A high-performance Python package wrapping a Rust-based local RAG kernel

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for surajkekan from gravatar.com surajkekan

Unverified details

These details have not been verified by PyPI
Meta
  • License: Apache Software License, MIT License (MIT OR Apache-2.0)
  • Author: Suraj Kekan
  • Tags rag , retrieval , embeddings , vector-search , nlp
  • Requires: Python >=3.8
  • Provides-Extra: dev
Classifiers
Report project as malware

Project description

MemAlloy

"The High-Performance Memory Kernel for AI Agents."

Executive Summary

MemAlloy is an open-source "Memory Kernel" designed to solve the data ingestion and retrieval bottleneck in Python AI applications. By offloading heavy tasks (file watching, parsing, embedding, and vector storage) to a high-performance Rust core, MemAlloy provides Python developers with a "Second Brain" that is 100x faster, memory-efficient, and privacy-first compared to existing pure-Python solutions like LangChain or LlamaIndex.

The Problem

Python is the language of AI, but it is ill-suited for the infrastructure of AI Memory.

  • Latency: Watching thousands of files and chunking text in Python introduces massive lag (the "Global Interpreter Lock" problem).
  • Complexity: Building a RAG (Retrieval Augmented Generation) pipeline currently requires gluing together 5+ disparate libraries (watchdog, pypdf, sentence-transformers, chromadb, tiktoken).
  • Resource Heaviness: Existing vector databases often require running a separate Docker container or server, consuming gigabytes of RAM even when idle.

The Solution

MemAlloy consolidates the entire RAG pipeline into a single, installable binary that exposes a clean Python API. It acts as an embedded OS service for memory.

Key Features

  • ⚡ Zero-Latency Ingestion: Uses Rust’s notify crate to detect file changes instantly.
  • 🧠 Local Intelligence: Runs quantized embedding models (ONNX) localy on the CPU. No API keys required.
  • 💾 Embedded Storage: Uses LanceDB to store millions of vectors in a single file on disk (Serverless).
  • 🐍 Python Native: Installs via pip install memalloy. No Rust knowledge required for the user.

Technical Architecture

Layer Component Technology (Rust Crate)
Interface Python Bindings PyO3 + Maturin
Control Async Runtime Tokio
Senses File System Watcher Notify (Recursive)
Processing Neural Embeddings FastEmbed (ONNX Runtime)
Storage Vector Database LanceDB (Apache Arrow)

Installation

From Source

# Install maturin (Rust-Python build tool)
pip install maturin

# Build and install memalloy
maturin develop --release

Quick Start

from memalloy import RAGKernel

# Initialize the RAG kernel
rag = RAGKernel()

# Add documents
rag.add_document("MemAlloy is a high-performance memory kernel.")

# Search
results = rag.search("memory kernel", top_k=1)
print(results)

License

Relationships imply responsibility. Licensed under Apache 2.0 or MIT.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for surajkekan from gravatar.com surajkekan

Unverified details

These details have not been verified by PyPI
Meta
  • License: Apache Software License, MIT License (MIT OR Apache-2.0)
  • Author: Suraj Kekan
  • Tags rag , retrieval , embeddings , vector-search , nlp
  • Requires: Python >=3.8
  • Provides-Extra: dev
Classifiers

Release history Release notifications | RSS feed

This version

0.1.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

memalloy-0.1.0.tar.gz (951.8 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

memalloy-0.1.0-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.3 MB view details)

Uploaded PyPymanylinux: glibc 2.17+ x86-64

memalloy-0.1.0-cp314-cp314-win_amd64.whl (50.2 MB view details)

Uploaded CPython 3.14Windows x86-64

memalloy-0.1.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.3 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.17+ x86-64

memalloy-0.1.0-cp314-cp314-macosx_11_0_arm64.whl (51.8 MB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

memalloy-0.1.0-cp314-cp314-macosx_10_12_x86_64.whl (55.1 MB view details)

Uploaded CPython 3.14macOS 10.12+ x86-64

memalloy-0.1.0-cp313-cp313-win_amd64.whl (50.2 MB view details)

Uploaded CPython 3.13Windows x86-64

memalloy-0.1.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.3 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64

memalloy-0.1.0-cp313-cp313-macosx_11_0_arm64.whl (51.8 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

memalloy-0.1.0-cp313-cp313-macosx_10_12_x86_64.whl (55.1 MB view details)

Uploaded CPython 3.13macOS 10.12+ x86-64

memalloy-0.1.0-cp312-cp312-win_amd64.whl (50.2 MB view details)

Uploaded CPython 3.12Windows x86-64

memalloy-0.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.3 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

memalloy-0.1.0-cp312-cp312-macosx_11_0_arm64.whl (51.8 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

memalloy-0.1.0-cp312-cp312-macosx_10_12_x86_64.whl (55.1 MB view details)

Uploaded CPython 3.12macOS 10.12+ x86-64

memalloy-0.1.0-cp311-cp311-win_amd64.whl (50.2 MB view details)

Uploaded CPython 3.11Windows x86-64

memalloy-0.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.3 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

memalloy-0.1.0-cp311-cp311-macosx_11_0_arm64.whl (51.8 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

memalloy-0.1.0-cp311-cp311-macosx_10_12_x86_64.whl (55.1 MB view details)

Uploaded CPython 3.11macOS 10.12+ x86-64

memalloy-0.1.0-cp310-cp310-win_amd64.whl (50.2 MB view details)

Uploaded CPython 3.10Windows x86-64

memalloy-0.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.3 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

memalloy-0.1.0-cp310-cp310-macosx_11_0_arm64.whl (51.8 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

memalloy-0.1.0-cp310-cp310-macosx_10_12_x86_64.whl (55.1 MB view details)

Uploaded CPython 3.10macOS 10.12+ x86-64

memalloy-0.1.0-cp39-cp39-win_amd64.whl (50.2 MB view details)

Uploaded CPython 3.9Windows x86-64

memalloy-0.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.3 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

memalloy-0.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.3 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

File details

Details for the file memalloy-0.1.0.tar.gz.

File metadata

  • Download URL: memalloy-0.1.0.tar.gz
  • Upload date:
  • Size: 951.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for memalloy-0.1.0.tar.gz
Algorithm Hash digest
SHA256 359c31ceb17aca4ec644dc1c487cd265759270f4f9c07be254cc5739c5d098f5
MD5 0e05f66de71b47b5248e79ad65b0d414
BLAKE2b-256 c4b7682ae2498ef8264f3372c23b41e51273011a4351edc839a872a9540a7fe1

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 fee2b7f06859b22fd900861b2f0c43ae6c95d072a7ab868e769f393fe4d75a1e
MD5 c3fb128c9a0320e7987cf0a94d661b95
BLAKE2b-256 2a137dceaccc8c2138b81052c7efdc21722b5c079f1bdc822324e415f0b7d2fe

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp314-cp314-win_amd64.whl.

File metadata

  • Download URL: memalloy-0.1.0-cp314-cp314-win_amd64.whl
  • Upload date:
  • Size: 50.2 MB
  • Tags: CPython 3.14, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for memalloy-0.1.0-cp314-cp314-win_amd64.whl
Algorithm Hash digest
SHA256 b73029d3d4aba232d616455107bd4017e446071df3f1f6595f39d18ce283a933
MD5 4cb547e6accd30ef5e1c6ec6d0d5794a
BLAKE2b-256 2c292448d1c8d6996e13dbf91572c07d2fe2b2722faf9e979ccc3e8abf14c7d6

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 5d3e5fab1b2133a89fdd50e4a999d30420c8b2a56f1e9bc6f4a81e279a991650
MD5 09ebd872f61f438ab5cb91b36414bb32
BLAKE2b-256 40dea77a977d190510328831773818d4e2c05f6f914e9c0155b514bf7ecebbae

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 616fe82437ccf539f0ba5166a75a393b2247a3570ac84a44fb5676982c082fa1
MD5 f99feef9ac160f893d543e85b4355f98
BLAKE2b-256 563fad602e51d8189036b1dc2e1f938369e319026a3e5f5da169929be64c1123

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp314-cp314-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp314-cp314-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 4dd82b9741018c4c1a3e199b9b366abe02c239eeb29a5dcc63dd0b947b9a0859
MD5 9fa181e0f133da261cdaf07627a49a59
BLAKE2b-256 5d0e2db066b764e01f5d551e1c45dfc9ec731e94bf9ba450f1d5066acc14cbc0

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: memalloy-0.1.0-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 50.2 MB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for memalloy-0.1.0-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 071a6fca1bf4035ebb01b5ee5e0c4ed7d6277a73d22dfc3ac3ec67332f25c4d5
MD5 f3f9bba1c3e6e45852dbb0d8cc57a99b
BLAKE2b-256 39fbf99ed6e914218953edf8e82e6eeb3e9b7f2cafd65d032e90b55be9e4865f

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 53e81fdc2992eefe733bf5baacedaa0e4a35ded94d499712f5f410630fb06ca8
MD5 2f471b951ae2f77f579aad043eedfd90
BLAKE2b-256 78e9cb02110541a2b0d7bfb7c0c175ca4dcdf4b813529a5a57c322bc14e3cbc2

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 5723d3aa11d5242b23328fb45c2f171c1c7fc6f292633c5b6fc0c28749d4955e
MD5 1704f9a7a68477b698469990b79f95c3
BLAKE2b-256 b3da8c4725e01740e3dee6e218c7cfa9dadbdef07c61aee9eb57d59692f0b1e5

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp313-cp313-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp313-cp313-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 7e88b4e42938d5b9646ed61a7dabff7c5461ceab7935378504ce97dc5dffe635
MD5 d60d8582332c071002369959dde870fe
BLAKE2b-256 ac318023fb61fd01657a45967b34d840e0b347f6505441f3caa32fa4f6475004

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: memalloy-0.1.0-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 50.2 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for memalloy-0.1.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 01f59d4a6f5b76018f95a48a3293011646e2924146686aeaeba0f38b517ef397
MD5 f0ca425415ac90c8f3f0c0f2691e81cf
BLAKE2b-256 0fa65fff142cfc9f7faf53e76f0e26c4c8efebae8735aff91a29c29810276f86

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 91ca346307d212d7f19c624a030d3670cd83eebf32f99aa336cc898b9cef3e02
MD5 ddb5c1cc02ddb11f9cbe16f7fbf9bf53
BLAKE2b-256 52d67aea21c0af08069de3a11d83e0e03e9051928f054911bd76671eee9f75e9

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c7b51568cca43d01a335d5496312a40c350e0cac0bf320d9fee4b9a167f3d327
MD5 3b3441ff683f1eb7f1b8fca93f5a29e5
BLAKE2b-256 2eacb5f84993bf114bd89644a0182c5354ab0d89bb7c45c9c09d6a3fe2c4a297

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp312-cp312-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp312-cp312-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 ac079b688add9513ed3e52880e0d142dae11482879a7a831ef0bf4d8f520ee99
MD5 36fd2b3d061e7f77a73061f9e18c3f45
BLAKE2b-256 205ff3bf0918f22ab13c80769c458e42498363e363bfe887b4a460de6d78d3f5

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: memalloy-0.1.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 50.2 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for memalloy-0.1.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 03ffec12a4f6cf1440b64237de5f1cf88571ac2ff26adb8983eb8b6946d1f2c6
MD5 77aee35b254b4adc606c544948ed579c
BLAKE2b-256 f629443b2559aad43bc64a0b41b50ecbaecb55b3b846a0fb951b5c95bdcb2427

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 01d6338a5d29d77cd13f58f0c5de37dc0ce1e362e1ea14f41b7271a9a3505e00
MD5 e509b101cd3a422dbb0b5b9aeffba97b
BLAKE2b-256 aa580f586113b6ef8990a05bcf3a8860a28ce767de6b48d15a16643a4c0e4bf2

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 fe2eb0d9762d782a1597812bbfb8e1145d4bd84ac5f262dbc45ecc081160b0de
MD5 316d7d2a5f2e7f9aa848736261b80910
BLAKE2b-256 a4fd4b913d285b6ccc8768355a2f1f77dc852c6215f5b194425d1ee670eb2975

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp311-cp311-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp311-cp311-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 691434d5d02951cb79d0a5422f6c9210a160a6973f886b580f717081bd5acda5
MD5 2cc42472a2850ba65eeb865536943231
BLAKE2b-256 b67e5dd32ca0a0bdf8ab2fbc525b2a64ce4201678267dd0b1eda4967fbb9e34b

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: memalloy-0.1.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 50.2 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for memalloy-0.1.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 ee854086e0bac86ed2f3ccdf2fe71915a3d7cef2058529a5d87a85418bfaa087
MD5 a53737df950b448bfe334a38d76540e9
BLAKE2b-256 692ccd323063252cc79d957473974f3aed455b3219fb7736770cfcca867d29a8

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b9093412e609c5ac061b0ecede2561019c2d76d2f8f6cbb30f8603e0bfc14bcf
MD5 3dd99c7b8497690638f60c72f05f5c79
BLAKE2b-256 b441757b3fc6b232fdf1bb63b631a84c6c8cb8f5a16bb4a1ab6ed8796133051e

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 dcd4c7bbd1b249ac45081e6eff1244aded0f69fd84d80d055f9d4020a06ed513
MD5 b037d1a7bfd665aca13d4e779e50b638
BLAKE2b-256 bfc6b421212926113b0f1e5898ad91d0e43d5866dccd0efe50ec9c4cbd9bd17e

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp310-cp310-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp310-cp310-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 835db92d428b57d058aa790068f76c1435a5b58738e9f19da861aeaee15a1d67
MD5 de40e8dba1ed495ebef8ec47d1263b14
BLAKE2b-256 addf742a39fff1058f99345ab5384e52d8ecb172b84a69b079fd839a1678f214

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: memalloy-0.1.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 50.2 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for memalloy-0.1.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 77348b053f7e1c351e37ffa1e88f5f4c0c4bd2427817fd75ac327a0d5071c30e
MD5 c75b166e1827f8d43c4c984ef4b9787e
BLAKE2b-256 3e0b0baf9b09674277612103e8da2d299ac85160d1e022fafe4d7826d7cc077b

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 7a6837531bb8e0a8d4d7392683d3e5928080e68e91402f0cbf4a54c9b0302a57
MD5 6347a85e1f74678f6a6cef763f8fee9a
BLAKE2b-256 d9a46313ec4ee90a08450477e5432674751cb2861df72cc6f65e4d9412777297

See more details on using hashes here.

File details

Details for the file memalloy-0.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for memalloy-0.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3798756cba4981e3c473ff5aae90e13a901c3c631d6f8c2a3c410c9a4cf73056
MD5 32e4b6ef0107610640ba061b0fbbb417
BLAKE2b-256 020c4053149c1527bed5a901a95195d799014d8488c9c659a327a0b458aa6994

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page