How to Clear and Rebuild Memory in OpenClaw

If you've spent more than a few hours building agents in OpenClaw, you've hit the wall. You know the one. Your agent starts strong — crisp responses, relevant context, clear reasoning — and then somewhere around step 15 or 20, it starts acting drunk. Repeating itself. Forgetting instructions you gave it three messages ago. Pulling in memories that have absolutely nothing to do with the current task. Or worse, confidently acting on information that was true two hours ago but has since been corrected.

This is the memory problem. And it's the single biggest reason OpenClaw agents fail in production after working perfectly in demos.

The good news: it's fixable. The bad news: the default memory setup that ships with most configurations isn't going to cut it for anything beyond toy projects. You need to understand what's actually happening under the hood, clear out the garbage, and rebuild your memory architecture with intention.

Let's do exactly that.

Why OpenClaw Memory Breaks Down

Before you start ripping things apart, it helps to understand why memory degrades. There are really four failure modes, and most struggling agents are experiencing at least two simultaneously.

1. Context Bloat

Every interaction your agent has gets stored. Every tool call, every intermediate reasoning step, every user message. This grows linearly, and eventually you're either blowing past your context window or spending absurd amounts on tokens. Your agent isn't getting dumber — it's drowning in its own history.

2. Stale Memories Poisoning Retrieval

Your agent learned that the user's preferred language is Python. Great. Then the user switched to Rust three sessions ago. But the old Python preference is still sitting in memory with a high relevance score because it was reinforced across dozens of interactions. Now your agent keeps generating Python examples and the user is getting increasingly frustrated. The memory isn't wrong — it's outdated. And there's no automatic mechanism to clean it up.

3. Retrieval Noise

Vector similarity search is a blunt instrument. When your agent queries memory for "what does the user want?", it pulls back the top-k most similar entries. But similar doesn't mean relevant. You get fragments of old conversations, half-completed thoughts, and context from completely different tasks — all mixed in with the actually useful stuff. The agent then tries to synthesize all of this into a coherent response, and the result is mush.

4. No Memory Structure

This is the root cause behind most of the other problems. If you're dumping everything into a single memory store with no hierarchy, no categorization, and no eviction policy, you're essentially building a hoarder's attic and then asking your agent to find specific items in the dark.

Step 1: Audit Your Current Memory State

Before you clear anything, you need to see what you're working with. OpenClaw gives you tools to inspect the memory state of any agent. Use them.

from openclaw import Agent, MemoryInspector

# Load your agent
agent = Agent.load("my-production-agent")

# Get memory stats
inspector = MemoryInspector(agent)
stats = inspector.get_stats()

print(f"Total memory entries: {stats['total_entries']}")
print(f"Memory size (tokens): {stats['total_tokens']}")
print(f"Oldest entry: {stats['oldest_entry_date']}")
print(f"Stale entries (>30 days, never retrieved): {stats['stale_count']}")
print(f"Duplicate/near-duplicate entries: {stats['duplicate_count']}")

Run this. Look at the numbers. I've seen production agents with 40,000+ memory entries where fewer than 2,000 were ever actually retrieved. That's a 95% noise ratio. No wonder the agent is confused.

You can also do a targeted inspection to see what's actually being pulled during retrieval:

# See what memories get retrieved for a sample query
results = inspector.simulate_retrieval(
    query="What is the user's current project?",
    top_k=10
)

for i, entry in enumerate(results):
    print(f"\n--- Result {i+1} (score: {entry['score']:.3f}) ---")
    print(f"Created: {entry['created_at']}")
    print(f"Last accessed: {entry['last_accessed']}")
    print(f"Content: {entry['content'][:200]}")

What you'll typically find: a mix of vaguely relevant entries from different time periods, with the actually useful current information buried at position 6 or 7. That's your problem in black and white.

Step 2: Clear Memory (The Right Way)

Your first instinct might be to nuke everything and start fresh. Sometimes that's the right call. But usually, you want to be surgical about it.

Option A: Full Reset

For agents that are hopelessly polluted or where you're fundamentally changing the use case:

from openclaw import Agent, MemoryManager

agent = Agent.load("my-production-agent")
memory = MemoryManager(agent)

# Full clear - this is irreversible
memory.clear_all(confirm=True)

print("Memory cleared. Agent is now a blank slate.")

Simple. Brutal. Effective. But you lose everything, including the good stuff.

Option B: Selective Pruning (Recommended)

This is what you should do 90% of the time. Remove the garbage, keep the gold.

from openclaw import Agent, MemoryManager
from datetime import datetime, timedelta

agent = Agent.load("my-production-agent")
memory = MemoryManager(agent)

# Remove entries older than 60 days that haven't been accessed in 30 days
cutoff_created = datetime.now() - timedelta(days=60)
cutoff_accessed = datetime.now() - timedelta(days=30)

pruned = memory.prune(
    created_before=cutoff_created,
    last_accessed_before=cutoff_accessed,
    min_retrieval_count=0  # never been useful
)

print(f"Pruned {pruned['removed_count']} entries")
print(f"Freed {pruned['tokens_freed']} tokens")

# Remove near-duplicates (keeps the most recent version)
deduped = memory.deduplicate(similarity_threshold=0.95)
print(f"Removed {deduped['removed_count']} duplicate entries")

# Remove entries matching specific patterns (e.g., old project context)
removed = memory.remove_by_query(
    query="ProjectAlpha requirements",
    threshold=0.85
)
print(f"Removed {removed['removed_count']} ProjectAlpha-related entries")

Option C: Summarize and Compress

For long-running agents where you want to preserve knowledge but reduce volume:

# Compress old memories into summaries
compressed = memory.compress(
    entries_older_than=timedelta(days=30),
    strategy="summarize",  # or "extract_facts" or "key_points"
    keep_originals=False
)

print(f"Compressed {compressed['original_count']} entries into {compressed['summary_count']} summaries")

This is particularly powerful. Instead of 500 individual memory entries from a month-long project, you get maybe 15-20 dense summaries that capture the essential knowledge without the noise.

Step 3: Rebuild with Proper Architecture

Here's where most people go wrong. They clear memory, feel good about it, and then immediately start accumulating the same mess again. Don't do that. Before you let your agent start building new memories, set up a proper architecture.

The pattern that works in production is layered memory with explicit management. Think of it like a computer: you need RAM (working context), a well-organized file system (structured long-term memory), and a clear policy for what goes where.

from openclaw import Agent, MemoryConfig, MemoryLayer

config = MemoryConfig(
    layers=[
        MemoryLayer(
            name="working",
            type="buffer",
            max_entries=20,
            description="Current conversation and immediate task context"
        ),
        MemoryLayer(
            name="episodic",
            type="vector",
            max_entries=500,
            auto_summarize_after=50,
            description="Past interactions and outcomes"
        ),
        MemoryLayer(
            name="semantic",
            type="structured",
            description="Facts, preferences, and learned knowledge",
            allow_updates=True,  # Can overwrite old facts
            require_confidence=0.8  # Only store high-confidence info
        ),
        MemoryLayer(
            name="procedural",
            type="indexed",
            description="How-to knowledge, workflows, patterns that worked"
        )
    ],
    retrieval=RetrievalConfig(
        strategy="hybrid",  # vector + recency + importance
        max_context_tokens=4000,
        rerank=True
    ),
    eviction=EvictionConfig(
        strategy="importance_decay",
        decay_rate=0.95,
        min_importance=0.1
    )
)

agent = Agent.load("my-production-agent")
agent.configure_memory(config)
agent.save()

Let me break down what each layer does and why it matters.

Working Memory is your agent's short-term context. It holds the current conversation, active task details, and anything immediately relevant. It's a simple buffer with a hard cap. When it's full, the oldest entries get either promoted to episodic memory or discarded. This prevents context bloat during long interactions.

Episodic Memory stores past interactions and their outcomes. "Last time the user asked about deployment, they wanted Docker examples." "The last three API calls to the payment service failed with timeout errors." This is what gives your agent the ability to learn from experience. The auto-summarize feature is key — after 50 entries accumulate, they get compressed into summaries so the layer doesn't grow unbounded.

Semantic Memory is for facts and knowledge. User preferences, project details, domain knowledge. The critical feature here is allow_updates=True. When your agent learns that the user switched from Python to Rust, it doesn't add a new entry — it updates the existing one. This eliminates the stale memory problem almost entirely.

Procedural Memory stores "how to do things." Successful workflows, effective prompting patterns, tool usage sequences. This is your agent's skill library.

Step 4: Implement Memory Hygiene

Architecture is necessary but not sufficient. You also need ongoing maintenance. Set up automated memory hygiene that runs periodically:

from openclaw import Agent, MemoryMaintenance

agent = Agent.load("my-production-agent")
maintenance = MemoryMaintenance(agent)

# Run this daily or weekly depending on agent activity
report = maintenance.run(
    deduplicate=True,
    prune_stale=True,
    reindex=True,
    validate_facts=True,  # Flag potentially outdated semantic memories
    compress_old_episodes=True
)

print(report.summary())

You can also hook this into your agent's lifecycle so it self-maintains:

agent.on_session_end(
    callback=lambda: maintenance.run(
        deduplicate=True,
        prune_stale=True
    )
)

This means every time a conversation session ends, your agent does a quick cleanup. It's the equivalent of closing your browser tabs at the end of the day. Small habit, massive impact over time.

Step 5: Monitor and Iterate

After you've rebuilt your memory architecture, you need to actually watch it work. OpenClaw's monitoring tools let you track memory health over time:

from openclaw import Agent, MemoryMonitor

agent = Agent.load("my-production-agent")
monitor = MemoryMonitor(agent)

# Enable memory quality tracking
monitor.enable(
    track_retrieval_relevance=True,
    track_memory_growth=True,
    track_eviction_rate=True,
    alert_on_bloat=True,
    bloat_threshold=10000  # Alert if entries exceed this
)

The metric you care about most is retrieval relevance — what percentage of retrieved memories actually get used in the agent's response. If you're below 60%, your retrieval is too noisy. If you're above 85%, you're in great shape.

Getting Started Without the Pain

If all of this feels like a lot to set up from scratch, it is. That's exactly why I'd recommend grabbing Felix's OpenClaw Starter Pack before you spend a weekend fighting configuration files. It bundles the kind of pre-configured memory architecture I've described here — layered memory, proper retrieval settings, eviction policies, and maintenance hooks — into a ready-to-go package. Instead of building this from the ground up, you get a solid foundation that you can customize for your specific use case. It's saved me and plenty of other OpenClaw developers a lot of frustrating setup time and lets you jump straight to the interesting part: building the actual agent logic.

Common Mistakes to Avoid

A few things I see people do repeatedly that sabotage their memory systems:

Storing everything at maximum detail. Not every interaction needs to be preserved word-for-word. Most tool call results, intermediate reasoning steps, and clarification questions are ephemeral. Let them die in working memory.

Using a single similarity threshold for all memory types. Episodic memories need lower thresholds (you want broad context). Semantic facts need higher thresholds (you want precision). Procedural memories need exact matching when possible.

Never testing retrieval quality. Build a test suite of queries and expected memories. Run it periodically. If retrieval quality degrades, you'll catch it before your users do.

Ignoring token costs. Every memory retrieval adds tokens to your prompt. If you're pulling 4,000 tokens of context per query and making 50 queries per hour, that's 200,000 additional tokens per hour just for memory. Monitor this. Optimize ruthlessly.

Treating memory as set-and-forget. Memory architecture needs to evolve with your agent's use case. What works for a customer support agent won't work for a research assistant. Revisit your configuration quarterly at minimum.

Next Steps

Here's your action plan:

1. Today: Run the memory audit on your current agent. See how bad things actually are. The numbers might surprise you.

2. This week: Do a selective prune. Remove duplicates, stale entries, and irrelevant content. This alone will probably improve agent performance noticeably.

3. This weekend: Implement the layered memory architecture. Set up the four layers (working, episodic, semantic, procedural) and configure proper retrieval and eviction.

4. Next week: Add memory maintenance hooks and monitoring. Start tracking retrieval relevance.

5. Ongoing: Review memory health monthly. Adjust thresholds and eviction policies based on what you're seeing.

Memory management isn't glamorous. Nobody gets excited about eviction policies and deduplication thresholds. But it's the difference between an agent that works in a demo and one that works in production, day after day, without degrading. Get this right, and everything else about your OpenClaw agent gets easier.

Now go clean up that memory. Your agent will thank you.