How to Make OpenClaw Remember Everything (Memory Tuning)

Let me be real with you: the single biggest reason people rage-quit AI agent frameworks isn't hallucinations, it isn't cost, and it isn't model quality. It's memory. Or more accurately, the complete absence of it.

You spend an hour setting up context with your agent — your tech stack, your coding conventions, your project architecture — and then the session ends. Next time you come back, it's like talking to someone with amnesia. You're back to square one, re-explaining everything. Every. Single. Time.

If you've felt this frustration, you're not alone. It's the number one complaint across every AI developer community I follow. And it's the reason I spent a stupid amount of time figuring out how to make OpenClaw's memory system actually work properly — so your agents remember what matters, forget what doesn't, and behave like collaborative partners instead of goldfish.

This post is everything I've learned about OpenClaw memory configuration. We're going from zero to a production-ready memory setup that persists across sessions, scales without slowing down, and doesn't require you to babysit it.

The Problem Nobody Warns You About

Here's the typical lifecycle of someone building AI agents:

Week 1: "This is amazing! My agent is so helpful!"

Week 2: "Wait, why doesn't it remember what we discussed yesterday?"

Week 3: "I'm now spending more time re-explaining context than actually building."

Week 4: Gives up, goes back to copy-pasting into ChatGPT.

The root issue is that most frameworks treat memory as an afterthought. They give you a ConversationBufferMemory class and wish you luck. You're left figuring out when to save, what to save, how to retrieve it, when to prune it, and how to make it work when you deploy to production and your container restarts nuking everything.

OpenClaw takes a fundamentally different approach: memory is a first-class citizen. It's not bolted on. It's baked in. But — and this is important — the default configuration is just the starting point. To get truly reliable, long-term memory, you need to tune it.

Let's do that.

Step 1: Understand the Three Memory Layers

OpenClaw organizes memory into three distinct layers, and understanding them is the key to everything:

1. Session Memory — What happened in this conversation. Short-term. Think of it as working memory.

2. Episodic Memory — Summaries and key facts extracted from past sessions. Medium-term. This is what lets your agent say "last time we discussed X."

3. Core Memory — Permanent facts, preferences, and decisions. Long-term. Your tech stack, your coding standards, your project architecture. Things that should never be forgotten unless explicitly updated.

By default, OpenClaw uses session memory only. That means out of the box, you get the goldfish experience. Let's fix that.

Step 2: Enable Persistent Memory

In your OpenClaw agent configuration, you need to explicitly enable persistence. Here's the baseline config:

# openclaw.config.yaml
agent:
  name: "dev-assistant"
  model: "gpt-4o"

memory:
  persistence: true
  backend: "sqlite"          # Options: sqlite, postgresql, redis
  path: "./memory/agent.db"  # For sqlite
  
  session:
    enabled: true
    max_turns: 50            # Keep last 50 exchanges in active context
    
  episodic:
    enabled: true
    auto_summarize: true     # Automatically summarize sessions on close
    retention_days: 90       # Keep episodic memories for 90 days
    
  core:
    enabled: true
    auto_extract: true       # Automatically extract key facts

That persistence: true with a backend is doing the heavy lifting. Without it, everything lives in RAM and dies when your process stops.

If you're running locally for development, sqlite is perfectly fine. For production, switch to postgresql or redis:

memory:
  persistence: true
  backend: "postgresql"
  connection: "postgresql://user:pass@localhost:5432/openclaw_memory"

This single change — enabling persistence with a real backend — solves the "it forgot everything after restart" problem that plagues 90% of agent setups.

Step 3: Configure Auto-Extraction (This Is the Magic)

Here's where OpenClaw gets genuinely impressive. With auto_extract: true on core memory, the system automatically identifies and stores important facts from your conversations. But the default extraction is conservative. You probably want to tune it.

memory:
  core:
    enabled: true
    auto_extract: true
    extraction:
      categories:
        - "tech_stack"        # Languages, frameworks, tools
        - "architecture"      # Design decisions, patterns
        - "preferences"       # Coding style, conventions
        - "project_context"   # What we're building, goals
        - "decisions"         # Agreed-upon choices with reasoning
      confidence_threshold: 0.7  # Only store if >70% confident it's important
      update_strategy: "merge"    # merge, replace, or ask

The update_strategy is crucial. Set it to "merge" and OpenClaw will intelligently combine new information with existing memories. Set it to "replace" and new info overwrites old. Set it to "ask" and the agent will confirm before updating — useful when you're worried about corrupting good memories with bad ones.

Here's what this looks like in practice:

You: "We're building the API with FastAPI and Python 3.12"

[OpenClaw Core Memory Auto-Extract]:
  → tech_stack.language: "Python 3.12"
  → tech_stack.framework: "FastAPI"
  → project_context.type: "API"

[Two weeks later]

You: "Add rate limiting to our endpoints"

[OpenClaw retrieves from core memory]:
  → Framework: FastAPI
  → Language: Python 3.12
  
Agent: "I'll add rate limiting using slowapi, which integrates 
        natively with FastAPI. Based on our Python 3.12 setup, 
        we can also use the new asyncio improvements for the 
        rate limit backend..."

No copy-pasting. No re-explaining. It just knows.

Step 4: Set Up Semantic Retrieval (Not Just Keyword Matching)

This is where most DIY memory solutions fall apart. They do keyword matching — searching for exact terms in stored memories. OpenClaw uses hybrid search: a combination of keyword matching and semantic vector similarity. This means your agent can find relevant memories even when you phrase things differently.

memory:
  retrieval:
    strategy: "hybrid"           # Options: keyword, semantic, hybrid
    semantic_weight: 0.7         # 70% semantic, 30% keyword
    max_results: 10              # Return top 10 relevant memories
    recency_boost: true          # Recent memories ranked higher
    recency_half_life: 7         # Recency weight halves every 7 days

The recency_boost with recency_half_life is subtle but powerful. It means that if you established a pattern last week and a different pattern six months ago, the recent one gets priority. But the old one doesn't disappear — it's still there if needed.

You can also do explicit retrieval in code when you want precise control:

from openclaw import Agent, Memory

agent = Agent.from_config("openclaw.config.yaml")

# Explicit recall
past_decisions = agent.memory.recall(
    query="error handling pattern",
    scope="core",           # Search core memory only
    limit=5,
    min_confidence=0.8
)

# Use in agent context
agent.run(
    "Implement error handling for the new endpoint",
    context=past_decisions
)

This is the escape hatch for when automatic retrieval isn't pulling the right context. In my experience, you need explicit recall maybe 10% of the time once your config is properly tuned.

Step 5: Multi-Agent Shared Memory

If you're running multiple agents (and you should be — specialized agents beat one generalist agent every time), you need shared memory. Without it, your research agent discovers something and your coding agent has no idea.

agents:
  - name: "researcher"
    model: "gpt-4o"
    memory:
      shared_pool: "project-alpha"   # All agents in this pool share memories
      
  - name: "coder"  
    model: "gpt-4o"
    memory:
      shared_pool: "project-alpha"   # Same pool = shared context
      
  - name: "reviewer"
    model: "gpt-4o"
    memory:
      shared_pool: "project-alpha"

memory:
  shared_pools:
    project-alpha:
      backend: "postgresql"
      connection: "postgresql://user:pass@localhost:5432/openclaw_shared"
      isolation: "read-write"        # All agents can read and write

Now when your researcher discovers that PostgreSQL is the best database choice for your use case, your coder immediately has access to that conclusion — including the reasoning behind it.

# In the researcher agent's flow:
agent.memory.shared().store(
    key="database_decision",
    value="PostgreSQL 16 - chosen for JSONB support and row-level security",
    category="decisions"
)

# The coder agent automatically has access:
# "Based on our decision to use PostgreSQL 16 for its JSONB support..."

No more agents working in silos. No more re-researching decisions that were already made.

Step 6: Memory Hygiene — Correcting, Updating, and Forgetting

Your agent will learn something wrong at some point. An outdated API endpoint, a pattern you've since abandoned, a wrong assumption. You need to be able to fix it without nuking everything.

# Update a specific memory
agent.memory.update(
    key="tech_stack.database",
    value="Switched from MySQL to PostgreSQL as of project phase 2",
    reason="Migration completed"    # Keeps audit trail
)

# Delete a specific incorrect memory
agent.memory.forget(
    key="api.auth_endpoint",
    confirm=True                    # Requires explicit confirmation
)

# Nuclear option: clear a specific category
agent.memory.clear(scope="session")     # Clear session only
agent.memory.clear(scope="episodic", older_than="180d")  # Clear old episodic

# GDPR-compliant full purge for a user
agent.memory.purge_user(user_id="user-123")

The reason parameter on updates is something I'd strongly recommend always using. It creates a versioned history:

tech_stack.database:
  v1 (2026-01-15): "MySQL 8.0"
  v2 (2026-03-22): "PostgreSQL 16" [reason: "Migration completed"]

This means your agent can say "we originally used MySQL but switched to PostgreSQL in March" instead of just knowing the current state. Context about changes is often as valuable as the current facts.

Step 7: Prevent Unbounded Growth

Memory that grows forever will eventually slow your system to a crawl. Here's the config that keeps things fast:

memory:
  maintenance:
    auto_summarize_threshold: 100    # Summarize after 100 session turns
    episodic_merge_interval: "7d"    # Merge similar episodic memories weekly
    max_core_entries: 500            # Cap core memory at 500 facts
    decay:
      enabled: true
      unreferenced_ttl: "180d"       # Remove unreferenced memories after 6 months
      min_access_count: 2            # Must be accessed 2+ times to survive decay

The min_access_count is clever — it means memories that were stored but never actually retrieved (i.e., probably not that useful) get cleaned up automatically. Memories that keep getting pulled into conversations are clearly valuable and stick around.

The Complete Production Config

Here's the full config I'd recommend for a production OpenClaw setup:

agent:
  name: "dev-assistant"
  model: "gpt-4o"

memory:
  persistence: true
  backend: "postgresql"
  connection: "${DATABASE_URL}"    # Use environment variables
  
  session:
    enabled: true
    max_turns: 50
    
  episodic:
    enabled: true
    auto_summarize: true
    retention_days: 90
    
  core:
    enabled: true
    auto_extract: true
    extraction:
      categories:
        - "tech_stack"
        - "architecture"
        - "preferences"
        - "project_context"
        - "decisions"
      confidence_threshold: 0.7
      update_strategy: "merge"
      
  retrieval:
    strategy: "hybrid"
    semantic_weight: 0.7
    max_results: 10
    recency_boost: true
    recency_half_life: 7
    
  maintenance:
    auto_summarize_threshold: 100
    episodic_merge_interval: "7d"
    max_core_entries: 500
    decay:
      enabled: true
      unreferenced_ttl: "180d"
      min_access_count: 2
      
  security:
    encryption: true
    user_isolation: true
    audit_log: true

Skip the Setup: Felix's OpenClaw Starter Pack

Look, I just walked you through a pretty involved configuration process. It's not hard, but there are a lot of knobs to turn, and getting them right requires some trial and error.

If you'd rather skip the experimentation phase and start with something that already works, Felix's OpenClaw Starter Pack on Claw Mart is genuinely the fastest way to get going. For $29, you get pre-configured skills that include production-ready memory configuration out of the box — the kind of setup I outlined above, but already tuned and tested. It also includes a bunch of other useful pre-built skills for common agent patterns.

I'm not saying you can't build all of this yourself — you clearly can with the guide above. But Felix has done the trial-and-error part already, and there's real value in starting with a config that someone has already battle-tested across dozens of projects. It's the difference between spending an afternoon tweaking settings and spending that afternoon actually building your application.

Common Gotchas and How to Fix Them

"My agent retrieves irrelevant memories" Lower the confidence_threshold for extraction and increase semantic_weight in retrieval. The agent is probably storing too many low-quality memories.

"Memory retrieval is slow" Check your backend. SQLite struggles past ~10,000 entries. Switch to PostgreSQL with proper indexing. Also make sure decay is enabled — you might have unbounded growth.

"Agent keeps using outdated information" Increase recency_boost and decrease recency_half_life. Also use memory.update() to explicitly correct outdated entries instead of hoping the agent figures it out.

"Shared memory between agents is chaotic" Set one agent to read-write and others to read-only for the shared pool. Having multiple agents writing to shared memory simultaneously can create conflicts. Designate one agent as the "memory owner."

Next Steps

1. Start with the basic config — Enable persistence with SQLite, turn on all three memory layers, and see how it feels.
2. Tune extraction categories — Add categories specific to your domain. Building a data pipeline? Add "data_sources" and "transformations." Building a SaaS? Add "user_requirements" and "business_logic."
3. Monitor memory quality — Periodically check what's being stored with agent.memory.inspect(). You'll quickly see if extraction is too aggressive or too conservative.
4. Move to production backend — Once you're happy with the behavior, switch to PostgreSQL and deploy with confidence.

Memory is what turns an AI agent from a fancy autocomplete into something that actually feels like a collaborator. It's the difference between an agent you fight with and one you build with. Get the config right, and you'll wonder how you ever worked without it.