azure-dynamic-sessions-code-interpreter 1.0.0

Python client for Azure Container Apps dynamic code interpreter sessions (dynamic sessions API).

azure-dynamic-sessions-code-interpreter

Standalone Python client for Azure Container Apps dynamic (code interpreter) sessions. Use it to run sandboxed Python against your session pool from your own apps or LLM tooling, without pulling in LangChain.

Repository: github.com/Akindu23/azure-dynamic-sessions-code-interpreter-tool

Installation

pip install -U azure-dynamic-sessions-code-interpreter

Requires Python 3.10+.

Configuration

Create an Azure Container Apps session pool and copy its management endpoint. See Configure a pool in Azure Container Apps.

Authentication uses DefaultAzureCredential. If you use a user-assigned managed identity, set AZURE_CLIENT_ID to that identity’s client ID. Only Microsoft Entra ID tokens from a principal granted the Azure ContainerApps Session Executor role on the session pool may call the pool management API.

az role assignment create \
    --role "Azure ContainerApps Session Executor" \
    --assignee <PRINCIPAL_ID> \
    --scope <SESSION_POOL_RESOURCE_ID>

Usage

Import CodeInterpreterTool from the installed package (sources live under src/ in this repo):

from io import BytesIO

from azure_dynamic_sessions_code_interpreter import CodeInterpreterTool

POOL_MANAGEMENT_ENDPOINT = "https://<your-pool-management-endpoint>/"

tool = CodeInterpreterTool(pool_management_endpoint=POOL_MANAGEMENT_ENDPOINT)

# Run Python in the session (returns JSON with result / stdout / stderr)
result = tool.execute("print('Hello, world!')")

# Upload a file into the session
tool.upload_file(data=BytesIO(b"Hello, world!"), remote_file_path="hello.txt")

# List files visible to the session
files = tool.list_files()

# Download a file from the session
tool.download_file(remote_file_path="hello.txt")

Optional arguments include session_id (reuse a session), timeout_connect, timeout_read, max_retries, and sanitize_input. See the docstring on CodeInterpreterTool in core.py for details.

Azure OpenAI: tool definition, arguments, and tool results

CodeInterpreterTool includes helpers aligned with function calling for Chat Completions and the Responses API. They expect tool_call to be the object returned by the model (with .function / .arguments or .call_id, etc.), matching how the Azure OpenAI Python SDK structures tool calls—not a plain dict.

1. Tool definition (what you send to the model)

Chat Completions — wrap the function under type / function:

code_interpreter = CodeInterpreterTool(pool_management_endpoint=POOL_MANAGEMENT_ENDPOINT)

tools = [
    code_interpreter.get_tool_definition(is_chat_completions_api=True),
]
# Pass `tools` into your chat completion request.

Responses API — flatter tool shape:

code_interpreter = CodeInterpreterTool(pool_management_endpoint=POOL_MANAGEMENT_ENDPOINT)

available_tools = [
    code_interpreter.get_tool_definition(is_chat_completions_api=False),
]
# Pass `available_tools` into your responses request.

2. Extract Python code from the model’s tool call

After the model emits a tool call, parse the python_code argument:

is_chat_completions_api = True  # or False for Responses API

python_code = code_interpreter.get_tool_call_arguments(
    tool_call=tool_call_object_from_model,
    is_chat_completions_api=is_chat_completions_api,
)

3. Run the sandbox and format the result for the next model turn

Execute the code and get a message-shaped payload to append to the conversation (role tool for Chat Completions, or function_call_output for Responses):

tool_call_result = code_interpreter.execute_tool_call(
    tool_call=tool_call_object_from_model,
    python_code=python_code,
    is_chat_completions_api=is_chat_completions_api,
)
# Append `tool_call_result` to your messages / response inputs and call the model again.

4. Typical branch when the model picks code_interpreter

This mirrors a common orchestration pattern (names like output / logger are only illustrative):

if output_name == "code_interpreter":
    python_code = code_interpreter.get_tool_call_arguments(
        tool_call=output,
        is_chat_completions_api=is_chat_completions_api,
    )
    # e.g. log a short preview: python_code[:200]

    tool_call_result = code_interpreter.execute_tool_call(
        tool_call=output,
        python_code=python_code,
        is_chat_completions_api=is_chat_completions_api,
    )
    # feed `tool_call_result` back to Azure OpenAI

This library is aimed at Azure OpenAI (Chat Completions or Responses API) workflows; support for other providers is work in progress.

Third-party notices

Parts of this project are derived from langchain-azure (MIT). Full attribution and license text are in THIRD_PARTY_NOTICES.TXT.

License

This project is licensed under the MIT License — see LICENSE.