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Releases: IBM/Agentics

Release 0.3.4: Pydantic v2 Compatibility Fixes

25 Mar 22:49

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What's Changed

This release includes critical Pydantic v2 compatibility fixes for Langflow 1.9 integration.

Bug Fixes

  • Fixed Pydantic v2 mutable default values in AG class (states, areduce_batches, crew_prompt_params)
  • Fixed merge_states method data alignment bug that caused sentiment analysis results to misalign
  • Improved error handling for empty LLM responses with detailed logging

Dependencies

  • Added apscheduler>=3.10.0 to suppress litellm warnings
  • Added fastapi-sso>=0.10.0 for complete litellm proxy support

Installation

pip install git+https://github.com/IBM/Agentics.git@v0.3.4

Full Changelog: https://github.com/IBM/Agentics/compare/v0.3.3...v0.3.4

v0.3.3

13 Mar 14:24
3e9034b

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What's Changed

Full Changelog: v0.3.2...v0.3.3

v0.3.3a1

12 Mar 11:35

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v0.3.3a1 Pre-release
Pre-release

Full Changelog: v0.3.1...v0.3.3a1

v0.3.2 langflow fix

11 Mar 14:27

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v0.3.1

17 Feb 14:14
4084f75

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Official Langflow Version of Agentics

What's Changed

Full Changelog: v0.3.0...v0.3.1

0.3.0 - Semantic Operators & Documentation

12 Feb 10:14
ba3a5e7

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🎯 Key Features

Semantic Operators

  • sem_map: Apply semantic transformations across datasets
  • sem_filter: Semantically filter data based on LLM-powered predicates
  • sem_agg: Aggregate semantic information across collections

Documentation & Architecture

  • Comprehensive documentation improvements for semantic operators
  • Refactored LLM connections module for better architecture
  • Enhanced server_url parameter support for flexible LLM configuration
  • Updated README with semantic operators integration guide

Bug Fixes & Improvements

  • Fixed undefined variable in PydanticTransducerVLLM executor
  • Corrected dependency references in pyproject.toml
  • Fixed package imports in async_executor.py
  • Improved code formatting and pre-commit compliance
  • Enhanced module organization and separation of concerns

📦 Installation

Install the latest version:

pip install agentics-py==0.3.0

📖 Documentation


Release Date: February 12, 2026
Status: Latest Stable Release

v0.2.4

09 Feb 13:15
4889ccb

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What's Changed

  • Relax pandas version constraint to >=2 by @D3f0 in #126

Full Changelog: v0.2.3...v0.2.4

v0.2.3

02 Feb 08:03
324a908

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What's Changed

  • Add semantic operators module by @D3f0 in #124

Full Changelog: v0.2.2...v0.2.3

v0.2.2

30 Jan 07:29
0707ccb

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Changes

Core Dependencies

• Added duckdb>=1.4.3 for efficient data querying
• Added pandas>=2.3.3 for enhanced data manipulation
• Added async-lru>=2.0.5 for async caching capabilities

Type System Enhancements ( src/agentics/core/atype.py )

• Added comprehensive docstring to pydantic_model_from_dict() function explaining:
• Dynamic Pydantic model creation from sample dictionaries
• Type inference and field name normalization
• Usage examples and edge cases
• Renamed composition fields from left/right to target/source for semantic clarity:
• target : The destination type in a composition
• source : The source type in a composition
• Removed debug print statement from create_pydantic_model()

Default Types ( src/agentics/core/default_types.py )

• Added Abool type: A new semantic type for boolean values with type validation
• Ensures only boolean values are accepted during construction
• Follows the same pattern as existing Astr type

Transduction Improvements ( src/agentics/core/transducible_functions.py )

• Enhanced Transduce class with string representation methods:
• Added str() : Human-readable string output
• Added repr() : Developer-friendly representation
• Added _one_to_str() : Handles Pydantic models, dicts, lists, and generic types
• Added new parameters to transducible() decorator:
• transduce_fields : List of specific fields to transduce
• prompt_template : Custom prompt template for transduction
• Fixed transduction return type: Now returns TransductionResult consistently
• Fixed generate_prototypical_instances() : Updated field definition to use optional list

Core AG Enhancements ( src/agentics/core/agentics.py )

• Improved batch processing:
• Transduction now chunks inputs using amap_batch_size for better memory management
• Processes chunks sequentially to handle large datasets
• Fixed state merging logic:
• Now filters output fields against allowed model fields (Pydantic v2 compatibility)
• Prevents errors from extra fields in merged states
• Validates AG lengths before merging
• Enhanced explanation generation:
• Improved prompt instructions for better semantic understanding
• Renamed explanation variables for clarity ( left/right → target/source )
• Added confidence scoring guidance to LLM
• Improved compose_states() method:
• Changed from nested loops to paired zip() iteration (matching AG lengths)
• Fixed semantic naming: left/right → target/source
• More efficient and semantically correct composition
• Better error handling:
• Added validation in merge_states() to check length compatibility
• Meaningful error messages for debugging
• Truncated display names: Limited AG names to 30 characters in progress descriptions

v0.2.2a2 Preview - Semantic operators improvements

29 Jan 14:14

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Changes

This alpha release includes significant improvements to the Agentics framework's type composition system and transduction capabilities.

Key Features

  • Enhanced Type Composition: Renamed composition fields from left/right to target/source for better semantic clarity
  • New Abool Type: Added semantic type for boolean values with validation
  • Improved Batch Processing: Transduction now chunks inputs for better memory management
  • Better State Merging: Fixed Pydantic v2 compatibility with proper field filtering
  • Enhanced Explanations: Improved LLM instructions for more meaningful semantic transductions
  • New Dependencies: Added duckdb, pandas, and async-lru for improved data handling

Technical Improvements

  • Chunked transduction prevents memory overload with large datasets
  • Better error handling with meaningful validation messages
  • Improved string representations for Transduce objects
  • Pre-commit fixes applied

For detailed information, see PR #115