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README
From the repo.
TCAM Memory System
Resilient 4-Layer Memory Architecture for TCAM v1.4 (Triadic Cognitive Augmentation Model)
Overview
The TCAM Memory System implements a fault-tolerant memory architecture where each layer operates independently, ensuring that system failures in one layer do not cascade to others. Memory operations are handled by a dedicated Memory Service running on Qwen 3.5 9B (local LLM).
Architecture
Four Independent Layers
- L1: Chronicle - Immutable historical record (file system, markdown)
- L2: Active Stream - Volatile working memory (Cloud LLM context + Redis)
- L3: Hive Mind - Semantic memory (Qdrant vector DB + Mem0)
- L4: Agent Codex - Personal knowledge base (file system, markdown)
Memory Service
Dedicated process running on Qwen 3.5 9B for:
- Truth extraction from dialogue
- Chronicle inscription
- Hive Mind indexing
- Agent Codex updates
- Sleeping cycle orchestration
Technology Stack
- TypeScript/Node.js - Core implementation
- Qwen 3.5 9B - Local LLM for memory operations
- Mem0 - Automatic fact extraction
- Qdrant - Vector database for semantic search
- Redis - Fast state persistence (LangGraph checkpointer)
- LangGraph - Multi-agent orchestration
- Jest - Testing framework
- fast-check - Property-based testing
Project Structure
.
├── src/ # Source code
├── tests/ # Test files
├── data/ # Chronicle storage
├── codex/ # Agent Codex (L4)
├── docs/ # Documentation
└── .kiro/specs/ # Specification documents
Getting Started
Prerequisites
- Node.js 18+
- Ollama (for Qwen 3.5 9B)
- Docker (for Qdrant and Redis)
Installation
# Install dependencies
npm install --legacy-peer-deps
# Pull Qwen 3.5 9B model
ollama pull qwen2.5:9b-instruct-q4_K_M
# Pull embedding model
ollama pull nomic-embed-text
# Start Qdrant (using setup script)
# Windows PowerShell:
.\scripts\setup-qdrant.ps1
# Linux/Mac:
bash scripts/setup-qdrant.sh
# Or manually with Docker:
docker run -d --name qdrant -p 6333:6333 -p 6334:6334 -v qdrant_storage:/qdrant/storage qdrant/qdrant
# Start Redis
docker run -d --name redis -p 6379:6379 redis
# Build and install globally
npm run build
npm install -g . --legacy-peer-deps
CLI Usage
# Interactive main menu
anots
# Real-time monitoring dashboard (Terminal UI)
anots dashboard
# Chat with Axiom (TCAM Node C)
anots axiom
# Start MCP server (19 tools for IDE integration)
anots mcp:start
# Start REST API server
anots api:start
# Start API with Axiom chat endpoint
anots api:start --axiom
# Search memory
anots memory:search "your query"
# Import conversation (JSON/Markdown)
anots import conversation.json --type general
# System status
anots status
# Configuration wizard
anots setup
# Show all commands
anots --help
Dashboard Features
The Terminal UI dashboard (anots dashboard) provides:
- Real-time memory layer health monitoring
- System statistics (chapters, sessions, memories)
- Activity log with timestamps
- Keyboard shortcuts (F1-F3, R, Q)
- Auto-refresh every 2 seconds
- Cyberpunk aesthetic with 90s BBS vibes
Verify Setup
# Check Qdrant
curl http://localhost:6333/health
# Check Redis
redis-cli ping
# Check Ollama
ollama list
Development
# Build
npm run build
# Run tests
npm test
# Run tests with coverage
npm run test:coverage
# Lint
npm run lint
# Format code
npm run format
Importing Conversations
Import large conversation files into the ANOTS memory system:
# Import Ubik conversation
npm run import -- data/import/conversation.json --type ubik
# Import Axiom conversation
npm run import -- data/import/conversation.md --type axiom
# Preview import without writing (dry run)
npm run import -- data/import/conversation.json --dry-run
# Custom chunk size (messages per chapter)
npm run import -- data/import/large-file.json --type ubik --chunk-size 100
What Gets Imported
- Chronicle: Conversation history in chapters
- Hive Mind: Semantic indexing for search
- Codex: Extracted truths and insights
See data/import/README.md for detailed documentation and examples.
Specification
See .kiro/specs/memory-system/ for complete specification:
requirements.md- Functional requirementsdesign.md- Technical designtasks.md- Implementation tasks
License
MIT
Config for your environment
Use the endpoint URL below in your config. No API key — you connect directly.
Tool
OS
Config file: ~/.cursor/mcp.json
{
"mcpServers": {
"mcp-server": {
"url": "https://api.anots.com/mcp"
}
}
}Paste into mcpServers in the config file. Restart Cursor after saving.
If this MCP is also published on mcpchannel.ai, you can subscribe from Browse and use the gateway config there instead.