Skynet Agent

What if AI could not only access memories, but consciously choose what to remember? With MCP tool access fully supported?

AI conversation platform implementing dual-layer memory architecture inspired by human cognition. Combines automatic background memory with conscious, deliberate memory operations that AI controls. Tool access powers similar to Claude Desktop.

Core Features

LangGraph-Powered Autopilot

Purpose-driven autonomous execution replacing simple query generation with sophisticated multi-step workflows:

• Purpose analysis and strategic planning
• Context gathering from all memory systems
• Smart tool orchestration with error recovery
• Progress monitoring with adaptive replanning
• Reflection engine for continuous learning
• Configurable aggressiveness and safety controls

Dual-Layer Memory

Automatic Memory (RAG): Non-volitional background memory using ChromaDB vectors and Google text-embedding-004
Conscious Memory: Volitional operations via MCP tools - save, search, update, delete with tags and importance scoring
Knowledge Graph: Neo4j-powered relationship mapping with automatic synchronization and retry mechanisms

MCP Tool Ecosystem

Exposes memory operations as Model Context Protocol tools for natural conversation flow. Clean separation between UI, memory, and AI operations.

Quick Setup

Prerequisites

• Node.js 18+
• Docker & Docker Compose
• LLM API key (free Google AI Studio recommended)

Installation

git clone https://github.com/esinecan/skynet-agent.git
cd skynet-agent
npm install

cp .env.example .env.local
# Edit .env.local with your API keys

docker-compose up -d    # ChromaDB (8000) + Neo4j (7474, 7687)
npm run dev             # Or npm run dev:next if Neo4j issues

Access:

• Application: http://localhost:3000
• Conscious Memory: http://localhost:3000/conscious-memory
• Neo4j Browser: http://localhost:7474 (neo4j/password123)

Supported LLMs

Configuration

Essential Environment Variables

# LLM (pick one)
GOOGLE_API_KEY=your_key
DEEPSEEK_API_KEY=your_key

# Main LLM Configuration
LLM_PROVIDER=google
LLM_MODEL=gemini-2.5-flash-preview-05-20

# Motive Force (Autopilot) LLM Configuration (optional - defaults to main LLM)
LLM_PROVIDER_MOTIVE_FORCE=deepseek
LLM_MODEL_MOTIVE_FORCE=deepseek-chat

# Services
CHROMA_URL=http://localhost:8000
NEO4J_URI=bolt://localhost:7687
NEO4J_PASSWORD=password123

# Autopilot
MOTIVE_FORCE_ENABLED=false
MOTIVE_FORCE_MAX_CONSECUTIVE_TURNS=10
MOTIVE_FORCE_TEMPERATURE=0.8

Autopilot Usage

Enable via UI toggle. Your next message becomes the objective:

Using timestamps and normal querying, organize today's memories into 5-10 groups. 
Delete redundant items, consolidate similar ones, add insights. Check with autopilot 
periodically. Daily maintenance cultivates curated memory over time.

Configure via gear icon: turn delays, limits, memory integration, aggressiveness modes.

Development

Scripts

# Development
npm run dev              # Full stack + KG sync
npm run dev:debug        # With Node debugging
npm run dev:next         # Frontend only
npm run dev:kg           # KG sync only

# Knowledge Graph
npm run kg:sync          # One-time sync
npm run kg:sync:full     # Complete resync
npm run kg:sync:queue    # Process retry queue

# Testing
npm run test             # All tests
npm run test:rag         # RAG system
npm run test:neo4j       # Neo4j integration

Project Structure

skynet-agent/
├── src/
│   ├── app/                    # Next.js routes
│   ├── components/             # React components
│   ├── lib/                    # Core libraries
│   │   ├── motive-force-graph.ts    # LangGraph workflow
│   │   ├── conscious-memory.ts      # Volitional memory
│   │   ├── rag.ts                   # Automatic memory
│   │   └── knowledge-graph-*.ts     # Neo4j integration
│   └── types/                  # TypeScript definitions
├── docker-compose.yml          # Services setup
└── motive-force-prompt.md      # Autopilot personality

Memory Architecture

Automatic Memory (RAG)

interface Memory {
  id: string;
  text: string;
  embedding: number[];  // Google text-embedding-004
  metadata: {
    sender: 'user' | 'assistant';
    timestamp: string;
    summary?: string;  // Auto-summarized if over limit
  };
}

Conscious Memory

interface ConsciousMemory {
  id: string;
  content: string;
  tags: string[];
  importance: number;  // 1-10
  source: 'explicit' | 'suggested' | 'derived';
  metadata: {
    accessCount: number;
    lastAccessed: string;
  };
}

LangGraph State

interface MotiveForceGraphState {
  messages: BaseMessage[];
  currentPurpose: string;
  subgoals: SubGoal[];
  executionPlan: ExecutionStep[];
  toolResults: ToolResult[];
  reflections: Reflection[];
  overallProgress: number;
  blockers: string[];
  needsUserInput: boolean;
}

API Reference

Conscious Memory

POST /api/conscious-memory
{
  "action": "save|search|update|delete|stats|tags",
  "content": "string",
  "tags": ["array"],
  "importance": 7
}

Autopilot

POST /api/motive-force
{
  "action": "generate|generateStreaming|saveConfig|getState",
  "sessionId": "string",
  "data": {}
}

Advanced Features

Hybrid Search

1. Semantic: Vector similarity via embeddings
2. Keyword: Exact match fallback
3. Smart Merge: Intelligent ranking with deduplication

Knowledge Graph Sync

• Automatic extraction from chat history
• Background service with retry queue
• Metrics collection and error handling
• Eventually consistent with ChromaDB

Safety Mechanisms

• Turn limits and error counting
• Manual override capabilities
• Resource usage monitoring
• Emergency stop functionality

Troubleshooting

"Embeddings service unavailable": Falls back to hash-based embeddings. Check Google API key.

"ChromaDB connection failed": Ensure docker-compose up -d and port 8000 available.

"Neo4j sync errors": Check credentials, run npm run kg:sync:queue for retries.

"Actually Looks Very Ugly": I suck at UI design.

Development Philosophy

Inspired by cognitive science:

• Dual-Process Theory: Automatic vs controlled processes
• Memory Consolidation: Active organization
• Working Memory: Conscious manipulation

Technical innovations:

• Hybrid Search: Solves subset query limitations
• MCP Architecture: Natural language memory control
• Importance Weighting: Smart prioritization
• LangGraph Integration: Complex autonomous workflows

Contributing

Fork, improve, PR. Areas: memory algorithms, UI/UX, MCP tools, autopilot intelligence, testing, performance.

License

MIT - Lok Tar Ogar!

Acknowledgments

ChromaDB, Google AI, Anthropic MCP, Next.js, Neo4j teams. Open source MCP servers and Ollama Vercel AI SDK library.