Temporal vs Hatchet

Agent loops

1. agent builderI run LLM agent loops as durable workflows — model and tool calls as checkpointed, retried steps that survive crashes mid-run

   weight 3 · round to Temporal
   Temporal✓fullcommunity9/10

   Temporal has a dedicated AI/agent page describing exactly this use case — long-running agent loops calling LLMs and tools, wrapped as Workflows/Activities with automatic checkpointing, retries, and crash/Worker-restart recovery — plus a concrete OpenAI Agents SDK integration and a durable agentic-loop cookbook with Claude tool calling. Community evidence corroborates durable execution and retry semantics in production use, though one report notes rough edges in error/anomaly handling and documentation quality. Missing for 10: independent hands-on verification specifically of an LLM-agent workflow (vs. general workflow durability) and no third-party benchmark of failure-recovery behavior in agent loops.

   • [claimed-docs] “Long-running, stateful agent loops that call LLMs and tools, wait on humans, and pick up exactly where they left off after a failure.”
   • [claimed-docs] “a Workflow resumes automatically after a crash, a network timeout, or a multi-day wait for a human to approve a step”
   • [claimed-docs] “model calls are executed as Activities, so they retry durably and are not repeated during Workflow replay. Your agents survive Worker restar…”
   • [claimed-docs] “Temporal's integration with the OpenAI Agents SDK for Python lets you run agents as Temporal Workflows.”
   • [claimed-docs] “Build a durable agentic loop in Python with Claude tool calling and Temporal.”
   • [claimed-docs] “Call an LLM from a durable Temporal Workflow in Python using the OpenAI API library.”
   • [claimed-docs] “Temporal's default behavior is to automatically retry an Activity that fails, so transient or intermittent failures require no action on you…”
   • [community] “No, the whole point of Temporal is to distribute work across machines without worrying too much about orchestration. You can retry any unit …”
   • [community] “Temporal is completely above and beyond Asyncio. It's a full scheduling of work and queues that's cross-machine, cross-language, and very tr…”

   proof ↗⚑ flag

   Hatchet✓fullclaimed7/10

   Hatchet is explicitly positioned as an orchestration engine for AI agents and durable workflows, with first-party docs on retry policies, durability, concurrency, cron/scheduled runs, and dedicated cookbooks showing Claude Agent SDK / OpenAI Agents SDK integration and a support-agent workflow example, matching the checkpointed/retried step model for agent loops. Missing for 10: independent/hands-on evidence of crash-mid-run recovery specifically for LLM tool-call loops, and more detail on how model/tool call state is checkpointed rather than just task-level retries.

   • [github] “Hatchet provides a full platform for queuing, automatic retries, durability, real-time monitoring, alerting, and logging.”
   • [github] “Hatchet is a platform for orchestrating background tasks, AI agents, and durable workflows at scale. It supports applications written in Pyt…”
   • [claimed-docs] “Retry Policies”
   • [claimed-docs] “expose Hatchet workflows and standalone tasks as tools that frameworks like the Claude Agent SDK and OpenAI Agents SDK can invoke”
   • [claimed-docs] “Hatchet Agent Tools”
   • [claimed-docs] “Support Agent”
   • [claimed-docs] “Claude Agent SDK”
   • [claimed-docs] “OpenAI Agents SDK”
   • [claimed-docs] “Concurrency”

   proof ↗⚑ flag

Ai integrations

1. agent builderFirst-party integrations wrap my AI stack — AI SDKs, agent frameworks, model providers — so agent steps get durability without glue code

   weight 2 · round drawn
   Temporal~partialclaimed6/10

   Temporal ships a first-party integration with the OpenAI Agents SDK (Python) that runs agents as Workflows with durable, non-repeated model calls, plus a cookbook for durable agent loops with Claude and raw OpenAI API calls, showing genuine glue-code elimination for those specific stacks. Missing for 10: broader first-party SDK coverage beyond OpenAI Agents SDK/Anthropic cookbook, evidence of framework integrations (LangChain, LlamaIndex, Vercel AI SDK), and independent/hands-on confirmation of these integrations working in production.

   • [claimed-docs] “model calls are executed as Activities, so they retry durably and are not repeated during Workflow replay. Your agents survive Worker restar…”
   • [claimed-docs] “Temporal's integration with the OpenAI Agents SDK for Python lets you run agents as Temporal Workflows.”
   • [claimed-docs] “Build a durable agentic loop in Python with Claude tool calling and Temporal.”
   • [claimed-docs] “Call an LLM from a durable Temporal Workflow in Python using the OpenAI API library.”
   • [claimed-docs] “Long-running, stateful agent loops that call LLMs and tools, wait on humans, and pick up exactly where they left off after a failure.”
   • [claimed-docs] “a Workflow resumes automatically after a crash, a network timeout, or a multi-day wait for a human to approve a step”

   proof ↗⚑ flag

   Hatchet~partialclaimed6/10

   Hatchet documents first-party cookbooks/integrations for the Claude Agent SDK and OpenAI Agents SDK (exposing Hatchet workflows/tasks as durable tools these frameworks can invoke) plus an MCP-based agent-tools pattern and a support-agent cookbook, directly supporting durability for agent steps without custom glue code for those specific frameworks. However, coverage is limited to two named agent SDKs and MCP — there's no evidence of broader first-party wrappers for other AI SDKs (e.g., LangChain, LlamaIndex) or direct model-provider clients (OpenAI/Anthropic APIs) beyond the agent-SDK cookbooks. Missing for 10: integrations/cookbooks for additional AI SDKs and model providers, and independent confirmation of ease-of-use/durability in production for these integrations.

   • [claimed-docs] “expose Hatchet workflows and standalone tasks as tools that frameworks like the Claude Agent SDK and OpenAI Agents SDK can invoke”
   • [claimed-docs] “Hatchet Agent Tools”
   • [claimed-docs] “Claude Agent SDK”
   • [claimed-docs] “OpenAI Agents SDK”
   • [claimed-docs] “Support Agent”

   proof ↗⚑ flag

Streaming

1. ai-native userI stream live progress — step updates or model tokens — from a running workflow into my frontend as it executes

   weight 2 · round to Temporal
   Temporal~partialclaimed4/10

   Temporal supports Signals/Queries/Updates and Web UI state visibility, which can be used to relay step updates from a workflow (e.g. via Query polling or Signal-based push), and its AI docs mention agent loops calling LLMs/tools, but there is no documented mechanism for streaming token-level or real-time push updates directly to a frontend — Queries are pull-based and Updates/Signals are not designed for continuous streaming. missing for 10: a documented real-time streaming/push API (e.g. websockets/SSE) to a frontend, token-level streaming support, and any hands-on example of live UI progress updates from a running workflow.

   • [claimed-docs] “Temporal supports three types of messages: Signals, Queries, and Updates”
   • [claimed-docs] “Workflows can be thought of as stateful web services that can receive messages. The Workflow can have message handlers, akin to endpoints”
   • [claimed-docs] “The Temporal Web UI provides users with Workflow Execution state and metadata for debugging purposes.”
   • [claimed-docs] “Long-running, stateful agent loops that call LLMs and tools, wait on humans, and pick up exactly where they left off after a failure.”
   • [claimed-docs] “model calls are executed as Activities, so they retry durably and are not repeated during Workflow replay. Your agents survive Worker restar…”

   proof ↗⚑ flag

   Hatchet—none0/10

   Hatchet's evidence covers real-time observability via its TUI/dashboard and general monitoring/logging, but nothing describes a mechanism for streaming step updates or model tokens into a consumer's own frontend as a workflow executes (no SSE/websocket API, event-stream docs, or token-streaming cookbook is cited). Given Hatchet explicitly targets AI-agent orchestration, this is a fair axis to expect evidence for, but none is present.

   ⚑ flag

Agent access

1. ai-native userPoint an agent at llms.txt or agent-oriented docs

   weight 2 · round drawn
   Temporal✓fullprobed9/10

   A direct probe confirms https://docs.temporal.io/llms.txt returns HTTP 200 and is a structured llms.txt index of Temporal's docs, plus Temporal has dedicated agent-oriented docs (docs.temporal.io/ai) for AI-native use cases. Missing for 10: independent third-party confirmation that agents successfully consume this file in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.temporal.io/llms.txt # Temporal Platform Documentation > This file is a structured index of Tempor…”
   • [claimed-docs] “Long-running, stateful agent loops that call LLMs and tools, wait on humans, and pick up exactly where they left off after a failure.”
   • [claimed-docs] “a Workflow resumes automatically after a crash, a network timeout, or a multi-day wait for a human to approve a step”

   proof ↗⚑ flag

   Hatchet✓fullprobed9/10

   A direct probe confirms llms.txt is live at https://docs.hatchet.run/llms.txt returning HTTP 200 with structured documentation content, which an agent could be pointed at directly. Missing for 10: no independent/community corroboration of agents actually consuming it in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.hatchet.run/llms.txt # Hatchet Documentation > Hatchet is a distributed task queue and workflow en…”

   proof ↗⚑ flag

2. ai-native userRun the product headlessly / in CI for automation

   weight 2 · round drawn
   Temporal✓fullclaimed8/10

   Temporal ships an official CLI (available on macOS/Linux/Windows/Docker) and a local dev server ('temporal server start-dev'), plus Workers/Workflows that run headlessly as background processes with no UI requirement, making it well-suited for CI/automation pipelines; docs also describe a test server for CI-friendly integration testing with time-skipping. missing for 10: no explicit CI pipeline example (e.g., GitHub Actions) or independent hands-on report confirming CI usage.

   • [claimed-docs] “The CLI includes a local Temporal development service for fast feedback while building or testing your application.”
   • [claimed-docs] “The CLI is available for macOS, Linux, and Windows, or as a Docker image.”
   • [github] “temporal server start-dev”
   • [claimed-docs] “Because the test server supports skipping time, use the test server for both end-to-end and integration tests with Workers.”
   • [claimed-docs] “The Temporal CLI (`temporal`) provides direct access to a Temporal Service via the terminal. Use it to manage, monitor, and debug Temporal a…”
   • [claimed-docs] “This starts a complete Temporal Service with Web UI on your local machine.”

   proof ↗⚑ flag

   Hatchet✓fullclaimed8/10

   Hatchet is a headless orchestration engine by design: workers run as background processes (hatchet worker dev / production worker run), SDKs exist for Python/TypeScript/Go/Ruby for scripted automation, and self-hosting/control-plane docs plus cron/scheduled runs support CI-driven and unattended execution. missing for 10: explicit CI pipeline examples (e.g., GitHub Actions integration) and independent confirmation of headless CI usage beyond docs.

   • [claimed-docs] “the `hatchet worker dev` command lets you run a worker locally with automatic reloading when code changes are detected”
   • [claimed-docs] “the `hatchet worker dev` command lets you run a worker locally with automatic reloading when code changes are detected.”
   • [claimed-docs] “Running Workers Locally”
   • [claimed-docs] “Python SDK TypeScript SDK Go SDK Ruby SDK”
   • [claimed-docs] “Cron Runs”
   • [claimed-docs] “Scheduled Runs”
   • [claimed-docs] “Self-hosting Hatchet means running your own instance of the **Hatchet Control Plane** - the central orchestration system that manages workfl…”
   • [github] “Hatchet is a platform for orchestrating background tasks, AI agents, and durable workflows at scale. It supports applications written in Pyt…”

   proof ↗⚑ flag

3. ai-native userUse an official CLI

   weight 2 · round drawn
   Temporal✓fullprobed8/10

   Temporal ships an official, well-documented CLI (temporal) available on macOS/Linux/Windows/Docker, including a local dev server, batch/query commands, and coverage of core operations (start/query/signal workflow executions), making it usable for scripting and automating agentic workflows. missing for 10: independent/hands-on confirmation of CLI usage, and explicit AI-native automation features like structured JSON output or agent-specific CLI subcommands.

   • [claimed-docs] “The CLI includes a local Temporal development service for fast feedback while building or testing your application.”
   • [claimed-docs] “The CLI is available for macOS, Linux, and Windows, or as a Docker image.”
   • [claimed-docs] “The Temporal CLI (`temporal`) provides direct access to a Temporal Service via the terminal. Use it to manage, monitor, and debug Temporal a…”
   • [claimed-docs] “Show the progress of an ongoing batch job. Pass a valid job ID to display its information”
   • [github] “temporal server start-dev”
   • [probe] “official CLI documented at https://docs.temporal.io/cli”

   proof ↗⚑ flag

   Hatchet✓fullprobed8/10

   Hatchet ships an official CLI documented with multiple subcommands (worker dev, tui, quickstart, profile) for local dev, observability, scaffolding, and environment management, confirmed by docs and a dedicated CLI reference page. Missing for 10: independent hands-on community review specifically of the CLI's agentic/AI-native workflows and broader third-party validation beyond docs.

   • [claimed-docs] “the `hatchet worker dev` command lets you run a worker locally with automatic reloading when code changes are detected”
   • [claimed-docs] “the `hatchet tui` command lets you interact with your Hatchet deployment through a terminal user interface (TUI) that provides real-time obs…”
   • [claimed-docs] “the `hatchet quickstart` command generates a worker project from a template to help you get started quickly”
   • [claimed-docs] “Profiles: the `hatchet profile` commands allow you to manage multiple Hatchet instances and tenants with named profiles, making it easy to s…”
   • [probe] “official CLI documented at https://docs.hatchet.run/reference/cli”

   proof ↗⚑ flag

4. ai-native userDrive the product through a documented public API

   weight 3 · round to Temporal
   Temporal✓fullprobed7/10

   Temporal exposes a well-documented client API (start/query/signal/list workflow executions) plus a documented CLI and multi-language SDKs, and even publishes an llms.txt AI-friendly doc index, making it programmatically drivable and discoverable by AI agents. However, probes show no machine-readable OpenAPI/REST spec is published (all openapi.json/swagger paths 404), so 'documented public API' relies on prose docs/SDK references rather than a formal spec. Missing for 10: a machine-readable OpenAPI/gRPC spec exposed publicly, and independent community confirmation of API completeness/stability for AI-driven use.

   • [claimed-docs] “Start a Workflow Execution - Get the result of Workflow Execution - List Workflow Executions - Query a Workflow Execution - Signal a Workflo…”
   • [claimed-docs] “The Temporal CLI (`temporal`) provides direct access to a Temporal Service via the terminal. Use it to manage, monitor, and debug Temporal a…”
   • [claimed-docs] “a short walkthrough of how to use the Temporal primitives (Activities, Workflows, and Workers) to build and run a Temporal application”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.temporal.io/llms.txt # Temporal Platform Documentation > This file is a structured index of Tempor…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.temporal.io/openapi.json, https://docs.temporal.io/swagger.json, https://docs.temporal.…”
   • [claimed-docs] “Long-running, stateful agent loops that call LLMs and tools, wait on humans, and pick up exactly where they left off after a failure.”

   proof ↗⚑ flag

   Hatchet~partialprobed6/10

   Hatchet documents multi-language SDKs (Python, TypeScript, Go, Ruby) with detailed API surfaces (retry policies, cron, concurrency, rate limits, webhooks, OpenTelemetry, embedded mode) and a CLI, giving AI-native users multiple documented ways to drive the product programmatically, including MCP/agent-tool integrations. However, a probe for a standard public REST/OpenAPI spec returned 404s across all expected paths, suggesting no first-class REST API doc is discoverable, only SDK/CLI-level docs. Missing for 10: a discoverable OpenAPI/REST API reference, and independent hands-on confirmation that the documented API surface is complete/accurate.

   • [claimed-docs] “Python SDK TypeScript SDK Go SDK Ruby SDK”
   • [claimed-docs] “Retry Policies”
   • [claimed-docs] “Cron Runs”
   • [claimed-docs] “Scheduled Runs”
   • [claimed-docs] “Concurrency”
   • [claimed-docs] “Rate Limits”
   • [claimed-docs] “Webhooks”
   • [claimed-docs] “OpenTelemetry”
   • [claimed-docs] “Embedded Mode”
   • [claimed-docs] “expose Hatchet workflows and standalone tasks as tools that frameworks like the Claude Agent SDK and OpenAI Agents SDK can invoke”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.hatchet.run/openapi.json, https://docs.hatchet.run/swagger.json, https://docs.hatchet.r…”
   • [probe] “official CLI documented at https://docs.hatchet.run/reference/cli”

   proof ↗⚑ flag

5. ai-native userIssue scoped/least-privilege API credentials for an agent

   weight 2 · round to Temporal
   Temporal~partialclaimed4/10

   Temporal Cloud documents 'Service Accounts & API Keys' and 'User Roles' as pricing-tier features, implying some capability to issue scoped credentials, and Temporal is positioned for building AI agent workflows (docs-9, docs-25, docs-33). However, there is no documentation describing fine-grained, least-privilege credential issuance specifically scoped to an individual AI agent's actions or tools. Missing for 10: agent-specific credential scoping, least-privilege policy examples for agents, documentation tying API keys/service accounts to agent identity rather than general platform access control.

   • [claimed-docs] “99.9% SLA, 99.99% HA options Multi-Cloud & Multi-Region User Roles Service Accounts & API Keys Audit Logging”
   • [claimed-docs] “User Roles * Service Accounts & API Keys * Audit Logging * Workflow Management * Workflow Availability”
   • [claimed-docs] “Long-running, stateful agent loops that call LLMs and tools, wait on humans, and pick up exactly where they left off after a failure.”
   • [claimed-docs] “model calls are executed as Activities, so they retry durably and are not repeated during Workflow replay. Your agents survive Worker restar…”
   • [claimed-docs] “Build a durable agentic loop in Python with Claude tool calling and Temporal.”

   proof ↗⚑ flag

   Hatchet—none0/10

   No evidence of scoped or least-privilege API credential/token issuance for agents — the pack covers CLI tooling, concurrency, self-hosting, and pricing tiers (SSO/audit logs) but never describes API key scoping or credential minimization features.

   ⚑ flag

6. ai-native userBuild against official SDKs

   weight 2 · round to Hatchet
   Temporal✓fullclaimed7/10

   Temporal ships official SDKs (TypeScript, Python, etc.) with dedicated AI-agent integration docs (OpenAI Agents SDK, Claude tool-calling cookbook) showing agentic loops built directly against the SDK primitives (Workflows/Activities) with durable retries and state survival. This directly supports building AI-native agent applications on official SDKs rather than community wrappers. Missing for 10: independent/hands-on developer reports specifically building AI agents with the SDK, and a complete list of all officially supported languages beyond TS/Python in the AI context.

   • [claimed-docs] “Long-running, stateful agent loops that call LLMs and tools, wait on humans, and pick up exactly where they left off after a failure.”
   • [claimed-docs] “a short walkthrough of how to use the Temporal primitives (Activities, Workflows, and Workers) to build and run a Temporal application”
   • [claimed-docs] “model calls are executed as Activities, so they retry durably and are not repeated during Workflow replay. Your agents survive Worker restar…”
   • [claimed-docs] “Build a durable agentic loop in Python with Claude tool calling and Temporal.”
   • [claimed-docs] “Temporal's integration with the OpenAI Agents SDK for Python lets you run agents as Temporal Workflows.”
   • [claimed-docs] “Call an LLM from a durable Temporal Workflow in Python using the OpenAI API library.”
   • [claimed-docs] “An Activity is a normal function or method that executes a single, well-defined action (either short or long running), such as calling anoth…”
   • [claimed-docs] “Temporal's default behavior is to automatically retry an Activity that fails, so transient or intermittent failures require no action on you…”

   proof ↗⚑ flag

   Hatchet✓fullcommunity8/10

   Hatchet ships official SDKs for Python, TypeScript, Go, and Ruby, confirmed both in docs and independently by community members who verified multi-language SDK support, and these SDKs are the basis for building AI agent workflows (including cookbooks integrating Claude Agent SDK and OpenAI Agents SDK). missing for 10: no independent quality/maturity comparison across SDKs and a community complaint that SDK/doc specs are sometimes incomplete or have broken links.

   • [claimed-docs] “Python SDK TypeScript SDK Go SDK Ruby SDK”
   • [github] “Hatchet is a platform for orchestrating background tasks, AI agents, and durable workflows at scale. It supports applications written in Pyt…”
   • [community] “This looks very cool! I see a lot of Python in the docs; is it usable in other languages? [confirmed SDKs exist for Python, Typescript and G…”
   • [claimed-docs] “Claude Agent SDK”
   • [claimed-docs] “OpenAI Agents SDK”
   • [community] “Why not fix all the broken doc links and make sure you have the full sdk spec down first, ready to go? ... this came off as extremely unprof…”

   proof ↗⚑ flag

7. ai-native userSubscribe to events via webhooks

   weight 2 · round to Hatchet
   Temporal—none0/10

   No evidence Temporal offers webhook subscriptions for events; its messaging model is Signals/Queries/Updates and client polling, not outbound webhooks, and no docs mention webhook subscription support.

   ⚑ flag

   Hatchet~partialclaimed3/10

   There is a dedicated docs page titled "Webhooks" indicating Hatchet supports webhook functionality, but no detail is given on whether this covers subscribing to Hatchet's own workflow/task events (vs. using webhooks as workflow triggers), and no independent confirmation of this specific direction. missing for 10: description of webhook event subscription semantics, example payloads/config, and community/hands-on confirmation of outbound event webhooks.

   • [claimed-docs] “Webhooks”

   proof ↗⚑ flag

Agentic features

1. ai-native userSet up automations that run autonomously in the background

   weight 2 · round drawn
   Temporal✓fullcommunity8/10

   Temporal provides durable Workflows, Schedules, Activities with automatic retries, and crash-proof execution that resumes exactly where it left off — the core primitives for autonomous background automation — and has dedicated docs/cookbooks for long-running agentic loops that call LLMs, wait on humans, and survive worker restarts. Community reports corroborate that Workflows run cross-machine and long-running without manual orchestration, though docs quality complaints exist on other issues. Missing for 10: independent hands-on validation specifically of the AI-agent autonomous background use case (current AI-specific evidence is vendor docs only).

   • [claimed-docs] “A Schedule contains instructions for starting a Workflow Execution at specific times.”
   • [claimed-docs] “Long-running, stateful agent loops that call LLMs and tools, wait on humans, and pick up exactly where they left off after a failure.”
   • [claimed-docs] “a Workflow resumes automatically after a crash, a network timeout, or a multi-day wait for a human to approve a step”
   • [claimed-docs] “model calls are executed as Activities, so they retry durably and are not repeated during Workflow replay. Your agents survive Worker restar…”
   • [claimed-docs] “Temporal's integration with the OpenAI Agents SDK for Python lets you run agents as Temporal Workflows.”
   • [claimed-docs] “Build a durable agentic loop in Python with Claude tool calling and Temporal.”
   • [claimed-docs] “Temporal delivers crash-proof execution by guaranteeing that applications resume exactly where they left off after crashes, network failures…”
   • [claimed-docs] “Temporal's default behavior is to automatically retry an Activity that fails, so transient or intermittent failures require no action on you…”
   • [community] “No, the whole point of Temporal is to distribute work across machines without worrying too much about orchestration. You can retry any unit …”
   • [community] “Temporal is completely above and beyond Asyncio. It's a full scheduling of work and queues that's cross-machine, cross-language, and very tr…”

   proof ↗⚑ flag

   Hatchet✓fullcommunity8/10

   Hatchet is fundamentally a background task/workflow orchestration engine with cron triggers, scheduled runs, durable retries, concurrency control, and explicit support for orchestrating AI agents autonomously (docs-16, docs-26, docs-27, docs-25, docs-11, gh-2, gh-3), and community evidence confirms real production use as a background job/workflow runner replacing Celery. Cookbooks show integration with Claude/OpenAI Agents SDKs for agentic workflows (hatchet-docs-34/36/37). missing for 10: no independent hands-on account of a fully autonomous multi-day agent running unattended, and some community reports note scaling/reliability caveats under heavy load (hatchet-comm-11) rather than confirming flawless long-running autonomy.

   • [claimed-docs] “A single platform for orchestrating AI agents, scheduling background tasks, and running mission-critical workflows”
   • [claimed-docs] “Create and manage cron triggers that run workflows on a schedule”
   • [claimed-docs] “Cron Runs”
   • [claimed-docs] “Scheduled Runs”
   • [claimed-docs] “Retry Policies”
   • [claimed-docs] “Hatchet Agent Tools”
   • [claimed-docs] “Claude Agent SDK”
   • [claimed-docs] “OpenAI Agents SDK”
   • [github] “Hatchet is a platform for orchestrating background tasks, AI agents, and durable workflows at scale. It supports applications written in Pyt…”
   • [github] “An orchestration engine for background tasks, AI agents, and durable workflows”
   • [community] “Looks pretty great! My biggest issue with Celery has been that the observability is pretty bad. Even if you use Celery Flower, it still just…”
   • [community] “Great job so far- The flow-based UI with triggers is killer! AFAIK, this surpasses what Celery includes.”

   proof ↗⚑ flag

2. ai-native userOperate the product with natural-language commands

   weight 2 · round drawn
   Temporal—none0/10

   Temporal's documented interfaces are code-based SDKs, a structured CLI, and a Web UI dashboard for workflow state — there is no evidence of any natural-language command interface, chat-based control, or AI copilot for operating Temporal itself. The AI-related docs (temporal-docs-9, -25, -33, -47) describe using Temporal to orchestrate LLM/agent workflows, not letting users control Temporal via natural language.

   • [claimed-docs] “The Temporal CLI (`temporal`) provides direct access to a Temporal Service via the terminal. Use it to manage, monitor, and debug Temporal a…”
   • [claimed-docs] “The Temporal Web UI provides users with Workflow Execution state and metadata for debugging purposes.”
   • [claimed-docs] “Long-running, stateful agent loops that call LLMs and tools, wait on humans, and pick up exactly where they left off after a failure.”
   • [claimed-docs] “model calls are executed as Activities, so they retry durably and are not repeated during Workflow replay. Your agents survive Worker restar…”

   proof ↗⚑ flag

   Hatchet—none0/10

   Hatchet's evidence shows a structured CLI, TUI, and API/SDKs, plus the ability to expose Hatchet workflows as tools *for* external AI agents (Claude Agent SDK, OpenAI Agents SDK) — but nothing shows a user operating Hatchet itself via natural-language commands (e.g., a chat interface or NL-to-command translation). The MCP/agent-tools integration is about agents consuming Hatchet, not about a human issuing NL commands to Hatchet.

   • [claimed-docs] “the `hatchet tui` command lets you interact with your Hatchet deployment through a terminal user interface (TUI) that provides real-time obs…”
   • [claimed-docs] “expose Hatchet workflows and standalone tasks as tools that frameworks like the Claude Agent SDK and OpenAI Agents SDK can invoke”
   • [claimed-docs] “Hatchet Agent Tools”
   • [probe] “official CLI documented at https://docs.hatchet.run/reference/cli”

   proof ↗⚑ flag

Api quality

1. ai-native userExplore an interactive API reference with runnable examples

   weight 2 · round drawn
   Temporal—none0/10

   Evidence shows only standard prose documentation and CLI/SDK guides; explicit probes for an OpenAPI/Swagger spec all returned 404, and no interactive API reference or runnable-example sandbox is mentioned anywhere in the pack.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.temporal.io/openapi.json, https://docs.temporal.io/swagger.json, https://docs.temporal.…”
   • [claimed-docs] “a short walkthrough of how to use the Temporal primitives (Activities, Workflows, and Workers) to build and run a Temporal application”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.temporal.io/llms.txt # Temporal Platform Documentation > This file is a structured index of Tempor…”

   proof ↗⚑ flag

   Hatchet—none0/10

   No evidence of an interactive API reference or runnable-example explorer; probes for OpenAPI/Swagger endpoints returned 404s and docs only reference CLI/SDK references and cookbooks, not an interactive API console.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.hatchet.run/openapi.json, https://docs.hatchet.run/swagger.json, https://docs.hatchet.r…”
   • [claimed-docs] “Python SDK TypeScript SDK Go SDK Ruby SDK”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.hatchet.run/llms.txt # Hatchet Documentation > Hatchet is a distributed task queue and workflow en…”

   proof ↗⚑ flag

2. ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)

   weight 2 · round drawn
   Temporal—none0/10

   A direct probe for OpenAPI/swagger spec files at docs.temporal.io returned 404 on all candidate paths, and no evidence pack item shows a published machine-readable API spec (Temporal's API surface is documented via gRPC/proto and SDKs, not OpenAPI). The axis is applicable since Temporal exposes a Client/Cloud API, but no evidence shows a downloadable spec exists.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.temporal.io/openapi.json, https://docs.temporal.io/swagger.json, https://docs.temporal.…”

   proof ↗⚑ flag

   Hatchet—none0/10

   A direct probe for OpenAPI/swagger spec files at all standard locations returned 404s, and no evidence pack item points to a downloadable API spec; Hatchet does expose an llms.txt but that is not a machine-readable API spec.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.hatchet.run/openapi.json, https://docs.hatchet.run/swagger.json, https://docs.hatchet.r…”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.hatchet.run/llms.txt # Hatchet Documentation > Hatchet is a distributed task queue and workflow en…”

   proof ↗⚑ flag

3. ai-native userTest against a sandbox environment without touching production data

   weight 1 · round to Temporal
   Temporal✓fullclaimed7/10

   Temporal ships a CLI-based local development service and a dedicated test server (with time-skipping) explicitly for running end-to-end/integration tests without needing a production Temporal Service, effectively providing a sandbox for testing workflows and activities. This directly supports testing without touching production data, though the evidence doesn't explicitly discuss data isolation or seeding sandbox data. missing for 10: explicit documentation on production-data isolation/mocking, independent hands-on confirmation of sandbox fidelity, and any data-masking or synthetic-data tooling.

   • [claimed-docs] “The CLI includes a local Temporal development service for fast feedback while building or testing your application.”
   • [claimed-docs] “Because the test server supports skipping time, use the test server for both end-to-end and integration tests with Workers.”
   • [claimed-docs] “Because the test server supports skipping time, use the t”
   • [claimed-docs] “This starts a complete Temporal Service with Web UI on your local machine.”
   • [github] “temporal server start-dev”

   proof ↗⚑ flag

   Hatchet~partialclaimed4/10

   Hatchet supports local development via `hatchet worker dev` for local execution and `hatchet profile` for switching between separate named environments/tenants, which could be used to isolate a sandbox from production, but there is no explicit documentation of a dedicated sandbox/staging environment or test-data isolation feature. missing for 10: explicit sandbox/test-environment documentation, isolation guarantees between environments, and any hands-on confirmation that switching profiles avoids touching production data.

   • [claimed-docs] “Profiles: the `hatchet profile` commands allow you to manage multiple Hatchet instances and tenants with named profiles, making it easy to s…”
   • [claimed-docs] “the `hatchet worker dev` command lets you run a worker locally with automatic reloading when code changes are detected.”
   • [claimed-docs] “Profiles: the `hatchet profile` commands allow you to manage multiple Hatchet instances and tenants with named profiles, making it easy to s…”
   • [claimed-docs] “Running Workers Locally”

   proof ↗⚑ flag

4. ai-native userRely on versioned APIs with a documented deprecation policy

   weight 2 · round drawn
   Temporal—none0/10

   Evidence shows Temporal has 'Worker Versioning' features for versioning Workflow code (temporal-docs-7, temporal-docs-39), but this is about workflow-definition compatibility, not a documented deprecation policy for Temporal's own SDK/gRPC/HTTP APIs. No OpenAPI spec or API changelog with deprecation timelines was found (temporal-probe-2 shows all openapi.json paths 404), and no docs reference an API deprecation policy.

   • [claimed-docs] “You can declare each Workflow type to have a Versioning Behavior, either Pinned or Auto-Upgrade, in your Workflow config”
   • [claimed-docs] “This page defines some of the underlying concepts used in Worker Versioning ... Worker Deployments ... Worker Deployment Versions ... Versio…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.temporal.io/openapi.json, https://docs.temporal.io/swagger.json, https://docs.temporal.…”

   proof ↗⚑ flag

   Hatchet—none0/10

   No evidence of API versioning scheme or a documented deprecation policy; the OpenAPI spec probe returned 404s and no changelog/deprecation policy content is present, only a v1 docs path exists without explanation of versioning/deprecation guarantees.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.hatchet.run/openapi.json, https://docs.hatchet.run/swagger.json, https://docs.hatchet.r…”
   • [claimed-docs] “Retry Policies”

   proof ↗⚑ flag

1. ai-native userPerform bulk operations across many items at once

   weight 2 · round to Temporal
   Temporal~partialclaimed6/10

   Temporal's CLI includes a documented 'batch' command for tracking bulk job progress, and the client API supports listing/signaling many workflow executions at once, indicating built-in bulk-operation support across workflows. However, evidence is thin — only one CLI reference to batch jobs with no detail on how batch signal/cancel/terminate operations are invoked or scoped, and no independent/hands-on confirmation of this specific capability. Missing for 10: fuller documentation of batch operation types (signal/cancel/terminate/query by visibility filter), examples of large-scale batch usage, and community validation of bulk operations at scale.

   • [claimed-docs] “Show the progress of an ongoing batch job. Pass a valid job ID to display its information”
   • [claimed-docs] “Start a Workflow Execution - Get the result of Workflow Execution - List Workflow Executions - Query a Workflow Execution - Signal a Workflo…”

   proof ↗⚑ flag

   Hatchet~partialclaimed5/10

   Hatchet is a workflow/task orchestration engine that supports bulk-style operations via concurrency rules across many workflow runs, cron-triggered batch scheduling, and per-key concurrency limits (e.g., letting many tasks/workflows run concurrently up to a limit), which indirectly supports bulk processing of many items. However, there is no explicit evidence of a bulk API/CLI operation (e.g., bulk-cancel, bulk-retry, batch-trigger-many-runs-at-once) designed for an AI-native user to invoke as a single bulk action. missing for 10: explicit bulk-operation API/CLI (batch trigger, bulk cancel/retry across many runs), documentation or examples showing an AI agent invoking such bulk operations, and independent confirmation of bulk operation reliability at scale.

   • [claimed-docs] “set concurrency limits on a per-key basis using a CEL expression. For example, let premium-tier users run 10 workflows simultaneously while …”
   • [claimed-docs] “create concurrency rules that many tasks and workflows consume simultaneously, e.g. limiting `workflow_a` and `workflow_b` to 10 concurrent …”
   • [claimed-docs] “Create and manage cron triggers that run workflows on a schedule”
   • [claimed-docs] “Two new concurrency strategies, `CANCEL_QUEUED_EXCEPT_NEWEST` and `CANCEL_QUEUED_EXCEPT_OLDEST`: configure the queue depth for each concurre…”

   proof ↗⚑ flag

2. ai-native userDefine rules that trigger actions automatically on events

   weight 3 · round to Hatchet
   Temporal~partialclaimed7/10

   Temporal supports event-driven automation via Signals/Queries/Updates for external events, Schedules for time-based triggers, and automatic retries on failure events, and explicitly targets AI agent loops that react to events and resume after failures (temporal-docs-6, temporal-docs-5, temporal-docs-4, temporal-docs-9, temporal-docs-41). However, this is developer-defined workflow code rather than a declarative 'rules' interface, and no evidence shows a no-code/rule-authoring UI for defining event-trigger conditions. Missing for 10: a declarative rule-definition mechanism (vs. code-based signal handlers), independent hands-on validation of event-triggered automation specifically for AI use cases.

   • [claimed-docs] “A Schedule contains instructions for starting a Workflow Execution at specific times.”
   • [claimed-docs] “Temporal supports three types of messages: Signals, Queries, and Updates”
   • [claimed-docs] “Temporal's default behavior is to automatically retry an Activity that fails, so transient or intermittent failures require no action on you…”
   • [claimed-docs] “Long-running, stateful agent loops that call LLMs and tools, wait on humans, and pick up exactly where they left off after a failure.”
   • [claimed-docs] “a Workflow resumes automatically after a crash, a network timeout, or a multi-day wait for a human to approve a step”
   • [claimed-docs] “Workflows can be thought of as stateful web services that can receive messages. The Workflow can have message handlers, akin to endpoints”

   proof ↗⚑ flag

   Hatchet✓fullclaimed7/10

   Hatchet's docs describe core event-driven automation: workflows can be triggered on schedules (cron, scheduled runs), via webhooks, and filtered/gated with CEL-based event filters and concurrency rules, all managed by a control plane that automatically dispatches tasks to workers on these triggers. This directly matches 'define rules that trigger actions automatically on events' for an orchestration/automation platform. Missing for 10: explicit first-party documentation of arbitrary custom event triggers (e.g., pub/sub event names beyond cron/webhook) and independent hands-on confirmation that event-triggered rules work reliably in production.

   • [claimed-docs] “Create and manage cron triggers that run workflows on a schedule”
   • [claimed-docs] “Cron Runs”
   • [claimed-docs] “Scheduled Runs”
   • [claimed-docs] “Webhooks”
   • [claimed-docs] “Evaluate and debug CEL expressions used in event filters and concurrency keys”
   • [claimed-docs] “Concurrency”
   • [github] “Hatchet is a platform for orchestrating background tasks, AI agents, and durable workflows at scale. It supports applications written in Pyt…”

   proof ↗⚑ flag

3. ai-native userSchedule recurring jobs or workflows

   weight 2 · round to Temporal
   Temporal✓fullclaimed9/10

   Temporal has a first-class Schedules feature (and legacy Cron Jobs) explicitly documented for starting Workflow Executions at specific times, with recurring/repeated execution, retries, and durable state, plus CLI/UI/Web tooling to manage them — directly enabling scheduled/recurring AI workflows (e.g., agent loops per temporal-docs-9/25/33). missing for 10: independent hands-on validation specifically of the Schedules feature (community evidence is about general durability, not scheduling specifically).

   • [claimed-docs] “A Schedule contains instructions for starting a Workflow Execution at specific times.”
   • [claimed-docs] “Schedules provide a more flexible and user-friendly approach than Temporal Cron Jobs.”
   • [claimed-docs] “Workflow Definition ... Workflow Execution ... Schedules ... Dynamic Handler ... Cron Job”
   • [claimed-docs] “A Schedule contains instructions for starting a Workflow Execution at specific times. Schedules provide a more flexible and user-friendly ap…”
   • [claimed-docs] “Long-running, stateful agent loops that call LLMs and tools, wait on humans, and pick up exactly where they left off after a failure.”
   • [claimed-docs] “model calls are executed as Activities, so they retry durably and are not repeated during Workflow replay. Your agents survive Worker restar…”
   • [claimed-docs] “Build a durable agentic loop in Python with Claude tool calling and Temporal.”

   proof ↗⚑ flag

   Hatchet✓fullclaimed8/10

   Hatchet explicitly documents Cron Runs and Scheduled Runs as first-class workflow trigger types, alongside concurrency/rate-limit controls, giving clear support for scheduling recurring jobs/workflows (hatchet-docs-16, hatchet-docs-26, hatchet-docs-27). This is corroborated by the general orchestration platform framing in GitHub docs and self-hosting control plane description. Missing for 10: independent/hands-on community confirmation specifically of cron/scheduled run reliability at scale, and more detail on failure/edge-case handling for recurring jobs.

   • [claimed-docs] “Create and manage cron triggers that run workflows on a schedule”
   • [claimed-docs] “Cron Runs”
   • [claimed-docs] “Scheduled Runs”
   • [github] “Hatchet is a platform for orchestrating background tasks, AI agents, and durable workflows at scale. It supports applications written in Pyt…”
   • [claimed-docs] “Self-hosting Hatchet means running your own instance of the **Hatchet Control Plane** - the central orchestration system that manages workfl…”

   proof ↗⚑ flag

4. ai-native userVersion, review, and roll back my automations

   weight 1 · round to Temporal
   Temporal~partialclaimed5/10

   Temporal's Worker Versioning feature lets teams declare Pinned vs Auto-Upgrade behavior per Workflow type, giving a real mechanism for managing multiple code versions of an automation (temporal-docs-7, temporal-docs-39), and the Web UI exposes execution state/history useful for reviewing runs (temporal-docs-8, temporal-docs-40). However, there's no documented explicit 'rollback' UI/action or version diff/review tooling analogous to a no-code automation platform's version history — it's a developer-code versioning mechanism rather than a click-to-review/rollback feature. Missing for 10: explicit rollback UI/CLI command, workflow-definition diff/audit trail, and independent evidence of using versioning for rollback in practice.

   • [claimed-docs] “You can declare each Workflow type to have a Versioning Behavior, either Pinned or Auto-Upgrade, in your Workflow config”
   • [claimed-docs] “This page defines some of the underlying concepts used in Worker Versioning ... Worker Deployments ... Worker Deployment Versions ... Versio…”
   • [claimed-docs] “The Temporal Web UI provides users with Workflow Execution state and metadata for debugging purposes.”
   • [claimed-docs] “The Temporal Web UI provides users with Workflow Execution state and metadata for debugging purposes. It ships with every Temporal CLI relea…”

   proof ↗⚑ flag

   Hatchet—none0/10

   No evidence in the pack describes versioning, review, or rollback of workflows/automations — the docs cover CLI tools, concurrency, retries, cron/scheduled runs, and self-hosting, but nothing about workflow version history, diff/review workflows, or rollback to a prior version. This is a fair axis for a workflow orchestration platform, so absence of evidence yields 'none' rather than 'na'.

   ⚑ flag

Local dev

1. backend developerOne command runs the whole engine locally, and testing utilities let me unit-test workflows with time skipping and mocked steps

   weight 2 · round to Temporal
   Temporal✓fullclaimed8/10

   Temporal's CLI provides a one-command local dev server (`temporal server start-dev`) that spins up a complete Temporal Service with Web UI, and the TypeScript testing suite explicitly supports a test server with time-skipping for unit/integration testing workflows and workers. Mocked activities/steps are implied by the testing-suite framework but not explicitly detailed in the pack. Missing for 10: explicit documentation of mocking individual activities/steps in unit tests, and independent/hands-on confirmation of the testing workflow experience.

   • [claimed-docs] “The CLI includes a local Temporal development service for fast feedback while building or testing your application.”
   • [github] “temporal server start-dev”
   • [claimed-docs] “This starts a complete Temporal Service with Web UI on your local machine.”
   • [claimed-docs] “Because the test server supports skipping time, use the test server for both end-to-end and integration tests with Workers.”
   • [claimed-docs] “Because the test server supports skipping time, use the t”

   proof ↗⚑ flag

   Hatchet~partialclaimed3/10

   Docs show `hatchet worker dev` for running a worker locally with auto-reload and mention an 'Embedded Mode' page, which partially supports a lightweight local run, but there is no evidence of a single command spinning up the full engine (control plane) locally, nor any documented unit-testing utilities, time-skipping, or step mocking for workflows. Missing for 10: dedicated single-command full-engine local runner, workflow unit-testing framework, time-skipping/mocked-step utilities, and any independent confirmation of these testing features.

   • [claimed-docs] “the `hatchet worker dev` command lets you run a worker locally with automatic reloading when code changes are detected”
   • [claimed-docs] “the `hatchet worker dev` command lets you run a worker locally with automatic reloading when code changes are detected.”
   • [claimed-docs] “Embedded Mode”
   • [claimed-docs] “Running Workers Locally”

   proof ↗⚑ flag

Approvals

1. backend developerA workflow pauses for human approval or input for hours or days and resumes the moment the response arrives

   weight 3 · round to Temporal
   Temporal✓fullcommunity8/10

   Temporal explicitly documents Signals/Updates for external input, durable state that survives multi-day waits, and directly states workflows resume after 'a multi-day wait for a human to approve a step' (temporal-docs-41, temporal-docs-6, temporal-docs-38, temporal-docs-9). Community evidence corroborates long-running, stateful workflow execution in production use. missing for 10: no hands-on/independent case study specifically demonstrating a human-approval pause-and-resume in production, and no mention of timeout/escalation handling for indefinite waits.

   • [claimed-docs] “a Workflow resumes automatically after a crash, a network timeout, or a multi-day wait for a human to approve a step”
   • [claimed-docs] “Temporal supports three types of messages: Signals, Queries, and Updates”
   • [claimed-docs] “Workflows can be thought of as stateful web services that can receive messages. The Workflow can have message handlers, akin to endpoints”
   • [claimed-docs] “Long-running, stateful agent loops that call LLMs and tools, wait on humans, and pick up exactly where they left off after a failure.”
   • [claimed-docs] “Temporal delivers crash-proof execution by guaranteeing that applications resume exactly where they left off after crashes, network failures…”
   • [community] “Temporal is completely above and beyond Asyncio. It's a full scheduling of work and queues that's cross-machine, cross-language, and very tr…”

   proof ↗⚑ flag

   Hatchet—none0/10

   No evidence describes a durable pause/resume mechanism for human approval or external event input (e.g., a wait-for-event or signal API); the pack covers retries, cron, scheduling, concurrency, webhooks, and AI-agent tool integrations but never a human-in-the-loop pause/resume pattern.

   ⚑ flag

Signals

1. backend developerI send signals, events, or messages into a specific running workflow from outside — an API call, webhook, or another workflow

   weight 2 · round to Temporal
   Temporal✓fullclaimed8/10

   Temporal explicitly documents Signals, Queries, and Updates as first-class message types for sending data into running workflows, describes workflows as 'stateful web services that can receive messages' with handler endpoints, and the client API/CLI exposes 'Signal a Workflow Execution' plus batch signal capability — directly enabling external API calls, webhooks, or other workflows to inject events into a running workflow. missing for 10: no independent/hands-on evidence specifically validating signal delivery reliability or webhook-triggered signal patterns beyond docs.

   • [claimed-docs] “Temporal supports three types of messages: Signals, Queries, and Updates”
   • [claimed-docs] “Workflows can be thought of as stateful web services that can receive messages. The Workflow can have message handlers, akin to endpoints”
   • [claimed-docs] “Start a Workflow Execution - Get the result of Workflow Execution - List Workflow Executions - Query a Workflow Execution - Signal a Workflo…”
   • [claimed-docs] “a Workflow resumes automatically after a crash, a network timeout, or a multi-day wait for a human to approve a step”
   • [claimed-docs] “Show the progress of an ongoing batch job. Pass a valid job ID to display its information”

   proof ↗⚑ flag

   Hatchet~partialclaimed3/10

   Docs reference 'Webhooks' and CEL-based 'event filters' for triggering workflows externally, implying some external event-to-workflow capability, but there is no concrete documentation of sending a signal/event into a specific *already-running* workflow instance (e.g., a signal API) as opposed to triggering new runs. Missing for 10: explicit signal-to-running-workflow API docs, examples of external systems pushing data into an in-flight workflow, and independent confirmation this works in practice.

   • [claimed-docs] “Webhooks”
   • [claimed-docs] “Evaluate and debug CEL expressions used in event filters and concurrency keys”

   proof ↗⚑ flag

Metrics

1. platform engineerFailure rates, latencies, and queue depths export to my observability stack, and alerts fire when workflows misbehave

   weight 1 · round to Temporal
   Temporal~partialclaimed6/10

   Temporal Cloud explicitly supports streaming metrics (including failure/latency-type signals) into Prometheus, Datadog, Grafana Cloud, New Relic, Elastic via an OpenMetrics/Prometheus endpoint, and separate docs cover task-queue/worker performance metrics for tuning. However, there's no first-party documentation of alerting rules or thresholds being configured within Temporal itself (alerting is presumably left to the downstream observability tool), and self-hosted metrics/alerting setup isn't detailed. Missing for 10: explicit alerting configuration/integration guidance, self-hosted metrics export details, and independent confirmation that queue-depth/latency dashboards work as advertised in production.

   • [claimed-docs] “Start with the OpenMetrics Quickstart to create a Service Account, generate an API key, and stream metrics into Datadog, Elastic, Grafana Cl…”
   • [claimed-docs] “Use the pages in this guide for practical methods for querying Task Queue information, and strategies for tuning Workers and Task Queue proc…”
   • [claimed-docs] “This guide covers metrics and configurations that drive the efficiency of your Worker fleet.”
   • [claimed-docs] “The Temporal Web UI provides users with Workflow Execution state and metadata for debugging purposes. It ships with every Temporal CLI relea…”

   proof ↗⚑ flag

   Hatchet~partialclaimed4/10

   Hatchet documents an OpenTelemetry integration page and its GitHub README claims 'real-time monitoring, alerting, and logging' as part of the platform, plus a TUI for real-time observability into tasks/workflows/workers. However, there is no concrete detail on which metrics (failure rates, latencies, queue depths) are exported, no evidence of alert rule configuration, and no independent confirmation that alerting actually fires as described. Missing for 10: detailed OpenTelemetry metrics/export documentation, explicit failure-rate/latency/queue-depth metric definitions, documented alerting configuration/integration with observability stacks (Grafana/Datadog/PagerDuty), and hands-on or community confirmation that alerts work in practice.

   • [claimed-docs] “OpenTelemetry”
   • [github] “Hatchet provides a full platform for queuing, automatic retries, durability, real-time monitoring, alerting, and logging.”
   • [claimed-docs] “the `hatchet tui` command lets you interact with your Hatchet deployment through a terminal user interface (TUI) that provides real-time obs…”

   proof ↗⚑ flag

Replay

1. platform engineerI replay or rerun a failed workflow from its recorded history — optionally from a specific step — to debug and recover

   weight 2 · round to Temporal
   Temporal~partialclaimed4/10

   Temporal's docs confirm durable execution history that lets workflows 'resume exactly where they left off' after failures (temporal-docs-1, temporal-docs-41), and the CLI/Web UI are positioned for managing, monitoring, and debugging workflow executions (temporal-docs-8, temporal-docs-40, temporal-docs-42, temporal-docs-26 batch job commands). However, the evidence pack never explicitly documents the specific 'replay' or 'reset workflow to a step' feature/CLI command that lets an engineer manually rerun a failed workflow from a chosen point in its history — the closest is generic resume-after-crash and debugging-via-UI language. Missing for 10: explicit documentation of a replay/reset-to-event command, guidance on selecting a specific historical step to resume from, and independent/hands-on confirmation that this workflow works as described.

   • [claimed-docs] “Temporal delivers crash-proof execution by guaranteeing that applications resume exactly where they left off after crashes, network failures…”
   • [claimed-docs] “a Workflow resumes automatically after a crash, a network timeout, or a multi-day wait for a human to approve a step”
   • [claimed-docs] “The Temporal Web UI provides users with Workflow Execution state and metadata for debugging purposes.”
   • [claimed-docs] “The Temporal Web UI provides users with Workflow Execution state and metadata for debugging purposes. It ships with every Temporal CLI relea…”
   • [claimed-docs] “The Temporal CLI (`temporal`) provides direct access to a Temporal Service via the terminal. Use it to manage, monitor, and debug Temporal a…”
   • [claimed-docs] “Show the progress of an ongoing batch job. Pass a valid job ID to display its information”

   proof ↗⚑ flag

   Hatchet—none0/10

   The evidence pack covers retries, concurrency, scheduling, and TUI-based observability, but nothing describes replaying or rerunning a failed workflow from recorded history, let alone from a specific step. Retry Policies (hatchet-docs-25) is only a title with no detail confirming step-level replay/rerun-from-history semantics.

   ⚑ flag

Run visibility

1. platform engineerEvery run has a step-by-step timeline — inputs, outputs, retries, and errors per step — in a dashboard my team can search and filter

   weight 3 · round drawn
   Temporal~partialcommunity5/10

   Temporal's Web UI shows Workflow Execution state and metadata for debugging, and the CLI/visibility APIs let you list and query executions, which points toward a searchable dashboard with retry/error visibility per Activity (retries are automatic and Activities are the per-step unit). However, the evidence never explicitly documents fine-grained search/filter UI features or explicit per-step input/output display, and a hands-on community report notes that error/anomaly details can be 'buried in the event log' and hard to act on, undercutting the smoothness of the debugging experience. Missing for 10: explicit docs on search/filter capabilities in the Web UI, clear per-step input/output rendering evidence, and resolution of the community-reported difficulty surfacing errors.

   • [claimed-docs] “The Temporal Web UI provides users with Workflow Execution state and metadata for debugging purposes.”
   • [claimed-docs] “The Temporal Web UI provides users with Workflow Execution state and metadata for debugging purposes. It ships with every Temporal CLI relea…”
   • [claimed-docs] “The Temporal CLI (`temporal`) provides direct access to a Temporal Service via the terminal. Use it to manage, monitor, and debug Temporal a…”
   • [claimed-docs] “Start a Workflow Execution - Get the result of Workflow Execution - List Workflow Executions - Query a Workflow Execution - Signal a Workflo…”
   • [claimed-docs] “Temporal's default behavior is to automatically retry an Activity that fails, so transient or intermittent failures require no action on you…”
   • [community] “We migrated from an in-house redis queuing system to Temporal. The worst part has been error/anomaly handling: workflows can hit a state tha…”

   proof ↗⚑ flag

   Hatchet~partialcommunity5/10

   Hatchet's docs and GitHub description claim 'real-time monitoring, alerting, and logging' and a TUI/dashboard for observability into tasks and workflows, and community feedback confirms a 'flow-based UI' seen as an improvement over Celery's observability. However, no evidence explicitly describes a searchable/filterable dashboard showing per-step inputs, outputs, retries, and errors in a timeline view — retry policies and OpenTelemetry integration are documented separately but not tied to a unified searchable timeline UI. Missing for 10: explicit documentation or screenshots of a step-level timeline view, confirmation of search/filter capability in the dashboard, and independent hands-on verification of this specific UI feature.

   • [github] “Hatchet provides a full platform for queuing, automatic retries, durability, real-time monitoring, alerting, and logging.”
   • [claimed-docs] “the `hatchet tui` command lets you interact with your Hatchet deployment through a terminal user interface (TUI) that provides real-time obs…”
   • [claimed-docs] “the `hatchet tui` command lets you interact with your Hatchet deployment through a terminal user interface (TUI) that provides real-time obs…”
   • [claimed-docs] “Retry Policies”
   • [claimed-docs] “OpenTelemetry”
   • [community] “Looks pretty great! My biggest issue with Celery has been that the observability is pretty bad. Even if you use Celery Flower, it still just…”
   • [community] “Great job so far- The flow-based UI with triggers is killer! AFAIK, this surpasses what Celery includes.”

   proof ↗⚑ flag

1. ai-native userDo everything through the API that I can do in the UI

   weight 2 · round to Temporal
   Temporal~partialprobed6/10

   Temporal's client API (Start/Get Result/List/Query/Signal Workflow Executions) and CLI (`temporal` command for manage/monitor/debug) mirror much of what the Web UI shows for workflow execution state and debugging, and the CLI even ships the Web UI itself. However, there's no explicit documentation asserting full API/UI parity, and a probe for a public OpenAPI/REST spec returned 404s, leaving ambiguity about whether all UI-surfaced admin/account features (e.g., Cloud user roles, project org) are equally scriptable via API. missing for 10: explicit parity statement, public OpenAPI/REST spec, evidence that Cloud-console-only settings (users, projects, billing) are API-accessible.

   • [claimed-docs] “Start a Workflow Execution - Get the result of Workflow Execution - List Workflow Executions - Query a Workflow Execution - Signal a Workflo…”
   • [claimed-docs] “The Temporal CLI (`temporal`) provides direct access to a Temporal Service via the terminal. Use it to manage, monitor, and debug Temporal a…”
   • [claimed-docs] “The Temporal Web UI provides users with Workflow Execution state and metadata for debugging purposes.”
   • [claimed-docs] “The Temporal Web UI provides users with Workflow Execution state and metadata for debugging purposes. It ships with every Temporal CLI relea…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.temporal.io/openapi.json, https://docs.temporal.io/swagger.json, https://docs.temporal.…”
   • [claimed-docs] “User Roles * Service Accounts & API Keys * Audit Logging * Workflow Management * Workflow Availability”

   proof ↗⚑ flag

   Hatchet~partialprobed5/10

   Hatchet offers SDKs (Python/TS/Go/Ruby) and CLI tools for managing workflows, tasks, cron, concurrency, etc., implying broad API-driven access, but there is no explicit documentation or OpenAPI spec confirming full parity between API/SDK capabilities and the web UI, and the openapi probe returned 404s. missing for 10: a published OpenAPI/API reference confirming API parity, explicit statement or evidence that every UI action (e.g., dashboards, tenant/user management, alerting config) is achievable via API/SDK, and independent confirmation of this parity.

   • [claimed-docs] “Python SDK TypeScript SDK Go SDK Ruby SDK”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.hatchet.run/openapi.json, https://docs.hatchet.run/swagger.json, https://docs.hatchet.r…”
   • [claimed-docs] “Self-hosting Hatchet means running your own instance of the **Hatchet Control Plane** - the central orchestration system that manages workfl…”
   • [claimed-docs] “Profiles: the `hatchet profile` commands allow you to manage multiple Hatchet instances and tenants with named profiles, making it easy to s…”
   • [community] “This looks very cool! I see a lot of Python in the docs; is it usable in other languages? [confirmed SDKs exist for Python, Typescript and G…”

   proof ↗⚑ flag

2. ai-native userExport all of my data in open formats and leave

   weight 3 · round to Hatchet
   Temporal~partialclaimed3/10

   Temporal is open-source and self-hostable, meaning operators can run their own persistence layer (Docker/Kubernetes/manual deployment) rather than being locked into Temporal Cloud, which implies some data ownership/portability, but no evidence shows a documented data-export feature or open-format export of workflow histories/event logs. Missing for 10: explicit export/import tooling, documented open data formats (e.g., JSON/event-history export), and independent confirmation that self-hosted data is portable without vendor lock-in.

   • [claimed-docs] “Deployment: Choose a deployment approach (Docker, Kubernetes, or manual) and set up a production-ready Temporal Service.”
   • [claimed-docs] “This starts a complete Temporal Service with Web UI on your local machine.”
   • [github] “temporal server start-dev”

   proof ↗⚑ flag

   Hatchet~partialclaimed4/10

   Hatchet is open-source and self-hostable, meaning users control their own Postgres-backed control plane and inherently retain access to underlying data (hatchet-docs-8, hatchet-docs-17, hatchet-docs-23), which supports an exit path. However, there is no explicit documented data-export feature, open-format export tooling, or migration/export API mentioned anywhere in the evidence. Missing for 10: explicit data export command/API, documentation on open-format export (e.g. JSON/CSV dumps), and independent confirmation that a full data export is possible beyond raw DB access.

   • [claimed-docs] “Self-hosting Hatchet means running your own instance of the **Hatchet Control Plane** - the central orchestration system that manages workfl…”
   • [claimed-docs] “Self-hosting Hatchet means running your own instance of the Hatchet Control Plane - the central orchestration system that manages workflows,…”
   • [claimed-docs] “Run Hatchet yourself”

   proof ↗⚑ flag

3. ai-native userRead the product's source under an open license

   weight 2 · round to Temporal
   Temporal✓fullclaimed8/10

   Temporal's server and CLI are open-source on GitHub (temporalio/temporal), confirming source is readable under an open license, and self-hosted guide explicitly refers to it as 'open source infrastructure software.' missing for 10: explicit license file/name citation (e.g., MIT/Apache-2.0 text) and confirmation that all SDKs (not just server) are open source.

   • [github] “temporal server start-dev”
   • [github] “Temporal—durable execution platform”
   • [claimed-docs] “This guide shows you how to self-host open source infrastructure software that orchestrates your durable applications.”

   proof ↗⚑ flag

   Hatchet~partialclaimed3/10

   The product's source is publicly hosted on GitHub (hatchet-dev/hatchet), which lets users read the code, but the evidence pack never states or documents an explicit open-source license (e.g., MIT/Apache) for that repository. missing for 10: explicit license text/badge, license file reference, confirmation the full engine (not just SDKs) is open-licensed.

   • [github] “Hatchet provides a full platform for queuing, automatic retries, durability, real-time monitoring, alerting, and logging.”
   • [github] “Hatchet is a platform for orchestrating background tasks, AI agents, and durable workflows at scale. It supports applications written in Pyt…”
   • [github] “An orchestration engine for background tasks, AI agents, and durable workflows”

   proof ↗⚑ flag

4. ai-native userSelf-host the core product

   weight 3 · round to Temporal
   Temporal✓fullclaimed9/10

   Temporal explicitly documents self-hosting the open-source core: 'self-host open source infrastructure software that orchestrates your durable applications' with deployment options (Docker, Kubernetes, manual), and the CLI/GitHub repo provide 'temporal server start-dev' for local runs. This is corroborated by the public GitHub repo itself, showing it's a genuine open, self-hostable product, not just SaaS-only. Missing for 10: independent hands-on production self-hosting reports beyond dev-server usage.

   • [claimed-docs] “This guide shows you how to self-host open source infrastructure software that orchestrates your durable applications.”
   • [claimed-docs] “Deployment: Choose a deployment approach (Docker, Kubernetes, or manual) and set up a production-ready Temporal Service.”
   • [claimed-docs] “This starts a complete Temporal Service with Web UI on your local machine.”
   • [github] “temporal server start-dev”
   • [github] “Temporal—durable execution platform”
   • [claimed-docs] “The CLI is available for macOS, Linux, and Windows, or as a Docker image.”

   proof ↗⚑ flag

   Hatchet✓fullclaimed8/10

   Hatchet has dedicated self-hosting docs explicitly stating you can run your own Hatchet Control Plane instance, with a full self-hosting guide and 'Run Hatchet yourself' section, and is open-source on GitHub. Missing for 10: independent hands-on confirmation of a successful self-hosted deployment (community discussion focuses on managed/product feedback rather than self-host experience) and more detail on infra requirements/HA in self-hosted mode.

   • [claimed-docs] “Self-hosting Hatchet means running your own instance of the **Hatchet Control Plane** - the central orchestration system that manages workfl…”
   • [claimed-docs] “Self-hosting Hatchet means running your own instance of the Hatchet Control Plane - the central orchestration system that manages workflows,…”
   • [claimed-docs] “Run Hatchet yourself”
   • [github] “Hatchet provides a full platform for queuing, automatic retries, durability, real-time monitoring, alerting, and logging.”
   • [github] “Hatchet is a platform for orchestrating background tasks, AI agents, and durable workflows at scale. It supports applications written in Pyt…”

   proof ↗⚑ flag

Deployment model

1. platform engineerI run workers in my own infrastructure while the vendor manages the orchestration control plane — code and data stay inside my network

   weight 2 · round to Temporal
   Temporal✓fullclaimed7/10

   Temporal Cloud is explicitly a managed control plane where customers run their own Workers connecting over mTLS, with data encryption designed so Temporal Cloud never accesses sensitive workflow data (temporal-docs-31, temporal-docs-13/44). This matches the exact 'bring your own workers, vendor manages orchestration, data stays in your network' architecture. Missing for 10: no independent/hands-on verification of the network-isolation claim, and no detailed architecture diagram or third-party audit confirming Workers-only-outbound connectivity model.

   • [claimed-docs] “Learn how Temporal Cloud provides provable security by design - orchestrating encrypted workflows without ever accessing your sensitive data…”
   • [claimed-docs] “Explore the security features of our SaaS offering, including mTLS, end-to-end encryption, and enterprise compliance.”
   • [claimed-docs] “Explore the security features of our SaaS offering, including mTLS, end-to-end en”
   • [claimed-docs] “99.9% SLA, 99.99% HA options Multi-Cloud & Multi-Region User Roles Service Accounts & API Keys Audit Logging”
   • [claimed-docs] “SOC 2 Type II GDPR HIPAA CCPA”

   proof ↗⚑ flag

   Hatchet~partialclaimed4/10

   Hatchet's architecture separates a 'worker' component (run via `hatchet worker dev`) from a central control plane, and `hatchet profile` lets you connect a local worker to different named Hatchet instances/tenants, which is consistent with running workers in your own infra against a vendor-hosted control plane; pricing tiers also imply a hosted/Cloud offering distinct from self-hosting. However, the evidence never explicitly states that when using the hosted (Cloud) control plane, task code/data stays inside the customer's network — the only explicit control-plane documentation describes full self-hosting (running your own control plane), not the hybrid vendor-managed-plane/self-hosted-worker model the story describes. Missing for 10: explicit vendor statement or docs describing the hybrid 'Cloud control plane + self-hosted workers, data stays in your network' architecture, and any data-residency/security claims tied to that specific topology.

   • [claimed-docs] “the `hatchet worker dev` command lets you run a worker locally with automatic reloading when code changes are detected”
   • [claimed-docs] “Profiles: the `hatchet profile` commands allow you to manage multiple Hatchet instances and tenants with named profiles, making it easy to s…”
   • [claimed-docs] “Self-hosting Hatchet means running your own instance of the Hatchet Control Plane - the central orchestration system that manages workflows,…”
   • [claimed-docs] “Get up and running in minutes. When you’re ready to scale, upgrade anytime and only pay for what you use.”
   • [claimed-docs] “Running Workers Locally”

   proof ↗⚑ flag

Flow control

1. platform engineerI cap concurrency and set rate limits per workflow, per key, or per tenant so one hot customer can't starve the rest

   weight 2 · round to Hatchet
   Temporal~partialclaimed4/10

   Temporal exposes Worker/Task Queue tuning to control concurrent Activity/Workflow task execution (docs-29, docs-49) and Temporal Cloud enforces its own rate limits (docs-45), which platform engineers could leverage (e.g., separate task queues per tenant) to approximate concurrency capping. However, there is no documented first-class primitive for per-workflow, per-key, or per-tenant rate limiting/quota configuration exposed to application teams. Missing for 10: explicit per-tenant/per-key rate-limiting API or policy, tenant isolation guarantees, and any hands-on evidence of using it to stop a 'hot customer' from starving others.

   • [claimed-docs] “Use the pages in this guide for practical methods for querying Task Queue information, and strategies for tuning Workers and Task Queue proc…”
   • [claimed-docs] “This guide covers metrics and configurations that drive the efficiency of your Worker fleet.”
   • [claimed-docs] “Temporal Cloud enforces three kinds of limits to keep the service reliable: rate limits ... resource limits ... and configuration limits”

   proof ↗⚑ flag

   Hatchet✓fullclaimed8/10

   Docs explicitly cover per-key concurrency limits via CEL expressions (e.g., premium vs standard tiers), shared concurrency rules across workflows per user, configurable queue-depth strategies (CANCEL_QUEUED_EXCEPT_NEWEST/OLDEST), and a dedicated Rate Limits reference page alongside a Concurrency reference page, directly matching the per-workflow/per-key/per-tenant throttling story. missing for 10: no independent/hands-on validation of rate-limit or per-tenant isolation behavior under real load, and no explicit doc example scoping limits strictly 'per tenant' as opposed to per-key/user.

   • [claimed-docs] “set concurrency limits on a per-key basis using a CEL expression. For example, let premium-tier users run 10 workflows simultaneously while …”
   • [claimed-docs] “create concurrency rules that many tasks and workflows consume simultaneously, e.g. limiting `workflow_a` and `workflow_b` to 10 concurrent …”
   • [claimed-docs] “Two new concurrency strategies, `CANCEL_QUEUED_EXCEPT_NEWEST` and `CANCEL_QUEUED_EXCEPT_OLDEST`: configure the queue depth for each concurre…”
   • [claimed-docs] “Concurrency”
   • [claimed-docs] “Rate Limits”
   • [claimed-docs] “Evaluate and debug CEL expressions used in event filters and concurrency keys”

   proof ↗⚑ flag

Prioritization

1. platform engineerI assign priorities to runs and get fair scheduling across tenants instead of a single FIFO queue

   weight 1 · round to Hatchet
   Temporal—none0/10

   Evidence covers Task Queues, worker performance tuning, and retries, but nothing about assigning per-run priorities or fair/weighted scheduling across multiple tenants sharing a queue. missing for 10: priority assignment API, fair-share/weighted scheduling across tenants, multi-tenant isolation guarantees for queue fairness.

   • [claimed-docs] “Use the pages in this guide for practical methods for querying Task Queue information, and strategies for tuning Workers and Task Queue proc…”
   • [claimed-docs] “This guide covers metrics and configurations that drive the efficiency of your Worker fleet.”

   proof ↗⚑ flag

   Hatchet~partialclaimed4/10

   Hatchet documents per-key concurrency limits driven by CEL expressions that can differentiate tenants/tiers (e.g., premium users get 10 concurrent runs vs 1 for others) and multiple concurrency strategies, which gives some tenant-level fairness/resource allocation. However, there is no documented 'priority' field or priority-based scheduler for runs, nor explicit anti-starvation FIFO-replacement mechanism — the evidence only covers concurrency caps, not priority assignment. Missing for 10: explicit run/task priority attribute, priority-aware scheduler documentation, and independent confirmation that concurrency limits actually produce fair scheduling across tenants under load.

   • [claimed-docs] “set concurrency limits on a per-key basis using a CEL expression. For example, let premium-tier users run 10 workflows simultaneously while …”
   • [claimed-docs] “create concurrency rules that many tasks and workflows consume simultaneously, e.g. limiting `workflow_a` and `workflow_b` to 10 concurrent …”
   • [claimed-docs] “Two new concurrency strategies, `CANCEL_QUEUED_EXCEPT_NEWEST` and `CANCEL_QUEUED_EXCEPT_OLDEST`: configure the queue depth for each concurre…”
   • [claimed-docs] “Concurrency”

   proof ↗⚑ flag

Scaling

1. platform engineerThroughput scales by adding workers — the platform load-balances tasks across the fleet and tolerates worker loss

   weight 2 · round to Temporal
   Temporal✓fullcommunity7/10

   Temporal's task-queue model lets any number of Workers poll the same queue, and docs explicitly describe tuning Worker fleets for throughput and efficient resource use (temporal-docs-29, temporal-docs-49), plus serverless autoscaling Worker options (temporal-docs-22, temporal-docs-28). Community users confirm the core promise: 'distribute work across machines without worrying about orchestration... retry any unit easily on failure' and 'full scheduling of work and queues that's cross-machine' (temporal-comm-7, temporal-comm-8), which implies load-balancing and worker-loss tolerance via retries/task requeueing. Missing for 10: explicit documentation/benchmarks of task-queue rebalancing when a worker dies mid-task, and independent throughput-scaling benchmarks beyond anecdotal community reports.

   • [claimed-docs] “Use the pages in this guide for practical methods for querying Task Queue information, and strategies for tuning Workers and Task Queue proc…”
   • [claimed-docs] “This guide covers metrics and configurations that drive the efficiency of your Worker fleet.”
   • [claimed-docs] “Serverless Workers for AWS Lambda allows Temporal to control the scaling and lifecycle of Workers running as Lambda functions, enabling dire…”
   • [claimed-docs] “Serverless Workers for Google Cloud Run allows Temporal to control the scaling of Worker Pools in Cloud Run, allowing for direct responses t…”
   • [community] “No, the whole point of Temporal is to distribute work across machines without worrying too much about orchestration. You can retry any unit …”
   • [community] “Temporal is completely above and beyond Asyncio. It's a full scheduling of work and queues that's cross-machine, cross-language, and very tr…”

   proof ↗⚑ flag

   Hatchet~partialcommunity6/10

   Hatchet's architecture (separate control-plane service, Postgres-backed queue, retries/durability, concurrency-key limits) implies workers can be added to a fleet and tasks distributed/retried on failure (hatchet-gh-1, hatchet-gh-2, hatchet-docs-5/6/18), and the founder describes the control plane as coordinating worker execution independently of client polling (hatchet-comm-6). However, the founder's own account of a scaling pathology at ~25k queries/s with many workers long-polling causing 'runaway CPU deterioration' (hatchet-comm-11) shows real limits to clean horizontal scaling that aren't fully resolved in the evidence, and there's no explicit documentation of automatic failover/rebalancing when a worker dies. Missing for 10: explicit docs on worker-loss recovery/rebalancing, independent throughput benchmarks confirming linear scaling with added workers, and confirmation the 25k qps bottleneck is fully resolved.

   • [github] “Hatchet provides a full platform for queuing, automatic retries, durability, real-time monitoring, alerting, and logging.”
   • [github] “Hatchet is a platform for orchestrating background tasks, AI agents, and durable workflows at scale. It supports applications written in Pyt…”
   • [claimed-docs] “set concurrency limits on a per-key basis using a CEL expression. For example, let premium-tier users run 10 workflows simultaneously while …”
   • [claimed-docs] “create concurrency rules that many tasks and workflows consume simultaneously, e.g. limiting `workflow_a` and `workflow_b` to 10 concurrent …”
   • [claimed-docs] “Two new concurrency strategies, `CANCEL_QUEUED_EXCEPT_NEWEST` and `CANCEL_QUEUED_EXCEPT_OLDEST`: configure the queue depth for each concurre…”
   • [community] “pg-boss comparison from founder: 'The core difference is that pg-boss is a library while Hatchet is a separate service which runs independen…”
   • [community] “Founder on scaling: 'FOR UPDATE SKIP LOCKED does not scale to 25k queries/s'... pathological case was many tasks in backlog, many workers, w…”

   proof ↗⚑ flag

1. ai-native userChoose where my data is stored (region/residency)

   weight 2 · round to Temporal
   Temporal~partialclaimed4/10

   Temporal's self-hosted deployment guide (Docker/K8s/manual) lets a customer run the Temporal Service in any region they choose, and Temporal Cloud advertises 'Multi-Cloud & Multi-Region' plus SOC2/GDPR/HIPAA/CCPA compliance, implying some data-locality control, but there is no explicit documentation of a residency/region-selection feature for Temporal Cloud namespaces or data storage. Missing for 10: explicit Cloud region-selection UI/API, data residency guarantees, and independent confirmation that region choice is enforced.

   • [claimed-docs] “Deployment: Choose a deployment approach (Docker, Kubernetes, or manual) and set up a production-ready Temporal Service.”
   • [claimed-docs] “99.9% SLA, 99.99% HA options Multi-Cloud & Multi-Region User Roles Service Accounts & API Keys Audit Logging”
   • [claimed-docs] “SOC 2 Type II GDPR HIPAA CCPA”
   • [claimed-docs] “This guide shows you how to self-host open source infrastructure software that orchestrates your durable applications.”

   proof ↗⚑ flag

   Hatchet~partialclaimed3/10

   Hatchet supports full self-hosting of its Control Plane, which lets an operator choose the physical location/region of their own infrastructure and thus control data residency indirectly, but there is no documented cloud-based region-selection feature or explicit data-residency guarantee. Missing for 10: an explicit region/residency selector for Hatchet Cloud, documentation of where hosted data is stored, and compliance certifications tied to specific regions.

   • [claimed-docs] “Self-hosting Hatchet means running your own instance of the **Hatchet Control Plane** - the central orchestration system that manages workfl…”
   • [claimed-docs] “Self-hosting Hatchet means running your own instance of the Hatchet Control Plane - the central orchestration system that manages workflows,…”
   • [claimed-docs] “Everything in Team, plus:Unlimited users & tenants7-day data retentionAudit logs & HIPAA”
   • [claimed-docs] “Enterprise-grade security, compliance, and SSO.”

   proof ↗⚑ flag

2. ai-native userControl data retention and deletion

   weight 2 · round drawn
   Temporal~partialclaimed3/10

   Evidence shows Temporal claims GDPR/CCPA/SOC2/HIPAA compliance and 'provable security' without accessing customer data, which implies some data-handling and deletion controls exist, but no docs specify workflow history retention periods, data deletion APIs, or user-controlled purge mechanisms. Missing for 10: explicit namespace/history retention configuration docs, a documented data-deletion or right-to-be-forgotten workflow, and independent confirmation that these controls work as described.

   • [claimed-docs] “SOC 2 Type II GDPR HIPAA CCPA”
   • [claimed-docs] “Learn how Temporal Cloud provides provable security by design - orchestrating encrypted workflows without ever accessing your sensitive data…”

   proof ↗⚑ flag

   Hatchet~partialclaimed3/10

   Evidence shows fixed data-retention windows tied to pricing tiers (3-day/7-day retention) and the option to self-host the control plane, which gives an operator ultimate control over their own Postgres-backed data including deletion. However, there is no documented user-facing deletion API, configurable retention policy, or GDPR-style erasure workflow for the hosted product. missing for 10: explicit data-deletion/purge API, user-configurable retention settings, compliance documentation (e.g., GDPR erasure) beyond generic 'Audit logs & HIPAA' mention.

   • [claimed-docs] “Everything in Team, plus:Unlimited users & tenants7-day data retentionAudit logs & HIPAA”
   • [claimed-docs] “Everything in Developer, plus:10 users & 5 tenants3-day retention500 RPS throughput”
   • [claimed-docs] “Self-hosting Hatchet means running your own instance of the **Hatchet Control Plane** - the central orchestration system that manages workfl…”
   • [claimed-docs] “Run Hatchet yourself”
   • [claimed-docs] “Enterprise-grade security, compliance, and SSO.”

   proof ↗⚑ flag

3. ai-native userOpt out of telemetry and usage tracking

   weight 2 · round drawn
   Temporal—none0/10

   No evidence pack item discusses telemetry, usage tracking, or opt-out settings for Temporal (CLI, SDKs, or Cloud). While self-hosting implies some data control, there's no documented telemetry disclosure or opt-out mechanism, so this cannot be credited as delivered.

   ⚑ flag

   Hatchet—none0/10

   No evidence pack item mentions telemetry, usage tracking, or any opt-out mechanism for Hatchet; this is a self-hostable orchestration platform where such a privacy control is a fair question but unaddressed in the evidence.

   ⚑ flag

Exactly once

1. backend developerIdempotency keys and exactly-once step semantics stop duplicate triggers from double-charging or double-sending

   weight 2 · round to Temporal
   Temporal~partialcommunity5/10

   Temporal's docs show relevant building blocks — automatic Activity retries (temporal-docs-4/36), deterministic replay that avoids re-executing side-effecting calls (temporal-docs-25: 'model calls are executed as Activities, so they retry durably and are not repeated during Workflow replay'), and crash-proof resume semantics (temporal-docs-1, temporal-docs-41) — which together reduce duplicate-trigger risk. However, the pack never explicitly documents Workflow ID uniqueness/reuse policies or an 'idempotency key' concept for external side effects like payments, and community feedback (temporal-comm-5) notes real-world difficulty handling anomalous workflow states, suggesting exactly-once guarantees for external actions still require developer diligence. Missing for 10: explicit documentation of Workflow ID-based deduplication/idempotency keys, guidance on making Activities idempotent for financial-grade exactly-once side effects, and independent verification that double-charging/double-sending is prevented in practice.

   • [claimed-docs] “Temporal's default behavior is to automatically retry an Activity that fails, so transient or intermittent failures require no action on you…”
   • [claimed-docs] “model calls are executed as Activities, so they retry durably and are not repeated during Workflow replay. Your agents survive Worker restar…”
   • [claimed-docs] “Temporal delivers crash-proof execution by guaranteeing that applications resume exactly where they left off after crashes, network failures…”
   • [claimed-docs] “a Workflow resumes automatically after a crash, a network timeout, or a multi-day wait for a human to approve a step”
   • [community] “We migrated from an in-house redis queuing system to Temporal. The worst part has been error/anomaly handling: workflows can hit a state tha…”

   proof ↗⚑ flag

   Hatchet—none0/10

   The evidence pack covers retries, concurrency limits, cron/scheduled runs, and rate limits, but nowhere mentions idempotency keys or exactly-once step execution guarantees to prevent duplicate side effects like double-charging. Missing for 10: any documentation of idempotency key APIs, deduplication mechanisms, or exactly-once semantics guarantees.

   ⚑ flag

Long running

1. backend developerA workflow can sleep or wait for days to months without holding a server, connection, or billable compute

   weight 2 · round to Temporal
   Temporal✓fullclaimed8/10

   Temporal's durable execution model explicitly supports workflows resuming after multi-day waits (e.g., human approval) without holding compute, backed by Activities/retries, timers, and crash-proof resumption docs, plus AI agent examples citing extended-period runs without losing state. missing for 10: independent/hands-on verification of long-duration (months-scale) sleep with zero billable compute claim, and no third-party benchmark confirming server/connection is not held during sleep.

   • [claimed-docs] “Temporal delivers crash-proof execution by guaranteeing that applications resume exactly where they left off after crashes, network failures…”
   • [claimed-docs] “Long-running, stateful agent loops that call LLMs and tools, wait on humans, and pick up exactly where they left off after a failure.”
   • [claimed-docs] “a Workflow resumes automatically after a crash, a network timeout, or a multi-day wait for a human to approve a step”
   • [claimed-docs] “model calls are executed as Activities, so they retry durably and are not repeated during Workflow replay. Your agents survive Worker restar…”
   • [claimed-docs] “By shifting the burden of failure handling from the application to the platform, there is less code for application developers to write, tes…”
   • [claimed-docs] “Temporal's default behavior is to automatically retry an Activity that fails, so transient or intermittent failures require no action on you…”

   proof ↗⚑ flag

   Hatchet~partialclaimed4/10

   Hatchet's docs reference Scheduled Runs and Cron Runs, which imply workflows can be deferred to future times without an always-on process, and the control-plane architecture persists workflow state in Postgres rather than in worker memory. However, no evidence explicitly documents a 'durable sleep' primitive, confirms sleeps can span months, or states that no billable compute/connection is held during a sleep. Missing for 10: explicit sleep/durable-execution API docs, confirmation of long-duration (weeks/months) sleeps, and any statement about compute/connection cost during sleep.

   • [claimed-docs] “Cron Runs”
   • [claimed-docs] “Scheduled Runs”
   • [claimed-docs] “Self-hosting Hatchet means running your own instance of the **Hatchet Control Plane** - the central orchestration system that manages workfl…”
   • [github] “Hatchet provides a full platform for queuing, automatic retries, durability, real-time monitoring, alerting, and logging.”

   proof ↗⚑ flag

Recovery

1. backend developerA workflow interrupted by a process crash, deploy, or infrastructure failure resumes from its last completed step with state intact

   weight 3 · round to Temporal
   Temporal✓fullcommunity9/10

   Temporal's core value proposition is crash-proof durable execution: docs explicitly state workflows resume exactly where they left off after crashes, network failures, or infrastructure outages, with automatic Activity retries and state preserved via event history/replay. Community evidence corroborates real-world use for long-running, cross-machine reliable orchestration, though one user noted difficulty debugging certain stuck/erroring workflow states. Missing for 10: independent third-party benchmark or incident post-mortem specifically validating recovery after a real deploy/crash scenario beyond vendor docs and general community sentiment.

   • [claimed-docs] “Temporal delivers crash-proof execution by guaranteeing that applications resume exactly where they left off after crashes, network failures…”
   • [claimed-docs] “Temporal's default behavior is to automatically retry an Activity that fails, so transient or intermittent failures require no action on you…”
   • [claimed-docs] “By shifting the burden of failure handling from the application to the platform, there is less code for application developers to write, tes…”
   • [claimed-docs] “a Workflow resumes automatically after a crash, a network timeout, or a multi-day wait for a human to approve a step”
   • [claimed-docs] “The usual answer is retry logic, a state table, a message queue, a scheduler, and a reconciliation job — code that has nothing to do with th…”
   • [community] “No, the whole point of Temporal is to distribute work across machines without worrying too much about orchestration. You can retry any unit …”
   • [community] “Temporal is completely above and beyond Asyncio. It's a full scheduling of work and queues that's cross-machine, cross-language, and very tr…”
   • [community] “We migrated from an in-house redis queuing system to Temporal. The worst part has been error/anomaly handling: workflows can hit a state tha…”

   proof ↗⚑ flag

   Hatchet~partialclaimed6/10

   Hatchet is explicitly marketed as a 'durable workflow' engine with automatic retries and durability built into the queuing layer (gh-1, gh-2, gh-3), and has a dedicated Retry Policies doc (hatchet-docs-25) plus separate control-plane architecture (hatchet-docs-8/17) that decouples worker crashes from task state. However, there is no first-party or community evidence detailing exact step-level checkpointing/resume semantics after a crash, deploy, or infra failure, and no hands-on confirmation from users that a workflow actually resumes mid-execution rather than restarting. Missing for 10: explicit documentation of step-level state persistence/checkpointing, and independent/hands-on evidence confirming resume-from-last-step behavior after real crashes or deploys.

   • [github] “Hatchet provides a full platform for queuing, automatic retries, durability, real-time monitoring, alerting, and logging.”
   • [github] “Hatchet is a platform for orchestrating background tasks, AI agents, and durable workflows at scale. It supports applications written in Pyt…”
   • [github] “An orchestration engine for background tasks, AI agents, and durable workflows”
   • [claimed-docs] “Retry Policies”
   • [claimed-docs] “Self-hosting Hatchet means running your own instance of the **Hatchet Control Plane** - the central orchestration system that manages workfl…”
   • [claimed-docs] “Self-hosting Hatchet means running your own instance of the Hatchet Control Plane - the central orchestration system that manages workflows,…”

   proof ↗⚑ flag

Retries

1. backend developerEvery step retries automatically with configurable backoff, timeouts, and failure policies — no try/catch scaffolding

   weight 3 · round to Temporal
   Temporal✓fullcommunity8/10

   Temporal Activities have automatic retry policies with configurable backoff, timeouts, and failure/non-retryable error policies built into the platform, explicitly removing app-level retry/try-catch scaffolding, and community users confirm retrying units on failure with non-retryable error specification works in practice. missing for 10: independent hands-on benchmark of backoff/timeout tuning edge cases, and one community report notes error/anomaly handling can be confusing in practice which slightly tempers the polish.

   • [claimed-docs] “Temporal's default behavior is to automatically retry an Activity that fails, so transient or intermittent failures require no action on you…”
   • [claimed-docs] “Temporal's default behavior is to automatically ... A Retry Policy is a collection of settings that tells Temporal how and when to try again…”
   • [claimed-docs] “By shifting the burden of failure handling from the application to the platform, there is less code for application developers to write, tes…”
   • [claimed-docs] “The usual answer is retry logic, a state table, a message queue, a scheduler, and a reconciliation job — code that has nothing to do with th…”
   • [community] “No, the whole point of Temporal is to distribute work across machines without worrying too much about orchestration. You can retry any unit …”
   • [community] “We migrated from an in-house redis queuing system to Temporal. The worst part has been error/anomaly handling: workflows can hit a state tha…”

   proof ↗⚑ flag

   Hatchet~partialcommunity6/10

   Hatchet has a dedicated 'Retry Policies' docs page and GitHub explicitly touts 'automatic retries' as part of the platform, and the founder's HN comments describe a declarative workflow model that avoids try/catch scaffolding for step execution. However, the evidence pack gives only page titles for retries/rate-limits/concurrency without concrete detail on configurable backoff curves, per-step timeouts, or failure-policy semantics. Missing for 10: explicit documentation of backoff configuration options, timeout settings per step, and failure-policy examples beyond a bare page title.

   • [claimed-docs] “Retry Policies”
   • [github] “Hatchet provides a full platform for queuing, automatic retries, durability, real-time monitoring, alerting, and logging.”
   • [community] “abelanger (Hatchet founder) explained: 'unlike many alternatives, the execution path of a multi-step workflow in Hatchet is declared ahead o…”
   • [claimed-docs] “Rate Limits”

   proof ↗⚑ flag

Events

1. backend developerEvents from my app, webhooks, or queues trigger workflows declaratively, and one event can fan out to many functions

   weight 2 · round drawn
   Temporal~partialclaimed6/10

   Temporal's Client API lets application code start or signal Workflow Executions in response to app events (temporal-docs-50, temporal-docs-6/38), and Activities/child workflows let a single Workflow fan out to many functions (temporal-docs-3, temporal-docs-35); Schedules cover time-based triggers (temporal-docs-5, temporal-docs-23). However this is done via imperative SDK calls in your own event/webhook/queue handler, not a native declarative trigger binding, and no first-party webhook/queue-connector or explicit fan-out pattern documentation is present. Missing for 10: documented native webhook/queue ingestion connectors, a declarative event-trigger config (vs code-driven client calls), and explicit fan-out-to-many-activities examples/docs.

   • [claimed-docs] “Start a Workflow Execution - Get the result of Workflow Execution - List Workflow Executions - Query a Workflow Execution - Signal a Workflo…”
   • [claimed-docs] “Temporal supports three types of messages: Signals, Queries, and Updates”
   • [claimed-docs] “Workflows can be thought of as stateful web services that can receive messages. The Workflow can have message handlers, akin to endpoints”
   • [claimed-docs] “An Activity is a normal function or method that executes a single, well-defined action (either short or long running), such as calling anoth…”
   • [claimed-docs] “An Activity is a normal function or method ... that executes a single, well-defined action (either short or long running), such as calling a…”
   • [claimed-docs] “A Schedule contains instructions for starting a Workflow Execution at specific times.”
   • [claimed-docs] “Schedules provide a more flexible and user-friendly approach than Temporal Cron Jobs.”

   proof ↗⚑ flag

   Hatchet~partialclaimed6/10

   Hatchet's docs show declarative workflow triggering via webhooks, event filters (CEL expressions), and cron/scheduled runs, and its DAG-based workflow model with shared concurrency rules across multiple workflows (docs-6) implies coordinated multi-workflow execution from shared triggers. However, there's no explicit worked example or documentation of a single event fanning out to multiple distinct functions/workflows, and no independent hands-on confirmation of this exact pattern. missing for 10: concrete example/doc of one event triggering multiple downstream workflows/functions, independent verification of fan-out behavior.

   • [claimed-docs] “Webhooks”
   • [claimed-docs] “Evaluate and debug CEL expressions used in event filters and concurrency keys”
   • [claimed-docs] “Create and manage cron triggers that run workflows on a schedule”
   • [claimed-docs] “Cron Runs”
   • [claimed-docs] “Scheduled Runs”
   • [claimed-docs] “create concurrency rules that many tasks and workflows consume simultaneously, e.g. limiting `workflow_a` and `workflow_b` to 10 concurrent …”
   • [github] “Hatchet is a platform for orchestrating background tasks, AI agents, and durable workflows at scale. It supports applications written in Pyt…”

   proof ↗⚑ flag

Flow shaping

1. backend developerI debounce, batch, or delay triggers so noisy event streams collapse into the runs I actually want

   weight 1 · round to Temporal
   Temporal~partialclaimed5/10

   Temporal provides the primitives a backend developer would use to build debounce/batch/delay logic — Signals for event ingestion, Workflow-as-stateful-service message handlers, and Schedules/timers for delayed execution — but there is no documented first-class 'debounce' or 'batch trigger' feature; developers must hand-roll the coalescing logic inside Workflow code. missing for 10: an explicit debounce/coalescing primitive or documented pattern for collapsing noisy signal streams, and independent evidence of teams successfully using it for this exact use case.

   • [claimed-docs] “Temporal supports three types of messages: Signals, Queries, and Updates”
   • [claimed-docs] “Workflows can be thought of as stateful web services that can receive messages. The Workflow can have message handlers, akin to endpoints”
   • [claimed-docs] “A Schedule contains instructions for starting a Workflow Execution at specific times.”
   • [claimed-docs] “Schedules provide a more flexible and user-friendly approach than Temporal Cron Jobs.”
   • [claimed-docs] “A Schedule contains instructions for starting a Workflow Execution at specific times. Schedules provide a more flexible and user-friendly ap…”
   • [claimed-docs] “Start a Workflow Execution - Get the result of Workflow Execution - List Workflow Executions - Query a Workflow Execution - Signal a Workflo…”

   proof ↗⚑ flag

   Hatchet~partialclaimed4/10

   Hatchet's concurrency strategies (e.g. CANCEL_QUEUED_EXCEPT_NEWEST/OLDEST) and per-key concurrency limits can approximate collapsing noisy triggers into a single desired run, and rate limits/cron/scheduled runs give some control over trigger cadence, but there is no explicit debounce, batching-window, or delay-trigger primitive described in the evidence. missing for 10: explicit debounce/delay trigger API, event batching/aggregation over a time window, and any hands-on confirmation these concurrency strategies are actually used for debounce-style use cases.

   • [claimed-docs] “Two new concurrency strategies, `CANCEL_QUEUED_EXCEPT_NEWEST` and `CANCEL_QUEUED_EXCEPT_OLDEST`: configure the queue depth for each concurre…”
   • [claimed-docs] “set concurrency limits on a per-key basis using a CEL expression. For example, let premium-tier users run 10 workflows simultaneously while …”
   • [claimed-docs] “create concurrency rules that many tasks and workflows consume simultaneously, e.g. limiting `workflow_a` and `workflow_b` to 10 concurrent …”
   • [claimed-docs] “Concurrency”
   • [claimed-docs] “Rate Limits”
   • [claimed-docs] “Create and manage cron triggers that run workflows on a schedule”
   • [claimed-docs] “Scheduled Runs”

   proof ↗⚑ flag

Schedules

1. backend developerI schedule workflows on cron expressions with overlap policies, pause/resume, and visibility into upcoming runs

   weight 2 · round drawn
   Temporal~partialclaimed5/10

   Temporal's Schedule feature (docs-5, docs-23, docs-37) explicitly supports cron-based workflow starts and is positioned as more flexible than plain Cron Jobs (docs-34 lists both Schedules and Cron Job as workflow-start mechanisms), and the Web UI (docs-8/40) and CLI (docs-42) give execution-state visibility/debugging. However, the evidence pack never explicitly documents overlap policies, pause/resume controls, or a view of upcoming scheduled runs — these specific sub-capabilities are asserted by the story but not directly cited in the pack. Missing for 10: explicit documentation of overlap policy configuration, pause/resume schedule actions, and next-run/upcoming-run listing UI or CLI output.

   • [claimed-docs] “A Schedule contains instructions for starting a Workflow Execution at specific times.”
   • [claimed-docs] “Schedules provide a more flexible and user-friendly approach than Temporal Cron Jobs.”
   • [claimed-docs] “Workflow Definition ... Workflow Execution ... Schedules ... Dynamic Handler ... Cron Job”
   • [claimed-docs] “A Schedule contains instructions for starting a Workflow Execution at specific times. Schedules provide a more flexible and user-friendly ap…”
   • [claimed-docs] “The Temporal Web UI provides users with Workflow Execution state and metadata for debugging purposes.”
   • [claimed-docs] “The Temporal CLI (`temporal`) provides direct access to a Temporal Service via the terminal. Use it to manage, monitor, and debug Temporal a…”

   proof ↗⚑ flag

   Hatchet~partialclaimed5/10

   Hatchet documents cron triggers, scheduled runs, and concurrency strategies (including queue-depth overlap-like policies such as CANCEL_QUEUED_EXCEPT_NEWEST/OLDEST) plus TUI/dashboard observability into tasks and workflows, giving partial coverage of scheduling and visibility. However, there is no explicit documentation tying overlap policies specifically to cron jobs, and no evidence of a pause/resume feature for scheduled workflows. Missing for 10: explicit cron-specific overlap/skip policy docs, pause/resume of cron schedules, and independent confirmation of upcoming-run visibility UI.

   • [claimed-docs] “Create and manage cron triggers that run workflows on a schedule”
   • [claimed-docs] “Cron Runs”
   • [claimed-docs] “Scheduled Runs”
   • [claimed-docs] “Two new concurrency strategies, `CANCEL_QUEUED_EXCEPT_NEWEST` and `CANCEL_QUEUED_EXCEPT_OLDEST`: configure the queue depth for each concurre…”
   • [claimed-docs] “the `hatchet tui` command lets you interact with your Hatchet deployment through a terminal user interface (TUI) that provides real-time obs…”

   proof ↗⚑ flag

Versioning

1. platform engineerI deploy new workflow code while in-flight runs finish on the version they started with — versioning without breaking determinism

   weight 3 · round to Temporal
   Temporal✓fullcommunity8/10

   Temporal's Worker Versioning feature explicitly supports declaring Pinned vs Auto-Upgrade Versioning Behavior per Workflow type, with Worker Deployments and Worker Deployment Versions concepts letting in-flight workflows continue on their starting version while new workflows use new code — directly addressing safe deployment without breaking determinism. Docs detail the underlying mechanics (deployment versions, versioning behaviors) precisely for this use case. Missing for 10: independent/hands-on community validation of the versioning feature specifically (community evidence in the pack discusses determinism generally but not this exact versioning workflow), and no evidence of edge-case failure handling during version transitions.

   • [claimed-docs] “You can declare each Workflow type to have a Versioning Behavior, either Pinned or Auto-Upgrade, in your Workflow config”
   • [claimed-docs] “This page defines some of the underlying concepts used in Worker Versioning ... Worker Deployments ... Worker Deployment Versions ... Versio…”
   • [community] “No, the whole point of Temporal is to distribute work across machines without worrying too much about orchestration. You can retry any unit …”

   proof ↗⚑ flag

   Hatchet—none0/10

   No evidence describes workflow-version pinning for in-flight runs, deterministic replay guarantees, or any mechanism to keep already-started runs on the code version they began with while new versions roll out — the docs cover retries, concurrency, cron, self-hosting, and CLI tooling but never versioning/determinism semantics. This is a reasonable axis for a durable workflow engine, so absence of evidence means 'none' rather than 'na'.

   ⚑ flag

Authoring

1. backend developerI write workflows as ordinary code in my language — steps with automatic checkpointing — not YAML or a proprietary DSL

   weight 3 · round to Temporal
   Temporal✓fullcommunity9/10

   Temporal's core model is Workflows and Activities written as ordinary functions/methods in supported languages (TypeScript, Python, etc.), with automatic checkpointing/durable execution and crash recovery built into the runtime, corroborated by community users describing it as modeling business logic as ordinary code with side effects in Activities. This directly matches the story of code-based workflow authoring versus YAML/DSL. Missing for 10: no independent benchmark or critique specifically addressing 'no DSL' claim beyond community anecdotes, and some community feedback notes friction with error/anomaly handling in practice.

   • [claimed-docs] “An Activity is a normal function or method that executes a single, well-defined action (either short or long running), such as calling anoth…”
   • [claimed-docs] “Temporal's default behavior is to automatically retry an Activity that fails, so transient or intermittent failures require no action on you…”
   • [claimed-docs] “Temporal delivers crash-proof execution by guaranteeing that applications resume exactly where they left off after crashes, network failures…”
   • [claimed-docs] “a short walkthrough of how to use the Temporal primitives (Activities, Workflows, and Workers) to build and run a Temporal application”
   • [community] “I haven't yet used Temporal, but spent a lot of time evaluating it and its predecessor Cadence. It models long-running business logic as ord…”
   • [community] “No, the whole point of Temporal is to distribute work across machines without worrying too much about orchestration. You can retry any unit …”

   proof ↗⚑ flag

   Hatchet~partialcommunity6/10

   Hatchet's SDKs (Python, TypeScript, Go, Ruby) and cookbooks show workflows/tasks defined as ordinary code rather than YAML, and 'durability'/retry features are documented, implying step-level state persistence, and a founder comment confirms multi-step execution paths are declared in code ahead of time. However, there's no explicit documentation of 'automatic checkpointing' semantics or a direct contrast against YAML/DSL approaches, and no independent hands-on report specifically validating this authoring model works smoothly in production. Missing for 10: explicit automatic-checkpointing mechanics, explicit no-DSL/no-YAML framing, independent developer confirmation of code-first authoring experience.

   • [claimed-docs] “Python SDK TypeScript SDK Go SDK Ruby SDK”
   • [github] “Hatchet provides a full platform for queuing, automatic retries, durability, real-time monitoring, alerting, and logging.”
   • [community] “abelanger (Hatchet founder) explained: 'unlike many alternatives, the execution path of a multi-step workflow in Hatchet is declared ahead o…”
   • [claimed-docs] “Retry Policies”

   proof ↗⚑ flag

Composition

1. backend developerI compose workflows from parallel steps, fan-out/fan-in over dynamic batches, and child workflows without hand-rolling coordination

   weight 2 · round to Temporal
   Temporal~partialclaimed6/10

   Temporal's docs establish the core primitives (Workflows as ordinary code, Activities, automatic retries, worker/task-queue tuning) that back parallel/fan-out patterns, and a CLI batch-job feature (temporal-docs-26) plus 'why Temporal' messaging about eliminating hand-rolled coordination (temporal-docs-18, temporal-docs-46) support the theme. Missing for 10: explicit documentation or examples of child workflow APIs, parallel/fan-out-fan-in composition patterns, and dynamic batch workflow orchestration are not directly cited in this evidence pack, so the story is only partially substantiated.

   • [claimed-docs] “An Activity is a normal function or method that executes a single, well-defined action (either short or long running), such as calling anoth…”
   • [claimed-docs] “Temporal's default behavior is to automatically retry an Activity that fails, so transient or intermittent failures require no action on you…”
   • [claimed-docs] “By shifting the burden of failure handling from the application to the platform, there is less code for application developers to write, tes…”
   • [claimed-docs] “Show the progress of an ongoing batch job. Pass a valid job ID to display its information”
   • [claimed-docs] “Use the pages in this guide for practical methods for querying Task Queue information, and strategies for tuning Workers and Task Queue proc…”
   • [claimed-docs] “The usual answer is retry logic, a state table, a message queue, a scheduler, and a reconciliation job — code that has nothing to do with th…”

   proof ↗⚑ flag

   Hatchet—none0/10

   The evidence pack contains no documentation or discussion of Hatchet's DAG/parallel-step syntax, fan-out/fan-in over dynamic batches, or child-workflow composition — only a general founder comment that 'the execution path of a multi-step workflow ... is declared ahead of time' (hatchet-comm-5), which does not demonstrate these specific coordination primitives. Missing for 10: docs/examples of parallel step definitions, fan-out/fan-in over dynamic batch sizes, and child workflow invocation/spawning.

   • [community] “abelanger (Hatchet founder) explained: 'unlike many alternatives, the execution path of a multi-step workflow in Hatchet is declared ahead o…”

   proof ↗⚑ flag

Language coverage

1. backend developerI author workflows in the language my team already uses — TypeScript, Python, Go, or more — with real feature parity across SDKs

   weight 2 · round to Hatchet
   Temporal~partialcommunity5/10

   Docs confirm dedicated TypeScript (temporal-docs-14) and Python (temporal-docs-25,47,48) developer guides with core Workflow/Activity primitives, and a community comment references additional SDKs like Ruby/PHP existing (though it mistakenly claims no JS, contradicted by the TypeScript docs). However, there's no explicit evidence of a Go SDK guide or any statement that SDKs maintain true feature parity across languages. Missing for 10: Go SDK documentation, an explicit cross-SDK feature-parity statement, and independent verification that all SDKs support identical capabilities.

   • [claimed-docs] “a short walkthrough of how to use the Temporal primitives (Activities, Workflows, and Workers) to build and run a Temporal application”
   • [claimed-docs] “model calls are executed as Activities, so they retry durably and are not repeated during Workflow replay. Your agents survive Worker restar…”
   • [claimed-docs] “Temporal's integration with the OpenAI Agents SDK for Python lets you run agents as Temporal Workflows.”
   • [claimed-docs] “Call an LLM from a durable Temporal Workflow in Python using the OpenAI API library.”
   • [community] “Surprised to see Ruby and PHP SDKs, but no JS [for Temporal].”

   proof ↗⚑ flag

   Hatchet~partialcommunity6/10

   Hatchet officially ships Python, TypeScript, Go and Ruby SDKs (hatchet-gh-2, hatchet-docs-24) and community members confirm using it across multiple languages (hatchet-comm-15), supporting the core 'author in your language' claim. However, several documented features (CEL expression debugging, cron creation via Python reference) are only shown in Python-specific docs, and there is no explicit cross-SDK feature-parity matrix or changelog confirming equal capabilities across all four SDKs. Missing for 10: explicit feature-parity documentation/matrix across SDKs, confirmation that newer features (e.g., CEL debug tools, concurrency strategies) are available uniformly in Go/Ruby/TypeScript, and independent hands-on validation of parity beyond basic usability.

   • [github] “Hatchet is a platform for orchestrating background tasks, AI agents, and durable workflows at scale. It supports applications written in Pyt…”
   • [claimed-docs] “Python SDK TypeScript SDK Go SDK Ruby SDK”
   • [community] “This looks very cool! I see a lot of Python in the docs; is it usable in other languages? [confirmed SDKs exist for Python, Typescript and G…”
   • [claimed-docs] “Evaluate and debug CEL expressions used in event filters and concurrency keys”
   • [claimed-docs] “Create and manage cron triggers that run workflows on a schedule”

   proof ↗⚑ flag