Omn1-ACE

Intelligent Context Management for AI Development Tools

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🚀 Stop your AI tools from sending 50 files when only 3 are relevant

Omn1-ACE prevents wasteful API calls by finding only relevant context through semantic search and smart caching—saving 85% on API costs.

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🚧 Project Status

Current Stage: Prototype / Early Development

• ✅ Architecture designed and documented
• ✅ Infrastructure setup (Docker, databases)
• ⚠️ Core API endpoints are placeholders (not yet implemented)
• ⚠️ Not production-ready

For production-ready microservices, see OmniMemory

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💡 Why Omn1-ACE?

The Problem: AI coding assistants send ALL potentially relevant files to expensive APIs—even when 90% are irrelevant.

The Solution: Smart retrieval finds only what's needed BEFORE hitting paid APIs.

Traditional vs Omn1-ACE

Aspect	Without Omn1-ACE	With Omn1-ACE
Files Searched	50+ files keyword search	50+ files semantic search (local)
Files Sent to API	All 50 files	Only 3 relevant files
Cache Check	None (re-send everything)	L1/L2/L3 (skip 2 already sent)
API Tokens	60,000 tokens	950 tokens
Cost per Query	$0.90	$0.014
Monthly Cost (500 queries)	~$450	~$68

How Savings Break Down:

Optimization	Impact	Savings
Smart Retrieval	Finds 3 of 50 files	80% ($340/mo)
Cache Hits	Skips 2 already sent	13% ($55/mo)
Compression	Reduces remaining size	5% ($22/mo)
Context Pruning	Trims conversation history	2% ($8/mo)

Total Savings: $382/month per developer (85% reduction)

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⚡ How It Works

Without Omn1-ACE

You: "Find the authentication bug"

AI Tool:
1. Searches all files for "auth" → 50 files
2. Sends all 50 files → Anthropic API
3. You pay: 60,000 tokens ($0.90)

Result: 47 files were completely irrelevant (wasted money)

With Omn1-ACE

You: "Find the authentication bug"

Omn1-ACE intercepts (before API):
1. Semantic search (local, free) → Finds 3 relevant of 50 files
2. Cache check (local, free) → 2 already sent, skip them
3. Sends 1 new file → Anthropic API
4. You pay: 950 tokens ($0.014)

Result: 59,050 tokens never hit paid API = $0.886 saved

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🔑 Key Features

🔍 Tri-Index Search Prevents sending irrelevant files Find only what's relevant using three methods: Dense: Semantic vector similarity Sparse: BM25 keyword matching Structural: AST code patterns Impact: 80% cost reduction

💾 Multi-Tier Caching Prevents re-sending files Three-layer cache avoids redundant API calls: L1: User cache (your history) L2: Team cache (shared knowledge) L3: Archive (long-term) Impact: 13% cost reduction

🧠 Predictive Prefetching Anticipates what you'll need Learns from patterns to prefetch: Workflow patterns Code structure relationships Team behavior Impact: Faster responses

Additional Capabilities

• Code-Aware Compression: Further reduces tokens while preserving semantic meaning (5% additional savings)
• Model-Specific Optimization: Context tailored for Claude, GPT, or Gemini
• Team Intelligence: L2 cache learns from what your teammates already sent
• LSP Integration: Enhanced code intelligence via Language Server Protocol

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🎯 Quick Start

Prerequisites

• Python 3.11+
• Docker & Docker Compose (recommended)
• 4GB+ RAM

🐳 Docker Compose (Recommended)

Get started in 5 minutes using convenience scripts:

# Clone the repository
git clone https://github.com/mrtozner/omn1-ace.git
cd omn1-ace

# Start all services (auto-creates .env from template)
./start.sh

# Check service status
./status.sh

# View logs
./logs.sh              # All services
./logs.sh postgres     # Specific service

# Restart services
./restart.sh

# Stop services
./stop.sh

Available scripts:

• start.sh - Start all Docker services with health checks
• stop.sh - Stop all services
• restart.sh - Restart all services
• logs.sh - View service logs (all or specific service)
• status.sh - Check service health and status

Manual Docker commands (if you prefer):

# Copy environment template
cp .env.example .env && nano .env

# Start services
docker-compose -f deploy/docker-compose.yml up -d

# Verify
curl http://localhost:8000/health

📖 Full Setup Guide →

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🏗️ Architecture

Omn1-ACE implements a 4-layer anticipatory system:

┌─────────────────────────────────────────────────┐
│         AI Development Tools                    │
│  (Claude Code, Cursor, Continue, etc.)          │
└───────────────────┬─────────────────────────────┘
                    │
        ┌───────────▼────────────┐
        │  Interception Layer    │  ← MCP Protocol
        │  (Before API call)     │
        └───────────┬────────────┘
                    │
        ┌───────────▼────────────┐
        │   Tri-Index Search     │  ← Find 3 of 50 relevant files
        │  (LOCAL, <100ms, FREE) │     (Dense + Sparse + Structural)
        └───────────┬────────────┘
                    │
        ┌───────────▼────────────┐
        │   Multi-Tier Cache     │  ← Check L1/L2/L3: Already sent?
        │  (LOCAL, <5ms, FREE)   │     Skip cached files
        └───────────┬────────────┘
                    │
        ┌───────────▼────────────┐
        │   Send to API          │  ← Only 1 new file (950 tokens)
        │  (PAID, Anthropic/     │     Instead of 50 files (60K tokens)
        │   OpenAI)              │
        └────────────────────────┘

Result: 85% cost reduction ($0.014 vs $0.90 per query)

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⚙️ API Endpoints

Endpoint	Method	Description
/health	GET	Health check
/api/v1/search	POST	Semantic search (find relevant files, not all files)
/api/v1/cache/check	POST	Cache lookup (skip files already sent to API)
/api/v1/embeddings	POST	Generate vector embeddings for semantic search
/api/v1/predict	POST	Predict likely context needs (prefetching)
/api/v1/compress	POST	Compress context (optional secondary optimization)
/api/v1/cache/stats	GET	Cache performance statistics

Interactive Docs: http://localhost:8000/docs (OpenAPI)

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📊 Real-World Example

Scenario: "Find the authentication bug"

WITHOUT Omn1-ACE:

Files sent to API: 50 files
- auth.ts ✓
- auth-middleware.ts ✓
- auth.test.ts ✓
- database-config.ts ✗ (irrelevant)
- logging-utils.ts ✗ (irrelevant)
- email-templates.ts ✗ (irrelevant)
- ...44 more irrelevant files ✗

Tokens sent: 60,000
Cost: $0.90
Waste: 47 files (78%) completely irrelevant

WITH Omn1-ACE:

Semantic search (local): Finds 3 relevant of 50
- auth.ts ✓ (similarity: 0.94)
- auth-middleware.ts ✓ (similarity: 0.89)
- auth.test.ts ✓ (similarity: 0.86)

Cache check (local):
- auth.ts: In L1 cache (sent 2 queries ago) → SKIP
- auth-middleware.ts: In L2 cache (teammate sent) → SKIP
- auth.test.ts: Not cached → SEND

Files sent to API: 1 file
Tokens sent: 950 (optionally compressed)
Cost: $0.014
Savings: $0.886 (98.5%)

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⚠️ Multi-Tool Context Considerations

Context Window Limits

Different AI models have different token limits:

Model	Context Window	Configuration
Claude 3.5 Sonnet	200,000 tokens	CLAUDE_CONTEXT_WINDOW=200000
GPT-4 Turbo	128,000 tokens	GPT_CONTEXT_WINDOW=128000
Gemini 1.5 Pro	1,000,000 tokens	GEMINI_CONTEXT_WINDOW=1000000
GPT-3.5 Turbo	16,000 tokens	GPT_CONTEXT_WINDOW=16000

Why this matters: Even with smart retrieval, you need to ensure your target model can handle the optimized context.

Configuration

Set your target model in .env:

DEFAULT_TARGET_MODEL=claude  # or gpt, gemini
CLAUDE_CONTEXT_WINDOW=200000
GPT_CONTEXT_WINDOW=128000
GEMINI_CONTEXT_WINDOW=1000000

Model-Specific Behavior

Claude (Anthropic):

• ✅ Best with structured, detailed context
• ✅ Excellent at following complex instructions
• ⚡ Prefers explicit task breakdowns

GPT (OpenAI):

• ✅ Works well with conversational context
• ⚠️ May need more explicit formatting
• ⚡ Better with shorter, focused context

Gemini (Google):

• ✅ Handles very large context windows
• ✅ Good with multimodal content
• ⚠️ May need different prompt engineering

Recommendation: Standardize on one model per team for consistent cache sharing (L2).

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📊 Performance

Recommended Resources

Component	Requirements
API Server	2+ CPU cores, 4GB+ RAM
PostgreSQL	4GB+ RAM, SSD storage
Qdrant	8GB+ RAM (scales with corpus)
Redis	2GB+ RAM (scales with cache)

Typical Performance

Operation	Time	Cost
Semantic search	<100ms	$0 (local)
Cache lookup	<5ms	$0 (local)
Vector embedding	<50ms	$0 (local)
API call (prevented)	N/A	$0.90 saved
API call (optimized)	1-3s	$0.014

Scaling

• Horizontal: API servers behind load balancer
• PostgreSQL: Read replicas for read-heavy workloads
• Qdrant: Clustering for large-scale vector search
• Redis: Clustering for high-availability caching

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🔒 Security

Before production deployment:

1. ✅ Change all default passwords in docker-compose.yml
2. ✅ Use environment variables for sensitive configuration
3. ✅ Enable TLS/SSL for all service connections
4. ✅ Configure authentication for API endpoints
5. ✅ Use network policies to restrict service access
6. ✅ Regular security updates for all dependencies

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🤝 Contributing

Contributions are welcome!

Before submitting a PR:

• All tests pass
• Code follows style guidelines (black, isort, pylint)
• New features include tests
• Documentation is updated

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📄 License

This project is licensed under the MIT License - see LICENSE for details.

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🔗 Related Projects

• OmniMemory: Production-ready microservices (13 independent services)
• Extensions: LSP integration for enhanced code intelligence (docs)

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🙏 Acknowledgments

Built with:

• FastAPI - Modern web framework
• Qdrant - Vector similarity search
• PostgreSQL - Relational database
• Redis - In-memory data store
• NetworkX - Graph analysis

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⭐ Star this repo if you find it useful!

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Made with ❤️ by Mert Ozoner