Set Up Persistent Memory in OpenClaw

If you've spent more than ten minutes building an agent in OpenClaw, you've already hit this wall: you start a session, feed the agent a bunch of context about your project, get it humming along nicely, close your laptop, come back the next morning, and your agent has the memory of a goldfish. Everything you told it—gone.

It's one of the most frustrating experiences in AI development, and it's the reason half the "AI assistant" projects on GitHub get abandoned after a week. Not because the agent wasn't useful, but because re-teaching it everything every single session makes it feel useless.

The good news: OpenClaw has a genuinely solid persistent memory system. The bad news: the documentation breezes past it like it's a footnote, and most people either don't set it up or set it up wrong. This post is everything I wish someone had told me six months ago.

The Core Problem (And Why Default Memory Doesn't Cut It)

By default, when you spin up an OpenClaw agent, it uses in-memory storage. That means everything your agent "remembers" lives in RAM for the duration of your session. The moment that process dies—whether you restart the agent, your terminal crashes, or you just close the window—poof. Gone.

This is fine for demos. It is absolutely not fine for anything real.

Even within a single long session, default memory causes problems. Once your conversation exceeds the context window, OpenClaw starts dropping older messages. There's no summarization, no prioritization. It just truncates. So you're 40 messages deep debugging an authentication module and suddenly your agent forgets which framework you're even using.

Here's what that looks like in practice:

Message 1-10: "I'm building a REST API with FastAPI, using PostgreSQL..."
Message 11-30: Detailed debugging of specific endpoint issues
Message 31-40: Discussion about deployment strategy
Message 41: "Which database are we using again?"

Agent: "Could you tell me what database you're working with?"

Maddening. Let's fix it.

Step 1: Enable Basic Persistent Memory

The simplest upgrade—and the one everyone should do immediately—is switching from default in-memory storage to PersistentMemory. This is a one-line conceptual change that fundamentally transforms how your agent works.

from openclaw.memory import PersistentMemory
from openclaw import Agent

# Create persistent memory tied to an agent identity
memory = PersistentMemory(
    agent_id="my_coding_assistant",
    # Automatically saves to ~/.openclaw/memory/my_coding_assistant/
)

agent = Agent(memory=memory)
agent.run("My project uses Python 3.11 with FastAPI and SQLAlchemy")

That's it. Now when you close everything down, come back tomorrow, and reinitialize with the same agent_id, your agent picks up right where it left off:

# Next day, different terminal session, same agent_id
agent = Agent(memory=PersistentMemory(agent_id="my_coding_assistant"))
agent.run("What Python version is my project using?")
# Response: "Your project uses Python 3.11"

Under the hood, OpenClaw is writing memory to ~/.openclaw/memory/my_coding_assistant/ in a local SQLite store. No external database needed. No Pinecone API key. No Docker container running a vector DB. Just local files that persist across sessions.

If you want to control where those files live:

memory = PersistentMemory(
    agent_id="project_assistant",
    storage_path="./my_project/agent_memory/",
    backup_frequency="hourly"  # Automatic backups, because losing memory hurts
)

This alone solves the most common complaint I see in every AI agent community: "My agent forgets everything when I restart." Done. Move on to the interesting stuff.

Step 2: Stop Storing Garbage

Here's a problem that sneaks up on you. You set up persistent memory, you're feeling great, and after a week your agent's retrieval starts getting... weird. It's pulling up irrelevant context. Responses feel off. What happened?

Your memory is full of junk.

Every "hi there," every "ok," every "thanks!" is sitting in your memory store with equal weight to "the production database is at prod-db.company.com." When the agent goes to retrieve relevant context, it's wading through a swamp of pleasantries to find the actual information.

OpenClaw's SelectiveMemory with ImportanceFilter fixes this:

from openclaw.memory import SelectiveMemory, ImportanceFilter

memory = SelectiveMemory(
    filters=[
        ImportanceFilter(
            store_greetings=False,       # "hi", "hey" → not stored
            store_confirmations=False,   # "ok", "yes", "got it" → not stored
            store_gratitude=False,       # "thanks!", "appreciate it" → not stored
            require_information_content=True  # Must contain actual substantive info
        )
    ]
)

Here's what this looks like in a real conversation:

User: "Hi there!"                          → NOT stored (greeting)
User: "I'm building an auth module"        → STORED (topic introduction)
User: "Okay"                               → NOT stored (confirmation)
User: "The endpoint is /v2/auth with JWT"  → STORED as CRITICAL (technical detail)
User: "thanks!"                            → NOT stored (gratitude)

Later, when someone asks "How does authentication work?", the retrieval pulls back the JWT endpoint info instead of a wall of "ok" and "thanks." Night and day difference.

You can also tag importance explicitly for things you absolutely cannot afford to lose:

memory.add(
    "Production API key is sk-prod-abc123...",
    importance="critical",     # Will never be pruned, even under memory pressure
    category="credentials"
)

memory.add(
    "User mentioned they prefer dark mode",
    importance="low"           # May be pruned if storage space is needed
)

OpenClaw also does automatic importance detection. If a user says something like "Never deploy to the staging server on Fridays—it breaks the CI pipeline," the system recognizes the explicit instruction pattern and tags it as high importance without you doing anything.

Step 3: Set Up Memory Scopes (This Is the Real Power)

This is where most people stop, and where things get really interesting if you keep going.

The problem with a single flat memory store is that not all information has the same lifecycle. Your current debugging task is irrelevant tomorrow. Your code style preferences are relevant forever. Your project's tech stack matters for months. These shouldn't all live in the same bucket with the same retention policy.

OpenClaw's CompositeMemory lets you define distinct scopes:

from openclaw.memory import CompositeMemory, MemoryScope

memory = CompositeMemory(
    scopes={
        # Task scope: What you're working on right now
        "task": MemoryScope(
            retention="until_task_complete",
            max_age_minutes=30
        ),
        
        # Session scope: This conversation
        "session": MemoryScope(
            retention="until_session_end",
            max_age_hours=24
        ),
        
        # Profile scope: Your preferences, forever
        "profile": MemoryScope(
            retention="permanent",
            storage="long_term"
        ),
        
        # Project scope: Until you explicitly delete it
        "project": MemoryScope(
            retention="until_explicitly_deleted",
            storage="long_term"
        )
    }
)

Now the magic: OpenClaw can automatically detect which scope information belongs to. When you say "I prefer tabs over spaces," it gets filed under profile. When you say "Debug the login function," that's task. When you say "This project uses React with TypeScript," that's project.

You can also be explicit:

memory.add("Current loop counter: iteration 5", scope="task")   # Temporary
memory.add("User timezone: PST", scope="profile")               # Permanent
memory.add("API base URL: api.example.com", scope="project")    # Project-level

Where this really shines is multi-project work. Most of us aren't working on one thing. We're juggling three projects, a side project, and that thing from last month we need to revisit occasionally.

# Working on Project A
agent.run("Setting up the auth module for the main app", project="project_a")
memory.add("Using JWT with refresh tokens", scope="project", project="project_a")

# Switching to Project B
agent.run("Now working on the data pipeline", project="project_b")
memory.add("Using Apache Kafka for event streaming", scope="project", project="project_b")

# Profile preferences carry across everything
agent.run("What message system am I using?", project="project_b")
# Returns: "Apache Kafka" — not JWT tokens from project_a

Project memories are isolated. Profile memories are universal. Task memories are ephemeral. This is how memory should work.

Step 4: Make It Inspectable (Because Black Boxes Are the Enemy)

One of the most common complaints I see on Reddit and in Discord servers: "I have no idea what my agent actually remembers." People are debugging bizarre agent behavior and can't figure out why the agent is giving weird responses because memory is a total black box.

OpenClaw gives you full inspection tools:

from openclaw.memory import InspectableMemory

memory = InspectableMemory()

# See what's in short-term memory
print(memory.inspect_short_term())
# Short-term Memory (last 15 messages):
# 1. [USER] "I need to build a REST API"
# 2. [ASSISTANT] "I can help with that..."

# Search long-term memory
print(memory.inspect_long_term(query="API"))
# Long-term Memory (matching "API"):
# 1. [CRITICAL] "Use FastAPI framework" (stored: 2026-01-10, retrieved: 5 times)
# 2. [HIGH] "API should use /v1 versioning" (stored: 2026-01-10, retrieved: 2 times)

# Get overall stats
stats = memory.get_stats()
# {
#   "total_memories": 47,
#   "short_term_count": 15,
#   "long_term_count": 32,
#   "critical_memories": 5,
#   "avg_retrieval_time_ms": 23,
#   "storage_size_mb": 1.2
# }

You can also fix mistakes. Agent remembered something wrong? Update it:

memory.update(
    query="User prefers Flask",
    new_value="User prefers FastAPI",
    reason="User corrected their preference"
)

# Or just delete it
memory.delete(query="test data from yesterday's debugging session")

There's also a CLI for when you want to manage memory outside your code:

$ openclaw memory list --agent my_dev_agent
[CRITICAL] Production DB: prod.example.com (retrieved: 12 times)
[HIGH] Preferred framework: React + TypeScript
[MEDIUM] Testing framework: Jest
[LOW] User mentioned coffee preference

$ openclaw memory delete --query "coffee preference"
Deleted 1 memory: "User mentioned coffee preference"

$ openclaw memory search "database"
Found 3 memories:
1. Production DB: prod.example.com
2. Test DB: test.example.com
3. Database ORM: Prisma

And if you want a visual interface:

$ openclaw memory serve
Memory inspector running at http://localhost:8000

This is the kind of tooling that separates "toy project" from "thing I actually rely on."

Step 5: Optimize for Speed (Production Considerations)

If you're building something that other people will use—a support bot, an internal tool, a customer-facing assistant—latency matters. Naive memory retrieval can add hundreds of milliseconds to every response.

OpenClaw's OptimizedMemory handles this:

from openclaw.memory import OptimizedMemory

memory = OptimizedMemory(
    cache_strategy="adaptive",      # Learns which memories are accessed frequently
    cache_size_mb=100,
    async_retrieval=True,           # Non-blocking memory queries
    batch_retrievals=True,          # Batch multiple lookups into one operation
    max_retrieval_time_ms=50,       # Hard latency ceiling
    fallback_on_timeout="short_term_only"  # Graceful degradation if long-term is slow
)

The adaptive cache strategy is particularly clever. It tracks which memories get retrieved most often and keeps them hot. For a customer support bot, this means common questions ("How do I reset my password?") hit the cache almost immediately, while rare edge cases take the full retrieval path.

You can monitor performance inline:

with memory.performance_monitor():
    agent.run("How do I reset my password?")

# Memory Performance:
#   Short-term retrieval: 2ms
#   Long-term retrieval: 18ms (cached: 85% hit rate)
#   Total memory impact: 20ms (4% of total response time)

For most use cases, this brings memory overhead down to 20-50ms per query. Barely noticeable.

Putting It All Together

Here's what a production-ready memory configuration looks like combining everything:

from openclaw import Agent
from openclaw.memory import (
    PersistentMemory,
    CompositeMemory,
    MemoryScope,
    SelectiveMemory,
    ImportanceFilter,
    OptimizedMemory,
    MemoryConfig
)

memory = PersistentMemory(
    agent_id="production_assistant",
    storage_path="./agent_memory/",
    backup_frequency="hourly",
    config=MemoryConfig(
        max_short_term_messages=20,
        summarization_strategy="semantic_compression",
        long_term_storage=True,
        relevance_threshold=0.7
    ),
    filters=[
        ImportanceFilter(
            store_greetings=False,
            store_confirmations=False,
            require_information_content=True
        )
    ],
    optimization={
        "cache_strategy": "adaptive",
        "async_retrieval": True,
        "max_retrieval_time_ms": 50
    }
)

agent = Agent(memory=memory)

That gives you: persistence across sessions, intelligent filtering, automatic importance detection, hierarchical summarization, caching, and performance guardrails. It's not trivial to configure from scratch, but once it's running, it just works.

The Shortcut (If You Don't Want to Wire All This Up Manually)

Look, I've spent weeks dialing in memory configurations for different use cases. If you want to skip that learning curve, Felix's OpenClaw Starter Pack on Claw Mart includes pre-configured memory skills that handle most of what I described above out of the box. It's $29, and the persistent memory setup alone—with scopes, filtering, and optimization already wired together—would have saved me a solid weekend of tinkering. It's not the only way to do this, but if you just want a working setup without manually configuring every layer, it's genuinely the fastest path I've found.

What to Do Next

1. Right now: Switch from default memory to PersistentMemory with a stable agent_id. This takes two minutes and immediately stops the "goldfish brain" problem.

2. This week: Add ImportanceFilter to stop polluting your memory with noise. You'll notice better retrieval quality within a day.

3. When you're ready: Set up CompositeMemory with scopes that match how you actually work. Profile, project, session, task—separate them and never look back.

4. For production: Layer on OptimizedMemory with caching and async retrieval. Monitor with the performance tools until you're confident in the latency profile.

Persistent memory is the difference between an AI agent that's a novelty and one that's genuinely useful over time. The whole point of an assistant is that it knows you—your preferences, your projects, your patterns. Without memory that persists, you're just talking to a very expensive autocomplete engine.

Set it up once. Set it up right. Then stop thinking about it and focus on the actual work your agent should be doing.