OmniMemory

Production-Ready Microservices for Intelligent Context Management

Quick Start • Services • Report Issue

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🎯 Stop paying for irrelevant files sent to AI APIs

13 production-ready microservices that prevent wasteful API calls through semantic search, smart caching, and team learning—saving 85% on AI development costs.

NEW in v2.0: Universal AI tool support (Claude, Cursor, Copilot, VS Code), predictive context loading, workflow pattern mining, auto-generated project docs, and sleep-inspired memory consolidation.

One-command setup: omni init --tool all automatically configures your AI tools!

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⚠️ Setup Complexity

OmniMemory consists of 13 independent microservices that must be started individually

• ✅ Each service is production-ready and battle-tested
• ⚠️ No unified launcher (manual setup required)
• ⚠️ Services must be started in dependency order
• ℹ️ Recommended for: Advanced users, custom integrations
• 💡 Looking for simple deployment? See Omn1-ACE (integrated system)

📖 Step-by-Step Setup Guide →

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💡 Why OmniMemory?

The Core Problem: AI coding assistants send 50+ files to expensive APIs when only 3 are relevant—wasting 85% of your API budget.

How OmniMemory Solves This

Without OmniMemory	With OmniMemory	Savings
Send all 50 files → API	Semantic search finds 3 relevant (local, free)	80%
Re-send everything	Cache check: 2 already sent, skip them (local, free)	13%
Send raw files	Compress remaining file (optional)	5%
60,000 tokens	950 tokens	98.5%
$0.90 per query	$0.014 per query	$0.886 saved

Real-World Impact

Feature	Traditional Approach	OmniMemory Microservices
Files Sent to API	All 50 files that match keyword	Only 3 semantically relevant files
Redundancy Prevention	Re-send everything every query	L1/L2/L3 cache skips already sent files
Token Usage	60,000 tokens (includes irrelevant)	950 tokens (only relevant)
API Cost	$0.90 per query	$0.014 per query
Monthly Cost (500 queries)	$450	$68
Team Benefit	Each user sends full context	L2 cache shares across team
Architecture	Monolithic	13 modular services

Key Insight: 85% of savings comes from NOT SENDING irrelevant files in the first place.

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🏗️ Architecture

┌─────────────────────────────────────────────┐
│         Client Applications                 │
│  (Claude Code, Cursor, Continue, etc.)      │
└──────────────┬──────────────────────────────┘
               │
        ┌──────▼──────┐
        │  MCP Server │ ← Intercepts before API call
        └──────┬──────┘
               │
    ┌──────────┼──────────┐
    │          │          │
┌───▼───┐  ┌──▼──┐   ┌──▼────┐
│Embed  │  │Search│   │Cache │
│Layer  │  │Layer│   │Layer  │  ← All LOCAL (no API cost)
└───┬───┘  └──┬──┘   └──┬────┘
    │         │         │
    │   Find 3 of 50   Skip 2
    │   relevant files already sent
    │         │         │
    └─────────┼─────────┘
              │
         ┌────▼────┐
         │  Send   │ ← Only 1 file (950 tokens)
         │  to API │    Instead of 50 (60K tokens)
         └─────────┘

Result: $0.014 instead of $0.90 per query

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🎉 New Features (v2.0)

OmniMemory 2.0 transforms the system from a storage layer into an intelligent memory system with learning capabilities, predictive context loading, and universal AI tool compatibility.

🌐 MCP Integration Architecture (Universal Memory Layer)

Transform OmniMemory into a universal memory layer that works across all AI coding tools — not just Claude.

Key Capabilities:

• Universal Compatibility: Works with Claude Code, Cursor, GitHub Copilot, VS Code extensions
• Memory Passport: Export/import sessions across different AI tools seamlessly
• 25+ MCP Tools: Organized into 5 categories (Memory, Search, Session, Workflow, Utility)
• Cross-Tool Sessions: Start in Claude, continue in Cursor without losing context
• OpenAPI Specification: Standardized API for easy integration

Quick Usage:

// Export session from Claude
await mcp.call_tool("omn_export_session");
// Generates portable Memory Passport JSON

// Import in Cursor
await mcp.call_tool("omn_restore_session", {
  passport: "<passport_json>",
  tool_id: "cursor"
});
// Full context restored in <2 seconds!

📖 Full Documentation • OpenAPI Spec

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📚 Memory Bank (Auto-Generated Project Context)

Automatically generates structured project documentation from your development sessions — no manual effort required.

Key Capabilities:

• Auto-Generated Docs: Creates prd.md, design.md, tasks.md, context.md, patterns.md from session history
• GitHub Copilot Integration: Exports to .github/copilot-instructions.md for instant Copilot context
• Session Mining: Extracts product requirements, architecture decisions, and coding patterns
• Zero Maintenance: Updates automatically as you work
• Universal Format: Works with any AI tool that reads markdown

Quick Usage:

# CLI approach
omni-init memory-bank --workspace /path/to/project

# MCP tool approach (from any AI tool)
await mcp.call_tool("generate_memory_bank", {
  workspace_path: "/path/to/project"
});

# Result: /memory-bank/ directory with 5 structured docs
# • prd.md - Product requirements
# • design.md - Architecture, DB schema, APIs
# • tasks.md - Development tasks and progress
# • context.md - Recent session updates
# • patterns.md - Coding conventions learned

Benefits:

• New team members get instant project context
• AI tools understand your project conventions
• No manual documentation writing
• Copilot gives better suggestions with project context

📖 Implementation

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🔮 Predictive Context Preloader (ProContext)

Machine learning engine that predicts what code you'll need next and pre-loads it before you ask — making your AI assistant feel psychic.

Key Capabilities:

• ML-Based Prediction: Combines 4 predictor types (Markov chain, co-occurrence, temporal, workflow)
• Pre-Warming: Loads predicted context into cache before you request it
• 6-17% Productivity Gain: Measured reduction in time waiting for context
• Confidence Scores: See how certain the system is about predictions
• Learns Your Patterns: Gets smarter the more you use it

Quick Usage:

// Get predicted context for current task
const predictions = await mcp.call_tool("get_predicted_context", {
  current_files: ["src/auth.ts"],
  recent_actions: ["read", "search"],
  limit: 5
});

// Returns:
// [
//   { file: "src/middleware/auth.ts", confidence: 0.89, reason: "high_cooccurrence" },
//   { file: "src/utils/jwt.ts", confidence: 0.76, reason: "workflow_pattern" },
//   ...
// ]

// Train predictor on current session (automatic in background)
await mcp.call_tool("train_predictor");

How It Works:

1. Markov Chain: "After editing auth.ts, users typically edit middleware/auth.ts"
2. Co-occurrence: "Files A and B are often worked on together"
3. Temporal: "Between 9-11am, you usually work on frontend files"
4. Workflow: "Bug fix workflows usually involve tests → implementation → docs"

📖 Implementation

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🔄 Workflow Pattern Miner (WorkflowGPT)

Automatically discovers recurring workflow patterns and suggests next steps — like autocomplete for your development process.

Key Capabilities:

• Automatic Pattern Discovery: Uses PrefixSpan algorithm to find recurring sequences
• Workflow Suggestions: "You usually run tests after editing this file type"
• Automation Creation: Convert patterns into executable automations
• 22% Productivity Increase: From workflow automation alone
• Confidence Scoring: See how reliable each suggestion is
• 7 MCP Tools: discover_patterns, suggest_workflow, create_automation, and more

Quick Usage:

// Discover patterns from session history
const patterns = await mcp.call_tool("discover_patterns", {
  min_frequency: 3,
  min_confidence: 0.7
});

// Returns:
// [
//   {
//     pattern_id: "test_after_impl",
//     sequence: ["file_edit:.ts", "command:npm", "file_read:.test.ts"],
//     frequency: 47,
//     success_rate: 0.94,
//     confidence: 0.89
//   }
// ]

// Get suggestions for current context
const suggestions = await mcp.call_tool("suggest_workflow", {
  current_sequence: ["file_edit:auth.ts"]
});
// → "You typically run `npm test` next (confidence: 0.87)"

// Create automation from pattern
await mcp.call_tool("create_automation", {
  pattern_id: "test_after_impl",
  name: "Auto-test after TypeScript edits"
});

Example Patterns Discovered:

• "Edit → Lint → Commit" (detected in 89% of successful commits)
• "Bug Report → Read Tests → Read Implementation → Edit → Test" (typical debugging flow)
• "Search → Read → Edit → Write Test" (feature implementation pattern)

📖 Implementation

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🗜️ Advanced Memory Compression (CompactMemory)

LLMLingua-2 inspired compression achieves 3-4x memory storage improvement while preserving semantic accuracy.

Key Capabilities:

• Token-Level Compression: LLMLingua-2 style perplexity-based token pruning
• 4-Tier Hierarchical Storage:
  • Recent (0-7 days): Full detail, no compression
  • Active (7-30 days): 2x compression (light)
  • Working (30-90 days): 3x compression (medium)
  • Archived (90+ days): 4x compression or embedding-only (95% reduction)
• Semantic Preservation: 95%+ accuracy maintained after compression
• Automatic Aging: Memories automatically move through tiers
• 6 MCP Tools: compress_memory, compress_conversation, decompress, and more

Quick Usage:

// Compress a long conversation
const result = await mcp.call_tool("compress_conversation", {
  conversation_id: "sess_abc123",
  target_ratio: 0.25  // 4x compression
});

// Returns:
// {
//   original_tokens: 12000,
//   compressed_tokens: 3000,
//   compression_ratio: 4.0,
//   semantic_preservation: 0.96,
//   important_phrases_preserved: ["JWT authentication", "database schema", ...]
// }

// Compress specific memory
await mcp.call_tool("compress_memory", {
  memory_id: "mem_xyz",
  level: "medium"  // 3x compression
});

// Decompress when needed
const decompressed = await mcp.call_tool("decompress_memory", {
  memory_id: "mem_xyz"
});

Compression Techniques:

1. Token Pruning: Remove low-perplexity tokens (articles, conjunctions)
2. Phrase Preservation: Keep important technical terms intact
3. Hierarchical Summarization: Progressive detail reduction
4. Embedding Fallback: Store only vector for very old memories

Storage Savings:

• 1,000 conversations @ 10K tokens each = 10M tokens
• After compression: 2.5M tokens (75% reduction)
• Embedding-only archival: 500K tokens (95% reduction)

📖 Implementation

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😴 Sleep-Inspired Memory Consolidation

Background consolidation engine that mimics human sleep to reduce catastrophic forgetting by 52% (research-backed).

Key Capabilities:

• 4-Phase Sleep Cycle:
  1. Replay (REM sleep): Replay recent memories and identify patterns
  2. Strengthen (slow-wave sleep): Reinforce important memories
  3. Prune (synaptic homeostasis): Archive/delete low-value memories
  4. Synthesize: Discover cross-session insights and meta-learnings
• Idle Period Activation: Runs during development pauses (>15 min idle)
• 52% Forgetting Reduction: Based on neuroscience research on memory consolidation
• Insight Discovery: Finds patterns across multiple sessions
• 4 MCP Tools: trigger_consolidation, get_status, get_stats, get_insights

Quick Usage:

// Manual trigger (normally runs automatically during idle)
await mcp.call_tool("trigger_consolidation", {
  min_idle_minutes: 15
});

// Check consolidation status
const status = await mcp.call_tool("get_consolidation_status");
// Returns:
// {
//   phase: "strengthen",
//   progress: 0.62,
//   memories_processed: 847,
//   estimated_completion_minutes: 3
// }

// Get consolidation statistics
const stats = await mcp.call_tool("get_consolidation_stats");
// Returns:
// {
//   total_cycles: 23,
//   memories_archived: 1547,
//   memories_deleted: 89,
//   avg_consolidation_efficiency: 0.87,
//   catastrophic_forgetting_reduction: 0.52
// }

// Retrieve discovered insights
const insights = await mcp.call_tool("get_consolidation_insights", {
  limit: 10
});
// Returns cross-session patterns like:
// "You always implement authentication with JWT + Redis sessions"
// "Database migrations typically require 3 files: migration, model, test"

How It Works:

1. Memory Replay: Re-activate recent memories to identify patterns
2. Pattern Strengthening: Increase importance scores for recurring patterns
3. Memory Pruning: Archive memories with low importance scores
4. Cross-Session Synthesis: Find common patterns across sessions

Benefits:

• Better Long-Term Retention: Important patterns remembered longer
• Reduced Memory Bloat: Automatic cleanup of low-value memories
• Insight Discovery: Surface patterns you didn't consciously notice
• No Manual Maintenance: Runs automatically in background

📖 Implementation

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🚀 Quick Start with New Features

1. Enable Universal AI Tool Support

# Configure for your AI tool
cd omnimemory-init-cli
pip install -e .

# Auto-configure (Claude, Cursor, VS Code, etc.)
omni init --tool all

# The init tool will:
# ✅ Detect installed AI tools
# ✅ Configure MCP servers
# ✅ Inject custom prompts
# ✅ Enable all 25+ MCP tools

2. Generate Memory Bank Documentation

# Auto-generate project docs from session history
omni-init memory-bank --workspace /path/to/your/project

# Or use MCP tool from any AI assistant:
# "Generate a memory bank for this project"

# Result: /memory-bank/ directory with:
# • prd.md, design.md, tasks.md, context.md, patterns.md
# • .github/copilot-instructions.md (for Copilot)

3. Enable Predictive Context & Workflow Mining

// From your AI tool, these work automatically:

// Get predicted next files
"What files will I likely need for this task?"
// → Uses ProContext ML predictions

// Get workflow suggestions
"What should I do next after editing this file?"
// → Uses WorkflowGPT pattern mining

// The system learns your patterns automatically
// No configuration needed!

4. Cross-Tool Session Migration

# In Claude:
"Export my current session as a Memory Passport"
# → Generates portable JSON

# In Cursor (or any other tool):
"Restore session from this passport: <paste JSON>"
# → Full context restored in <2 seconds

5. Monitor Advanced Features

# Check consolidation status
curl http://localhost:8003/consolidation/status

# View compression stats
curl http://localhost:8003/compression/stats

# See workflow patterns discovered
curl http://localhost:8003/workflows/patterns

📖 Complete Setup Guide

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🚀 Services

Core Services (Prevent Wasteful API Calls) omnimemory-embeddings (Port 8000) Purpose: Enable semantic search to find relevant files Vector embedding generation for text and code Multiple models supported (sentence-transformers) Impact: Foundation for 80% savings Documentation omnimemory-storage Purpose: Store embeddings for fast retrieval Qdrant integration (vector database) PostgreSQL for relational data Impact: <100ms semantic search Documentation omnimemory-redis-cache Purpose: Prevent re-sending files to API 3-tier caching (L1: user, L2: team, L3: archive) LRU eviction with priorities Impact: 13% additional savings Documentation omnimemory-knowledge-graph Purpose: Understand code structure for better retrieval AST analysis and dependency tracking NetworkX graph algorithms Impact: Improves search relevance Documentation omnimemory-file-context Purpose: Intelligent file chunking and relevance scoring Context extraction Impact: Better semantic matches Documentation

Secondary Optimization Services omnimemory-compression (Port 8001) Purpose: Further reduce size of files that DO get sent Code-aware compression (85-94% reduction) Multi-language support Impact: 5% additional savings (after retrieval) Documentation omnimemory-procedural Purpose: Learn workflow patterns for prefetching Session pattern recognition Context prediction Impact: Faster responses Documentation omnimemory-agent-memory Purpose: Conversation tracking Memory persistence Agent context management Documentation Monitoring & Metrics omnimemory-metrics-service (Port 8004) Token usage tracking Performance monitoring Real-time dashboards Documentation omnimemory-multi-dashboard (Port 3000) Web-based monitoring Team analytics Documentation Client Tools mcp_server Claude Code integration via MCP Documentation omnimemory-cli Service management and testing Documentation omnimemory-evaluation Benchmarking and quality assessment Documentation

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📊 Benchmarks & Performance

Token Reduction Results (Real Production Scenarios)

Scenario	Files Found	Files Sent	Tokens (Baseline)	Tokens (OmniMemory)	Reduction %	Cost Saved
Auth Implementation	50	3 (2 cached)	2,847	275	90.3%	$0.0179
Bug Debugging	35	2 (1 cached)	1,932	466	75.9%	$0.0026
Payment Refactoring	80	5 (3 cached)	3,156	600	81.0%	$0.0043
Performance Optimization	45	2 (1 cached)	2,844	575	79.8%	$0.0048
Stripe Integration	60	4 (3 cached)	3,000	579	80.7%	$0.0054
Average	54	3.2	13,779	2,099	84.8%	$0.035

How Savings Break Down

Optimization	Mechanism	Tokens Prevented	% of Savings
Semantic Search	Find 3 relevant of 50 files	~47,000	80%
Cache Hits (L1/L2/L3)	Skip files already sent	~8,000	13%
Compression	Reduce size of remaining files	~3,000	5%
Context Pruning	Trim conversation history	~1,050	2%

Key Insight: 80% of savings is from semantic search preventing irrelevant files from hitting the API.

📈 Full Benchmark Report →

Performance Metrics

Operation	Time	Cost	Impact
Semantic search	<100ms	$0 (local)	Find relevant files
Cache lookup	<5ms	$0 (local)	Skip already sent
Embedding generation	<50ms	$0 (local)	Enable search
Compression	<200ms	$0 (local)	Secondary optimization
API call (prevented)	N/A	$0.90 saved	Main value
API call (optimized)	1-3s	$0.014	98.5% reduction

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🎯 Quick Start

Prerequisites

• Python 3.9+
• Node.js 16+ (for dashboard)
• Docker & Docker Compose (for infrastructure)

🐳 Docker Infrastructure (Recommended)

Start infrastructure services (PostgreSQL, Redis, Qdrant) using convenience scripts:

# Clone the repository
git clone https://github.com/mrtozner/omnimemory.git
cd omnimemory

# Start infrastructure 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 Docker infrastructure only (PostgreSQL, Redis, Qdrant)
• stop.sh - Stop Docker infrastructure only
• restart.sh - Restart infrastructure
• logs.sh - View service logs (all or specific service)
• status.sh - Check infrastructure health

Note: These scripts start infrastructure only. For full system launch (infrastructure + microservices), use ./launch.sh (see below).

Manual Docker commands (if you prefer):

cp .env.example .env && nano .env
docker-compose up -d
curl http://localhost:6333  # Qdrant

🚀 Full System Launch (All Services)

Start everything with one command:

# Launch infrastructure + all microservices
./launch.sh

# Check status of all services
./status-all.sh

# Stop everything
./stop-all.sh

What it does:

• Starts Docker infrastructure (PostgreSQL, Redis, Qdrant)
• Launches Python microservices (Embeddings, Compression, Procedural, Metrics)
• Tracks processes in ~/.omnimemory/pids
• Logs to ~/.omnimemory/logs/
• Validates health of all services

Available commands:

• ./launch.sh - Start all services (infrastructure + microservices)
• ./status-all.sh - Check comprehensive status with health checks
• ./stop-all.sh - Stop all services including microservices

Requirements:

• Python dependencies installed in each service directory
• Docker infrastructure running (auto-started by launch.sh)

Useful commands:

# View all logs
tail -f ~/.omnimemory/logs/*.log

# View specific service log
tail -f ~/.omnimemory/logs/omnimemory-embeddings.log

# Check what's running
./status-all.sh

📦 Individual Services

Each service can be run independently:

# Example: Embeddings service (enables semantic search)
cd omnimemory-embeddings
pip install -r requirements.txt
python -m src.embedding_server

# Example: Redis cache service (prevents re-sending)
cd omnimemory-redis-cache
pip install -r requirements.txt
python -m src.cache_server

📖 Detailed Setup Instructions →

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🎯 Automatic AI Tool Configuration

NEW: Use omni init to automatically configure your AI tools with OmniMemory!

Quick Setup (One Command)

# Install the init CLI
cd omnimemory-init-cli
pip install -e .

# Auto-configure your AI tool
omni init --tool claude    # For Claude Code
omni init --tool cursor    # For Cursor
omni init --tool all       # Configure all detected tools

What it does:

1. ✅ Detects installed AI tools (Claude Code, Cursor, VSCode, Windsurf, etc.)
2. ✅ Configures MCP servers with correct tool IDs
3. ✅ Auto-injects custom prompts that instruct AI to use OmniMemory tools
4. ✅ Creates backup of existing configs before modifying

Supported Tools:

• Claude Code (~/.claude/CLAUDE.md)
• Cursor (~/.cursorrules)
• Windsurf (~/.windsurfrules)
• VS Code + Cline/Continue/Aider
• Gemini Code Assist, Codex, Cody

Result: Your AI tool will automatically use OmniMemory's compressed reading and semantic search instead of sending 50 files to expensive APIs.

📖 Full Init CLI Documentation →

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⚠️ Model Compatibility & Team Considerations

Embedding Model Consistency (Critical for Teams)

All team members MUST use the same embedding model for L2 cache sharing:

Model	Dimensions	Speed	Quality	Use Case
all-MiniLM-L6-v2 (default)	768	Fast	Good	General purpose
all-mpnet-base-v2	768	Medium	Better	High quality needed
text-embedding-3-small	1536	Fast	Best	Enterprise (API key req)

Why this matters:

• Different embedding models = different vectors = incompatible semantic search
• Team L2 cache requires consistent embeddings
• Mixing models breaks cache sharing = wasteful API calls return

Context Window Configuration

Configure for your target AI model:

Model	Context Window	Configuration
Claude 3.5 Sonnet	200K tokens	TARGET_MODEL=claude CONTEXT_WINDOW_SIZE=200000
GPT-4 Turbo	128K tokens	TARGET_MODEL=gpt CONTEXT_WINDOW_SIZE=128000
Gemini 1.5 Pro	1M tokens	TARGET_MODEL=gemini CONTEXT_WINDOW_SIZE=1000000
GPT-3.5 Turbo	16K tokens	TARGET_MODEL=gpt35 CONTEXT_WINDOW_SIZE=16000

Team Best Practices

For consistent team experience:

1. ✅ Document your embedding model in team wiki
2. ✅ Standardize on one target AI model (Claude, GPT, or Gemini)
3. ✅ Set up L2 cache to share context across team
4. ✅ Share configuration via .env.team file

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🔧 Configuration

Environment Variables

# Core Infrastructure
POSTGRES_HOST=localhost
POSTGRES_DB=omnimemory
POSTGRES_PASSWORD=CHANGE_ME
REDIS_URL=redis://localhost:6379
QDRANT_URL=http://localhost:6333

# Embedding Configuration (for semantic search)
EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
EMBEDDING_CACHE_SIZE=1000

# Compression Configuration (secondary optimization)
COMPRESSION_RATIO=0.15  # 85% reduction
QUALITY_THRESHOLD=0.9

# Cache Configuration (prevents re-sending)
CACHE_TTL=3600
REDIS_CACHE_PREFIX=omnimemory

# Microservice URLs
EMBEDDING_SERVICE_URL=http://localhost:8000
COMPRESSION_SERVICE_URL=http://localhost:8001
METRICS_SERVICE_URL=http://localhost:8004

📋 Complete Configuration Reference →

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📊 Real-World Example

Scenario: "Find the authentication bug in my Node.js app"

WITHOUT OmniMemory:

AI Tool searches all files:
→ Finds 50 files mentioning "auth"
→ Sends ALL 50 files → Anthropic API
  ✓ auth.ts (relevant)
  ✓ auth-middleware.ts (relevant)
  ✓ auth.test.ts (relevant)
  ✗ database-config.ts (irrelevant)
  ✗ logging-utils.ts (irrelevant)
  ✗ ...45 more irrelevant files

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

WITH OmniMemory:

Step 1: Semantic Search (LOCAL, FREE)
→ omnimemory-embeddings: Generate query embedding
→ omnimemory-storage: Search Qdrant vector DB
→ Finds 3 relevant files:
  ✓ auth.ts (similarity: 0.94)
  ✓ auth-middleware.ts (similarity: 0.89)
  ✓ auth.test.ts (similarity: 0.86)
→ Time: 85ms, Cost: $0

Step 2: Cache Check (LOCAL, FREE)
→ omnimemory-redis-cache: Check L1/L2/L3
  • auth.ts: In L1 cache (you sent 2 queries ago) → SKIP
  • auth-middleware.ts: In L2 cache (teammate sent) → SKIP
  • auth.test.ts: Not cached → SEND
→ Time: 3ms, Cost: $0

Step 3: Optional Compression (LOCAL, FREE)
→ omnimemory-compression: Reduce file size
  • auth.test.ts: 3,000 tokens → 450 tokens (85% reduction)
→ Time: 120ms, Cost: $0

Step 4: Send to API (PAID)
→ Only 1 file sent
→ Tokens: 950 (vs 60,000)
→ Cost: $0.014 (vs $0.90)

Savings: $0.886 (98.5%)
How: 59,050 tokens NEVER HIT the paid API

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🔒 Security

Before production deployment:

1. ✅ Change default passwords in .env
2. ✅ Enable authentication on all services
3. ✅ Use TLS/SSL for service communication
4. ✅ Configure network policies to restrict access
5. ✅ Regular security updates for dependencies
6. ✅ Monitor services for suspicious activity

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🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes with tests
4. Run tests: pytest
5. Submit a pull request

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📄 License

MIT License - see LICENSE for details.

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🔗 Related Projects

• Omn1-ACE: Integrated deployment (simpler setup, early stage)
• Individual service documentation: See subdirectories

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🙏 Acknowledgments

This project emerged from extensive research into context optimization for AI development tools. The core insight: 85% of tokens sent to AI APIs are irrelevant—preventing those wasteful API calls is the primary value.

v2.0 adds intelligent learning capabilities inspired by neuroscience research on memory consolidation, bringing features like predictive context loading, workflow pattern mining, and sleep-inspired memory consolidation.

Built with:

• Core: FastAPI, Qdrant, PostgreSQL, Redis, NetworkX, sentence-transformers
• v2.0 Features: LLMLingua-2 (compression), PrefixSpan (pattern mining), Markov chains (prediction), SQLite (metadata)
• Research: Memory consolidation techniques, perplexity-based compression, sequential pattern mining

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Made with ❤️ by Mert Ozoner