recognizers-text-date-time
recognizers-text-date-time README
Decision gist · record as of 2026-08-14
Yes, with conditions. The package is stable and permissively licensed, with low install friction and no known vulnerabilities. However, it is in alpha status and has not been released since 2019, which may indicate incomplete language support or unresolved issues. Install if you need multilingual date/time entity extraction in a non-critical context or as part of a larger Recognizers-Text integration; avoid if you require active maintenance or guaranteed stability for production systems.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Package is in alpha; no official Python version specified in metadata, though classifiers indicate Python 3.6+ support.
- Requires recognizers-text and related packages to be installed.
- Low install friction with a pure-Python wheel.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects without legal concern.
last release 2019-11-12 (2467 days) · last repo commit 2026-04-17 · 1,793 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 99,211 downloads/mo, #13,035 on PyPI
Alternatives
Verify before relying
pip install recognizers-text-date-time
from recognizers_text_date_time import recognize_datetime
results = recognize_datetime('tomorrow at 3pm', 'en-us')
for result in results:
print(result.resolution)- Actual Python version compatibility beyond classifier hints (3.6+)
- Whether alpha status reflects incomplete implementation or stable-but-unversioned code
- Performance characteristics on large text volumes or complex temporal expressions
- Accuracy rates for date/time extraction across supported languages
What it is and what it does
recognizers-text-date-time is a specialized component of Microsoft's Recognizers-Text suite that extracts and normalizes date and time expressions from natural language text. It handles temporal phrases like "tomorrow", "next Friday", "3 PM", and "in two weeks", converting them into structured date/time values. The package supports multiple languages including English, Chinese, French, Spanish, Portuguese, German, Italian, Turkish, Hindi, and Dutch, with partial support for Japanese, Korean, Arabic, and Swedish.
The package is designed as a building block for NLP pipelines, chatbots, and language understanding systems. It depends on recognizers-text as its foundation, along with regex and datedelta for pattern matching and date arithmetic. As an alpha-stage library that has not released since 2019, it represents a snapshot of the recognizer suite's date-time capabilities; the upstream repository remains maintained, but this PyPI package itself is not actively versioned.
Use it for
- Extract temporal references from user input in chatbots or virtual assistants to schedule tasks or set reminders.
- Parse date/time mentions in customer support tickets or logs to identify when issues occurred.
- Normalize temporal expressions in multilingual documents for downstream analysis or database storage.
- Build NLP preprocessing pipelines that need to identify and resolve date/time entities before further processing.
- Augment text analytics workflows to tag and extract temporal context from unstructured text.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
The package is stable and permissively licensed, with low install friction and no known vulnerabilities. However, it is in alpha status and has not been released since 2019, which may indicate incomplete language support or unresolved issues. Install if you need multilingual date/time entity extraction in a non-critical context or as part of a larger Recognizers-Text integration; avoid if you require active maintenance or guaranteed stability for production systems.
Install
recognizers-text-date-time on PyPI
Before you install
Low install friction with a pure-Python wheel. The package is in alpha status (version 1.0.2a2) and has not seen a release since 2019-11-12, though the upstream repository remains active as of 2026-04-17. Depends on recognizers-text and related recognizer packages plus regex and datedelta.
Package is in alpha; no official Python version specified in metadata, though classifiers indicate Python 3.6+ support. Requires recognizers-text and related packages to be installed.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects without legal concern.
Quickstart
pip install recognizers-text-date-time
from recognizers_text_date_time import recognize_datetime
results = recognize_datetime('tomorrow at 3pm', 'en-us')
for result in results:
print(result.resolution)Verify before relying
- Actual Python version compatibility beyond classifier hints (3.6+)
- Whether alpha status reflects incomplete implementation or stable-but-unversioned code
- Performance characteristics on large text volumes or complex temporal expressions
- Accuracy rates for date/time extraction across supported languages
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesrecognizers-textrecognizers-text-numberrecognizers-text-number-with-unitregexdatedelta |
| Maintenance | Actively maintained 2,467 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 99,211 / month, #13,035 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3.6Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: recognizers_text_date_time-1.0.2a2-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “date time entity extraction”
- recognizers-text-date-timeRecognizes and resolves date and time entities from text in multiple…
- recognizers-text-choiceRecognizes and resolves entities like numbers, units, and date/time…
- recognizers-textRecognizes and extracts entities like numbers, units, dates, and…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also recognizers-text · date-spacy · recognizers-text-choice · recognizers-text-number · recognizers-text-number-with-unit · presidio-analyzer · gliner2 · commonregex · prefixdate · ctparse