From ed09a77d9f88621ccf49cd6706ec0f891aa1e602 Mon Sep 17 00:00:00 2001
From: Harsh Nevse <110392719+HarshNevse@users.noreply.github.com>
Date: Wed, 29 Jan 2025 22:18:54 +0530
Subject: [PATCH] Update multi-agent-collaboration.ipynb (#3233)

grammar
---
 docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb b/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb
index 225e56b22..6bdaf978e 100644
--- a/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb
+++ b/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb
@@ -14,7 +14,7 @@
     "\n",
     "A single agent can usually operate effectively using a handful of tools within a single domain, but even using powerful models like `gpt-4`, it can be less effective at using many tools. \n",
     "\n",
-    "One way to approach complicated tasks is through a \"divide-and-conquer\" approach: create an specialized agent for each task or domain and route tasks to the correct \"expert\". This is an example of a [multi-agent network](https://langchain-ai.github.io/langgraph/concepts/multi_agent/#network) architecture.\n",
+    "One way to approach complicated tasks is through a \"divide-and-conquer\" approach: create a specialized agent for each task or domain and route tasks to the correct \"expert\". This is an example of a [multi-agent network](https://langchain-ai.github.io/langgraph/concepts/multi_agent/#network) architecture.\n",
     "\n",
     "This notebook (inspired by the paper [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://arxiv.org/abs/2308.08155), by Wu, et. al.) shows one way to do this using LangGraph.\n",
     "\n",