research-to-article

Automate research-to-article workflows by launching five parallel research agents to collect data, compiling findings into a story arc, and generating a narrative article with voice-first delivery and deterministic validation.

A multi-agent pipeline for voice content generation coordinates parallel agents to gather research, structures the data into a narrative, and applies deterministic validation to produce voice-authentic articles.

Compile multi-source research by orchestrating parallel agents that collect data, then aggregate the findings into a coherent document with a structured story arc before generating the final article.

Yes, the research-to-article pipeline gracefully degrades when parallel agents timeout or return incomplete data, ensuring the workflow continues to compile available findings and generate the article.

Yes, you can validate generated articles deterministically using voice_validator.py to check the voice-authentic output before saving the final artifacts to disk.