# Journaled LLM Call

> Record every non-deterministic step (LLM calls, tool results, timestamps) to an append-only journal on first execution — replay crashes by replaying the journal, not re-invoking the model.

- **Category**: Agentic AI
- **Subcategory**: Governance & Observability
- **Canonical URL**: https://designpattern.fyi/patterns/journaled_llm_call/

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## Description
**Intent**: Record every non-deterministic step on first execution and replay that recorded value during crash-recovery instead of re-invoking the model.
**Context**: Durable-execution engines (Temporal, Inngest) recover from crashes by replaying workflow code from a recorded history. If your workflow contains non-deterministic steps — LLM calls, tool results, timestamps, random draws — replaying those steps will produce different values on recovery, causing divergence or duplicate side effects.
**Solution**: Classify every step as deterministic workflow logic or non-deterministic effect. Run each effect exactly once and append its result to an append-only journal keyed by step position. On crash-recovery, the engine replays workflow code from the start — deterministic logic recomputes freely, but each effect call short-circuits to its journaled output. The model is queried only the first time a step is reached; the recorded response stands in for all subsequent replays. This trades a possibly-stale recorded answer for deterministic, fault-tolerant replay without double-billing.



## Use Cases
- The agent runs on a durable-execution engine that recovers by replaying workflow code from a recorded history.
- The workflow contains non-deterministic steps — LLM calls, tool results, timestamps, or random draws.
- A recovered run must follow the same path as the original, and re-invoking the model on recovery is unacceptable on cost or correctness grounds.






## Trade-offs


### Advantages

- Replay is deterministic — recovered runs follow the identical path the original took.

- Each model call is billed once; recovery reuses the recorded output.

- The journal doubles as an audit trail of every non-deterministic decision the workflow made.




### Considerations & Drawbacks

- Journaled responses can be stale — replay reuses a value the world has since changed.

- Missing one non-deterministic step reintroduces divergence that's hard to spot and debug.

- The append-only journal grows with every effect and must be stored and garbage-collected.

- Changing workflow code between original run and replay can invalidate journaled step positions.







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**Reference**: [Original Source](https://www.agentpatternscatalog.org/patterns/journaled-llm-call/)