Fix Typo in open-source-llms-as-agents (huggingface#1771)

Author: freddyaboulton

open-source-llms-as-agents.md

@@ -35,7 +35,7 @@ In this post, we explain the inner workings of ReAct agents, then show how to bu
 
 The definition of LLM agents is quite broad: LLM agents are all systems that use LLMs as their engine and can perform actions on their environment based on observations. They can use several iterations of the Perception ⇒ Reflexion ⇒ Action cycle to achieve their task and are often augmented with planning or knowledge management systems to enhance their performance. You can find a good review of the Agents landscape in [Xi et al., 2023](https://huggingface.co/papers/2309.07864).
 
-Today, we are focusing on **ReAct agents**. [ReAct](https://huggingface.co/papers/2210.03629) is an approch to building agents based on the concatenation of two words, "**Reasoning**" and "**Acting**." In the prompt, we describe the model, which tools it can use, and ask it to think “step by step” (also called [Chain-of-Thought](https://huggingface.co/papers/2201.11903) behavior) to plan and execute its next actions to reach the final answer. 
+Today, we are focusing on **ReAct agents**. [ReAct](https://huggingface.co/papers/2210.03629) is an approach to building agents based on the concatenation of two words, "**Reasoning**" and "**Acting**." In the prompt, we describe the model, which tools it can use, and ask it to think “step by step” (also called [Chain-of-Thought](https://huggingface.co/papers/2201.11903) behavior) to plan and execute its next actions to reach the final answer. 
 
 <p align="center">
     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/open-source-llms-as-agents/ReAct.png" alt="drawing" width=90%>