> ## Documentation Index > Fetch the complete documentation index at: https://docs.getcara.ai/llms.txt > Use this file to discover all available pages before exploring further. # Code Execution > Run Python inside a workspace to compute, transform, and verify data Some tasks — reconciling a schedule of locations, checking premium math on a proposal, reshaping an exported spreadsheet — are more reliably done in code than in natural language. Cara can execute Python inside a sandboxed environment attached to your workspace and use the result to continue the task. Code execution is always visible. Every run appears in the workspace's **Execution** tab with the exact code, inputs, stdout, stderr, and any files produced. ## When Cara uses it Cara reaches for code execution whenever a task benefits from deterministic computation, including: * **Tabular reshaping** — pivoting a schedule of locations, joining two spreadsheets, deduping rows, normalizing carrier names * **Verification** — recomputing a premium sum, checking that TIV rolls up correctly, confirming that ACORD 125 totals match the underlying schedule * **File conversions** — `.xlsx` → `.csv`, extracting specific sheets or columns, filtering by date * **Small parsers** — pulling structured data out of an unusual PDF layout or a text dump You can also ask directly: "Use code execution to..." or "Run Python to check that...". Cara will draft the code, execute it, and quote the result back to you. ## What it can access The sandbox has read access to any file you've attached to the workspace and any artifact Cara has generated in that workspace. It does **not** have network access, and it does not have direct access to your integrations or knowledge base — Cara pulls that data via her regular tools first, then hands it to the sandbox. Cara treats code-execution output as evidence, not as truth: she still narrates her reasoning in the **Messages** tab and cites the specific run when relevant. ## Reviewing a run Open the **Execution** tab and click any code-execution step to expand it. You'll see: * The Python source that ran * The input files referenced * `stdout` and `stderr` * Any new files produced (they'll also show up in the **Artifacts** tab) If a run failed, Cara automatically retries with a corrected version and keeps both attempts in the history.