Temporal for Durable Agents Trial
Overview
Temporal provides durable execution for long-running workflows with automatic retries, timers, and human signals. Agent builders use it when LLM steps, tool calls, and approvals must survive process restarts and partial failures (Temporal).
Trial for agentic workflows that outgrow in-memory loops. Design idempotent activities and separate model calls from side effects before production scale.
Adoption Signals
- Named AI production users: Temporal lists OpenAI, ADP, Yum! Brands and Block among production customers, and OpenAI's VP of App Infrastructure calls durable execution "a core requirement for modern AI systems" (Temporal Series D announcement).
- Agent products built on it: Replit migrated its coding agent to Temporal, and Retool used it to ship Retool Agents with a small team (Temporal for AI).
- Usage growth: Temporal reports more than 20 million installs per month, 9.1 trillion lifetime actions on Temporal Cloud and 1.86 trillion from AI-native companies, alongside a $300M Series D at a $5B valuation in February 2026 (Temporal Series D announcement).
- The OpenAI Agents SDK integration for Python is generally available; sandbox support is pre-release, streaming experimental and OpenTelemetry in public preview (OpenAI Agents SDK integration).
- Integrations are listed for Pydantic AI, Google ADK, Vercel AI SDK, Mastra, Strands Agents and Spring AI, plus Langfuse and Braintrust for tracing (Temporal for AI).
Risks
- Determinism. Workflow code must issue the same calls in the same order on replay; code changes to running workflows and non-deterministic logic cause failures, so LLM calls, clocks and randomness belong in Activities (Workflow definition).
- History limits. An execution fails at 51,200 events or 50 MB of history (warnings at 10,240 events or 10 MB), which long multi-turn agents can reach; Continue-As-New and storing large prompts outside the history are needed (Workflow execution limits).
- Duplicate side effects. Activities are retried by default, so tool calls that send email, pay or write to external systems need idempotency keys or a retry policy that fits the action.
- Cost model. Temporal Cloud charges per action (from $50 per million, falling to $25 at volume) plus storage, and agents with many small steps generate many actions (Temporal pricing).
- Maturing agent features. Sandbox support, streaming and OpenTelemetry in the OpenAI Agents SDK integration can still change before GA (OpenAI Agents SDK integration).
Pros & Cons
Advantages
- Agent state survives crashes and restarts: the agent loop runs as a Workflow while model and tool calls run as retried Activities that are not repeated on replay, which removes most hand-built checkpointing code.
- Proven at AI-native scale: OpenAI is a named production customer, Replit and Retool run their agents on Temporal, and Temporal Cloud reports 1.86 trillion actions from AI-native companies.
- Open source (MIT) with a managed option and a growing set of agent-framework integrations, including a generally available OpenAI Agents SDK integration and listed integrations for Pydantic AI, Google ADK, Vercel AI SDK and Strands Agents.
Disadvantages
- Workflow code must be deterministic; changing a running workflow definition or calling an LLM directly from workflow code causes non-determinism errors, which requires versioning discipline that many agent teams do not have yet.
- Long agent conversations hit history limits (51,200 events or 50 MB per execution), so chatty agents need Continue-As-New and payload offloading by design.
- Self-hosting means operating a stateful cluster and database; Temporal Cloud removes that but bills per action (from $50 per million), and several agent-specific features (sandboxing, streaming) are still pre-release.
Recommendation
Trial Temporal for agents that run longer than a single request, wait for human approval, or call tools with real side effects, where losing state on a restart is not acceptable. Start with one agent: put the agent loop in a Workflow, every model and tool call in an Activity with explicit timeouts and retry policies, and approvals as Signals or Updates. Use a framework integration (OpenAI Agents SDK, Pydantic AI or Google ADK) rather than writing the loop by hand when one fits.
Before production, add workflow versioning to the release process, test replay in CI, keep large prompts and documents out of the event history, and set per-namespace action budgets. For short request-response agents or single-step tool calls, Temporal adds more machinery than it saves; a simpler framework-level checkpoint (for example in LangGraph) can be enough. European teams choosing Temporal Cloud should confirm the region and data-residency terms for workflow payloads, which include prompts and model outputs.
Sources
Consulting
From radar to implementation
You have assessed the technologies — we help you bring them into production systems safely and measurably.
