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Restate uses an execution log to replay operations after failures and suspensions. Non-deterministic operations (database calls, HTTP requests, UUID generation) must be wrapped to ensure deterministic replay.

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Run

Use ctx.run to safely wrap any non-deterministic operation, like HTTP calls or database responses, and have Restate store its result in the execution log. 
async def call_llm(prompt: str) -> str:
    # ... implement ...
    return "llm response"

# specify the (async) function to call and its arguments
result = await ctx.run_typed("LLM call", call_llm, prompt="What is the weather?")

# or use a lambda to capture a single value
my_number = await ctx.run_typed("generate number", lambda: random.randint(0, 10))
Note that inside ctx.run, you cannot use the Restate context (e.g., ctx.get, ctx.sleep, or nested ctx.run).
By default, the SDK serializes the journal entry with the json library. Alternatively, you can specify a Pydantic model or custom serializer.
Failures in ctx.run are treated the same as any other handler error. Restate will retry it unless configured otherwise or unless a TerminalError is thrown.You can customize how ctx.run retries via:
try:
    retry_opts = restate.RunOptions(
        max_attempts=10, max_retry_duration=timedelta(seconds=30)
    )
    await ctx.run_typed("write", write_to_other_system, retry_opts)
except TerminalError as err:
    # Handle the terminal error after retries exhausted
    # For example, undo previous actions (see sagas guide) and
    # propagate the error back to the caller
    raise err
  • You can limit retries by time or count
  • When the policy is exhausted, a TerminalError is thrown
  • See the Error Handling Guide and the Sagas Guide for patterns like compensation
If Restate doesn’t receive new journal entries from a service for more than one minute (by default), it will automatically abort the invocation and retry it.However, some business logic can take longer to complete—for example, an LLM call that takes up to 3 minutes to respond.In such cases, you can adjust the service’s abort timeout and inactivity timeout settings to accommodate longer execution times.For more information, see the error handling guide.

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Deterministic randoms

When you do non-deterministic operations, like generating UUIDs or random numbers, you must ensure that the results are deterministic on replay. For example, to generate stable UUIDs for things like idempotency keys: 
my_uuid = ctx.uuid()
The SDK provides deterministic helpers for random values — seeded by the invocation ID — so they return the same result on retries.

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UUIDs

To generate stable UUIDs for things like idempotency keys: 
my_uuid = ctx.uuid()
Do not use this in cryptographic contexts.

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Random numbers

To generate a deterministic float between 0 and 1: 
ctx.random().random()
This behaves like Math.random() but is deterministically replayable.

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Deterministic time

To get the current millis since midnight, January 1, 1970, that is consistent across retries: 
current_time = await ctx.time()