Consistency Python Packages

agentcrdt

CRDTs for distributed AI agent state and conflict-free belief merging

rankseg

Boost segmentation model mIoU/Dice instantly WITHOUT retraining. A plug-and-play, training-free optimization module. Published in NeurIPS & JMLR. Compatible with SAM, DeepLab, SegFormer, and more. 🧩

memstate

Transactional Memory for AI Agents - Keep SQL and Vector DBs in sync with ACID-like guarantees

llm-testlab

Comprehensive testing suite for LLM evaluation: hallucination detection, consistency, robustness, safety, and multi-language code generation assessment.

engram-team

Multi-agent memory consistency for engineering teams.

mlscorecheck

Testing the consistency of binary classification performance scores reported in papers

agent-convergence-scorer

Score how similar N agent outputs are — exact match, Jaccard token overlap, divergence point, composite 0-1 score. Stdlib-only.

text-to-struct

text-to-struct converts unstructured text into standardized, structured output

vitae-parser

vitae-parser extracts and structures biographical details from unstructured text using pattern matching.

texttoknowledge

Convert unstructured text into structured, queryable knowledge with llmatch-messages. Extract and organize key details for fast, reliable access—ideal for teams and researchers.

data-convertible

A new package that provides a structured and reliable way to process user input related to common developer utilities such as JSON, Base64, URL, and hash operations. It uses an LLM to interpret user r

leaderboard-summarizer

Extracts and organizes leaderboard/ranking data from text via smart pattern matching and retries, delivering clean, structured summaries effortlessly.

partnershipparser

partnershipparser extracts and structures key info from tech partnership news for easy analysis of companies, focus areas, and impacts

pre-v7-unix-summarizer

A new package that processes text input related to early Unix history (pre-V7) and returns structured, verified summaries using pattern matching and LLM interactions. It takes user-provided text (e.g.

vrscene-parser

A new package would process textual descriptions of virtual reality scenes or environments and return structured, validated outputs that describe the scene in a standardized format. It would use an LL

headline-parser

A new package that processes news headlines or short text snippets to generate structured summaries of current events. It uses an LLM to extract key entities, topics, and sentiment, ensuring the outpu

thematic-structurizer

A new package would process user-provided text input related to historical or thematic content—such as summaries, descriptions, or analyses of topics like persuasion techniques from antiquity—and retu

structuredxtract

A new package is designed to simplify the process of extracting structured information from user-provided text inputs by leveraging a language model with pattern matching capabilities. The system prom

sport-evo-summary

A new package that transforms unstructured text about sports evolution into a structured summary. Users input text describing changes in a sport, and the package returns a standardized breakdown of ke

scamp-analyzer

A new package that analyzes user-provided text descriptions of mischievous or cleverly unconventional behavior (a 'scamp' scenario) and returns a structured assessment. It evaluates the creativity, hu