ACE - Agentic Context Engineering Plugin

Context optimization plugin for gptme, providing hybrid retrieval, semantic matching, and context curation for the lesson system.

Research Background

Based on Agentic Context Engineering (ACE) research (Stanford/SambaNova/UC Berkeley, October 2025):

• Core Insight: Treat agent prompts as "living playbooks" that evolve through iterative generation, reflection, and curation
• Problem Solved: Addresses brevity bias (loss of nuanced details) and context collapse (degradation from repeated rewrites)
• Framework Components:
  • Generator: Creates candidate reasoning trajectories
  • Reflector: Evaluates outputs, distilling insights
  • Curator: Iteratively refines and prunes the playbook

Also incorporates insights from:

• HGM (Huxley-Gödel Machine): +10.6% performance via context optimization
• GEPA (Genetic-Pareto): 70-90% token savings through intelligent context selection

Features

Hybrid Lesson Matching

Replaces simple keyword matching with multi-signal retrieval:

Signal	Weight	Description
Keyword	25%	Traditional keyword matching
Semantic	40%	Embedding-based similarity
Effectiveness	25%	Historical usage success
Recency	10%	Recently used lessons boosted
Tool Bonus	+20%	Bonus for matching tools

Semantic Deduplication

Uses sentence embeddings to detect similar lessons:

• Prevents redundant lesson accumulation
• Identifies consolidation opportunities
• Supports multiple similarity thresholds

Retrieval Analytics

Tracks retrieval patterns for continuous improvement:

• Session-level retrieval logging
• Method comparison (keyword vs hybrid)
• Effectiveness correlation

Installation

# Basic installation
pip install gptme-ace

# With embeddings support (recommended)
pip install gptme-ace[embeddings]

# Full installation (includes analytics tools)
pip install gptme-ace[full]

Configuration

Enable hybrid matching via environment variable:

export GPTME_LESSONS_HYBRID=true

Usage

As gptme Plugin

The plugin automatically enhances gptme's lesson matching when enabled:

# gptme.toml
[plugins]
enabled = ["gptme_ace"]

Programmatic Usage

from gptme_ace import GptmeHybridMatcher, LessonEmbedder

# Initialize with embeddings
embedder = LessonEmbedder()
matcher = GptmeHybridMatcher(embedder=embedder)

# Match lessons
results = matcher.match(lessons, context, threshold=0.5)

Dependencies

Required:

• gptme
• pydantic ≥2.0.0
• pyyaml ≥6.0

Optional (embeddings):

• sentence-transformers ≥2.2.0
• faiss-cpu ≥1.7.0
• numpy ≥1.24.0
• scipy ≥1.9.0

Migration from packages/ace

This plugin was migrated from Bob's workspace (packages/ace/) to gptme-contrib for broader use. The core functionality is preserved:

• GptmeHybridMatcher - Drop-in replacement for gptme's LessonMatcher
• LessonEmbedder - Embedding generation and similarity search
• HybridLessonMatcher - Core hybrid retrieval algorithm
• RetrievalTracker - Analytics and tracking

License

MIT