> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Azure container apps dynamic sessions integration

> Integrate with the Azure container apps dynamic sessions tool using LangChain JavaScript.

> [Azure Container Apps dynamic sessions](https://learn.microsoft.com/azure/container-apps/sessions) provide fast access to secure sandboxed environments that are ideal for running code or applications that require strong isolation from other workloads.

You can learn more about Azure Container Apps dynamic sessions and its code interpretation capabilities on [this page](https://learn.microsoft.com/azure/container-apps/sessions). If you don't have an Azure account, you can [create a free account](https://azure.microsoft.com/free/) to get started.

## Setup

You'll first need to install the [`@langchain/azure-dynamic-sessions`](https://www.npmjs.com/package/@langchain/azure-dynamic-sessions) package:

<Tip>
  See [this section for general instructions on installing LangChain packages](/oss/javascript/langchain/install).
</Tip>

```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm install @langchain/azure-dynamic-sessions @langchain/core
```

You'll also need to have a code interpreter session pool instance running. You can deploy a version using [Azure CLI](https://learn.microsoft.com/cli/azure/install-azure-cli) following [this guide](https://learn.microsoft.com/azure/container-apps/sessions-code-interpreter).

Once you have your instance running, you need to make sure you have properly [set up the Azure Entra authentication for it](https://learn.microsoft.com/azure/container-apps/sessions?tabs=azure-cli#authentication).

After you've added the role for your identity, you need to retrieve the **session pool management endpoint**. You can find it in the Azure Portal, under the "Overview" section of your instance. Then you need to set the following environment variable:

```bash .env example theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
AZURE_CONTAINER_APP_SESSION_POOL_MANAGEMENT_ENDPOINT=<your_endpoint>
```

## Usage example

Below is a simple example that creates a new Python code interpreter session, invoke the tool and prints the result.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { SessionsPythonREPLTool } from "@langchain/azure-dynamic-sessions";

const tool = new SessionsPythonREPLTool({
  poolManagementEndpoint:
    process.env.AZURE_CONTAINER_APP_SESSION_POOL_MANAGEMENT_ENDPOINT || "",
});

const result = await tool.invoke("print('Hello, World!')\n1+2");

console.log(result);

// {
//   stdout: "Hello, World!\n",
//   stderr: "",
//   result: 3,
// }
```

Here is a complete example where we use an Azure OpenAI chat model to call the Python code interpreter session tool to execute the code and get the result:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import type { ChatPromptTemplate } from "@langchain/core/prompts";
import { pull } from "@langchain/classic/hub";
import { AgentExecutor, createToolCallingAgent } from "@langchain/classic/agents";
import { SessionsPythonREPLTool } from "@langchain/azure-dynamic-sessions";
import { AzureChatOpenAI } from "@langchain/openai";

const tools = [
  new SessionsPythonREPLTool({
    poolManagementEndpoint:
      process.env.AZURE_CONTAINER_APP_SESSION_POOL_MANAGEMENT_ENDPOINT || "",
  }),
];

// Note: you need a model deployment that supports function calling,
// like `gpt-35-turbo` version `1106`.
const llm = new AzureChatOpenAI({
  temperature: 0,
});

// Get the prompt to use - you can modify this!
// If you want to see the prompt in full, you can at:
// https://smith.langchain.com/hub/jacob/tool-calling-agent
const prompt = await pull<ChatPromptTemplate>("jacob/tool-calling-agent");

const agent = await createToolCallingAgent({
  llm,
  tools,
  prompt,
});

const agentExecutor = new AgentExecutor({
  agent,
  tools,
});

const result = await agentExecutor.invoke({
  input:
    "Create a Python program that prints the Python version and return the result.",
});

console.log(result);
```

## Related

* Tool [conceptual guide](/oss/javascript/langchain/tools)
* Tool [how-to guides](/oss/javascript/langchain/tools)

***

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