TCAM Memory System
MCP serverSearchYour AI can discover and answer questions about any business on ANOTS. Once added, it can search the full business directory, ask about a business directly, or find one and ask about it in the same step.
Available today. Use it from your connected AI after setup.
No other account needed.
After adding the directory, ask your AI to search for a business on ANOTS or answer a question about one.
Then ask your AI: use TCAM Memory System to search businesses
What your AI can do with it
- Search the full list of businesses on ANOTS
- Ask questions about any business on ANOTS
- Find a business and ask about it in one step
- Look up any business in the ANOTS directory
From the project's README
As published by patheras/the-anots in README.md.
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
Tools it offers (3)
What this server listed when ahel dialed its public endpoint in Sep 2026, with no key and no account of yours. The names are the server’s own.
search_businessesask_businessfind_and_ask
Signals
- Last commit
- Apr 2026
Advanced
- Delivery
- directory MCP server → your ahel gateway (mcp.ahel.ai) → every connected AI client.
- Catalog kind
- mcp-server
- Gateway key
com-anots-directory- Source
- github.com/patheras/the-anots
- Hosted endpoint
https://api.anots.com/mcp