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.
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.
Step by step
- 01Ask 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`.
- 02Run `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/`.
- 03Read 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.
- 04Delegate 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.
- 05Have 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.
- 06Allow 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.
- 07Ask 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.
- 08Record 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"`.
Frequently asked questions
- 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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