They said it couldn't be done. We did it.
They said the sauce was secret. We figured it out.
In 1000+ head to head tests against the Big Boys. We win. Every. Single. Time.
Better content. Better relevancy. Better context.
Code context retrieval that actually works. 94% accuracy. Self-learning.
MCP - so it runs anywhere. Local - so what's yours stays yours. Open source - so it's available and transparent for all.
Don't take our word for it. It's free and open source. Run your own head to head benchmarks and see for yourself.
Like it? Star it. Issues? Report it. Ideas? Contribute.
pip install ace-frameworkfrom ace import UnifiedRetriever
retriever = UnifiedRetriever()
results = retriever.retrieve("your query")| Metric | Value |
|---|---|
| Accuracy | 94% |
| Test Queries | 1,000 |
| Response Time | <200ms |
LinUCB HyDE HDBSCAN Cross-Encoder BM25 Qdrant AST-Chunking Semantic-Dedup Confidence-Decay MiniLM Voyage
- Local: Ollama, LMStudio, any embedding model
- Cloud: OpenAI, Voyage, Gemini
- IDE: MCP server for VS Code, Cursor, Claude
Need fewer, more focused results? Set ACE_PRESET=precision:
# Environment variable
export ACE_PRESET=precision
# Or in .env
ACE_PRESET=precision| Preset | Results | Query Expansion | Use Case |
|---|---|---|---|
| precision | ~5 | None | Focused lookup, symbol search |
| fast | ~40 | None | Quick checks, no reranking |
| balanced | ~64 | 4 queries | Default - balanced recall/precision |
| deep | ~96 | 6 queries | Comprehensive analysis |
| diverse | ~80 | 4 queries | Multiple perspectives |
Precision mode gives Auggie-like focused results:
- Conservative fetch limits (fewer candidates)
- Higher min_score threshold (0.3 vs 0.0)
- No phrase text search fallback (trust embeddings)
- Ideal for: exact symbol lookup, specific file location, targeted searches
| Component | Recommendation | Notes |
|---|---|---|
| Embeddings (Text) | Qwen3-Embedding-8B (4096d) | Local via LM Studio, ~8GB VRAM |
| Embeddings (Code) | Voyage-code-3 | API, optimized for code |
| Vector DB | Qdrant | Local or cloud, free tier available |
| LLM | Any | Ollama, LM Studio, OpenAI, Gemini |
# Qdrant (Docker)
docker run -p 6333:6333 qdrant/qdrant
# LM Studio
# 1. Download: lmstudio.ai
# 2. Load: Qwen3-Embedding-8B
# 3. Start server on port 1234HyDE - Hypothetical Document Embeddings for query expansion
LinUCB Bandit - Learns which retrieval strategies work for your data
HDBSCAN Dedup - Kills near-duplicate chunks
Confidence Decay - Old memories fade, fresh data wins
AST Chunking - Code-aware splitting that doesn't break functions
Cross-Encoder Reranking - Precision filtering after retrieval
Self-Learning Memory - Cross-workspace patterns + project-specific knowledge. The one-two punch.
Research Foundation:
- Agentic Context Engineering - UC Berkeley & SambaNova Systems (Zhang et al.)
- Dynamic Cheatsheet methodology
- LinUCB Algorithm - Li et al. (2010)
Code Inspirations:
- Kayba.ai - Original implementation
- m1rl0k/Context-Engine - AST chunking, ReFRAG
- ELF - Confidence decay, golden rules
- r/Rag community - Memory architecture discussions
Built On:
- Qdrant - Vector search
- Sentence Transformers - MiniLM, Cross-Encoders
- Voyage AI - Embeddings
- HDBSCAN - Clustering
- LiteLLM - Multi-provider LLM
- FastMCP - MCP server framework
MIT.
