Feature Discussion: Code Generation + Execution

[taylorn-ai]

I'd like to suggest an enhancement to this project: supporting code generation and execution using Azure OpenAI and Azure Container Apps dynamic sessions.

Use Case
In many scenarios, especially technical or data-heavy contexts, it's useful to go beyond just generating code. Allowing users to execute the generated code (e.g. Python) inline and return results would open up more interactive and practical use cases - such as data analysis, file processing, or visualization. I have a basic working example, but it needs some work.

Potential Approach

1. Use an agent-based loop (LangChain or similar) to generate code.
2. Offload execution to a secure, ephemeral environment using Azure Container Apps session pools.
3. Return the result, including stdout, stderr, and any generated files or images, back to the UI.

Benefits

• Adds practical utility to LLM responses.
• Keeps code execution isolated and secure.
• Enables building intelligent agents that can reason and act on data.

Would this be in scope for the project? If so, I’d be interested in discussing implementation options or contributing a prototype.