# Onboard to an Unfamiliar Codebase in TensorFlow with Atlas in 2026

> Atlas enables TensorFlow developers to build a working mental model of unfamiliar codebases by semantically searching, exploring, and interacting directly with tools like `pytest` and `uv`.

Atlas helps TensorFlow developers in 2026 rapidly build a working mental model of unfamiliar repositories without reading every file by starting from meaning, not filenames. It leverages `codebase_search` for semantic understanding, `glob` for structural overview, and `read` for targeted file inspection, all while integrating direct with your existing TensorFlow toolchain like `pytest`, `uv`, and `black`. This approach allows you to pinpoint critical `tf.function` definitions or `tf.data` pipelines efficiently, ensuring you grasp the core logic quickly and safely.

## Key takeaways

- Atlas uses semantic search to pinpoint relevant TensorFlow code, like `tf.keras.Model` definitions, without reading every file.
- The `explore` subagent provides a safe, read-only environment for wide sweeps across TensorFlow projects, preventing accidental changes.
- Atlas integrates directly with TensorFlow's toolchain, executing `pytest` for tests, `uv` for packages, and `black` for formatting.
- Every proposed change to your TensorFlow code, from `tf.function` wraps to `tf.data` optimizations, requires explicit approval via unified diffs.
- Atlas understands TensorFlow-specific idioms, helping optimize `tf.data` pipelines and explain `tf.function` retracing warnings.
- Permission-gated tool calls and git patch snapshots ensure transparency and control over all Atlas interactions with your TensorFlow repository.

## How does Atlas build a mental model of a TensorFlow codebase?

Atlas builds a mental model of a TensorFlow codebase by starting from meaning, not just filenames, in 2026. It uses `codebase_search` to query a semantic index for concepts like 'how requests are authenticated', then `glob` to map the directory shape, and finally `read` to pull only the 2 or 3 most relevant files.

To understand an unfamiliar TensorFlow project, Atlas first indexes your code by AST declarations using tree-sitter, not blind line windows. This allows `codebase_search` to perform hybrid semantic and keyword retrieval, fused by reciprocal rank fusion, returning highly relevant snippets. For instance, you might ask `atlas codebase_search "how are Keras models defined and loaded?"` to find `model.py` or `layers/custom_layer.py` files. After identifying key areas, you can run `atlas glob "src/*"` to quickly grasp the project's top-level package layout and naming conventions, such as `src/data_pipeline/` or `src/training_loops/`. Finally, `atlas read "src/model.py"` allows you to inspect the most critical files identified by `codebase_search`, focusing your attention on `tf.keras.Model` subclasses or `tf.function` decorated methods without sifting through an entire directory.

## How does Atlas explore a TensorFlow project safely?

Atlas explores a TensorFlow project safely by delegating wide sweeps to the `explore` subagent, which operates with a deny-by-default permission set. This ensures that while the agent looks around for things like `tf.data` pipelines or custom training steps, it cannot make any changes, providing a secure environment for initial investigation in 2026.

When you need to perform a broad investigation across a TensorFlow repository, such as finding all instances of `tf.data` transformations or custom `training_step` overrides, Atlas uses the `explore` subagent. This subagent is invoked via the `task` tool, for example: `atlas task explore "find all custom tf.data pipelines and their prefetch stages"`. The `explore` subagent is specifically defined with a deny-by-default permission set, allowing only read-only operations like `grep`, `glob`, `read`, `bash` (for non-modifying commands), `webfetch`, and `websearch`. This strict permission model means the agent can search for patterns in `data_loader.py` or `input_pipeline.py`, read configuration files, and even fetch external documentation, all without the risk of accidentally modifying your TensorFlow code or `pyproject.toml` dependencies. Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, ensuring transparency and control.

## How does Atlas interact with TensorFlow's toolchain?

Atlas interacts directly with TensorFlow's standard toolchain, including `pytest` for testing, `uv` for package management, and `black` for formatting, ensuring a native developer experience in 2026. For example, Atlas can run `pytest` behind a permission prompt and then format any resulting diffs with `black`, integrating direct into your existing workflow.

Atlas is designed to be terminal-native and integrate directly with the tools TensorFlow developers already use. When working with a `pyproject.toml` that pins `tensorflow` and `keras`, Atlas can help you manage dependencies using `uv`. For instance, it can suggest running `uv install` if dependencies are missing. For testing, Atlas can execute your test suite by running `pytest` commands, such as `atlas bash "pytest tests/model_test.py"`, always behind a permission prompt for safety. If code changes are made, Atlas can automatically format the diff using `black`, ensuring code style consistency. You might see Atlas propose `atlas bash "black src/model.py"` to adhere to formatting standards. Beyond basic tool integration, Atlas understands TensorFlow-specific idioms. It can read your Keras model definitions, analyze `tf.data` input pipelines, and even wrap hot Python loops in `tf.function` to explain the retracing warnings it removes, or add `tf.data` prefetch and cache stages, then measure step time before and after, providing concrete performance insights.

## How does Atlas ensure safety and transparency in TensorFlow code edits?

Atlas ensures safety and transparency in TensorFlow code edits through a multi-stage approval process, starting with a read-only plan agent and culminating in a unified diff for every file edit. This means any proposed change to your `tf.keras` model or `tf.data` pipeline, even a minor one, requires your explicit approval before it's written to disk in 2026.

Before any modification is made to your TensorFlow codebase, Atlas drafts a plan in a read-only plan agent and asks for your approval before switching to a build agent. This initial planning phase allows you to review the intended actions, such as refactoring a `tf.function` or optimizing a `tf.data` pipeline, without any risk of accidental changes. Once a plan is approved, Atlas computes a unified diff for every file edit it proposes, surfacing it for your approval before writing. This applies to changes in files like `src/model.py`, `src/data_pipeline.py`, or even `pyproject.toml`. Atlas also snapshots file changes as git patches, so edits can be diffed and rolled back easily. Furthermore, every Atlas tool call is permission-gated against allow, ask, and deny rules, providing granular control over what the agent can execute. For example, if Atlas suggests running `pytest` or `black`, it will prompt you for permission, ensuring you maintain full oversight of your TensorFlow development environment.

## Steps

1. Ask Atlas to semantically search your TensorFlow codebase for key concepts, like `atlas codebase_search "how are Keras models compiled and trained?"`, to identify core files such as `src/model.py` or `train.py`.
2. Run `atlas glob "src/*"` to quickly visualize the top-level directory structure and understand the package layout of the TensorFlow project, noting common patterns like `src/data/` or `src/tests/`.
3. Read the 2 or 3 most relevant TensorFlow files identified by `codebase_search`, for example, `atlas read "src/model.py"`, then use `lsp goToDefinition` to follow `tf.keras.layers` imports or custom `tf.function` definitions.
4. Delegate wide-ranging exploration tasks to the `explore` subagent, such as `atlas task explore "find all custom tf.data input pipelines and their batching strategies"`, knowing it operates with read-only permissions.
5. Have Atlas run `pytest` for specific TensorFlow test files, like `atlas bash "pytest tests/test_data_pipeline.py"`, and review the output behind a permission prompt.
6. Allow Atlas to suggest and apply `black` formatting to any modified TensorFlow Python files, for instance, `atlas bash "black src/training_loop.py"`, ensuring code style consistency.
7. Ask Atlas to analyze and optimize `tf.data` pipelines, such as adding `prefetch` or `cache` stages, and measure the step time before and after these changes.
8. Record your findings and open questions about the TensorFlow codebase as a `todowrite` list, ensuring continuity for your next session, for example, `atlas todowrite "Investigate tf.function retracing warnings in src/utils.py"`.

## FAQ

### How does Atlas find relevant TensorFlow code quickly?

Atlas uses `codebase_search` with a semantic index built by AST declarations, not blind line windows. This allows it to understand the meaning of your TensorFlow code, like `tf.keras.layers` or `tf.data.Dataset` operations, and return highly relevant snippets and file paths, such as `src/models/resnet.py`.

### Can Atlas modify my TensorFlow code without my permission?

No. Atlas operates with strict permission controls. It drafts plans in a read-only agent, asks for approval before switching to a build agent, and computes a unified diff for every proposed file edit, requiring your explicit approval before writing any changes to your TensorFlow files or `pyproject.toml`.

### How does Atlas handle TensorFlow testing with `pytest`?

Atlas can execute `pytest` commands directly within your terminal, such as `atlas bash "pytest tests/unit_tests/"`. All such commands are permission-gated, meaning Atlas will prompt you for approval before running the tests, ensuring you maintain control over your TensorFlow test suite.

### Does Atlas understand `tf.function` and `tf.data` specifics?

Yes, Atlas is designed to understand TensorFlow-specific idioms. It can analyze your `tf.function` decorated methods, explain retracing warnings, and suggest optimizations for `tf.data` pipelines, such as adding `prefetch` or `cache` stages, and even measure performance impacts.

### How does Atlas ensure code style for TensorFlow projects?

Atlas integrates with `black`, the standard Python formatter. If Atlas makes or suggests changes to your TensorFlow Python files, it can automatically format the diff using `black`, ensuring your codebase adheres to established style guidelines, for example, `atlas bash "black src/utils.py"`.

### What if I need to explore a large TensorFlow repository without making changes?

For wide-ranging exploration, Atlas delegates to the `explore` subagent via the `task` tool. This subagent operates with a deny-by-default permission set, allowing only read-only operations like `grep`, `glob`, and `read`, ensuring you can safely investigate files like `data_processing.py` or `config.py` without risk of modification.

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