How does Atlas handle outdated Polars stack traces?

Atlas validates stack trace offsets against the current file. If a trace from an older Polars build points to an 'Offset is out of range for this file', Atlas will loudly fail and prompt you to re-read the file from the top, ensuring you're always looking at the correct code in your Polars project.

Can Atlas help optimize Polars `LazyFrame` performance?

Yes, Atlas can help optimize Polars `LazyFrame` performance. You can ask Atlas to convert `scan_csv` plus filter operations into a lazy chain to enable `predicate pushdown`. Atlas can also print `explain()` on your query plan, showing how projections are pruned before and after optimizations, providing clear insights into performance gains.

What Polars testing tools does Atlas integrate with?

Atlas integrates with `pytest` for testing Polars code. Specifically, it supports adding regression tests that use `assert_frame_equal` to validate data frame equality after a bug fix. Atlas will prompt for permission before running `pytest` to ensure control.

How does Atlas ensure code quality for Polars fixes?

Atlas ensures code quality by integrating with `ruff format`. After an `edit` to fix a Polars bug, Atlas can automatically format the changes and present a unified diff for your approval. This maintains consistent code style across your Polars codebase.

How does Atlas understand Polars' `expression API`?

Atlas builds its code index using AST declarations via tree-sitter, not blind line windows. This allows it to understand Polars' `LazyFrame` chains, `expression contexts`, and where each `.collect()` call actually materializes data, providing a deep semantic understanding of your Polars code.

Is it safe to let Atlas modify my Polars codebase?

Yes, it is safe. Atlas operates with strict permission controls. It drafts a plan in a read-only agent and asks for approval before making any changes. Every `edit` operation results in a unified diff that you must approve before Atlas writes to your Polars files. File changes are also snapshotted as git patches for easy rollback.

How does Atlas use `pyproject.toml` in a Polars project?

Atlas reads your Polars project's `pyproject.toml` file to understand pinned dependencies, including the Polars version. This allows Atlas to build an accurate code index and ensure its analysis and proposed fixes are relevant to your specific Polars environment, integrating with `uv` for package management.

Trace a runtime bug from a stack trace in Polars with Atlas in 2026

In 2026, Polars developers can trace a runtime bug from a production stack trace to a precise fix without a debugger attached, using Atlas. Atlas reads each frame, greps for error messages, and uses LSP to find callers, enabling rapid resolution and the addition of `pytest (assert_frame_equal)` regression tests, all while managing dependencies with `uv` and formatting code with `ruff format`.

How Atlas traces Polars runtime bugs from a stack trace

Tracing a runtime bug in Polars from a stack trace with Atlas in 2026 involves a precise, multi-step process that leverages Atlas's deep code understanding. Atlas begins by consuming the stack trace, which is a list of file:line pairs, and then uses its `read` tool to examine each frame at its reported offset.

Atlas is designed to consume a production stack trace directly, treating it as a series of `file:line` pairs. For Polars codebases, this means Atlas will `read` each frame at its specified offset, understanding the context of `LazyFrame` chains and expression contexts. If a trace originates from an older build, Atlas will loudly report that an 'Offset <n> is out of range for this file', preventing misdiagnosis by prompting a re-read from the top of the file. This ensures that the line numbers correspond to the current codebase. Following the initial read, Atlas employs its `grep` tool to search for the exact error message string. This often reveals where the error message is constructed, providing more insight than just the top frame of the stack trace. For instance, if a Polars `scan_csv` operation fails due to malformed data, `grep` can pinpoint the specific validation logic. Finally, Atlas utilizes its `lsp` tool's `findReferences` operation on the identified failing function. This allows Polars developers to see all callers that could potentially reach the problematic function with the bad input, helping to understand the data flow through `LazyFrame` transformations before a `collect()` call materializes the result.

Concrete commands and files for Polars bug tracing with Atlas

Working with Atlas to trace Polars bugs involves specific commands and file interactions, ensuring a robust and reproducible workflow in 2026. The process starts by ensuring your Polars project's `pyproject.toml` file correctly pins the Polars version, which Atlas reads to understand your environment and dependencies.

To effectively trace Polars bugs, Atlas integrates directly with your project's configuration. The `pyproject.toml` file is crucial, as it pins your Polars version and other dependencies, which Atlas uses to build its code index. When you paste a stack trace, Atlas's `read` tool will access the specified Polars source files. For example, if a trace points to an issue within a `LazyFrame` transformation, Atlas will read that specific Python file. After identifying the problematic area, Atlas's `grep` tool can be used with specific error strings, such as those related to `scan_csv` or `filter` operations, to locate the exact code constructing the error. The `lsp` tool then leverages language server capabilities to perform `findReferences` on Polars functions, helping to trace data flow through complex expression APIs. When a fix is drafted using Atlas's `edit` tool, it's common practice to add a regression test. This test will use `pytest` and `assert_frame_equal` to validate the fix, ensuring the bug cannot recur silently. Before committing, Atlas can run `ruff format` to ensure the new code adheres to your project's style guidelines, and it will present a unified diff for approval.

Ensuring safety and review in Polars bug fixes with Atlas

Atlas prioritizes safety and review throughout the Polars bug-fixing process, providing multiple checkpoints before any code changes are finalized in 2026. Every Atlas tool call, including `read`, `grep`, `lsp`, and `edit`, is permission-gated against allow, ask, and deny rules, giving developers full control over agent actions.

When Atlas assists in fixing a Polars bug, safety is paramount. Before any action, Atlas drafts a plan in a read-only plan agent and explicitly asks for approval before switching to a build agent that can make changes. This ensures that Polars developers understand the proposed steps, such as using `lsp` to analyze `LazyFrame` chains or `edit` to modify a `scan_csv` call. All tool calls are permission-gated, meaning Atlas will ask for explicit permission before running `pytest` or `ruff format`. Once Atlas proposes an `edit` to fix a Polars bug, it computes a unified diff for every file change. This diff is surfaced for approval, allowing developers to review the exact modifications to their Polars code, including new `pytest (assert_frame_equal)` tests. Atlas also snapshots file changes as git patches, enabling easy diffing and rolling back of edits if necessary. This robust review process ensures that any fix, whether it's adjusting a `predicate pushdown` or refining an `expression API` usage, is thoroughly vetted before being applied to the codebase.

Optimizing Polars query plans with Atlas's insights

Atlas provides valuable insights into Polars query plans, helping developers optimize `LazyFrame` operations and understand `predicate pushdown` in 2026. Atlas can be asked to convert a `scan_csv` plus filter into a lazy chain, ensuring predicate pushdown reaches the reader for improved performance.

Beyond just tracing bugs, Atlas helps Polars developers optimize their code. The `expression API` and lazy query optimizer are core to Polars' performance, and Atlas understands how these work. Developers can ask Atlas to convert eager operations, like a `scan_csv` followed by an immediate filter, into a lazy chain. This ensures that `predicate pushdown` can be applied, allowing the filter to be executed as early as possible, often directly at the data source, which significantly reduces memory usage and processing time. Atlas can also print the `explain()` output on a Polars query plan, showing which projections got pruned both before and after optimizations. This visual representation helps developers understand the impact of their `LazyFrame` transformations and identify areas for further optimization, ensuring that `collect()` calls only materialize necessary data. Atlas's ability to read and interpret these complex Polars constructs makes it an invaluable tool for performance tuning.

Step by step

01Paste the production Polars stack trace into Atlas to initiate the debugging process.
02Allow Atlas to `read` each `file:line` frame from the Polars stack trace. If Atlas reports 'Offset <n> is out of range for this file', re-read the file from the top to ensure line numbers are current for your Polars codebase.
03Instruct Atlas to `grep` for the specific error message string found in the Polars stack trace. This often reveals the construction point of the error, providing more context than the top frame alone, especially for `LazyFrame` or `expression API` issues.
04Use Atlas's `lsp` tool with the `findReferences` operation on the identified failing Polars function. This helps trace which callers can reach it with the bad input, particularly useful for understanding data flow before a `collect()` call.
05Approve Atlas's plan to `edit` the Polars source code to implement the fix. Atlas will present a unified diff for review.
06Have Atlas add a regression test using `pytest` and `assert_frame_equal` to prevent the Polars bug from recurring silently. Atlas will prompt for permission before running `pytest`.
07Allow Atlas to format the modified Polars code and the new test with `ruff format` to maintain code style consistency.
08Review and approve the final changes, including the Polars code fix and the new `pytest (assert_frame_equal)` test, presented as a git diff by Atlas.

Frequently asked questions

How does Atlas handle outdated Polars stack traces?: Atlas validates stack trace offsets against the current file. If a trace from an older Polars build points to an 'Offset <n> is out of range for this file', Atlas will loudly fail and prompt you to re-read the file from the top, ensuring you're always looking at the correct code in your Polars project.
Can Atlas help optimize Polars `LazyFrame` performance?: Yes, Atlas can help optimize Polars `LazyFrame` performance. You can ask Atlas to convert `scan_csv` plus filter operations into a lazy chain to enable `predicate pushdown`. Atlas can also print `explain()` on your query plan, showing how projections are pruned before and after optimizations, providing clear insights into performance gains.
What Polars testing tools does Atlas integrate with?: Atlas integrates with `pytest` for testing Polars code. Specifically, it supports adding regression tests that use `assert_frame_equal` to validate data frame equality after a bug fix. Atlas will prompt for permission before running `pytest` to ensure control.
How does Atlas ensure code quality for Polars fixes?: Atlas ensures code quality by integrating with `ruff format`. After an `edit` to fix a Polars bug, Atlas can automatically format the changes and present a unified diff for your approval. This maintains consistent code style across your Polars codebase.
How does Atlas understand Polars' `expression API`?: Atlas builds its code index using AST declarations via tree-sitter, not blind line windows. This allows it to understand Polars' `LazyFrame` chains, `expression contexts`, and where each `.collect()` call actually materializes data, providing a deep semantic understanding of your Polars code.
Is it safe to let Atlas modify my Polars codebase?: Yes, it is safe. Atlas operates with strict permission controls. It drafts a plan in a read-only agent and asks for approval before making any changes. Every `edit` operation results in a unified diff that you must approve before Atlas writes to your Polars files. File changes are also snapshotted as git patches for easy rollback.
How does Atlas use `pyproject.toml` in a Polars project?: Atlas reads your Polars project's `pyproject.toml` file to understand pinned dependencies, including the Polars version. This allows Atlas to build an accurate code index and ensure its analysis and proposed fixes are relevant to your specific Polars environment, integrating with `uv` for package management.

Try Atlas in your terminal

The terminal-native AI coding agent. Free core, single binary.

Install Atlas

Related guides

Trace a Runtime Bug from a Stack Trace with Atlas in 2026

How to trace a runtime bug from a stack trace with Atlas in 2026: read each frame at its offset, grep for the error string, and use the lsp tool to find callers.

Atlas for Polars: Terminal-Native AI Coding in 2026

Atlas is a terminal-native AI coding agent for Polars. Build LazyFrame chains, push scan_csv predicates into the reader, and read explain() plans in 2026.

Automate GitHub Issue and Pull Request Triage in Polars with Atlas in 2026

Streamline GitHub issue and pull request triage for your Polars projects using Atlas. Safely automate responses, code formatting with ruff format, and testing with pytest (assert_frame_equal) for trusted users.

Onboard to an Unfamiliar Codebase in Polars with Atlas in 2026

Onboard to unfamiliar Polars codebases in 2026 with Atlas. Build a mental model fast using semantic search, explore subagents, and review diffs for LazyFrame chains and pytest.

Review a Polars Pull Request with Atlas in 2026

In 2026, Atlas helps Polars developers review pull requests by providing deep context beyond the diff. Catch subtle bugs in LazyFrame chains, validate `pytest (assert_frame_equal)` results, and ensure `ruff format`

Extract a Shared Helper from Duplicated Polars Code with Atlas in 2026

In 2026, Polars developers use Atlas to find and refactor duplicated logic into shared helpers. Leverage semantic search, pytest (assert_frame_equal), and ruff format for robust code.

Diagnose a hanging or long-running Polars command with Atlas in 2026

Quickly diagnose hanging Polars scripts or builds with Atlas. Determine if your `uv` or `pytest` commands are genuinely slow or silently blocked on input, and get unstuck efficiently in 2026.

Run Atlas Headless in CI for Polars Projects in 2026

Automate Atlas in your Polars CI/CD pipelines. Get machine-readable output for non-interactive sessions, integrate with `pytest` and `uv`, and ensure code quality with `ruff format` in 2026.

Browse this resource hub