Update README

README.md

@@ -9,7 +9,7 @@
 
 ## Overview
 
-[LangGraph.js](https://langchain-ai.github.io/langgraphjs/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Built on top of [LangChain.js](https://github.com/langchain-ai/langchainjs), it offers these core benefits compared to other LLM frameworks: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
+[LangGraph.js](https://langchain-ai.github.io/langgraphjs/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
 
 LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
 
@@ -19,7 +19,7 @@ LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [A
 - **Persistence**: Automatically save state after each step in the graph. Pause and resume the graph execution at any point to support error recovery, human-in-the-loop workflows, time travel and more.
 - **Human-in-the-Loop**: Interrupt graph execution to approve or edit next action planned by the agent.
 - **Streaming Support**: Stream outputs as they are produced by each node (including token streaming).
-- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain](https://github.com/langchain-ai/langchainjs/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
+- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain.js](https://github.com/langchain-ai/langchainjs/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
 
 ## Installation
 

libs/langgraph/README.md

@@ -9,7 +9,7 @@
 
 ## Overview
 
-[LangGraph.js](https://langchain-ai.github.io/langgraphjs/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Built on top of [LangChain.js](https://github.com/langchain-ai/langchainjs), it offers these core benefits compared to other LLM frameworks: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
+[LangGraph.js](https://langchain-ai.github.io/langgraphjs/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
 
 LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
 
@@ -19,7 +19,7 @@ LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [A
 - **Persistence**: Automatically save state after each step in the graph. Pause and resume the graph execution at any point to support error recovery, human-in-the-loop workflows, time travel and more.
 - **Human-in-the-Loop**: Interrupt graph execution to approve or edit next action planned by the agent.
 - **Streaming Support**: Stream outputs as they are produced by each node (including token streaming).
-- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain](https://github.com/langchain-ai/langchainjs/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
+- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain.js](https://github.com/langchain-ai/langchainjs/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
 
 ## Installation