Update description

.env.example

@@ -1,9 +1,4 @@
 # To separate your traces from other application
 LANGSMITH_PROJECT=new-agent
 
-# The following depend on your selected configuration
-
-## LLM choice:
-ANTHROPIC_API_KEY=....
-FIREWORKS_API_KEY=...
-OPENAI_API_KEY=...
+# Add API keys for connecting to LLM providers, data sources, and other integrations here

README.md

@@ -9,9 +9,9 @@ This template demonstrates a simple application implemented using [LangGraph](ht
   <img src="./static/studio_ui.png" alt="Graph view in LangGraph studio UI" width="75%" />
 </div>
 
-The core logic defined in `src/agent/graph.py`, showcases a straightforward application that responds with a fixed string and the configuration provided.
+The core logic defined in `src/agent/graph.py`, showcases an single-step application that responds with a fixed string and the configuration provided.
 
-This template provides a basic foundation for a LangGraph application that can be extended to create more complex agents.
+You can extend this graph to orchestrate more complex agentic workflows that can be visualized and debugged in LangGraph Studio.
 
 ## Getting Started
 
@@ -21,7 +21,7 @@ This template provides a basic foundation for a LangGraph application that can b
 pip install --upgrade "langgraph-cli[inmem]"
 ```
 
-2. Create a `.env` file.
+2. Create a `.env` file. While this starter app does not require any secrets, if you later decide to connect to LLM providers and other integrations, you will likely need to provide API keys.
 
 ```bash
 cp .env.example .env
@@ -46,6 +46,7 @@ End setup instructions
 ```bash
 cd path/to/your/app
 pip install -e .
+pip install "langgraph-cli[inmem]"
 ```
 
 5. Customize the code as needed.
@@ -59,20 +60,15 @@ For more information on getting started with LangGraph Server, [see here](https:
 
 ## How to customize
 
-1. **Define a configuration**: Create a `configuration.py` file and define a configuration schema. For example, in a chatbot application you may want to define a dynamic system prompt or LLM to use. For more information on configurations in LangGraph, [see here](https://langchain-ai.github.io/langgraph/concepts/low_level/?h=configuration#configuration).
+1. **Define configurable parameters**: Modify the `Configuration` class in the `graph.py` file to expose the arguments you want to configure. For example, in a chatbot application you may want to define a dynamic system prompt or LLM to use. For more information on configurations in LangGraph, [see here](https://langchain-ai.github.io/langgraph/concepts/low_level/?h=configuration#configuration).
+
 2. **Extend the graph**: The core logic of the application is defined in [graph.py](./src/agent/graph.py). You can modify this file to add new nodes, edges, or change the flow of information.
 
-You can also quickly extend this template by:
-
-- Adding custom tools or functions to enhance the chatbot's capabilities.
-- Implementing additional logic for handling specific types of user queries or tasks.
-- Integrating external APIs or databases to provide more dynamic responses.
-
 ## Development
 
-While iterating on your graph, you can edit past state and rerun your app from previous states to debug specific nodes. Local changes will be automatically applied via hot reload.
+While iterating on your graph in LangGraph Studio, you can edit past state and rerun your app from previous states to debug specific nodes. Local changes will be automatically applied via hot reload.
 
-Follow-up requests will be appended to the same thread. You can create an entirely new thread, clearing previous history, using the `+` button in the top right.
+Follow-up requests extend the same thread. You can create an entirely new thread, clearing previous history, using the `+` button in the top right.
 
 For more advanced features and examples, refer to the [LangGraph documentation](https://langchain-ai.github.io/langgraph/). These resources can help you adapt this template for your specific use case and build more sophisticated conversational agents.