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Changelog

All notable changes to ACE Framework will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

0.7.0 - 2025-12-04

⚠️ Breaking Changes

  • Complete terminology rename - Playbook → Skillbook, Bullet → Skill
    • Playbook → Skillbook
    • Bullet → Skill
    • Generator → Agent
    • Curator → SkillManager
    • OfflineAdapter → OfflineACE
    • OnlineAdapter → OnlineACE
    • DeltaOperation → UpdateOperation
    • DeltaBatch → UpdateBatch
    • Migration: Update imports and method calls to use new names
    • JSON files: Change "bullets" key to "skills" in saved skillbooks

Added

  • Deduplication consolidation_operations field - SkillManagerOutput now properly captures consolidation operations from LLM responses

Fixed

  • Deduplication not working - Added consolidation_operations field to SkillManagerOutput Pydantic model. Previously, Instructor was silently dropping these operations.

0.5.0 - 2025-11-20

⚠️ Breaking Changes

  • Playbook format changed to TOON (Token-Oriented Object Notation)
    • Playbook.as_prompt() now returns TOON format instead of markdown
    • Reason: 16-62% token savings for improved scalability and reduced inference costs
    • Migration: No action needed if using playbook with Generator/Curator/Reflector
    • Debugging: Use playbook._as_markdown_debug() or str(playbook) for human-readable output
    • Details: Uses tab delimiters and excludes internal metadata (created_at, updated_at)

Added

  • ACELiteLLM integration - Simple conversational agent with automatic learning
  • ACELangChain integration - Wrap LangChain Runnables with ACE learning
  • Custom integration pattern - Wrap ANY agentic system with ACE learning
    • Base utilities in ace/integrations/base.py with wrap_playbook_context() helper
    • Complete working example in examples/custom_integration_example.py
    • Integration Pattern: Inject playbook → Execute agent → Learn from results
  • Integration exports - Import ACEAgent, ACELiteLLM, ACELangChain from ace package root
  • TOON compression for playbooks - 16-62% token reduction vs markdown
  • Citation-based tracking - Strategies cited inline as [section-00001], auto-extracted from reasoning
  • Enhanced browser traces - Full execution logs (2200+ chars) passed to Reflector
  • Test coverage - Improved from 28% to 70% (241 tests total)

Changed

  • Renamed SimpleAgent → ACELiteLLM - Clearer naming for conversational agent integration
  • Playbook.__str__() returns markdown (TOON reserved for LLM consumption via as_prompt())

Fixed

  • Browser-use trace integration - Reflector now receives complete execution traces
    • Fixed initial query duplication (task appeared in both question and reasoning)
    • Fixed missing trace data (reasoning field now contains 2200+ chars vs 154 chars)
    • Fixed screenshot attribute bug causing AttributeError on step.state.screenshot
    • Fixed invalid bullet ID filtering - hallucinated/malformed citations now filtered out
    • Added comprehensive regression tests to catch these issues
    • Impact: Reflector can now properly analyze browser agent's thought process
    • Test coverage improved: 69% → 79% for browser_use.py
  • Prompt v2.1 test assertions updated to match current format
  • All 206 tests now pass (was 189)

0.4.0 - 2025-10-26

Added

  • Production Observability with Opik integration
    • Enterprise-grade monitoring and tracing
    • Automatic token usage and cost tracking for all LLM calls
    • Real-time cost monitoring via Opik dashboard
    • Graceful degradation when Opik is not installed
  • Browser Automation Demos showing ACE vs baseline performance
    • Domain checker demo with learning capabilities
    • Form filler demo with adaptive strategies
    • Side-by-side comparison of baseline vs ACE-enhanced automation
  • Support for UV package manager (10-100x faster than pip)
    • Added uv.lock for reproducible builds
    • UV-specific installation and development instructions
  • Improved documentation structure with multiple guides
    • QUICK_START.md for 5-minute quickstart
    • API_REFERENCE.md for complete API documentation
    • PROMPT_ENGINEERING.md for advanced techniques
    • SETUP_GUIDE.md for development setup
    • TESTING_GUIDE.md for testing procedures
  • Optional dependency groups for modular installation
    • observability for Opik integration
    • demos for browser automation examples
    • langchain for LangChain support
    • transformers for local model support
    • dev for development tools
    • all for all features combined

Changed

  • Replaced explainability module with observability
    • Removed empty ace/explainability directory
    • Migrated to production-grade Opik monitoring
    • Updated all documentation to reflect this change
  • Improved Python version requirements consistency (3.11+ everywhere)
  • Enhanced README with clearer examples and installation options
  • Reorganized examples directory for better discoverability
  • Updated CLAUDE.md with comprehensive codebase guidance

Fixed

  • Package configuration in pyproject.toml
  • Documentation references to non-existent explainability module
  • Python version inconsistencies across documentation files

Removed

  • Empty ace/explainability module (replaced by observability)
  • Outdated references to explainability features in documentation

0.3.0 - 2025-10-16

Added

  • Experimental v2 Prompts with state-of-the-art prompt engineering
    • Confidence scoring at bullet and answer levels
    • Domain-specific variants for math and code generation
    • Hierarchical structure with identity headers and metadata
    • Concrete examples and anti-patterns for better guidance
    • PromptManager for version control and A/B testing
  • Comprehensive prompt engineering documentation (docs/PROMPT_ENGINEERING.md)
  • Advanced examples demonstrating v2 prompts (examples/advanced_prompts_v2.py)
  • Comparison script for v1 vs v2 prompts (examples/compare_v1_v2_prompts.py)
  • Playbook persistence with save_to_file() and load_from_file() methods
  • Example demonstrating playbook save/load functionality (examples/playbook_persistence.py)
  • py.typed file for PEP 561 type hint support
  • Mermaid flowchart visualization in README showing ACE learning loop

Changed

  • Enhanced docstrings with comprehensive examples throughout codebase
  • Improved README with v2 prompts section and visual diagrams
  • Updated formatting to comply with Black code style

Fixed

  • README incorrectly referenced non-existent docs/ directory
  • Test badge URL in README (test.yml → tests.yml)
  • Code formatting issues detected by GitHub Actions

0.2.0 - 2025-10-15

Added

  • LangChain integration via LangChainLiteLLMClient for advanced workflows
  • Router support for load balancing across multiple model deployments
  • Comprehensive example for LangChain usage (examples/langchain_example.py)
  • Optional installation group: pip install ace-framework[langchain]
  • PyPI badges and Quick Links section in README
  • CHANGELOG.md for version tracking

Fixed

  • Parameter filtering in LiteLLM and LangChain clients (refinement_round, max_refinement_rounds)
  • GitHub Actions workflow using deprecated artifact actions v3 → v4

Changed

  • Improved README with better structure and badges
  • Updated .gitignore to exclude build artifacts and development files

Removed

  • Unnecessary development files from repository

0.1.1 - 2025-10-15

Fixed

  • GitHub Actions workflow for PyPI publishing
  • Updated artifact upload/download actions from v3 to v4

0.1.0 - 2025-10-15

Added

  • Initial release of ACE Framework
  • Core ACE implementation based on paper (arXiv:2510.04618)
  • Three-role architecture: Generator, Reflector, and Curator
  • Playbook system for storing and evolving strategies
  • LiteLLM integration supporting 100+ LLM providers
  • Offline and Online adaptation modes
  • Async and streaming support
  • Example scripts for quick start
  • Comprehensive test suite
  • PyPI packaging and GitHub Actions CI/CD

Features

  • Self-improving agents that learn from experience
  • Delta operations for incremental playbook updates
  • Support for OpenAI, Anthropic, Google, and more via LiteLLM
  • Type hints and modern Python practices
  • MIT licensed for open source use