From 73bed2cf7c57d0e9d8b9278645dd77e0f3f65966 Mon Sep 17 00:00:00 2001
From: Rauf Parchiev <54438900+J0hnArren@users.noreply.github.com>
Date: Tue, 24 Jun 2025 02:54:45 +0300
Subject: [PATCH] Update workflows.md (#5116)

---
 docs/docs/tutorials/workflows.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/docs/tutorials/workflows.md b/docs/docs/tutorials/workflows.md
index 8099a3b92..8be5cf07b 100644
--- a/docs/docs/tutorials/workflows.md
+++ b/docs/docs/tutorials/workflows.md
@@ -648,7 +648,7 @@ With orchestrator-worker, an orchestrator breaks down a task and delegates each
     Because orchestrator-worker workflows are common, LangGraph **has the `Send` API to support this**. It lets you dynamically create worker nodes and send each one a specific input. Each worker has its own state, and all worker outputs are written to a *shared state key* that is accessible to the orchestrator graph. This gives the orchestrator access to all worker output and allows it to synthesize them into a final output. As you can see below, we iterate over a list of sections and `Send` each to a worker node. See further documentation [here](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/) and [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#send).
 
     ```python
-    from langgraph.constants import Send
+    from langgraph.types import Send
 
 
     # Graph state