Make OpenClaw Memory Persist After Restart

Look, I'm going to save you the frustration I went through. You built something cool with OpenClaw. Your agent is learning, adapting, getting smarter with every interaction. Then you restart the system — maybe a server reboot, maybe a deploy, maybe your laptop died — and your agent wakes up with total amnesia. Everything it learned? Gone. Every preference it picked up? Vanished. Every pattern it recognized over hundreds of interactions? Poof.

This is the single most common "what the hell?" moment people hit with AI agents. Not just OpenClaw — it's the dirty secret of the entire agent ecosystem. Most frameworks treat memory as this ephemeral, in-session thing that evaporates the moment the process stops. And most tutorials conveniently skip over this because persistence is "an implementation detail."

No. It's not an implementation detail. It's the entire point. An agent that can't remember anything between sessions isn't an assistant — it's a goldfish with an API key.

Let me show you exactly how to fix this in OpenClaw so your agent's memory survives restarts, stays performant over time, and actually gets smarter the longer it runs.

Why Your Agent's Memory Disappears (And Why It's Not a Bug)

First, let's understand what's actually happening. By default, when you spin up an OpenClaw agent, its memory lives in-process. It's stored in RAM. This is fast and convenient for development, but it means the moment that process ends — whether gracefully or because your cat stepped on the power strip — everything in memory is gone.

Here's what a default setup typically looks like:

from openclaw import Agent, Memory

agent = Agent(
    name="my_assistant",
    memory=Memory()  # Default: in-memory only
)

# Agent learns things during the session
agent.remember("User prefers dark mode")
agent.remember("Project deadline is March 15")

# Session ends... and so do these memories

This isn't a bug. In-memory storage is the sane default for development and testing. You don't want persistent state cluttering things up while you're iterating on prompts and skills. But the moment you move to anything resembling production — or even just a personal agent you want to use daily — you need to flip the switch to persistent storage.

The Fix: Persistent Memory in OpenClaw

OpenClaw has first-class support for persistent memory. It's not a hack, not a workaround, not a third-party plugin. It's built into the core framework. You just need to tell it where to store things.

Here's the minimal change:

from openclaw import Agent, PersistentMemory

agent = Agent(
    name="my_assistant",
    memory=PersistentMemory(
        storage_path="./agent_memory",
        auto_save=True
    )
)

# These memories now survive restarts
agent.remember("User prefers dark mode")
agent.remember("Project deadline is March 15")

That's it for the basic case. When you restart your agent, PersistentMemory automatically loads everything from ./agent_memory on initialization. Your agent picks up exactly where it left off.

But "basic" isn't why you're reading a 2000-word blog post. Let's get into the real configuration that makes this production-worthy.

Step-by-Step: Setting Up Robust Memory Persistence

Step 1: Choose Your Storage Backend

OpenClaw supports multiple storage backends for persistent memory. Your choice depends on your use case:

# Option A: SQLite (great for single-agent, local setups)
memory = PersistentMemory(
    storage_path="./agent_memory",
    backend="sqlite",
    auto_save=True
)

# Option B: JSON files (human-readable, git-friendly)
memory = PersistentMemory(
    storage_path="./agent_memory",
    backend="json",
    auto_save=True
)

# Option C: Vector database (best for semantic retrieval at scale)
memory = PersistentMemory(
    storage_path="./agent_memory",
    backend="vector",
    embedding_model="default",
    auto_save=True
)

My recommendation: Start with SQLite. It's zero-config, handles thousands of memories without breaking a sweat, and you can always migrate later. If you're building something that needs to semantically search through memories (e.g., "what did we discuss about authentication last month?"), go with the vector backend from the start.

Step 2: Configure Memory Management (So It Doesn't Bloat)

Here's where most people mess up. They enable persistence, pat themselves on the back, and then three months later their agent is slow as molasses because it's trying to process 50,000 unsummarized memories on every interaction.

You need a retention policy:

memory = PersistentMemory(
    storage_path="./agent_memory",
    backend="sqlite",
    auto_save=True,
    max_memories=5000,
    summarization_strategy="hierarchical",
    retention_policy={
        "solutions": "forever",
        "preferences": "forever",
        "business_rules": "forever",
        "conversation": "30_days",
        "chitchat": "7_days"
    },
    retrieval_limit=15
)

Let me break down what each of these does:

• max_memories: Hard cap on total stored memories. When you hit the limit, the lowest-priority memories get summarized and consolidated.
• summarization_strategy: "hierarchical" means old memories get progressively summarized — individual memories become daily summaries, daily summaries become weekly summaries, and so on. Key facts are always preserved.
• retention_policy: Different memory categories have different lifespans. Your agent should remember business rules forever but can safely forget casual conversation after a week.
• retrieval_limit: On any given interaction, the agent only pulls the 15 most relevant memories into context. This keeps your token costs sane and your responses fast.

Step 3: Tag and Prioritize What Matters

The default auto-tagging in OpenClaw is decent, but for critical information, be explicit:

# High-priority: will persist even during aggressive pruning
agent.remember(
    "Client requires HIPAA compliance for all data handling",
    importance=10,
    tags=["compliance", "critical", "business_rules"]
)

# Medium-priority: standard retention
agent.remember(
    "User prefers weekly status updates on Mondays",
    importance=5,
    tags=["preferences", "scheduling"]
)

# Low-priority: will be pruned first
agent.remember(
    "User mentioned they had a good weekend",
    importance=1,
    tags=["chitchat"]
)

The importance score (1-10) directly affects what gets kept during memory consolidation. A memory with importance 10 will survive long after importance 1 memories have been summarized away.

Step 4: Enable Selective Forgetting

This one matters more than people think — both for performance and for compliance. Your agent needs to be able to forget things:

# Delete specific sensitive information
agent.forget(query="credit card numbers")
agent.forget(query="social security numbers")

# Forget everything from a specific tag
agent.forget(tags=["deprecated_project"])

# Forget all memories before a certain date
agent.forget(before="2026-01-01")

# Nuclear option: wipe all memories (fresh start)
agent.memory.clear()

If you're handling any kind of user data, build forgetting into your workflow. It's not just good practice — it might be legally required depending on your jurisdiction.

Step 5: Set Up Cross-Session Memory Verification

Here's a pro tip that took me way too long to figure out: after configuring persistence, actually verify it works before you build your entire system on top of it.

# Session 1: Store a test memory
agent = Agent(
    name="persistence_test",
    memory=PersistentMemory(
        storage_path="./test_memory",
        backend="sqlite",
        auto_save=True
    )
)

agent.remember(
    "persistence_test_token_12345",
    importance=10,
    tags=["test"]
)

# Explicitly close the session
agent.memory.save()

# Session 2: Verify retrieval (simulate restart)
agent2 = Agent(
    name="persistence_test",
    memory=PersistentMemory(
        storage_path="./test_memory",
        backend="sqlite"
    )
)

test_recall = agent2.recall("persistence_test_token")
print(f"Memory persisted: {test_recall is not None}")
print(f"Content: {test_recall}")

# Inspect total stored memories
print(f"Total memories: {agent2.memory.count()}")

Run this. If you see your test token come back in session 2, you're golden. If not, check your storage_path — the most common issue is a relative path that changes between runs because you launched from a different directory.

Shared Memory Across Multiple Agents

If you're running multiple specialized agents (which is the OpenClaw sweet spot), you probably want them to share context. Nothing kills user experience like explaining the same thing to three different agents.

# Create a shared memory pool
shared_memory = PersistentMemory(
    storage_path="./shared_context",
    backend="vector",
    auto_save=True
)

# All agents share the same memory
sales_agent = Agent(name="sales", memory=shared_memory)
support_agent = Agent(name="support", memory=shared_memory)
billing_agent = Agent(name="billing", memory=shared_memory)

# When sales learns something about the customer...
sales_agent.remember(
    "Customer interested in enterprise plan, budget around $50k",
    tags=["sales", "customer_profile"],
    importance=8
)

# Support and billing automatically have access
# No re-explaining needed

You can also use namespaces if certain memories should be agent-specific:

sales_agent.remember(
    "Internal: approved 15% discount",
    namespace="sales_internal",  # Only sales agent sees this
    importance=7
)

Debugging Memory Issues

When things go sideways (and they will, because that's development), OpenClaw gives you tools to inspect what's happening:

# List all stored memories
for mem in agent.memory.list_all():
    print(f"[{mem.importance}] {mem.tags}: {mem.content[:100]}...")

# Understand why specific memories were recalled
result = agent.recall("project deadline", explain=True)
for mem in result:
    print(f"Score: {mem.relevance_score}")
    print(f"Reason: {mem.retrieval_reason}")
    print(f"Content: {mem.content}")
    print("---")

# Export everything for analysis
agent.memory.export("debug_dump.json", format="json")

That explain=True flag is invaluable. It shows you exactly why the agent recalled specific memories and what relevance score each one got. If your agent is recalling nonsense, this is how you diagnose it.

Backup and Portability

Because OpenClaw uses open storage formats, backing up your agent's memory is straightforward:

# Export for backup
agent.memory.export("backup_2024_12.json", format="json")

# Import into a new agent
new_agent = Agent(
    name="upgraded_assistant",
    memory=PersistentMemory(storage_path="./new_memory")
)
new_agent.memory.import_from("backup_2024_12.json")

If you're using the SQLite backend, you can also just copy the .db file. It's a standard SQLite database — you can open it with any SQLite client, query it directly, even write migration scripts if you need to restructure your memory schema.

The Shortcut: Skip the Setup Entirely

I've walked you through all the manual configuration because understanding it matters. But if I'm being honest? When I was setting this up for the first time, I burned a full afternoon getting the retention policies, summarization strategies, and storage backends dialed in correctly.

If you don't want to set this all up manually, Felix's OpenClaw Starter Pack on Claw Mart includes a pre-built version of this — persistent memory with sensible defaults, retrieval tuning, and retention policies already configured. It's $29 and comes with pre-configured skills that handle the memory management patterns I described above. I'd have happily paid that to get those hours back. It's genuinely the fastest way to go from "my agent forgets everything" to "my agent has been getting smarter for the last three months" without debugging storage paths and summarization configs.

What Persistent Memory Actually Gets You

Let me paint the picture of what this looks like in practice once it's working:

Week 1: Your agent handles basic tasks. It's learning your preferences, your project context, your communication style.

Week 4: Your agent remembers that you hate Monday morning meetings, that Project Atlas switched from React to Vue in week 2, that your client's billing contact changed last Tuesday. You never re-explained any of this.

Month 3: Your agent has handled hundreds of interactions. Old conversations have been automatically summarized. Solution patterns have been extracted and prioritized. It responds faster and more accurately than it did on day one because it's drawing from three months of accumulated context — and it survived four server restarts without losing a single important fact.

That's the difference between a demo and a tool. Between something you show off and something you actually rely on.

Next Steps

1. Switch to PersistentMemory with SQLite backend. Five-minute change, immediate impact.
2. Set up retention policies so your memory doesn't bloat. Use the template above as a starting point.
3. Test persistence explicitly with the verification script I showed. Don't assume it works.
4. Add importance scoring to your critical remember() calls. Default importance is fine for most things, but your business rules and user preferences deserve a 10.
5. Set up a backup schedule — even just a weekly export() to JSON gives you a safety net.

Or grab Felix's OpenClaw Starter Pack and have all of this configured out of the box. Either way, stop letting your agent wake up with amnesia. The entire value of an AI agent is that it learns and adapts. Without persistence, you're rebuilding that value from zero every single session.

Your agent should be getting smarter while you sleep. Make its memory stick.