqadE3pXneb7MSaBw==","signedAt":"2026-06-21T14:37:43.786Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/inngest","artifact":"https://unfragile.ai/inngest","verify":"https://unfragile.ai/api/v1/verify?slug=inngest","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"}}