### Answer
In rigid mode, the agent only processes the active node's prompt rather than compiling all nodes into a single prompt [(1) ](
) . This is fundamentally different from flex mode, where all node instructions, transitions, and tool descriptions are compiled into one prompt, which can significantly increase token count and LLM costs when flows exceed 3,500 tokens .
Since rigid mode sends only the current node's context to the LLM, having 250+ nodes shouldn't inherently overwhelm the system from a prompt-size perspective. The agent processes nodes sequentially, not all at once .
However, the complexity of your workflow could still contribute to issues:
Potential factors with large rigid-mode flows:
-
Transition logic complexity - With 250+ nodes, there are many potential transition paths that need to be evaluated
-
State management - The system needs to track which node is active and evaluate transition conditions
-
Network/processing overhead - More nodes mean more potential points where timing or execution issues could occur
The documented silence issues after function nodes [(2) ](
) and transition problems don't specifically mention node count as a factor. The sources focus more on:
- Function schema issues
- Transition condition precision
- Node instruction complexity
- LLM model selection
If you suspect the large workflow is causing problems, consider breaking it into smaller components or using modular conversation flows where appropriate. This aligns with the recommendation to split complex nodes for better consistency [(1) ](
) .
Without specific documentation on node count limits for rigid mode, I'd recommend t