{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hatchet","slug":"hatchet","name":"Hatchet","type":"framework","url":"https://github.com/hatchet-dev/hatchet","page_url":"https://unfragile.ai/hatchet","categories":["automation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hatchet__cap_0","uri":"capability://automation.workflow.dag.based.workflow.orchestration.with.hierarchical.concurrency.control","name":"dag-based workflow orchestration with hierarchical concurrency control","description":"Hatchet executes complex multi-step workflows defined as directed acyclic graphs (DAGs) stored in the v1_dag table, with built-in hierarchical concurrency management that enforces resource limits at workflow, step, and action levels. The system uses a state machine approach for task lifecycle management (v1_task table) with automatic persistence, enabling workflows to survive service restarts and coordinate dependencies across distributed workers via gRPC streaming.","intents":["Define multi-step AI pipelines where later steps depend on earlier outputs","Enforce concurrency limits to prevent overwhelming downstream APIs or databases","Build fault-tolerant workflows that automatically retry failed steps with exponential backoff","Coordinate work across multiple distributed workers without manual synchronization"],"best_for":["Teams building AI agent workflows with LLM calls and tool invocations","Developers needing reliable multi-step task orchestration at scale","Organizations requiring fairness scheduling for shared resource access"],"limitations":["DAG structure must be defined upfront — dynamic workflow generation requires code changes","Hierarchical concurrency adds complexity to workflow definition; requires understanding of semaphore semantics","No built-in support for cyclic workflows or loops — must be unrolled or implemented via event-driven retriggers","Task state machine is PostgreSQL-backed; high-frequency state transitions may create database contention"],"requires":["PostgreSQL 12+ with partitioning support","gRPC-capable worker runtime (Go, Python, TypeScript SDKs provided)","Message queue: RabbitMQ or PostgreSQL PGMQ for task distribution"],"input_types":["DAG definition (JSON/YAML via SDK)","Task input payloads (JSON, stored in v1_task_payload)"],"output_types":["Task execution results (JSON)","Workflow run status and history (via v1_workflow_run table)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hatchet__cap_1","uri":"capability://automation.workflow.event.driven.workflow.triggering.with.cel.expression.matching","name":"event-driven workflow triggering with cel expression matching","description":"Hatchet triggers workflow runs in response to external events using a CEL (Common Expression Language) expression matcher stored in v1_filter and v1_match tables. When an event is published to the system, the dispatcher evaluates CEL expressions against event payloads to determine which workflows should be triggered, enabling complex conditional logic without hardcoding trigger rules. This architecture decouples event producers from workflow definitions.","intents":["Trigger AI workflows when specific conditions in external events are met (e.g., 'user.tier == premium AND event.type == signup')","Route events to multiple workflows based on dynamic CEL expressions","Build event-driven systems where workflow triggers evolve without redeploying workers","Implement complex event filtering logic at the orchestration layer rather than in application code"],"best_for":["Event-driven AI systems (webhooks, message queues, streaming events)","Multi-tenant platforms where different tenants have different workflow triggers","Teams wanting to decouple event producers from workflow logic"],"limitations":["CEL expression evaluation adds latency per event (~5-50ms depending on expression complexity)","No support for stateful event correlation or windowing — each event is evaluated independently","CEL expressions must be pre-defined; runtime expression injection requires API calls to update v1_filter records","Event payload size is limited by PostgreSQL JSONB storage; very large payloads require offloading to external storage"],"requires":["Event payload as JSON-serializable object","CEL expression syntax knowledge (similar to Google Cloud CEL)","Workflow definition already registered in Hatchet"],"input_types":["JSON event payload","CEL expression string"],"output_types":["Workflow run ID (if triggered)","Boolean (matched or not)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hatchet__cap_10","uri":"capability://automation.workflow.horizontal.scaling.via.dispatcher.sharding.and.worker.pool.management","name":"horizontal scaling via dispatcher sharding and worker pool management","description":"Hatchet scales horizontally by running multiple dispatcher instances, each managing a subset of worker connections based on worker affinity or hash-based sharding. Workers register with a specific dispatcher instance, and the system routes task assignments to the appropriate dispatcher based on worker availability. The architecture supports adding/removing dispatcher instances without downtime, with workers automatically reconnecting to available dispatchers on failure.","intents":["Scale task assignment throughput beyond single dispatcher capacity (>10k concurrent workers)","Distribute worker connections across multiple dispatcher instances for fault isolation","Add dispatcher capacity without restarting existing workers","Balance load across heterogeneous worker pools with different capabilities"],"best_for":["Large-scale deployments with thousands of workers","Organizations requiring high availability and fault isolation","Teams running Hatchet on Kubernetes with dynamic scaling"],"limitations":["Dispatcher sharding requires careful configuration; incorrect sharding can cause task assignment failures","Worker affinity/sharding logic must be consistent across dispatcher instances; no built-in consensus mechanism","Scaling down dispatchers requires graceful shutdown to avoid orphaning worker connections","No automatic load balancing across dispatchers; requires external load balancer or service mesh"],"requires":["Multiple dispatcher instances (hatchet-engine)","Shared state store (PostgreSQL) for worker registration and task state","Load balancer or service discovery for routing workers to dispatchers"],"input_types":["Worker registration request","Task assignment request"],"output_types":["Dispatcher assignment (which dispatcher instance to connect to)","Task assignment (from assigned dispatcher)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hatchet__cap_11","uri":"capability://automation.workflow.observability.and.telemetry.with.structured.logging.and.metrics.export","name":"observability and telemetry with structured logging and metrics export","description":"Hatchet includes built-in observability through structured logging (api/v1/server/middleware/telemetry/telemetry.go) and metrics export to OpenTelemetry-compatible backends. The system logs task execution events, worker lifecycle events, and API requests with structured fields (tenant_id, workflow_id, task_id) for easy filtering and correlation. Metrics include task latency, success rates, worker utilization, and dispatcher throughput, exported via OpenTelemetry SDK.","intents":["Monitor task execution latency and success rates in production","Debug issues by correlating logs across dispatcher, workers, and API","Track worker utilization and identify bottlenecks","Export metrics to observability platforms (Datadog, New Relic, Prometheus) for dashboarding"],"best_for":["Operations teams monitoring production Hatchet deployments","Teams using centralized observability platforms (Datadog, New Relic, Honeycomb)","Organizations requiring audit trails and compliance logging"],"limitations":["Structured logging adds overhead; high-volume task execution may impact performance","Metrics export is asynchronous; metrics may lag actual execution by seconds","No built-in log aggregation; requires external log aggregation platform (ELK, Splunk, etc.)","Telemetry configuration is global; no per-tenant or per-workflow telemetry sampling"],"requires":["OpenTelemetry-compatible backend (Jaeger, Datadog, New Relic, etc.)","OpenTelemetry SDK configured in Hatchet deployment","Log aggregation platform (optional but recommended)"],"input_types":["Task execution event","Worker lifecycle event","API request"],"output_types":["Structured log entry (JSON)","Metric (latency, success rate, etc.)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hatchet__cap_12","uri":"capability://automation.workflow.postgresql.based.message.queue.pgmq.as.alternative.to.rabbitmq","name":"postgresql-based message queue (pgmq) as alternative to rabbitmq","description":"Hatchet supports PostgreSQL PGMQ as a built-in message queue alternative to RabbitMQ, eliminating the need for a separate message broker in simpler deployments. PGMQ uses PostgreSQL tables for queue storage, with the same API as RabbitMQ but without external dependencies. This is suitable for deployments where PostgreSQL is already required and operational complexity should be minimized.","intents":["Run Hatchet without external message broker dependencies","Simplify deployment by using PostgreSQL as the only external service","Reduce operational overhead for small to medium deployments","Avoid RabbitMQ licensing or operational complexity"],"best_for":["Small to medium deployments where operational simplicity is prioritized","Teams already running PostgreSQL and wanting to minimize external dependencies","Development and testing environments"],"limitations":["PGMQ performance is lower than RabbitMQ; not suitable for very high-throughput deployments (>10k tasks/sec)","PGMQ adds load to PostgreSQL; may impact operational database performance if not carefully tuned","No built-in clustering or replication for PGMQ; relies on PostgreSQL replication","PGMQ message ordering is not guaranteed; tasks may be processed out of order"],"requires":["PostgreSQL 12+ with PGMQ extension installed","Hatchet configured to use PGMQ instead of RabbitMQ"],"input_types":["Task message (JSON)"],"output_types":["Task message (dequeued by dispatcher)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hatchet__cap_13","uri":"capability://automation.workflow.workflow.versioning.and.rollback.with.immutable.run.history","name":"workflow versioning and rollback with immutable run history","description":"Hatchet stores workflow definitions with versioning, allowing multiple versions of a workflow to coexist. Each workflow run is bound to a specific workflow version, ensuring that historical runs can be replayed or analyzed against the exact workflow definition that executed them. The system maintains immutable run history in the v1_workflow_run table, preventing accidental modification of historical data.","intents":["Update workflow definitions without affecting in-flight runs","Replay historical workflow runs with the same logic for debugging or re-processing","Maintain audit trail of workflow definition changes","Safely rollback to previous workflow versions if new version has issues"],"best_for":["Production systems requiring workflow definition stability","Teams frequently updating workflow logic","Organizations requiring audit trails for compliance"],"limitations":["Workflow versioning adds complexity; no automatic migration of in-flight runs to new versions","Immutable run history prevents correcting historical data; errors are permanent","No built-in workflow diff or comparison tool; manual inspection required to understand version changes","Rollback is manual; no automatic rollback on deployment failure"],"requires":["Workflow definition with version identifier","PostgreSQL for immutable run history storage"],"input_types":["Workflow definition (new version)"],"output_types":["Workflow version ID","Immutable run history"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hatchet__cap_2","uri":"capability://automation.workflow.real.time.task.assignment.via.grpc.streaming.with.worker.heartbeat.monitoring","name":"real-time task assignment via grpc streaming with worker heartbeat monitoring","description":"Hatchet's dispatcher service (dispatcher_v1.go) maintains persistent gRPC streaming connections to workers, pushing task assignments in real-time rather than workers polling a queue. The dispatcher monitors worker heartbeats and automatically reassigns tasks from dead workers, implementing a pull-based model where workers declare availability and the dispatcher matches them to queued tasks. This architecture reduces latency and enables fair scheduling across heterogeneous worker pools.","intents":["Assign tasks to workers with minimal latency (sub-100ms vs polling-based systems)","Detect worker failures and automatically reassign tasks without manual intervention","Balance load fairly across workers with different capabilities or resource constraints","Stream task updates and status changes to workers in real-time"],"best_for":["Low-latency AI inference pipelines where task assignment overhead matters","Heterogeneous worker pools with varying compute capabilities","Systems requiring automatic failover and task reassignment"],"limitations":["gRPC streaming requires persistent network connections; not suitable for ephemeral/serverless workers","Worker registration and heartbeat monitoring add operational complexity vs simple HTTP polling","Dispatcher becomes a bottleneck if managing >10k concurrent worker connections; requires horizontal scaling via sharding","Network partitions between dispatcher and workers cause task reassignment delays (configurable timeout, default ~30s)"],"requires":["gRPC-compatible worker runtime (Go, Python, TypeScript SDKs provided)","Network connectivity between dispatcher and workers (no NAT traversal built-in)","Hatchet dispatcher service running and accessible"],"input_types":["Task definition (action name, input payload)","Worker availability signal (gRPC stream)"],"output_types":["Task assignment (action, inputs, timeout)","Task result (output payload, status)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hatchet__cap_3","uri":"capability://automation.workflow.automatic.task.retry.with.exponential.backoff.and.timeout.enforcement","name":"automatic task retry with exponential backoff and timeout enforcement","description":"Hatchet persists task state in the v1_task table with built-in retry logic that automatically re-executes failed tasks using exponential backoff (configurable base and max multiplier). Each task has a timeout enforced at the dispatcher level; if a task exceeds its timeout, the dispatcher marks it as failed and triggers the retry mechanism. The system tracks retry count and can enforce a maximum retry limit, with all retry history persisted for debugging.","intents":["Automatically retry transient failures (network timeouts, rate limits) without manual intervention","Prevent cascading failures by backing off exponentially between retries","Enforce strict timeouts on long-running tasks to prevent resource exhaustion","Debug failed tasks by inspecting full retry history and error messages"],"best_for":["AI workloads calling external APIs (LLMs, databases) that may be temporarily unavailable","Systems requiring resilience to transient failures without application-level retry logic","Teams needing visibility into task failure patterns and retry behavior"],"limitations":["Exponential backoff is fixed per task type; no adaptive backoff based on error type (e.g., 429 vs 500)","Retries are task-level only; no workflow-level rollback or compensation logic","Maximum retry limit is global; no per-task-type configuration without code changes","Timeout enforcement is dispatcher-side; worker-side timeouts require separate implementation"],"requires":["Task definition with retry policy (maxRetries, backoffMultiplier, initialBackoffMs)","PostgreSQL for task state persistence","Dispatcher service running to enforce timeouts"],"input_types":["Task definition with retry parameters","Task execution result (success/failure)"],"output_types":["Retry decision (retry or fail)","Updated task state in v1_task table"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hatchet__cap_4","uri":"capability://automation.workflow.multi.tenant.workflow.isolation.with.configurable.resource.limits","name":"multi-tenant workflow isolation with configurable resource limits","description":"Hatchet implements complete data isolation per tenant at the database level, with all tables partitioned or filtered by tenant_id. The system supports configurable resource limits per tenant (concurrency limits, rate limits, storage quotas) enforced at the API and dispatcher layers. Tenants cannot access each other's workflows, runs, or events, and resource consumption is tracked separately for billing and enforcement.","intents":["Build SaaS platforms where each customer has isolated workflow namespaces","Enforce per-tenant concurrency and rate limits to prevent one tenant from starving others","Track resource consumption per tenant for billing or quota enforcement","Ensure data privacy and compliance by preventing cross-tenant data leakage"],"best_for":["SaaS platforms offering workflow orchestration as a service","Multi-tenant AI platforms where different customers have different resource allocations","Organizations requiring strict data isolation for compliance (GDPR, SOC2)"],"limitations":["Tenant isolation is database-level; no row-level security at the application layer, requiring careful API design","Resource limits are enforced at dispatcher; no kernel-level isolation (e.g., CPU/memory cgroups) for worker processes","Cross-tenant analytics require careful query design to avoid exposing aggregate data across tenants","Tenant-specific configuration changes require API calls; no self-service tenant configuration UI built-in"],"requires":["Tenant identifier (tenant_id) in all API requests","PostgreSQL with support for partitioning or filtering by tenant_id","API authentication/authorization to enforce tenant boundaries"],"input_types":["Tenant ID (string)","Workflow definition","Resource limit configuration"],"output_types":["Tenant-scoped workflow runs","Resource usage metrics per tenant"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hatchet__cap_5","uri":"capability://data.processing.analysis.dual.database.architecture.for.operational.and.analytical.workloads","name":"dual-database architecture for operational and analytical workloads","description":"Hatchet uses a dual-database schema: v1-core for operational data (tasks, workflows, runs) optimized for transactional consistency and fast writes, and v1-olap for analytical data (event aggregations, metrics) optimized for reporting and analytics. The system asynchronously replicates data from v1-core to v1-olap, enabling complex analytical queries without impacting operational performance. This architecture allows operational tables to be heavily partitioned for scalability while analytical tables maintain denormalized views.","intents":["Query workflow execution metrics and analytics without impacting operational performance","Build dashboards showing task success rates, latency distributions, and worker utilization","Analyze event patterns and workflow trigger frequency over time","Maintain operational database performance while supporting complex analytical queries"],"best_for":["Large-scale deployments where operational and analytical query patterns diverge","Teams needing real-time dashboards and historical analytics","Organizations requiring audit trails and compliance reporting"],"limitations":["Analytical data is eventually consistent; real-time analytics queries may lag operational data by seconds to minutes","Dual-schema maintenance adds operational complexity; schema changes must be coordinated across both databases","OLAP table design requires upfront analysis of analytical query patterns; ad-hoc queries may be slow","Data replication pipeline is asynchronous; failures in replication can cause analytics data to diverge from operational truth"],"requires":["PostgreSQL 12+ with support for partitioning and materialized views","Async replication mechanism (built-in to Hatchet, no external tool required)","Separate logical or physical database for OLAP schema (optional but recommended)"],"input_types":["Operational data (task executions, workflow runs, events)","Analytical query (SQL)"],"output_types":["Analytical results (aggregations, metrics, time-series data)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hatchet__cap_6","uri":"capability://data.processing.analysis.payload.storage.with.automatic.offloading.to.external.object.storage","name":"payload storage with automatic offloading to external object storage","description":"Hatchet stores task input and output payloads in the v1_task_payload table, with automatic offloading to external object storage (S3, GCS, etc.) when payloads exceed a configurable size threshold. The system maintains references to offloaded payloads in the database, transparently fetching them when needed. This architecture prevents database bloat from large payloads while maintaining a single logical view of task data.","intents":["Handle large task payloads (multi-MB images, documents) without bloating the database","Automatically tier payloads between fast database storage and cheap object storage","Maintain referential integrity between tasks and their payloads across storage tiers","Avoid database size limits when processing large AI model outputs or file uploads"],"best_for":["AI workflows processing large files (images, videos, documents)","Systems with variable payload sizes (some tasks small, others large)","Cost-sensitive deployments where database storage is expensive"],"limitations":["Offloading adds latency for large payloads; fetching from object storage is slower than database reads","Requires external object storage configuration (S3, GCS); no built-in local file storage option","Payload offloading is one-way; once offloaded, payloads cannot be moved back to database","Object storage credentials must be configured; no built-in credential rotation or audit logging"],"requires":["External object storage (S3, GCS, Azure Blob Storage)","Object storage credentials (access key, secret key)","Configurable payload size threshold (default likely 1-10MB)"],"input_types":["Task payload (JSON, binary)","Payload size"],"output_types":["Payload reference (database row or object storage URL)","Payload data (transparently fetched)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hatchet__cap_7","uri":"capability://automation.workflow.rate.limiting.and.fairness.scheduling.for.llm.api.calls","name":"rate limiting and fairness scheduling for llm api calls","description":"Hatchet implements rate limiting at multiple levels: per-workflow, per-step, and per-action, with fairness scheduling that ensures no single workflow starves others when shared resources are constrained. The system uses token bucket algorithms with configurable rates and burst sizes, stored in the hierarchical concurrency control layer. This is specifically optimized for LLM API calls where rate limits are common and fairness is critical for multi-tenant systems.","intents":["Enforce rate limits on LLM API calls to stay within provider quotas (e.g., OpenAI TPM limits)","Fairly distribute limited API quota across multiple workflows in a multi-tenant system","Prevent one workflow from consuming all available API capacity","Implement backpressure when rate limits are exceeded, queuing tasks instead of failing"],"best_for":["Multi-tenant AI platforms where multiple customers share LLM API quotas","Teams building LLM-heavy workflows with strict rate limits","Systems requiring fair resource allocation across competing workflows"],"limitations":["Rate limiting is enforced at the dispatcher; worker-side rate limiting requires separate implementation","Fairness scheduling adds complexity; configuring rates and burst sizes requires understanding token bucket semantics","No adaptive rate limiting based on API response codes (e.g., 429 responses); rates are static","Rate limit configuration is per-workflow; no automatic discovery of provider rate limits"],"requires":["Rate limit configuration (requests per second, burst size) per workflow/step/action","Dispatcher service running to enforce limits","Shared state (Redis or PostgreSQL) to track token bucket state across dispatcher instances"],"input_types":["Rate limit policy (rps, burst size)","Task assignment request"],"output_types":["Task assignment (if rate limit allows) or queued task"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hatchet__cap_8","uri":"capability://code.generation.editing.python.and.typescript.sdk.with.automatic.code.generation.from.openapi.spec","name":"python and typescript sdk with automatic code generation from openapi spec","description":"Hatchet provides Python and TypeScript SDKs that are automatically generated from the OpenAPI specification (api-contracts/openapi/openapi.yaml), ensuring consistency between API and SDK. The SDKs include high-level abstractions for defining workflows, registering actions, and triggering runs, with type safety through generated data models. The generation pipeline (pkg/client/rest/gen.go) is part of the build process, ensuring SDKs stay in sync with API changes.","intents":["Define and register workflows in Python or TypeScript without manual API calls","Get type-safe access to workflow definitions and task results","Automatically stay in sync with Hatchet API changes through regenerated SDKs","Reduce boilerplate code for common operations (trigger run, get status, list workflows)"],"best_for":["Python and TypeScript developers building Hatchet workflows","Teams wanting type safety and IDE autocomplete for workflow definitions","Organizations with frequent API changes who want SDKs to auto-update"],"limitations":["SDKs are generated from OpenAPI spec; custom logic or optimizations require code generation changes","Type safety is limited to API contract; runtime validation of workflow definitions is minimal","No async/await support in Python SDK (if using synchronous HTTP client); TypeScript SDK may have better async support","Generated code can be verbose; manual SDK implementations might be more concise for simple use cases"],"requires":["Python 3.8+ (Python SDK) or Node.js 16+ (TypeScript SDK)","Hatchet API server running and accessible","API credentials (tenant ID, API key)"],"input_types":["Workflow definition (Python/TypeScript code)","Action handler (Python/TypeScript function)"],"output_types":["Workflow run ID","Task result (typed)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hatchet__cap_9","uri":"capability://automation.workflow.workflow.and.run.management.dashboard.with.real.time.status.updates","name":"workflow and run management dashboard with real-time status updates","description":"Hatchet provides a web-based dashboard (frontend/app) built with React that displays workflow definitions, execution history, and real-time task status. The dashboard queries the v1-olap analytical schema for historical data and the API for real-time status, with WebSocket support for live updates. Users can trigger workflow runs, inspect task inputs/outputs, view retry history, and debug failed tasks through the UI.","intents":["Visualize workflow definitions and execution DAGs","Monitor real-time task execution status across workers","Debug failed tasks by inspecting inputs, outputs, and error messages","Trigger manual workflow runs with custom input payloads","Track workflow execution metrics and success rates over time"],"best_for":["Operators monitoring production workflows","Developers debugging workflow failures","Teams wanting visibility into task execution without CLI tools"],"limitations":["Dashboard is read-mostly; limited ability to modify workflows or cancel running tasks","Real-time updates via WebSocket may not scale to thousands of concurrent users","No built-in role-based access control (RBAC); all authenticated users see all workflows","Dashboard queries v1-olap for historical data; real-time analytics may lag by seconds"],"requires":["Hatchet API server running and accessible","Web browser with JavaScript support","API authentication (tenant ID, API key or session token)"],"input_types":["Workflow run trigger request (via UI form)","Task input payload (JSON, via UI editor)"],"output_types":["Workflow run status (real-time)","Task execution history (from v1-olap)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hatchet__headline","uri":"capability://automation.workflow.distributed.task.queue.and.workflow.orchestration.for.ai.workloads","name":"distributed task queue and workflow orchestration for ai workloads","description":"Hatchet is a distributed task queue and workflow engine specifically designed for managing complex AI workloads, featuring DAG-based workflows, concurrency controls, and fairness scheduling for LLM calls.","intents":["best distributed task queue for AI","workflow orchestration for machine learning","AI workload management system","task scheduling for large language models","scalable workflow engine for AI applications"],"best_for":["AI developers","data scientists"],"limitations":[],"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":["PostgreSQL 12+ with partitioning support","gRPC-capable worker runtime (Go, Python, TypeScript SDKs provided)","Message queue: RabbitMQ or PostgreSQL PGMQ for task distribution","Event payload as JSON-serializable object","CEL expression syntax knowledge (similar to Google Cloud CEL)","Workflow definition already registered in Hatchet","Multiple dispatcher instances (hatchet-engine)","Shared state store (PostgreSQL) for worker registration and task state","Load balancer or service discovery for routing workers to dispatchers","OpenTelemetry-compatible backend (Jaeger, Datadog, New Relic, etc.)"],"failure_modes":["DAG structure must be defined upfront — dynamic workflow generation requires code changes","Hierarchical concurrency adds complexity to workflow definition; requires understanding of semaphore semantics","No built-in support for cyclic workflows or loops — must be unrolled or implemented via event-driven retriggers","Task state machine is PostgreSQL-backed; high-frequency state transitions may create database contention","CEL expression evaluation adds latency per event (~5-50ms depending on expression complexity)","No support for stateful event correlation or windowing — each event is evaluated independently","CEL expressions must be pre-defined; runtime expression injection requires API calls to update v1_filter records","Event payload size is limited by PostgreSQL JSONB storage; very large payloads require offloading to external storage","Dispatcher sharding requires careful configuration; incorrect sharding can cause task assignment failures","Worker affinity/sharding logic must be consistent across dispatcher instances; no built-in consensus mechanism","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":"2026-06-17T09:51:04.691Z","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=hatchet","compare_url":"https://unfragile.ai/compare?artifact=hatchet"}},"signature":"5L7MSP6hdUj9BfZcagHdOkbd00qs73kSjrnbvxceDUJ9nGNvFsZEXxT7ePvc1H/RjDVrLEwGWnc3s6KDJlCOAA==","signedAt":"2026-06-21T07:45:02.799Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hatchet","artifact":"https://unfragile.ai/hatchet","verify":"https://unfragile.ai/api/v1/verify?slug=hatchet","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"}}