I’m experiencing an intermittent issue with state persistence in a 7-stage LangGraph pipeline where fields from later stages randomly disappear from the state. The LangGraph deployment is deployed in SaaS mode. LangGraph is hosting this deployment at a us.langgraph.app sub-domain.
Problem Description:
- Pipeline has 7 sequential stages
- State values from stages 1-4 persist reliably
- Fields output from stages 5-7 intermittently vanish from state
- Pattern: 5-10 consecutive API requests will be missing the later fields, then they’ll suddenly reappear
- No obvious trigger for when this occurs
Current Behavior:
Request 1-5: Has fields from stages 1-4 only
Request 6-10: Has fields from stages 1-4 only
Request 11: Suddenly has all fields from stages 1-7
Request 12-15: Has all fields from stages 1-7
Request 16-20: Back to stages 1-4 only
Environment:
requires-python = ">=3.11"
dependencies = [
"aiofiles>=24.1.0",
"langchain>=0.3.25",
"langchain-openai>=0.3.19",
"langgraph>=0.4.8",
"langgraph-supervisor>=0.0.27",
"pydantic>=2.0.0",
"python-dotenv>=1.1.0",
"types-aiofiles>=24.1.0.20250606",
]
Questions:
- Is this a known issue with state management in longer pipelines?
- Are there state size limits or memory pressure issues that could cause this?
- Any debugging approaches to identify why later stage outputs are being dropped?
The inconsistency makes it difficult to rely on the full pipeline output in API requests. I know it is only a problem that shows up when called by API. When viewed in the tracing project for the deployment, the model completes and populates the later stage fields. Any insights into potential causes or debugging strategies would be appreciated.