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Example use cases: Multi-source research, parallel analysis, concurrent data processing Steps inside a Parallel block run concurrently. Their outputs are aggregated in the configured step order and passed to the next step in the workflow. Workflows parallel steps diagram Workflows parallel steps diagram

Example

parallel_workflow.py

Handling Session State Data in Parallel Steps

Custom Python functions can accept a run_context parameter and update run_context.session_state. Parallel branches share that session-state dictionary. Coordinate writes to the same keys, or assign separate keys to each branch, to avoid races.

Developer Resources

Reference

For complete API documentation, see Parallel Steps Reference.