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llm-receipts

This package has been consolidated into aiir.

Install the real package

pip install aiir

What is AIIR?

AI Integrity Receipts — lightweight cryptographic commitment records generated during AI inference that bind model identity, sampling configuration, and output tokens into a tamper-evident hash chain.

  • Zero external dependencies (Python stdlib only)
  • Receipt generation in < 100 µs
  • SHA-256 hash chains with O(1) amortized cost
  • Works with any model format (GGUF, ONNX, cloud APIs)
  • MCP server for Claude / Copilot / ChatGPT integration

Links


This package (llm-receipts) exists to redirect users to the canonical aiir package. Installing it will automatically install aiir as a dependency.

Metadata

Release files for llm-receipts 0.0.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for llm-receipts 0.0.1
File Size Uploaded Provenance
llm_receipts-0.0.1.tar.gz 1.7 kB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for llm-receipts 0.0.1
File Interpreter ABI Platform
llm_receipts-0.0.1-py3-none-any.whl Python 3 none any Details

Total release size: 3.9 kB

Release files / llm_receipts-0.0.1.tar.gz

Download URL llm_receipts-0.0.1.tar.gz
Size 1.7 kB
Tags Source
SHA-256 checksum
How to use checksums
4214d7577ac2d45268a05b6bb37a3458836ad25bdbc251c9d37864117b2642a9
BLAKE2b-256 checksum
How to use checksums
d0891cb612fde6ba7bee991827615f7f369665485b804bb440f0fd293dfd89ff
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.0rc1

Release files / llm_receipts-0.0.1-py3-none-any.whl

Download URL llm_receipts-0.0.1-py3-none-any.whl
Size 2.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
ae5e1bfa4214e6fe30c18c3ac5a2044f24595e59b82646e56c8758259890e7fa
BLAKE2b-256 checksum
How to use checksums
bf91f1743632b765697b1367b09732564f915a4c810ecf9b7f67c63a17a2292b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.0rc1

Security for llm-receipts 0.0.1

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This release

0.0.1 This release

2 release files No provenance

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