Agentic Long-Term Memory (ALTM)

A governed memory architecture for AI agents with cross-LLM continuity

"Context is the New Data" - Enterprises spent 30 years learning to govern data. AI context is following the same arc, compressed.

Overview

ALTM is a memory infrastructure platform that provides AI agents with persistent, governed, cross-LLM memory capabilities. Unlike simple RAG or vector store solutions, ALTM treats AI context like governed enterprise data - with quality scoring, lineage tracking, retention policies, and full lifecycle management.

Current Status: MVP in testing, running locally in Docker, designed for Kubernetes deployment.

The Problem

LLM agents suffer from "context rot" - losing learned behaviors, corrections, and institutional knowledge between sessions. Current solutions (RAG, vector DBs, Mem0, Zep) provide retrieval but not governed memory. They lack:

• Semantic structure - What type of memory is this?
• Quality signals - Is this accurate? Useful?
• Lineage tracking - Where did this come from?
• Retention policies - When should this expire?

The Architecture

ALTM uses a 4-store architecture, each optimized for its purpose:

┌─────────────────────────────────────────────────────────────────┐
│                     ANY LLM (Claude, GPT, etc.)                 │
│                              │                                  │
│                    ┌─────────▼─────────┐                        │
│                    │    MCP Server     │  (LLM-Agnostic)        │
│                    └─────────┬─────────┘                        │
└──────────────────────────────┼──────────────────────────────────┘
                               │ HTTPS
              ┌────────────────▼────────────────┐
              │        ALTM PLATFORM            │
              │      (Docker / Kubernetes)      │
              │                                 │
              │  ┌──────────┐  ┌──────────┐     │
              │  │  Neo4j   │  │  Qdrant  │     │
              │  │  (Graph) │  │ (Vector) │     │
              │  └────┬─────┘  └────┬─────┘     │
              │       │             │           │
              │  ┌────▼─────┐  ┌────▼─────┐     │
              │  │  MinIO   │  │PostgreSQL│     │
              │  │ (Object) │  │ (Audit)  │     │
              │  └──────────┘  └──────────┘     │
              └─────────────────────────────────┘

Why 4 stores?

Store	Purpose	Example Queries
Neo4j	Relationship queries	"What concepts connect to this learning?"
Qdrant	Similarity search	"Find semantically related memories"
MinIO	Large objects	"Store session transcripts, documents"
PostgreSQL	Compliance & audit	"Who accessed what, when?"

Key Capabilities

Capability	Description	Status
Read-write memory	MCP tools for capture, query, update	Working
Learning pipeline	Auto-extract from corrections, sessions	Working
Quality scoring	Automatic scoring with decay/promotion	Working
Lineage tracking	Full provenance for every memory	Working
Session checkpoints	Preserve and restore agent state	Working
Context governance	Retention policies, access control	Working
LLM-agnostic	Works with Claude, GPT, any LLM	Working
Enterprise security	Audit trails, CMMC-ready	Working

The Killer Feature: Cross-LLM Continuity

Start a task with Claude. Continue with GPT. Finish with local Llama.

The memory layer maintains identity and context regardless of which model is active. This doesn't exist today.

ALTM is the "Switzerland of AI memory" - neutral, portable, governed.

Why This Matters: The Path to AGI

It's not a poor man's LLM. It's something architecturally different.

Aspect	More Training Data	What ALTM Does
Learning	Static weights, fixed at training	Continuous - corrections persist immediately
Temporal awareness	"Paris is the capital" (fact)	"Last Tuesday we fixed the auth bug" (episode)
Self-correction	Requires retraining millions of params	Single correction → permanent behavior change
Knowledge lifecycle	Frozen	Draft → active → deprecated (evolves)
Metacognition	None	Quality scores, staleness awareness, lineage

What this gives an AI that training can't:

1. Autobiographical continuity - Maintain identity and goals across sessions. Without it, AI is a brilliant amnesiac reborn every conversation.
2. Learning from single examples - Corrections stick immediately. LLM training needs thousands of examples to shift weights.
3. Knowledge that can doubt - Quality scoring means the AI knows "this learning is stale" or "this came from a correction, not a guess."
4. Separation of reasoning from memory - Mirrors human architecture (prefrontal cortex + hippocampus). This is arguably the right architecture.

Documentation

Architecture

• Platform Overview
• Memory Stores
• Context Registry
• Governance Model

Concepts

• Context Governance
• Cross-LLM Continuity
• Metacognition Layer
• Human Cognitive Parallel

Competitive Positioning

Feature	Mem0 / Zep / LangGraph	ALTM
Focus	RAG retrieval	Full lifecycle governance
Quality	Store & retrieve	Score, decay, deprecate
Lineage	None	Know where knowledge came from
Promotion	Flat storage	Idea → Learning → Skill → Policy
Cross-LLM	Tied to one model	LLM-agnostic, portable identity
Enterprise	Basic multi-tenant	Hive mind, team knowledge flow
Security	Basic	Audit trails, CMMC-ready

Solution	Memory	Governance	LLM-Agnostic	Enterprise Security
Mem0	Yes	No	Yes	No
Zep	Yes	No	Yes	No
LangGraph	Partial	No	Yes	No
OpenClaw	Yes	No	Yes	No
ALTM	Yes	Yes	Yes	Yes

Core Thesis

Enterprises spent 30 years learning to govern data:

Era	Data	AI Context
Early	Warehouses, no governance	RAG, no governance
Middle	Quality initiatives, MDM	Vector DBs, still unstructured
Mature	Governance frameworks, lineage	ALTM: quality, lineage, retention

Whoever builds the governance layer for AI context will own the enterprise AI stack.

Technology Stack

• Graph Store: Neo4j (knowledge graphs, relationships)
• Vector Store: Qdrant (semantic similarity search)
• Object Store: MinIO (large objects, transcripts)
• Audit Store: PostgreSQL (compliance, analytics)
• API Layer: MCP (Model Context Protocol) for LLM-agnostic access
• Deployment: Docker (local), Kubernetes (production)

License

Apache 2.0

Author

Dallas McIntyre GitHub | LinkedIn