Tools extend Noema beyond plain text generation. Arm the ones you need for the current chat, let the model call them when they improve accuracy, and review the returned output before relying on the result.
Best when the assistant needs current news, fresh references, or recent web content.
Best for calculations, statistics, parsing, algorithms, and other tasks where running code is more reliable than mental estimation.
When a tool is armed, Noema can decide mid-conversation to call it. The result flows back into the model so the final answer can include live information, computed values, or structured execution output.
Current web information and source-backed answers.
Sandboxed Python execution for computational tasks.
Use the Python tool when code execution will improve accuracy or save time, especially for calculations, statistics, parsing, data processing, algorithms, or other computational tasks.
The setting is split into a global toggle in Settings and a per-chat armed state in the composer or tool menu.
The Python tool exposes a single callable action: noema.python.execute.
It accepts one argument: code, which is Python 3 source code to run.
The model should use print() for anything it wants returned in the output.
30-second timeout.In practice, networking, subprocess spawning, and filesystem access outside the temporary sandbox are blocked. The tool is intended for controlled computation, not general system automation.
Python execution returns structured output including stdout, stderr, exitCode, executionTimeMs, timedOut, and an optional error. That lets the model inspect the result and use it in the final answer.
MLX models do not expose the Python tool because tool calling is treated as unreliable there.Tell users to enable Python when they want the assistant to verify calculations or perform structured data work, and to leave it off for normal conversational prompts where code execution is unnecessary.