Prefer reading instead? The full article is available here. The podcast is also available on Spotify and Apple Podcasts. Subscribe to keep up with the latest drops.
Agent systems break down when simple workflows evolve into tangled 30+ node graphs with unclear dependencies and sequential bottlenecks. In this episode, we explore how to scale LangGraph architectures through strategic parallelization, modular subgraphs, and dynamic task distribution. You’ll learn:
When to use parallel execution vs. sequential flows and how to manage concurrent state updates with reducers?
How to structure multi-agent systems using subgraphs with either shared or isolated states?
When dynamic map-reduce patterns outperform static parallelization for variable workloads
If you’d rather read than listen, the full article (with diagrams, code examples, and implementation details) is available on Substack:
Scaling LangGraph Agents: Parallelization, Subgraphs, and Map-Reduce Trade-Offs
This article is also available as a podcast! If you’re on the go or just want to absorb the content in audio format, you can listen to the full episode below 👇
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