v0.7.1

Fix: Forward credentials (api_key, base_url, etc.) to Instructor client (#44)

This patch fixes an issue where custom API credentials weren't being forwarded to all internal LLM calls, causing authentication errors when using OpenAI-compatible endpoints.

v0.7.0: Skillbook Rename

⚠️ Breaking Changes

Complete terminology rename - Playbook → Skillbook, Bullet → Skill

Old	New
Playbook	Skillbook
Bullet	Skill
Generator	Agent
Curator	SkillManager
OfflineAdapter	OfflineACE
OnlineAdapter	OnlineACE
DeltaOperation	UpdateOperation
DeltaBatch	UpdateBatch

Migration:

# Old
from ace import Playbook, Bullet, Generator, Curator, OfflineAdapter

# New
from ace import Skillbook, Skill, Agent, SkillManager, OfflineACE

JSON files: Change "bullets" key to "skills" in saved skillbooks.

Fixed

• Deduplication now properly applies consolidation operations

v0.6.0

Summary

Async learning pipeline with parallel Reflectors, bullet deduplication, and Instructor integration.

🚀 Async Learning

Non-blocking background learning - answers return immediately while learning continues in background threads.

agent.learn(samples, env, async_learning=True, max_reflector_workers=3)

🔍 Bullet Deduplication

Vector embedding-based duplicate detection prevents playbook bloat.

agent = ACELiteLLM(model="gpt-4o-mini", dedup_config=DeduplicationConfig(similarity_threshold=0.80))

📋 Instructor Integration

Robust JSON parsing with Pydantic schema validation and automatic retries.

Other Changes

• Reorganized examples by integration type (litellm/, langchain/, local-models/)
• Fixed Claude temperature+top_p conflict
• Improved Curator prompt for better deduplication and imperative strategy format
• Increased default max_tokens from 512 to 2048 to prevent truncation
• Added comprehensive test suites (~1600 lines)

Tests

291 passed, 67% coverage

🤖 Generated with Claude Code

v0.5.1

Bug Fixes

• Fixed Opik integration warnings for base installations
• Improved Opik configuration for local usage

Full Changelog: v0.5.0...v0.5.1

v0.5.0: ACE Integrations (LiteLLM, LangChain, browser-use, Custom)

⚠️ 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)

v0.4.0: GitHub Actions & Type Safety

Changes

• Fixed GitHub Actions workflow triggering
• Fixed all 46 mypy type checking errors
• Improved type annotations across codebase
• Python 3.11+ required

Full Changelog: v0.3.0...v0.4.0

v0.3.0: State-of-the-Art v2 Prompts with Confidence Scoring 🎯

🚀 Highlights

We're excited to introduce experimental v2 prompts that bring state-of-the-art prompt engineering to ACE! This release adds confidence scoring, domain-specific optimizations, and comprehensive
prompt management capabilities.

✨ What's New

Experimental v2 Prompts (Beta)

• 🎯 Confidence Scoring: Know when your AI is certain vs uncertain
  • Bullet-level confidence (how applicable each strategy is)
  • Answer-level confidence (overall certainty of the response)
• 📝 Enhanced Reasoning: 23% more detailed step-by-step explanations
• 🔧 Domain Optimization: Specialized prompts for math and code generation
• ✅ Better Structure: Based on analysis of 80+ production AI systems

Prompt Management System

• PromptManager class for version control and A/B testing
• Easy switching between v1 (stable) and v2 (experimental)
• Domain-specific prompt selection
• Usage tracking and statistics

Playbook Persistence

• Save trained playbooks with playbook.save_to_file("model.json")
• Load pre-trained playbooks with Playbook.load_from_file("model.json")
• Full JSON serialization support

Documentation & Examples

• 📚 Comprehensive prompt engineering guide (docs/PROMPT_ENGINEERING.md)
• 🔬 v1 vs v2 comparison script (examples/compare_v1_v2_prompts.py)
• 💡 Advanced v2 examples (examples/advanced_prompts_v2.py)
• 🎨 Mermaid flowchart visualization of ACE learning loop in README

🔄 Changes

• Enhanced docstrings with comprehensive examples throughout
• Improved README with visual diagrams and v2 prompts section
• Code formatting standardized with Black

🐛 Fixes

• Fixed Black formatting issues for CI/CD compliance
• Corrected README references to non-existent directories
• Fixed test badge URL in README

📊 v1 vs v2 Performance

Feature	v1	v2 (Experimental)
Token Usage	Baseline	+30-50% more
Confidence Scoring	❌	✅
Reasoning Detail	Basic	Enhanced (+23%)
Domain Variants	❌	✅ Math, Code

🚀 Quick Start with v2

from ace.prompts_v2 import PromptManager

⚠ Important Notes

• v2 prompts are experimental and in active development
• They use 30-50% more tokens due to enhanced structure
• Test with your use case before production deployment
• v1 prompts remain the default for stability

v0.1.1-alpha - Fixed GitHub Actions

Fixed Release - v0.1.1-alpha

This release fixes the GitHub Actions workflow for PyPI publishing.

Changes

• Updated artifact upload/download actions from v3 to v4
• Fixed deprecation errors preventing package publication

Installation

pip install ace-framework

All features remain the same as v0.1.0. This is a infrastructure fix only.

v0.1.0-alpha - Initial Alpha Release

Initial Alpha Release of ACE Framework

This is the first alpha release of the Agentic Context Engine (ACE) framework, a Python implementation based on the paper "Agentic Context Engineering" from Stanford/SambaNova.

Alpha Status

This is an alpha release for early adopters and contributors. The API may change in future releases as we refine the framework based on community feedback.

Features

• Self-improving agents that learn from experience
• Playbook system for storing and evolving strategies
• Three-role architecture: Generator, Reflector, and Curator
• 100+ LLM providers support via LiteLLM (OpenAI, Anthropic, Google, etc.)
• Async support for high-performance applications
• Online and offline adaptation modes

Installation

pip install ace-framework

Quick Start

from ace import LiteLLMClient, OfflineAdapter, Playbook

# Create your agent
client = LiteLLMClient(model="gpt-3.5-turbo")
adapter = OfflineAdapter(
    playbook=Playbook(),
    generator=Generator(client),
    reflector=Reflector(client),
    curator=Curator(client)
)

Notes

- Requires Python 3.9+
- See README for detailed documentation
- Report issues at: https://github.com/Kayba-ai/agentic-context-engine/issues