rapidyaml
Parse and emit YAML, and do it fast. Python wrapper for the C++ library
Decision gist · record as of 2026-08-14
Yes, if you need to parse or emit YAML at scale and can work with a low-level index-based API and manual type conversion. No, if you expect automatic Python object construction or need a high-level interface. The package is actively maintained, has no known vulnerabilities, and carries a permissive MIT license.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Scalars are returned as memoryview objects to the source buffer; requires explicit encoding conversion to work with Python strings.
- Medium install friction due to compiled wheels; prebuilt binaries available for Python 3.10–3.12 across macOS, Linux, and Windows architectures.
- Active maintenance with a recent release 50 days ago.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; suitable for most projects.
last release 2026-06-25 (50 days) · last repo commit 2026-06-25 · 3 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 261,297 downloads/mo, #8,384 on PyPI
Alternatives
Verify before relying
pip install rapidyaml
import rapidyaml
yaml_bytes = b"{key: value, seq: [0, 1, 2]}"
tree = rapidyaml.parse_in_arena(yaml_bytes)
root_id = tree.root_id()
child_id = tree.find_child(root_id, b"key")
print(tree.val(child_id))- Whether the low-level index-based API is suitable for typical YAML workflows or primarily targets performance-critical use cases.
- Compatibility with YAML specification versions and dialect-specific parsing behavior.
- Memory overhead of the arena-based approach compared to in-place parsing for large documents.
- Actual performance gains in real-world applications versus the benchmark scenarios provided.
What it is and what it does
Rapidyaml is a Python wrapper around a C++ YAML parser and emitter designed for speed. It exposes a low-level, index-based API that operates on node indices and string views rather than constructing Python dictionaries and lists automatically. All scalar values are returned as untyped strings via memoryview objects, and you must manually walk the tree and convert types as needed. The tradeoff is substantial performance: the package documentation shows parsing speeds 100x and up to 400x faster than alternatives and emitting speeds as high as 3000x faster, though those gains come from avoiding Python-side type conversions and data structure construction.
The package is built on precompiled wheels for modern Python versions across common platforms. It requires you to manage the lifetime of input buffers when using in-place parsing, or to use the arena-based approach for safer operation. The single runtime dependency is deprecation. This is a tool for scenarios where YAML parsing speed is a bottleneck and you can tolerate a lower-level API and manual tree navigation.
Use it for
- Parsing large YAML configuration files where speed is critical and you can manually construct Python objects from the tree.
- Streaming or batch processing of YAML documents in performance-sensitive applications.
- Emitting YAML at high throughput in data pipelines or real-time systems.
- Building custom YAML-based serialization layers where you need fine-grained control over tree structure.
- Embedded or resource-constrained environments where the C++ backend's efficiency matters.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to parse or emit YAML at scale and can work with a low-level index-based API and manual type conversion.
No, if you expect automatic Python object construction or need a high-level interface. The package is actively maintained, has no known vulnerabilities, and carries a permissive MIT license.
Install
rapidyaml on PyPI
Before you install
Medium install friction due to compiled wheels; prebuilt binaries available for Python 3.10–3.12 across macOS, Linux, and Windows architectures. Active maintenance with a recent release 50 days ago. Single runtime dependency on deprecation.
Scalars are returned as memoryview objects to the source buffer; requires explicit encoding conversion to work with Python strings.
License in practice
MIT license permits commercial and private use with minimal restrictions; suitable for most projects.
Quickstart
pip install rapidyaml
import rapidyaml
yaml_bytes = b"{key: value, seq: [0, 1, 2]}"
tree = rapidyaml.parse_in_arena(yaml_bytes)
root_id = tree.root_id()
child_id = tree.find_child(root_id, b"key")
print(tree.val(child_id))Verify before relying
- Whether the low-level index-based API is suitable for typical YAML workflows or primarily targets performance-critical use cases.
- Compatibility with YAML specification versions and dialect-specific parsing behavior.
- Memory overhead of the arena-based approach compared to in-place parsing for large documents.
- Actual performance gains in real-world applications versus the benchmark scenarios provided.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagedeprecation |
| Maintenance | Actively maintained 50 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 261,297 / month, #8,384 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Operating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: rapidyaml-0.15.2-cp310-cp310-macosx_10_9_universal2.whl; rapidyaml-0.15.2-cp310-cp310-macosx_10_9_x86_64.whl; rapidyaml-0.15.2-cp310-cp310-macosx_11_0_arm64.whl; rapidyaml-0.15.2-cp310-cp310-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl; rapidyaml-0.15.2-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; rapidyaml-0.15.2-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; rapidyaml-0.15.2-cp310-cp310-win32.whl; rapidyaml-0.15.2-cp310-cp310-win_amd64.whl; rapidyaml-0.15.2-cp310-cp310-win_arm64.whl; rapidyaml-0.15.2-cp311-cp311-macosx_10_9_universal2.whl; rapidyaml-0.15.2-cp311-cp311-macosx_10_9_x86_64.whl; rapidyaml-0.15.2-cp311-cp311-macosx_11_0_arm64.whl; rapidyaml-0.15.2-cp311-cp311-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl; rapidyaml-0.15.2-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; rapidyaml-0.15.2-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; rapidyaml-0.15.2-cp311-cp311-win32.whl; rapidyaml-0.15.2-cp311-cp311-win_amd64.whl; rapidyaml-0.15.2-cp311-cp311-win_arm64.whl; rapidyaml-0.15.2-cp312-cp312-macosx_10_13_universal2.whl; rapidyaml-0.15.2-cp312-cp312-macosx_10_13_x86_64.whl
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