How to Build an Evolving Three-Layer Memory System for Your AI

In the realm of AI-assisted productivity, a fundamental pain point persists: 「most AI assistants are forgetful by default.」 Even with advanced systems like Clawdbot—which possess solid native primitives for persistence—memory is often static. It acts as a storage locker rather than a dynamic brain.

「This article aims to answer a core question: How can we upgrade a static AI memory system into a self-maintaining, compounding knowledge graph that evolves automatically as your life changes?」

The answer lies in implementing a “Three-Layer Memory Architecture.” By segmenting raw logs, entity-based knowledge graphs, and tacit knowledge, and introducing automated extraction and synthesis mechanisms, we can solve the issues of data staleness and manual cleanup once and for all.

The Status Quo & Challenge: Why Static Memory Falls Short

「The core question of this section: What are the fundamental structural flaws in current AI memory mechanisms that prevent them from adapting to the dynamic nature of real life?」

Most AI assistants ship with decent foundational modules: behavioral rules, persistent user preferences, heartbeats, and cron jobs. These features ensure basic continuity. The AI remembers your preferences, follows your rules, and can execute scheduled automations. However, a fatal structural flaw lies beneath the surface: 「all of this memory is static and requires manual maintenance.」

Real life is not static.

Consider this scenario: Six months ago, you told your AI, “My boss, Sarah, is difficult; she micromanages.” Since then, you’ve changed jobs. You love your new manager. A static-based Clawdbot, however, remains stuck in the past. It still “thinks” you hate your boss.

This stale context leads to frustration and poor decision-making. We don’t need a “notepad” that simply records text; we need a “brain” that corrects its understanding over time, just as a human would.

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「Author’s Reflection」

From an engineering perspective, many developers fall into the trap of thinking “storage equals solution.” We obsess over database schemas and vector stores but neglect the lifecycle of the data. If data only enters and never evolves or exits, we don’t end up with wisdom; we end up with a garbage dump full of noise and obsolete information.

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The Solution Overview: The Three-Layer Memory Architecture

「The core question of this section: How can we design a memory system that preserves the full history of facts while maintaining a sharp, accurate current perception?」

To solve the rigidity of static memory, we must upgrade the flat-file memory into a dynamic 「Knowledge Graph」. This system divides memory into three logical layers, each with distinct responsibilities, forming an organic, self-maintaining entity.

The Three-Layer Architecture isn’t just categorization; it’s an algorithm for distilling signal from noise. Here is the logic:

「Layer 1: The Knowledge Graph」 — The core system storing entity-specific info (people, companies, projects) containing atomic data points and periodically updated summaries.
「Layer 2: Daily Notes」 — The raw timeline recording “what happened and when.” It is the repository of unprocessed raw material.
「Layer 3: Tacit Knowledge」 — The metadata on “how you work,” including patterns, preferences, and learned lessons.

This structural design turns memory from a stagnant pond into a flowing river. Every conversation is captured, extracted into structured data, and then synthesized into a cleaner context. Six months from now, your AI’s understanding of your life will be structured, searchable, and current.

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「Author’s Reflection」

I appreciate the separation of “Explicit Facts” (Layers 1 & 2) from “Tacit Patterns” (Layer 3). In many past projects, we mixed user instructions with objective facts, causing the context window to fill with irrelevant noise. Separating long-term stable patterns (like “I prefer calls”) allows the AI to reason much more efficiently.

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Layer 1 Deep Dive: Building a Living Knowledge Graph

「The core question of this section: How do we ensure information about specific entities (like people or companies) is accurate while retaining a historical audit trail?」

This is where the magic happens. Instead of dumping everything into a single monolithic file, we create a dedicated folder for every meaningful entity in your life (people, companies, projects).

Entity-Based Folder Structure

For “People” and “Companies,” the system automatically generates a structure like this:

/life/areas/
├── people/
│   ├── sarah/              # Former boss (the previous villain)
│   │   ├── summary.md
│   │   └── items.json
│   ├── maria/              # Business partner
│   ├── emma/               # Family member
│   └── sarah-connor/       # Knows too much. Trust cautiously.
├── companies/
│   ├── acme-corp/          # Old job
│   ├── newco/              # Current job
│   └── skynet/             # Do not give cron access

This structure makes retrieval incredibly efficient. When the AI needs to know about Sarah, it only loads /people/sarah/, rather than scanning a massive database.

Atomic Facts & The “Supersede, Don’t Delete” Mechanism

Inside items.json, every fact is stored as a discrete, timestamped unit. This is key to memory evolution.

Initially, a fact might look like this:

{
  "id": "sarah-003",
  "fact": "Difficult manager, micromanages",
  "timestamp": "2025-06-15",
  "status": "active"
}

When you change jobs, the system doesn’t delete this. It generates a new fact and marks the old one as “superseded.”

{
  "id": "sarah-003",
  "status": "superseded",
  "supersededBy": "sarah-007"
},
{
  "id": "sarah-007",
  "fact": "No longer works together — left Acme Corp",
  "timestamp": "2026-01-15",
  "status": "active"
}

This mechanism preserves the full history. The AI knows Sarah isn’t your colleague anymore, but it can trace back the relationship history. This is crucial for understanding complex relationship evolutions.

Living Summaries

To avoid loading hundreds of raw facts (which consumes tokens and slows reasoning), every entity has a weekly-rewritten snapshot file, summary.md.

# Sarah

Former manager at Acme Corp (2024–2025).
No longer relevant after job change.

This ensures context stays lean. Old info naturally fades from the summary but remains in the underlying database.

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「Author’s Reflection」

The “Superseding” logic here is the highlight of this design. Traditional DB operations often UPDATE directly, losing history. In human memory and AI context, historical background is often the key to understanding current intent. Keeping “superseded” facts is preserving the AI’s “experience.”

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图片来源：Unsplash
Image Description: Data grows like tree roots, sprouting new structures over time while keeping the historical foundation intact.

Layer 2 Deep Dive: Daily Notes as a Raw Timeline

「The core question of this section: How can we record the raw timeline of life without cluttering the structured knowledge graph?」

While Layer 1 refines high-value information, we still need a place for “logs.” This is Layer 2: Daily Notes.

Typically located at memory/YYYY-MM-DD.md, this purely records what happened and when.

# 2026-01-27
- 10:30am: Shopping trip
- 2:00pm: Doctor follow-up
- Decision: Calendar events now use emoji categories

This is the “when” layer. Clawdbot writes these continuously. Later, a background automation agent scans these raw logs to extract durable facts (like “changed jobs,” “baby walked”) and moves them to Layer 1.

This “log first, distill later” process mimics human memory: we experience specific events, and after a period of rest, our brains solidify the important fragments into long-term memory.

Layer 3 Deep Dive: Capturing Tacit Knowledge & Behavioral Patterns

「The core question of this section: How does the AI capture deep personal traits that aren’t objective facts about the world?」

This layer usually corresponds to the MEMORY.md file. It isn’t about world facts; it’s “meta-knowledge” about how you operate in the world.

## How I Work
- Sprint worker — intense bursts, then rest
- Contact preference: Call > SMS > Email
- Early riser, prefers brief messages

## Lessons Learned
- Don't create cron jobs for one-off reminders

This information is vital for personalized service. If the AI knows you are a “sprint worker” who prefers concise communication, it won’t send you long emails during your rest periods.

In this upgrade, this file’s role is formalized. It’s no longer just a random note; it’s a core reference document for system planning and communication.

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「Author’s Reflection」

Lots of prompt engineering focuses on “telling” the AI your preferences. The brilliance of this system is that it can gradually “infer” these preferences from your daily behavior (Layer 2) and interactions, writing them into Layer 3. This is a paradigm shift from “configuration” to “observation.”

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The Compounding Engine: Automation & Memory Evolution

「The core question of this section: How do we use cheap automation processes to achieve a “compounding effect” on memory, making it smarter with use?」

Without automation, the three-layer architecture is just a complex manual note-taking system. The real power comes from the “Compounding Engine,” which involves two core automated processes: Real-time Extraction and Weekly Synthesis.

Real-Time Extraction

The system doesn’t call an expensive LLM on every chat line—that’s too costly. Instead, approximately every 30 minutes, a very cheap sub-model (e.g., Haiku, costing ~$0.001) wakes up to scan recent conversations for durable facts.

「Targets:」 Relationship changes, status updates, milestones.
「Ignores:」 Casual chat, temporary info.

For example, it might capture:

“Maria’s company hired two developers”
“Emma took her first steps”
“Started new job, reporting to James”

The main model stays idle unless you are actively chatting. Operational cost: pennies per day.

Weekly Synthesis

Every Sunday, a Cron job triggers the “Weekly Memory Review.” This process is like a human weekly retrospective:

「Review new facts:」 Load all facts added this week.
「Update summaries:」 Rewrite the relevant entity summary.md based on new active facts.
「Mark history:」 Mark contradictory facts as “historical/superseded.”
「Generate snapshot:」 Produce a clean, current view.

Through this loop, the AI’s memory self-corrects. You don’t need to manually tell it “I don’t work there anymore”; as long as you mention it in conversation, the weekly report updates Sarah’s status summary automatically.

图片来源：Unsplash
Image Description: Automated gears symbolizing the continuous processing and iteration of information in the background.

Implementation Guide: Building the System Step-by-Step

「The core question of this section: How do we implement this three-layer memory system using concrete folder structures and configuration code?」

To perform this upgrade, simply follow these steps. All logic is transparent and file-based.

1. Create the Folder Structure

First, establish the directory tree locally or in your Clawdbot environment:

mkdir -p ~/life/areas/people
mkdir -p ~/life/areas/companies
mkdir -p ~/clawd/memory

2. Update System Configuration

You need to write the memory rules into MEMORY.md so the AI understands the three-layer logic.

「Add to MEMORY.md:」

## Memory — Three Layers

### Layer 1: Knowledge Graph (`/life/areas/`)
- `people/` — Person entities
- `companies/` — Company entities

Tiered retrieval logic:
1. summary.md — Quick context
2. items.json — Atomic facts

Rules:
- Save facts immediately to items.json
- Weekly: Rewrite summary.md from active facts
- Never delete — Use "Supersede" mechanism

3. Define Fact Extraction Rules

Tell the bot what to do during a “heartbeat.” Add the following logic to your system prompt or automation script:

「Add to Heartbeat Rules:」

## Fact Extraction
On each heartbeat:
1. Check for new conversations
2. Spawn cheap sub-agent to extract durable facts
3. Write to relevant entity items.json
4. Track lastExtractedTimestamp

Focus: Relationships, status changes, milestones
Skip: Casual chat, temporary info

4. Setup Weekly Synthesis Cron Job

The Sunday Cron job is key to keeping memory fresh. Add this logic:

## Weekly Memory Review
For each entity with new facts:
1. Load summary.md
2. Load active items.json
3. Rewrite summary.md for current state
4. Mark contradicted facts as superseded

5. Define Atomic Fact Schema

To ensure machine readability, JSON must follow a strict Schema:

{
  "id": "entity-001",
  "fact": "The actual fact",
  "category": "relationship|milestone|status|preference",
  "timestamp": "YYYY-MM-DD",
  "source": "conversation",
  "status": "active|superseded",
  "supersededBy": "entity-002"
}

Why This Solution Beats Others

「The core question of this section: Compared to vector databases or large context files, what are the irreplaceable advantages of this file-based knowledge graph approach?」

In AI engineering, we typically face three主流 memory management solutions, each with drawbacks:

Solution Type	Flaw Analysis
「Vector DB / RAG」	It’s a “black box.” It’s hard to inspect exactly what the AI “knows,” and editing specific erroneous memories is as hard as editing text. Retrieval can be random.
「Monolithic Context Files」	Over time, files become massive. Slow to load, prone to stale info, and hard to query structurally.
「Basic Clawdbot」	Solid foundation, but essentially static. Lacks auto-evolution capabilities.
「Three-Layer Clawdbot」	「Clear Advantage:」 Files are readable, automatically maintained, and intelligently compounding. You can open JSON to edit, or let AI maintain it.

The three-layer system solves the tension between retrieval efficiency, data timeliness, and maintenance costs through clear architectural separation.

Conclusion: The Leap From Tool to Partner

When we copy this article into Clawdbot and execute the configuration, we get more than just a robot with a better memory.

「The core question of this section: What is the ultimate value of running this system long-term?」

The result is an AI that:

「Never forgets.」
「Never goes stale.」
Costs 「pennies」 to maintain.
Understands the difference between a “current boss” and a “former boss.”
Gets 「smarter every week.」

While other assistants wake up with amnesia, yours wakes up better informed than yesterday. The knowledge graph grows. The context optimizes. The responses improve.

This is the difference between an AI assistant and an AI that actually knows you.

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「Author’s Reflection」

This “compound effect” is rare in technology. Most software systems accumulate technical debt as they age; they slow down and get harder to maintain. This three-layer memory system is counter-intuitive—the more you use it, the more high-value structured data accumulates, and the more valuable the system becomes. This should be the ultimate goal we strive for when building Personal Knowledge Management (PKM) systems.

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Practical Summary / Action Checklist

Here is a simplified checklist to help you get started quickly based on the content above:

「Environment Setup:」 Create /life/areas/people and /life/areas/companies folders.
「Data Structure Definition:」 Define the three-layer architecture rules in MEMORY.md.
「Automation Configuration:」
- Set Heartbeat script to call sub-model every 30 mins to extract facts to items.json.
- Set Sunday Cron to rewrite summary.md based on items.json.
「Data Standardization:」 Strictly follow the JSON Schema, using status: "superseded" instead of deleting old data.

One-Page Summary

「Concept:」 Three-Layer Memory System (Knowledge Graph + Daily Logs + Tacit Knowledge).

「Core Mechanisms:」

「Real-time Extraction:」 Cheap sub-model scans conversations periodically to save atomic facts.
「Entity-Based Storage:」 Store by person/company, not a single blob.
「Weekly Synthesis:」 Rewrite summaries weekly, mark old facts.

「Key Advantages:」 Context auto-updates, history is never lost, maintenance cost is near zero, and it is fully human-readable.

Frequently Asked Questions (FAQ)

「1. Will this system delete my old memories?」
No. The system uses a “superseding” mechanism. Old facts are marked as superseded, but remain in the database forever, ensuring historical traceability.

「2. What is the daily cost of running this system?」
It is very low. The main cost comes from the cheap sub-model call (like Haiku) every 30 minutes, usually costing just pennies per day in total.

「3. What if I want to manually edit a specific fact?」
Since all data is stored as standard Markdown and JSON files, you can directly open the corresponding items.json and edit it manually. The system will read your changes during the next synthesis.

「4. Why do I need weekly synthesis?」
Weekly synthesis prevents the context window from becoming bloated. It compresses massive amounts of raw atomic facts into a refined summary.md, ensuring the AI only loads the most relevant and up-to-date info when called upon.

「5. Is this system suitable for non-technical users?」
While it involves file operations, the automation process is transparent to the user once configured. The user just chats with the AI as usual, and the system maintains memory automatically in the background.

「6. How is this better than a Vector Database (RAG)?」
Vector databases are often black boxes; they are hard to control precisely and debug. This solution is based on plain text and JSON—it is fully transparent, readable, editable, and avoids the hallucinations or fuzzy matching issues possible with vector retrieval.

「7. How should I handle temporary information (like tomorrow’s meeting)?」
Temporary info does not enter Layer 1’s Knowledge Graph. Layer 1 is for persistent facts (like relationships or preferences). Temporary events are logged in Layer 2 (Daily Notes) for short-term recall.

「8. What happens if I provide contradictory information to the AI?」
The system uses timestamps to identify the newer information and updates the old fact’s status to superseded, automatically resolving the conflict and ensuring the AI always follows the latest perception.