Skip to main content

Rust core for Entroly — information-theoretic context optimization (knapsack, entropy, dedup, SAST, query analysis)

Project description

entroly-core

Rust core for Entroly, the information-theoretic context optimization engine for AI coding agents.

Provides high-performance PyO3 bindings for:

  • Knapsack optimizer - 0/1 DP context selection within token budget
  • Shannon entropy scorer - boilerplate detection, information density
  • SimHash deduplication - near-duplicate fragment detection
  • Query analysis - TF-IDF vagueness scoring, heuristic refinement
  • SAST scanner - security rules for common vulnerability classes
  • LSH index - approximate nearest-neighbor semantic recall
  • PRISM RL optimizer - online feedback-driven fragment weight learning

Install

python -m pip install --upgrade pip
python -m pip install --no-cache-dir -U "entroly-core>=0.19.13"

Prebuilt abi3 wheels are published for Linux, macOS, and Windows. One wheel per platform covers Python 3.10+ including Python 3.14. If pip tries to compile an old source distribution such as 0.2.0, upgrade pip and retry with --no-cache-dir; the intended path is the prebuilt wheel, not a local PyO3 build.

Usage

Usually used via the higher-level entroly package:

python -m pip install entroly
entroly  # starts the MCP server

Or directly:

from entroly_core import ContextFragment, py_knapsack_optimize, py_shannon_entropy

License

Apache-2.0

Project details


Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

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

entroly_core-1.0.3-cp310-abi3-win_amd64.whl (2.0 MB view details)

Uploaded CPython 3.10+Windows x86-64

entroly_core-1.0.3-cp310-abi3-manylinux_2_28_aarch64.whl (2.2 MB view details)

Uploaded CPython 3.10+manylinux: glibc 2.28+ ARM64

entroly_core-1.0.3-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.10+manylinux: glibc 2.17+ x86-64

entroly_core-1.0.3-cp310-abi3-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl (4.2 MB view details)

Uploaded CPython 3.10+macOS 10.12+ universal2 (ARM64, x86-64)macOS 10.12+ x86-64macOS 11.0+ ARM64

File details

Details for the file entroly_core-1.0.3-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for entroly_core-1.0.3-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 0d2fefc91f43b8bac29366c3f84491a80a7e252037c5608f082bddd72afa2241
MD5 9f8c3b61be5a4f128122974431989da3
BLAKE2b-256 3ab77b9bd71761efd10e60bc584e1fb46f34e41d8f8cb0fa1f8aca7da49ee68f

See more details on using hashes here.

File details

Details for the file entroly_core-1.0.3-cp310-abi3-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for entroly_core-1.0.3-cp310-abi3-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 e77fa39b0d10b38f50bda7b179845cb3f367a4e1745dc182777dcada05d9871d
MD5 5a5b92d5220c2bdd37f75a4690636dd4
BLAKE2b-256 5cf75fb8e340f7ec708928a24410449f6e477024be9c0552098c97059d7c38b9

See more details on using hashes here.

File details

Details for the file entroly_core-1.0.3-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for entroly_core-1.0.3-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 9b14ea06ac67e0ef0aebdbbe45ec1e162e406cea147a07257f4dbc28033fad21
MD5 4fa1f4b51e5c18e2ebc80a3191c0abb1
BLAKE2b-256 d74471c1098431f107c73a60862845613d43515c58b841061d6ccf45584ac7e5

See more details on using hashes here.

File details

Details for the file entroly_core-1.0.3-cp310-abi3-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl.

File metadata

File hashes

Hashes for entroly_core-1.0.3-cp310-abi3-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl
Algorithm Hash digest
SHA256 1c688fbada188479ba73911b0d058070cd7d4226de03733b79d7bc6ede20abcd
MD5 f54e15378bfec18f7a9d75dbab577a09
BLAKE2b-256 fab43f0b866f7c0c1ab45196af366fbb1c425850aea208f3a89ae0f29514500f

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