Atlas empowers TensorFlow developers in 2026 to produce precise and up-to-date README documentation by directly analyzing live source code. It leverages the `lsp` tool to enumerate public APIs, `codebase_search` to understand real-world usage, and `bash` to verify every code sample, ensuring the documentation accurately reflects the current TensorFlow implementation and not what it was intended to do a year ago.
How Atlas documents TensorFlow modules with READMEs
Atlas streamlines README generation for TensorFlow modules in 2026 by directly analyzing source code, ensuring documentation reflects current implementation rather than outdated specifications. It uses `lsp` to enumerate public APIs and `codebase_search` to understand real-world usage, providing a traceable source for every claim.
Atlas approaches TensorFlow module documentation by first establishing a ground truth from the codebase itself. Instead of relying on stale comments or developer memory, Atlas uses its `lsp` tool's `documentSymbol` operation to enumerate the real exported surface of your TensorFlow module. This ensures that no public API, whether it is a `tf.keras.Model` subclass, a custom `tf.function` decorated utility, or a `tf.data.Dataset` pipeline component, is missed or invented in the documentation. Following this, Atlas employs its `read` tool to delve into the implementation details of each identified export. To provide context on how these components are actually used, Atlas then utilizes `codebase_search` to find practical examples within your TensorFlow project. This step is crucial for documenting the practical application of `tf.function` graph tracing or `tf.data` pipeline stages, ensuring the README describes what the code *does* today, not just what its signature implies. The entire process is anchored in your project's `pyproject.toml` file, which pins `tensorflow` and `keras`, providing Atlas with the correct environment context.
Concrete Atlas commands for TensorFlow README generation
To generate a README for a TensorFlow module, Atlas employs a sequence of 5 specific tools: `lsp`, `read`, `codebase_search`, `grep`, and `write`. This process ensures every claim in the documentation is traceable to the actual source code, providing a reliable and current resource for developers in 2026.
The workflow for generating a TensorFlow README with Atlas is a series of concrete, tool-driven steps. First, Atlas uses `lsp` with the `documentSymbol` operation to identify all public exports from your TensorFlow module, such as Keras 3 layers or custom `tf.data` transformations. Next, the `read` tool is invoked to extract the detailed implementation of these exports, including any `tf.function` decorators or custom `training_step` overrides. To understand how these components are integrated and used across your project, Atlas runs `codebase_search`, looking for calls to your module's functions or instantiations of its classes. Before writing, Atlas uses `grep` to scan your repository for an existing `README.md` file. This allows Atlas to match the established heading structure and tone, ensuring consistency rather than inventing a new format. Finally, the `write` tool is used to emit the new `README.md` content, quoting real TensorFlow signatures and actual file paths from your project. Throughout this process, Atlas respects your project's setup, understanding that `uv` manages packages and `black` handles code formatting, ensuring the generated documentation aligns with your existing TensorFlow development practices.
Verifying TensorFlow code samples in READMEs with Atlas
Atlas ensures the reliability of TensorFlow code samples within READMEs by executing them directly with `bash`. This critical step prevents the inclusion of broken or outdated examples, a common issue in documentation that can frustrate developers in 2026 and lead to wasted time.
A core principle of Atlas's documentation workflow for TensorFlow is that every code sample included in a README must be verifiable. To achieve this, Atlas utilizes its `bash` tool to run each code snippet directly. This means that if your README includes an example of setting up a `tf.data` pipeline, defining a Keras 3 model, or demonstrating a `tf.function` decorated utility, Atlas will attempt to execute that exact code. A sample that was never executed is considered a liability, as it can quickly become outdated or incorrect as the codebase evolves. By running these samples, Atlas catches syntax errors, API changes, or environmental issues that would otherwise lead to non-functional documentation. This rigorous verification process guarantees that when a TensorFlow developer in 2026 copies a code block from your README, it will work as described, fostering trust and reducing friction in adopting your module.
Atlas safety and review for TensorFlow documentation
Atlas prioritizes safety and developer control throughout the TensorFlow documentation process, implementing 3 distinct permission gates. Every Atlas tool call is permission-gated, and all proposed file edits are presented as unified diffs for explicit approval, ensuring your codebase remains secure and under your command.
Developer safety and control are paramount when Atlas interacts with your TensorFlow codebase. Atlas implements several layers of protection to ensure that documentation generation is a transparent and auditable process. Firstly, every Atlas tool call, including `lsp`, `read`, `codebase_search`, `grep`, `write`, and `bash`, is permission-gated against allow, ask, and deny rules. This means you have granular control over what Atlas can do. Secondly, Atlas drafts a plan in a read-only plan agent and asks for your approval before switching to a build agent that can make changes. This provides a crucial review point before any modifications are attempted. Thirdly, for every file edit, such as writing a new `README.md`, Atlas computes a unified diff and surfaces it for your explicit approval before writing to disk. This allows you to review the exact changes Atlas proposes. Furthermore, Atlas builds its code index with local Ollama embeddings, keeping your sensitive TensorFlow code off third-party servers. Atlas also reads git branches, status, and diffs, and can stage and create commits on your behalf, while also snapshotting file changes as git patches so edits can be diffed and rolled back if needed. This comprehensive safety framework ensures that documenting your TensorFlow module is both efficient and secure.
Step by step
- 01Run Atlas in your TensorFlow project, ensuring your `pyproject.toml` pins `tensorflow` and `keras` for correct context.
- 02Ask Atlas to enumerate the public APIs of your TensorFlow module using the `lsp` tool's `documentSymbol` operation to capture all exports.
- 03Instruct Atlas to `read` the implementation of `tf.function` decorated functions and use `codebase_search` to find their actual usage patterns within your project.
- 04Have Atlas `grep` your repository for an existing `README.md` to establish a consistent heading structure and tone for the new documentation.
- 05Approve Atlas's plan to `write` the new `README.md`, which will quote real TensorFlow signatures and actual file paths from your codebase.
- 06Verify every TensorFlow code sample in the drafted README by allowing Atlas to execute it with `bash`, confirming its functionality.
- 07Review the unified diff of the generated `README.md` and explicitly approve Atlas to write the changes to your TensorFlow project.
Frequently asked questions
- How does Atlas ensure TensorFlow documentation is current?
- Atlas directly reads your TensorFlow module's source code, using `lsp` to enumerate APIs and `codebase_search` to understand real-world usage. This ensures documentation reflects the code as it exists today in 2026, not what it was supposed to do a year ago.
- Can Atlas document custom Keras 3 models or tf.data pipelines?
- Yes, Atlas is designed to read your Keras 3 model definitions, `tf.data` input pipelines, and any custom `training_step` overrides. It incorporates these TensorFlow specifics into the README, providing comprehensive documentation.
- What TensorFlow tools does Atlas integrate with?
- Atlas integrates with your existing TensorFlow toolchain, including `pytest` for testing, `uv` for package management, and `black` for code formatting. This ensures a familiar and consistent developer experience within your TensorFlow project.
- How does Atlas verify code examples in TensorFlow READMEs?
- Atlas uses its `bash` tool to execute every TensorFlow code sample within the drafted README. This confirms its functionality and prevents outdated or broken examples from being published, ensuring reliability for developers in 2026.
- Is my TensorFlow code safe with Atlas?
- Yes, Atlas builds its code index with local Ollama embeddings, keeping your TensorFlow code off third-party servers. All tool calls are permission-gated, and every file edit requires explicit approval via a unified diff, ensuring security and control.
- Can Atlas help optimize my TensorFlow code?
- While focused on documentation here, Atlas can also wrap hot Python loops in `tf.function` and add `tf.data` prefetch and cache stages. It explains the performance benefits and retracing warnings it removes, contributing to optimized TensorFlow code.
- How does Atlas handle existing README structures for TensorFlow projects?
- Atlas uses its `grep` tool to search your repository for an existing `README.md` file. This allows it to match the established heading structure and tone, ensuring consistency rather than inventing a new format for your TensorFlow project's documentation.
- What if I need to roll back changes made by Atlas to my TensorFlow project?
- Atlas snapshots file changes as git patches, allowing edits to be easily diffed and rolled back. This provides a robust safety net for any modifications Atlas makes to your TensorFlow codebase, ensuring full control over your project history.
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