Anto01
773f8ff8af
Complete implementation of Agentic Context Engineering (ACE) framework: Core modules (optimization_engine/context/): - playbook.py: AtomizerPlaybook with helpful/harmful scoring - reflector.py: AtomizerReflector for insight extraction - session_state.py: Context isolation (exposed/isolated state) - feedback_loop.py: Automated learning from trial results - compaction.py: Long-session context management - cache_monitor.py: KV-cache optimization tracking - runner_integration.py: OptimizationRunner integration Dashboard integration: - context.py: 12 REST API endpoints for playbook management Tests: - test_context_engineering.py: 44 unit tests - test_context_integration.py: 16 integration tests Documentation: - CONTEXT_ENGINEERING_REPORT.md: Comprehensive implementation report - CONTEXT_ENGINEERING_API.md: Complete API reference - SYS_17_CONTEXT_ENGINEERING.md: System protocol - Updated cheatsheet with SYS_17 quick reference - Enhanced bootstrap (00_BOOTSTRAP_V2.md) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
124 lines
2.5 KiB
Python
124 lines
2.5 KiB
Python
"""
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Atomizer Context Engineering Module
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Implements state-of-the-art context engineering for LLM-powered optimization.
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Based on the ACE (Agentic Context Engineering) framework.
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Components:
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- Playbook: Structured knowledge store with helpful/harmful tracking
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- Reflector: Analyzes optimization outcomes to extract insights
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- SessionState: Context isolation with exposed/isolated separation
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- CacheMonitor: KV-cache optimization for cost reduction
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- FeedbackLoop: Automated learning from execution
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- Compaction: Long-running session context management
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Usage:
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from optimization_engine.context import (
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AtomizerPlaybook,
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AtomizerReflector,
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AtomizerSessionState,
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FeedbackLoop,
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CompactionManager
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)
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# Load or create playbook
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playbook = AtomizerPlaybook.load(path)
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# Create feedback loop for learning
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feedback = FeedbackLoop(playbook_path)
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# Process trial results
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feedback.process_trial_result(...)
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# Finalize and commit learning
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feedback.finalize_study(stats)
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"""
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from .playbook import (
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AtomizerPlaybook,
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PlaybookItem,
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InsightCategory,
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get_playbook,
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save_playbook,
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)
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from .reflector import (
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AtomizerReflector,
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OptimizationOutcome,
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InsightCandidate,
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ReflectorFactory,
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)
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from .session_state import (
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AtomizerSessionState,
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ExposedState,
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IsolatedState,
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TaskType,
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get_session,
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set_session,
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clear_session,
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)
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from .cache_monitor import (
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ContextCacheOptimizer,
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CacheStats,
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ContextSection,
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StablePrefixBuilder,
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get_cache_optimizer,
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)
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from .feedback_loop import (
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FeedbackLoop,
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FeedbackLoopFactory,
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)
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from .compaction import (
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CompactionManager,
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ContextEvent,
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EventType,
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ContextBudgetManager,
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)
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__all__ = [
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# Playbook
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"AtomizerPlaybook",
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"PlaybookItem",
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"InsightCategory",
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"get_playbook",
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"save_playbook",
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# Reflector
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"AtomizerReflector",
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"OptimizationOutcome",
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"InsightCandidate",
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"ReflectorFactory",
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# Session State
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"AtomizerSessionState",
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"ExposedState",
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"IsolatedState",
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"TaskType",
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"get_session",
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"set_session",
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"clear_session",
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# Cache Monitor
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"ContextCacheOptimizer",
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"CacheStats",
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"ContextSection",
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"StablePrefixBuilder",
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"get_cache_optimizer",
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# Feedback Loop
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"FeedbackLoop",
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"FeedbackLoopFactory",
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# Compaction
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"CompactionManager",
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"ContextEvent",
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"EventType",
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"ContextBudgetManager",
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]
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__version__ = "1.0.0"
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