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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

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.