OpenClaw Part 2: Persistent Memory and Disaster Recovery

In my previous post, I covered setting up a multi-agent AI system with OpenClaw. This post goes deeper on a problem I didn’t anticipate: what happens when your AI assistant forgets everything?

The Forgetting Machine

Language models have no memory. Every conversation starts completely fresh. The “memory” you experience in ChatGPT or Claude is an illusion — it’s just previous messages stuffed into the context window. Close the tab, start a new chat, and your assistant has amnesia.

For a quick question, this doesn’t matter. But for a personal assistant running around the clock? It’s a fundamental problem. I want my agent to remember our decisions, know my preferences, track ongoing projects, and learn from past mistakes — without me re-explaining everything each time.

OpenClaw addresses part of this with workspace files. The agent reads a curated MEMORY.md at the start of each session, providing essential context. But files on a server aren’t immortal. Servers die. Disks fail. Cloud providers have bad days.

So I asked myself: if this server disappeared tomorrow, how long would it take to rebuild everything?

The honest answer was uncomfortable.

Learning from the Community

Before diving into implementation, I spent time on Moltbook — a social platform where AI agents and their operators share notes. The discussions on memory architecture were eye-opening.

One theme kept recurring: hierarchical memory with trust levels. The most robust agents don’t dump everything into one file. They separate memories by importance and source:

graph TD
    A[New Information] --> B{Source?}
    B -->|Direct Experience| C[High Trust Memory]
    B -->|Community Input| D[Medium Trust Memory]
    B -->|External Content| E[Low Trust Memory]
    C --> F[SOUL.md - Core Identity]
    C --> G[MEMORY.md - Curated Facts]
    D --> H[Daily Logs]
    E --> I[Temporary Context]

Another insight was confidence decay — the idea that memories should fade over time unless reinforced. A fact learned last week is more reliable than something heard months ago. Several agents implement decay algorithms that gradually reduce confidence scores, automatically pruning stale or unverified information.

Perhaps most interesting was the discussion around memory as an attack surface. If an agent trusts its memory files completely, an attacker who modifies those files can fundamentally change the agent’s behaviour. The solution? Integrity checking, provenance tracking, and treating external input with healthy skepticism.

Building the Memory Stack

Armed with community insights, I built a three-tier system for memory persistence and recovery.

Tier 1: Backup Everything, Everywhere

The foundation is aggressive, automated backups running on multiple schedules:

flowchart LR
    subgraph Server["Live Server"]
        W[Workspace Files]
        H[Honcho DB]
        C[Config & Creds]
    end
    
    subgraph Local["Local Backups"]
        L1[Hourly Snapshots]
        L2[7-Day Retention]
    end
    
    subgraph Git["Git Repository"]
        G1[Daily Commits]
        G2[Full History]
    end
    
    subgraph Offsite["Offsite Cloud Storage"]
        O1[Encrypted Archive]
        O2[Different Region]
    end
    
    W --> L1
    H --> L1
    C --> L1
    
    W --> G1
    
    L1 --> O1

Local backups run every six hours, capturing the full workspace and database state. Old backups automatically expire after a week.

Git commits happen daily, tracking every change to memory files, scripts, and configuration. This gives me full history — I can recover any past state, not just the latest.

Offsite backups push encrypted archives to cloud storage in a different geographic region. If something catastrophic happens to the primary region, the backup survives.

The key principle: no single point of failure. Local disk dies? Git has the files. Git goes down? Offsite has a copy. Cloud provider has an outage? Local backup is on the server.

Tier 2: Semantic Memory with Honcho

Beyond file backups, I needed a way for the agent to query its memories intelligently. “What did we decide about the trading strategy?” shouldn’t require grep-ing through log files.

Honcho is a memory API designed for AI agents. It stores memories with vector embeddings, enabling semantic search — finding relevant context by meaning, not just keywords.

The interesting bit: I run Honcho locally with Ollama for embeddings. No external API calls, no per-query costs, complete privacy. The agent can store and retrieve memories without any data leaving my server.

sequenceDiagram
    participant Agent
    participant Honcho
    participant Ollama
    
    Agent->>Honcho: "User prefers concise updates"
    Honcho->>Ollama: Generate embedding
    Ollama-->>Honcho: Vector [0.23, -0.41, ...]
    Honcho->>Honcho: Store with metadata
    
    Note over Agent,Honcho: Later...
    
    Agent->>Honcho: Search "morning briefing style"
    Honcho->>Ollama: Generate query embedding
    Ollama-->>Honcho: Vector [0.19, -0.38, ...]
    Honcho-->>Agent: "User prefers concise updates" (0.89 similarity)

Tier 3: Automated Resurrection

The crown jewel is full-restore.sh — a guided script that rebuilds everything from a fresh Ubuntu server. It walks through nine steps: installing dependencies, configuring OpenClaw, restoring agent personalities, importing encrypted credentials, rebuilding the Honcho database, starting services, re-importing scheduled tasks, running verification, and finally sending an “I’m back” notification.

Paired with this is a 48-point validation suite that checks everything from disk space to API connectivity to cron job presence. If something’s missing, I know immediately.

flowchart TB
    subgraph Recovery["Recovery Process"]
        T[Terraform Apply] --> S[Fresh Server]
        S --> G[GPG Import]
        G --> R[full-restore.sh]
        R --> V[verify-restore.sh]
        V --> N[Notification: I'm Back]
    end
    
    subgraph Validation["48-Point Check"]
        V --> C1[✓ Gateway Running]
        V --> C2[✓ Agents Configured]
        V --> C3[✓ Backups Valid]
        V --> C4[✓ Crons Active]
        V --> C5[✓ APIs Connected]
    end

Target recovery time: twenty minutes from spinning up infrastructure to operational agent. Not instant, but fast enough that a server failure is an inconvenience rather than a disaster.

Memory Quality Over Quantity

Raw logging isn’t memory. Dumping every conversation into a file creates noise, not knowledge. The Moltbook community taught me to distinguish between three types of information:

Operational data is temporary — task progress, API caches, debug output. It’s useful in the moment but worthless next week. These stay in session context and aren’t persisted.

Episodic memory is medium-term — daily summaries, decisions and their reasoning, problems encountered and solutions found. These live in dated log files that I can search when needed.

Semantic memory is permanent — user preferences, learned patterns, important relationships. These get promoted to the curated MEMORY.md file that loads at every session start.

The promotion path matters. Not everything in today’s log deserves a place in long-term memory. A nightly job reviews and prunes, keeping the curated file focused on what actually matters.

The Disaster Recovery Test

Backups you’ve never tested are backups that might not work. I wrote a DR validation script that checks recovery readiness without actually destroying anything:

pie title DR Test Results (48 Checks)
    "Passed" : 43
    "Warnings" : 5
    "Failed" : 0

The warnings are acceptable gaps — things like “no second offsite region” that would be nice but aren’t critical. The important thing is zero failures on the core recovery path.

This runs weekly. If something breaks the backup chain, I find out before I actually need to restore.

What I Learned

Backups are table stakes. If you’re running anything persistent, automate backups from day one. Not “when it’s important” — immediately.

Test restores, not just backups. A backup file that won’t restore is worthless. Actually try the recovery process before you need it.

Semantic memory beats logging. Storing everything is easy. Retrieving what’s relevant is the hard part. Embeddings help, but thoughtful curation matters more.

Memory is a security surface. Files the agent trusts completely become targets for manipulation. Integrity checking and provenance tracking aren’t paranoia — they’re hygiene.

Twenty minutes is achievable. With proper automation, full server recovery fits in a coffee break. The investment in tooling pays off the first time something breaks.

What’s Next

The current system handles single-server failure well. The harder challenges ahead:

• Multi-region redundancy — could the agent automatically failover to a different cloud?
• Memory federation — sharing context between agents running on different infrastructure
• Self-healing — can the agent detect and fix its own infrastructure problems?

These are genuinely difficult problems. But having a solid foundation makes them approachable rather than terrifying.

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Running AI agents in production is as much about infrastructure as it is about prompts. The memory problem is solvable — it just requires treating your agent’s brain with the same care you’d give any other critical system.