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  3. Directory
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Health: ActiveRecent health check succeeded.Last checked 9/21/2026, 8:01:45 PM

Directory

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agent-memoryvector-databaselocal-llmself-hostedknowledge-base

TCAM Memory System is a self-hosted, 4-layer memory architecture for AI agents β€” an immutable chronicle, active working memory, a vector-backed semantic layer, and a personal knowledge base β€” run by a dedicated local-LLM memory service.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet β€” we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ
No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself β€” its README, its docs, or a verified owner. We haven’t found those for directory, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.
Install Directory Badge Claim listing Alternatives🧠 More in Knowledge & Memory

Overview

This project implements a fault-tolerant, four-layer memory architecture (Chronicle, Active Stream, Hive Mind, Agent Codex) where each layer operates independently so a failure in one doesn't cascade to the others. A dedicated Memory Service running on a local Qwen 3.5 9B model (via Ollama) handles truth extraction, chronicle inscription, semantic indexing (Qdrant + Mem0), and agent codex updates, with Redis and LangGraph handling state persistence and orchestration.

Use cases

β€’Give an agent an immutable historical record of past interactions (Chronicle)
β€’Maintain volatile working memory backed by Redis during a session
β€’Run semantic search over accumulated memory via a Qdrant vector index
β€’Keep a personal, file-based knowledge base that survives across sessions

Key features

β€’Four independent memory layers so one layer's failure doesn't cascade
β€’Local-LLM memory service (Qwen 3.5 9B via Ollama) for truth extraction and indexing
β€’Qdrant vector database plus Mem0 for semantic memory
β€’Redis + LangGraph for state persistence and multi-agent orchestration

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Directory.

Extracted Tool Capabilities
Four independent memory layers so one layer's failure doesn't cascade
Local-LLM memory service (Qwen 3.5 9B via Ollama) for truth extraction and indexing
Qdrant vector database plus Mem0 for semantic memory
Redis + LangGraph for state persistence and multi-agent orchestration

Documentation Overview

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

Code
.
β”œβ”€β”€ src/                    # Source code
β”œβ”€β”€ tests/                  # Test files
β”œβ”€β”€ data/                   # Chronicle storage
β”œβ”€β”€ codex/                  # Agent Codex (L4)
β”œβ”€β”€ docs/                   # Documentation
└── .kiro/specs/           # Specification documents

Getting Started

Prerequisites

  • Node.js 18+
  • Ollama (for Qwen 3.5 9B)
  • Docker (for Qdrant and Redis)

Installation

bash
# 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

server.ts
# 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

bash
# Check Qdrant
curl http://localhost:6333/health

# Check Redis
redis-cli ping

# Check Ollama
ollama list

Development

bash
# 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:

server.ts
# 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

  1. Chronicle: Conversation history in chapters
  2. Hive Mind: Semantic indexing for search
  3. 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 requirements
  • design.md - Technical design
  • tasks.md - Implementation tasks

License

MIT

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

npm downloads
2.7k
Package downloads in the last 30 days.
Last commit
5mo ago
Most recent push to the default branch.
Directory activity
2 views
Config copies, upvotes, and views on AllMCPs.

Reviews

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Frequently Asked Questions about Directory

The dedicated Memory Service runs on a local Qwen 3.5 9B model via Ollama; the Active Stream layer can use a cloud LLM context alongside Redis.

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Technical Specs & Signals

Category🧠Knowledge & Memory
PricingFree
More technical detailsExpand β–Ύ
AuthNo auth required
Last updatedAug 12, 2026
9/9 checks healthy over the last 46d
Views2
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars0
GitHub Star CountTotal stargazers on GitHub representing community popularity (0 stars).
Last commit5mo ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Apr 2, 2026
npm downloads2,661/mo
Monthly npm DownloadsAverage monthly package installs recorded from npm registry statistics.
36Quality signal: Fair Β· 36/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools13/30
Adoption & activity4/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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