{"slug": "building-a-book-recommendation-pipeline-with-langgraphs-mapreduce-pattern", "title": "Building a Book Recommendation Pipeline with LangGraph’s MapReduce Pattern", "summary": "LangGraph's MapReduce pattern, implemented via Send, enables dynamic parallel processing for LLM workflows, as demonstrated in a book recommendation pipeline where the number of recommendation steps is determined at runtime rather than fixed in advance.", "body_md": "Member-only story\n\n# Building a Book Recommendation Pipeline with LangGraph’s MapReduce Pattern\n\n**MapReduce** is a way to process a list of items and combine the results into a single output.\n\nThe idea has two steps:\n\n**Map**: Process each item in the list separately.** Reduce**: Combine all the individual results into one final result.\n\nThis pattern is commonly used in distributed systems, but it is also useful in LLM applications.\n\nIn an LLM workflow, the **map** step often means taking a list generated by one model call and running a separate model call (or API call) for each item. The **reduce** step then combines all those results with a final model call.\n\nIn **LangGraph**, this pattern is implemented using **Send**. In this article, I will build a simple **book recommendation workflow** to show *how **Send** distributes work across multiple parallel tasks and then combines the results into a final output.*\n\n## The Problem with a Fixed Graph\n\nA standard LangGraph graph has a **fixed** set of nodes and edges. This works well when the number of steps is known **in advance**. It becomes difficult when the number of steps depends on the data received at** runtime**.\n\nFor example — suppose a user asks for book recommendations on the **history of the Football World Cup**. The first LLM call may return 2 books, 4 books, or even 10. The graph doesn’t know this until that call finishes.", "url": "https://wpnews.pro/news/building-a-book-recommendation-pipeline-with-langgraphs-mapreduce-pattern", "canonical_source": "https://pub.towardsai.net/building-a-book-recommendation-pipeline-with-langgraphs-mapreduce-pattern-15869cc39094?source=rss----98111c9905da---4", "published_at": "2026-07-28 06:33:21+00:00", "updated_at": "2026-07-28 06:38:53.537296+00:00", "lang": "en", "topics": ["large-language-models", "ai-tools", "developer-tools"], "entities": ["LangGraph"], "alternates": {"html": "https://wpnews.pro/news/building-a-book-recommendation-pipeline-with-langgraphs-mapreduce-pattern", "markdown": "https://wpnews.pro/news/building-a-book-recommendation-pipeline-with-langgraphs-mapreduce-pattern.md", "text": "https://wpnews.pro/news/building-a-book-recommendation-pipeline-with-langgraphs-mapreduce-pattern.txt", "jsonld": "https://wpnews.pro/news/building-a-book-recommendation-pipeline-with-langgraphs-mapreduce-pattern.jsonld"}}