### Prompt management
Fetch [version-controlled prompts from Langfuse](https://langfuse.com/docs/prompts) as a durable step with `ctx.run`. Retries reuse the same prompt; new executions pick up the latest version.
### Async evals example
Run [LLM-as-a-Judge evaluations](https://langfuse.com/docs/evaluation/evaluation-methods/llm-as-a-judge) as async Restate workflows that don't block agent execution. Restate acts as both the queue and the orchestrator. See the example below.
You can submit an evaluation from the agent via a [one-way call](/develop/python/service-communication#sending-messages), so it runs asynchronously without blocking the main agent.
The eval workflow runs an LLM judge and writes the score back to the original trace in Langfuse:
```python evaluation.py {"CODE_LOAD::https://raw.githubusercontent.com/restatedev/ai-examples/refs/heads/main/openai-agents/examples/langfuse/evaluation.py#here"} theme={null}
langfuse = get_client()
judge_agent = Agent(
name="ClaimEvaluationJudge",
instructions=(
"You are an expert evaluator of insurance claim processing. "
"Rate the overall quality of the claim agent's response as a score "
"between 0.0 and 1.0, and provide a brief reason for your rating."
),
output_type=EvaluationScore,
)
evaluation_service = restate.Service("LLMJudgeEvaluation")
@evaluation_service.handler()
async def evaluate(ctx: restate.Context, req: EvaluationRequest) -> None:
# Step 1: Run the LLM judge (durable — retried on failure)
result = await DurableRunner.run(
judge_agent,
f"Evaluate this insurance claim processing:\n\n"
f"**Claim Input:**\n{req.input}\n\n"
f"**Agent Output:**\n{req.output}",
)
evaluation: EvaluationScore = result.final_output
# Step 2: Write the score to Langfuse on the original claim trace
async def score_trace() -> None:
langfuse.create_score(
trace_id=req.trace_id(),
name="quality",
value=evaluation.score,
data_type="NUMERIC",
comment=evaluation.reason,
)
langfuse.flush()
await ctx.run_typed("Score trace in Langfuse", score_trace)
```
For a full walkthrough of these features, see the [Restate + Langfuse blog post](https://restate.dev/blog/reliable-ai-agents-with-restate-and-langfuse/).