To audit a Python repo with parallel subagents, Atlas splits the sweep into independent slices and launches one task per slice, so each subagent reads your pyproject.toml, your packages, and your pytest suite in its own session. Only conclusions come back to the main session, so a repo-wide audit never blows the context window. The explore subagent is deny-by-default and read-only, which makes it the right choice when nothing in the Python source should change.
How do I audit a whole Python repo without running out of context?
Atlas audits a Python repo by launching subagents through the task tool, each in its own session, so the file dumps from src/, tests/, and pyproject.toml never enter your context. Only their conclusions come back, which is what makes a repo-wide sweep in 2026 practical.
A Python monorepo with dozens of packages will not fit in one window, and reading it linearly wastes the budget on files that turn out irrelevant. Atlas fans out work to subagents that can run in the foreground or in parallel background sessions, so the audit is split by directory, by package, or by rule. One subagent takes src/api/, another takes src/workers/, another takes the tests/ tree that pytest collects. Each returns a short finding list rather than the source it read, and the main session stays small enough to reason about the merged result.
Which Atlas subagent type should audit Python source, explore or general?
Atlas offers 2 subagent types for an audit, and explore is the right one for Python source. The explore subagent is deny-by-default and read-only, so it cannot edit a module or mutate a virtualenv while it looks. Choose general only when the subagent must also run commands, such as invoking pytest or uv during the sweep.
The distinction is a permission boundary, not a style preference. Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, and the explore subagent's deny-by-default set means an audit of your Python packages cannot become an unrequested refactor. When the audit genuinely needs to execute something, for example running pytest on a suspicious test module or checking a dependency with uv, subagent_type general is the one that can act. Picking explore where possible keeps a large parallel fan-out safe by construction.
How do I slice a Python audit so subagents do not overlap?
Split a Python audit into independent slices before launching anything: 1 slice per top-level package under src/, 1 per Django app or FastAPI router, or 1 per rule you are checking. Atlas then launches exactly 1 task per slice, issued together so they run concurrently and never duplicate each other's reading.
Overlap is the main way parallel audits waste money and produce contradictory findings. Natural Python boundaries make good slices: the directories listed in pyproject.toml, the app packages of a Django project, the router modules of a FastAPI service, or the test packages that pytest collects. Rule-based slices work too, for example one subagent hunting bare except blocks, another hunting missing type hints, another hunting modules that ruff format would rewrite. Issue the task calls together so they run concurrently rather than one after another, which is what turns a long serial sweep into a single parallel pass.
What happens when a Python audit subagent fails or is cancelled?
An Atlas subagent auditing a Python package produces 1 of 2 visible outcomes when it does not finish: the task tool surfaces the child's error text verbatim, or it returns Task cancelled. A failed slice of your src/ tree is therefore re-runnable rather than silently missing from the merged findings.
Verbatim error text matters in Python because the failure is usually informative on its own: an import error from a missing package, a pytest collection failure, a module that the subagent could not read. Because the task tool passes the child's error through rather than summarizing it, the main session sees the real traceback text and can decide to re-launch just that slice. Atlas fans out work to subagents that can run in the foreground or in parallel background sessions, so re-running one failed package while the others complete is a normal move, not a restart.
How do I turn parallel audit findings into fixes in a Python codebase?
Atlas merges every subagent's findings into 1 todowrite list, then fixes them in the main session with edit. Each fix in a Python module lands as a unified diff Atlas surfaces for approval before writing, and a pytest run plus ruff format at the end confirm the repository is still healthy.
The merge step is where a parallel audit becomes useful work. Each subagent's conclusions collapse into a single todowrite list in the main session, ordered however you like: by severity, by package, by file. Fixes are applied with edit, and Atlas computes a unified diff for every file edit and surfaces it for approval before writing, so a change to a Django model or a FastAPI dependency is reviewed rather than assumed. Running pytest after each batch and ruff format at the end keeps the audit's output as a clean, reviewable diff rather than a sprawl of half-applied changes.
Step by step
- 01Run atlas in a repo with a pyproject.toml or requirements.txt and let Atlas read your package layout, virtualenv, and installed dependencies.
- 02Split the audit into independent slices by directory, by package, or by rule so the subagents do not overlap.
- 03Launch one task per slice with subagent_type explore for a read-only sweep, since explore is deny-by-default and cannot edit your Python modules.
- 04Use subagent_type general only for slices that must run commands, for example invoking pytest or checking a dependency with uv.
- 05Issue the task calls together so they run concurrently rather than one after another.
- 06Collect each subagent's final message; the task tool surfaces the child's error text verbatim if it fails, and Task cancelled if it was cancelled.
- 07Merge the findings into one todowrite list in the main session.
- 08Fix each item with edit, approving the unified diff, then run pytest and ruff format before committing.
Frequently asked questions
- how to audit a large python codebase with an AI agent without hitting context limits
- Launch Atlas subagents through the task tool, one per slice of the repo. Each subagent reads your Python packages in its own session and returns only conclusions, so the source never lands in your main context window.
- what is the difference between the explore and general subagent in atlas
- The explore subagent is deny-by-default and read-only, so it can sweep your Python source without changing it. The general subagent can act, which you need only when a slice must run something like pytest or uv.
- can atlas run multiple subagents at the same time
- Yes. Atlas fans out work to subagents that can run in the foreground or in parallel background sessions. Issue the task calls together and the slices of your Python audit run concurrently rather than one after another.
- how do i know if an atlas subagent failed during a repo audit
- The task tool surfaces the child's error text verbatim when a subagent fails, and returns Task cancelled if it was cancelled. A Python import error or a pytest collection failure comes back as real text you can act on.
- how do i fix everything an atlas audit found in a python repo
- Merge the subagents' findings into one todowrite list, then fix them in the main session with edit. Atlas computes a unified diff for every file edit and surfaces it for approval before writing.
- does atlas need a pyproject.toml to work on python projects
- Run atlas in a repo with a pyproject.toml or requirements.txt. Atlas reads your package layout, virtualenv, and installed dependencies, then can add type hints, write pytest cases, or refactor a module.
- can atlas keep my python code off third-party servers during an audit
- Atlas can build its code index with local Ollama embeddings, keeping code off third-party servers, which matters when subagents are sweeping a private Python repository.
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