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Best practice for persisting large accumulated LangGraph state without hitting ScheduleActivityTask payload size limits

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#1,894 0 comentarios 0 reacciones 0 asignados Ver en GitHub

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
35/100
Tipo de issue
Documentación
Claridad
Necesita aclaración
Estado de actividad
Activo
Stack tecnológico
python

Línea de trabajo

Start with the LangGraphPlugin activity boundary and the workflow.execute_activity call described in the issue, then review how the Postgres-backed checkpointer and Temporal activity payload limits interact. The issue is complete when maintainers document a supported persistence pattern and clarify whether payload codecs, chunking, or streaming are appropriate for large state.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

We're running LangGraph-based agents through temporalio.contrib.langgraph.LangGraphPlugin, where each graph node executes as a Temporal Activity. The workflows accumulate state across many sequential (and some parallel) node executions — investigation-style agents with 20-30+ nodes producing tool-call transcripts, messages, etc.

Before wrapping these graphs with Temporal, we ran them directly against LangGraph's own Postgres-backed checkpointer, which persists automatically after every node (every "superstep"). Once we moved execution behind Temporal Activities, that per-node persistence went away — the nodes now run as separate Activities without a shared in-process checkpointer, so nothing writes to Postgres until we explicitly do so ourselves.

Our first attempt reintroduced persistence as a single workflow.execute_activity(persist_result_activity, args=[...full accumulated final state...]) call after ainvoke() returns, so it survives even if no client ever reconnects to read handle.result(). That worked for small/medium runs, but a larger run hit:

BadScheduleActivityAttributes: ScheduleActivityTaskCommandAttributes.Input exceeds size limit.

...which terminated the entire workflow server-side (WORKFLOW_EXECUTION_TERMINATED) — a total loss, worse than the gap we were trying to close. Individual per-node Activity payloads never approached the limit (29/29 node-Activities completed fine); only the one bulk end-of-run payload did.

Our leading fix: restore the original per-node persistence behavior — write each node's own delta to Postgres from inside that node's own Activity execution (no extra execute_activity hop needed, since the node body already runs as an Activity) — rather than shipping the whole accumulated state through Temporal in one call at the end. This keeps every persistence write proportional to a single node's output, matching what the pre-Temporal execution already did natively.

Questions for the maintainers:

  1. Is per-node/per-Activity persistence the idiomatic pattern here, or is there Temporal-native support for this (e.g. something LangGraphPlugin already offers) that we're missing?
  2. We also considered gzip-compressing the final payload before passing it as Activity args — it only raises the ceiling rather than removing it, and doesn't restore true mid-run durability (a crash just before that one activity still loses the whole run's state). Are there better-supported options for cases where a single large Activity payload is genuinely unavoidable (custom PayloadCodec, chunking, streaming activity results)?
  3. Any general guidance on structuring Activities around something like LangGraph, where per-node output sizes vary widely and the framework's own native checkpointing gets bypassed by the Activity boundary?
Lenguaje dominante
Python
Estrellas
1.2k
Forks
241
Merge medio
3 d 2 h
PR fusionados (30 d)
49

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