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@@ -15,24 +15,6 @@ You can extend this graph to orchestrate more complex agentic workflows that can
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## Getting Started
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1. Install the [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli/).
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```
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pip install --upgrade "langgraph-cli[inmem]"
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```
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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.
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```bash
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cp .env.example .env
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```
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3. If desired, add your LangSmith API key in your `.env` file.
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```
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LANGSMITH_API_KEY=lsv2...
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```
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<!--
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Setup instruction auto-generated by `langgraph template lock`. DO NOT EDIT MANUALLY.
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-->
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End setup instructions
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-->
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4. Install dependencies
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1. Install dependencies, along with the [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli/), which will be used to run the server.
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```bash
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cd path/to/your/app
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pip install -e .
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pip install "langgraph-cli[inmem]"
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pip install -e ."langgraph-cli[inmem]"
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```
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5. Customize the code as needed.
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6. Start the LangGraph Server.
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2. (Optional) Customize the code and project as needed. Create a `.env` file if you need to use secrets.
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```bash
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cp .env.example .env
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```
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If you want to enable LangSmith tracing, add your LangSmith API key to the `.env` file.
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