Neither is universally better: they solve different problems. Durable execution records workflow progress so work can resume after failures and waits. Persistent agent state retains context—such as conversation history—so an interaction can continue across turns. If an application needs both remembered context and recoverable work, use both layers and test how they behave together.
What is the difference?
“Persistent agents” can mean several ways of keeping an agent’s context between turns; it is not one specific recovery guarantee. Durable execution, by contrast, is about making workflow progress recoverable when a process, worker, or downstream service fails or work must wait.
| Question | Durable execution | Persistent agent state |
|---|---|---|
| What is retained? | Workflow progress and the information needed to resume recorded work. The exact guarantees depend on the runtime and its implementation. | Conversation history or other agent/session context, depending on the chosen persistence mechanism. |
| What problem does it address? | Recovering work after failures, handling retries, timers, and waits. | Continuing an interaction with prior context or choosing where session data is stored. |
| Does it automatically cover the other? | No. A recoverable workflow does not, by itself, define how an agent’s conversational context is managed. | No. Retained conversation history alone does not establish that in-flight tool work or a whole business process can resume after worker failure. |
OpenAI’s agent-running guide distinguishes application-held history, sessions backed by application storage, server-managed Conversations API state, and Responses API continuation using a prior response ID. Those are different choices for interaction continuity, not interchangeable evidence of workflow recovery.
When durable execution is the better fit
Evaluate a durable execution layer when a process must outlive the worker handling it, pause for an approval or external event, retry work safely, or continue after an outage. Temporal’s technical guide describes durable execution as persisting steps so work can continue in another process after a process or container failure, with developers retaining control over retry behavior. That is Temporal’s description of its approach, not a blanket guarantee for every workflow product or application.
#1 Best Overall
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- Long waits: The workflow may wait for a person, a timer, or an external system without relying on one live process.
- Failure recovery: You need recorded progress and defined retry behavior after a worker or downstream dependency fails.
- Business-process continuity: The workflow matters independently of any one agent turn or conversation session.
Read the recovery and replay semantics for the specific runtime you plan to use, especially around external side effects. A retry can be operationally safe only if the application and integrations handle repeated attempts appropriately.
When persistent agent state is the better fit
Choose a persistence strategy based on what “remembering” means in your application and who should own the stored data. OpenAI documents several patterns: keep replay-ready history in your application, use SDK sessions with application storage, use server-managed Conversations API state, or continue through a prior Responses API response ID. Its guide describes sessions as useful for durable memory, resumable approval flows, or application-controlled storage.
Rank #2
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- Application-held history: Your application controls the conversation history it sends to the agent.
- Storage-backed sessions: The SDK and application storage support continuity across turns.
- Server-managed state: OpenAI’s Conversations API manages conversation state.
- Response-ID continuation: The Responses API can continue from the previous response ID.
These options have different ownership and storage implications. A conversation identifier or session can preserve context, but do not assume it records every tool action, makes external side effects safe to repeat, or reconstructs an entire business workflow after a crash.
When to use both
Use both layers when an agent must remember prior conversation context and reliably complete a workflow that may pause, retry, or survive worker restarts. In this design, the agent/session layer handles interaction state; the workflow runtime handles orchestration and recovery. Define which layer owns each piece of state, and how the workflow retrieves the right conversation context when it resumes.
The Tool Desk
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- Sleek 7-in-1 USB-C Hub: Features an HDMI port, two USB-A 3.0 ports, and a USB-C data port, each providing 5Gbps transfer speeds. It also includes a USB-C PD input port for charging up to 100W and dual SD and TF card slots, all in a compact design.
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Temporal’s documented OpenAI Agents SDK integration is one example for TypeScript: agent orchestration runs inside a Workflow, while model calls run as Activities. Temporal’s guide says the integration makes model calls durable and avoids repeating them during Workflow replay, and describes agents surviving Worker restarts. These are claims about that documented integration; verify current behavior, version compatibility, and language-specific support for your own stack.
OpenAI’s Agents SDK documentation also lists durable-execution integrations, including Dapr, Temporal, Restate, and DBOS. It summarizes their intended roles differently, so treat those descriptions as starting points and confirm current capabilities in each provider’s primary documentation.
Rank #4
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How to choose for your workload
- Identify what must survive. Is it conversation context, workflow progress, or both? Name the records and actions that must be available after a restart.
- Map the failure and wait cases. List worker restarts, downstream outages, human approvals, timers, and long-running tool operations. Decide what should resume, retry, or require intervention.
- Assign state ownership. Specify which system stores conversation history, workflow progress, and any business data; decide how operators can inspect or migrate it.
- Check execution semantics. Verify how the chosen runtime records progress, replays workflow code, retries activities, and handles external side effects. Confirm what happens when workflow code changes.
- Evaluate the operating model. Account for required storage, workflow services, workers, hosted platforms, monitoring, and the team’s operational responsibilities.
- Test representative workflows. Simulate restarts, repeated attempts, long waits, and approvals. Measure your own cost and latency; the cited documentation does not establish a neutral benchmark that determines which approach is cheaper or faster.
What the comparison cannot decide for you
There is no source-backed universal winner on cost, latency, reliability, or staffing burden. Those outcomes depend on the workload, deployment model, versions, and recovery behavior you actually operate. LangChain’s June 6, 2026 comparison of LangGraph/LangSmith and Temporal is a vendor-authored perspective on the products’ respective orientations, not a neutral benchmark; use it as context rather than proof that one architecture wins for your use case: LangGraph vs. Temporal.
Quick Recap
Best Value
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