Beta Release 4.5.0b4

No changes since last release.

Nightly Alpha 4.5.0a3

No changes since last release.

Beta Release 4.5.0b3

No changes since last release.

Nightly Alpha 4.5.0a2

No changes since last release.

Beta Release 4.5.0b2

No changes since last release.

Nightly Alpha 4.5.0a1

No changes since last release.

DataFog v4.4.0

What's New

Released: 2026-04-26

Python 3.13 Support Scope

Python 3.13 support is certified for the core SDK and CLI: pip install datafog and pip install datafog[cli].

Optional extras including nlp, nlp-advanced, ocr, distributed, and all are available but not yet certified on Python 3.13. They will be validated separately based on user demand.

v5 Migration Bridge

This release adds the v5-preview top-level APIs datafog.scan, datafog.redact, and datafog.protect while keeping the legacy datafog.detect and datafog.process APIs working with targeted migration warnings.

Privacy Defaults

Telemetry is now opt-in. DataFog does not send telemetry unless DATAFOG_TELEMETRY=1 is explicitly set. DATAFOG_NO_TELEMETRY=1 and DO_NOT_TRACK=1 continue to force telemetry off.

🔧 Other Changes

• Prepare v4.4.0 stable release (#133)

📥 Installation

# Core package (lightweight)
pip install datafog

# With all features
pip install datafog[all]

📊 Metrics

• Package size: ~2MB (core)
• Install time: ~10 seconds
• Tests passing: ✅
• Commits this week: 1

DataFog v4.3.0

What's New

Released: 2026-02-13

🚀 New Features

• feat(telemetry): add anonymous opt-out PostHog telemetry for v4.3.0 (#115)
• fix(ci): add diagnostics and plugin verification for benchmark tests
• fix(ci): add diagnostics and plugin verification for benchmark tests
• Merge pull request #104 from DataFog/feature/sample-notebooks
• Merge branch 'dev' into feature/sample-notebooks
• Fix segmentation fault in beta-release workflow and add sample notebook
• Merge pull request #103 from DataFog/feature/sample-notebooks
• Fix segmentation fault in beta-release workflow and add sample notebook
• Merge pull request #102 from DataFog/feature/gliner-integration-v420
• Merge branch 'dev' into feature/gliner-integration-v420
• Merge branch 'feature/gliner-integration-v420' of github.com:DataFog/datafog-python into feature/gliner-integration-v420
• Merge pull request #101 from DataFog/feature/gliner-integration-v420
• Merge branch 'dev' into feature/gliner-integration-v420
• Merge pull request #100 from DataFog/feature/gliner-integration-v420
• docs: add release guidelines to Claude.md

🐛 Bug Fixes

• Fix release version extraction without importing package
• Fix release workflow versioning and dry-run safety
• Merge pull request #108 from DataFog/fix/beta-workflow-changelog-v2
• Merge branch 'dev' into fix/beta-workflow-changelog-v2
• Merge pull request #107 from DataFog/fix/beta-workflow-changelog-v2
• Merge branch 'fix/performance-regression' into dev
• fix(ci): improve beta version detection to check existing git tags
• Merge branch 'fix/performance-regression' of github.com:DataFog/datafog-python into fix/performance-regression
• fix(ci): improve beta versioning logic and use GH_PAT token
• fix(ci): improve beta versioning logic and use GH_PAT token
• fix(ci): replace invalid --benchmark-skip flag with simple performance test
• Merge pull request #106 from DataFog/fix/performance-regression
• Merge branch 'dev' into fix/performance-regression
• Merge pull request #105 from DataFog/fix/performance-regression
• fix(ci): reset benchmark baseline to resolve false regression alerts
• fix(performance): eliminate memory debugging overhead from benchmarks
• fix(performance): eliminate redundant regex calls in structured output mode
• fix(performance): eliminate redundant regex calls in structured output mode
• fix(ci): handle segfault gracefully while preserving test validation
• fix(tests): make spaCy address detection test more robust
• fix(ci): improve GLiNER validation to confirm PyTorch exclusion
• fix(ci): exclude PyTorch dependencies entirely to prevent segfault
• fix(ci): eliminate PyTorch segfaults and enhance README with GLiNER examples
• fix(ci): workaround for PyTorch segfault in CI environments
• fix(ci): split test execution to prevent memory segfault
• fix(ci): reduce coverage reporting to prevent segmentation fault
• fix(tests): resolve final GLiNER test failures
• fix(tests): update GLiNER test mocking for proper import paths
• fix(tests): resolve GLiNER dependency mocking for CI environments

🔧 Other Changes

• overhaul: audit-and-cleanup architecture + accuracy corpus + agent API (#121)
• chore(ci): consolidate 7 workflows into 3 (#117)
• chore: bump version to 4.3.0 for next development cycle
• chore: clean up test changelog file after merge
• chore: clean up test changelog file after merge
• chore: set version to 4.2.0b1 for beta testing of unreleased 4.2.0
• resolve: merge conflicts with enhanced segfault detection

📥 Installation

# Core package (lightweight)
pip install datafog

# With all features
pip install datafog[all]

📊 Metrics

• Package size: ~2MB (core)
• Install time: ~10 seconds
• Tests passing: ✅
• Commits this week: 51

release: prepare v4.2.0 with GLiNER integration

DataFog 4.2.0 - GLiNER Integration Release

Released: 2025-05-30

🚀 Major Features

GLiNER Integration

• Modern NER Engine: Added GLiNER (Generalist Named Entity Recognition) support
• Smart Cascading: Intelligent progression from regex → GLiNER → spaCy
• 32x Performance: GLiNER provides 32x faster NER compared to spaCy baseline
• PII-Specialized Models: Support for urchade/gliner_multi_pii-v1 and other models

Engine Selection

from datafog.services.text_service import TextService

# New GLiNER engine
service = TextService(engine="gliner")

# Smart cascading (recommended)
service = TextService(engine="smart")  # regex → GLiNER → spaCy

Performance Improvements

• 190x faster regex engine for structured PII (emails, phones, SSNs)
• Lightweight core: <2MB package with optional ML extras
• Memory optimization: Enhanced segfault handling and performance validation

🐛 Bug Fixes

• Fixed CI segmentation faults in test environments
• Resolved benchmark regression detection
• Improved dependency management for optional ML features
• Enhanced test stability across platforms

🔧 Infrastructure

• Comprehensive CI/CD improvements
• Enhanced GitHub Actions workflows
• Better error handling and diagnostics
• Sample notebooks and examples

📥 Installation

# Core package (lightweight)
pip install datafog

# With GLiNER support
pip install datafog[nlp-advanced]

# Everything included
pip install datafog[all]

📊 Performance Comparison

Engine	Speed vs spaCy	Accuracy	Use Case
regex	190x faster	High (structured)	Emails, phones, SSNs
gliner	32x faster	Very High	Modern NER
spacy	1x (baseline)	Good	Traditional NLP
smart	60x faster	Highest	Best balance

🔗 Links

• PyPI: https://pypi.org/project/datafog/4.2.0/
• Documentation: https://docs.datafog.ai
• GitHub: https://github.com/datafog/datafog-python

v4.1.1

What's Changed

• bump 4.1.1 for pypi release by @sidmohan0 in #90

Full Changelog: v4.1.0...v4.1.1