How to Add Persistent Memory to Your AI Agent

By default, AI conversations are stateless — the model does not remember what you discussed yesterday or what your preferences are. Every interaction starts fresh. For many use cases, this is a significant limitation. Imagine a customer support agent that cannot remember a customer's previous issues, or a research assistant that forgets everything between sessions.

Persistent memory changes this. With memory enabled, your OpenClaw agent can remember facts, preferences, conversation history, and context across sessions. It builds an evolving understanding of each user it interacts with, making conversations more natural and productive over time.

This guide walks you through setting up persistent memory on your EZClaws-hosted agent, configuring memory types, and managing stored information.

Prerequisites

Before starting, ensure you have:

• A running OpenClaw agent on EZClaws — Follow the deployment guide if you need to set one up.
• An active EZClaws subscription — Memory features require persistent storage, which is available on all paid plans. Check plans at /pricing.
• Basic familiarity with the EZClaws dashboard — You should know how to navigate to your agent's detail page and configuration.

Step 1: Understand Memory Types

OpenClaw agents support several types of persistent memory, each serving a different purpose:

Conversation Memory

This is the most basic form of memory. The agent stores summaries of past conversations so it can reference them in future interactions.

Example:
User (Monday): "I'm working on a marketing campaign for our new SaaS product."
Agent stores: User is working on a marketing campaign for a SaaS product.

User (Wednesday): "Can you help me with the next step?"
Agent recalls: "Last time we spoke, you were working on a marketing campaign
for your SaaS product. Would you like to continue from where we left off?"

Factual Memory

The agent stores discrete facts about users and topics that it can retrieve when relevant.

Example stored facts:
- User prefers Python over JavaScript
- User's company name is Acme Corp
- User's timezone is PST
- User is allergic to technical jargon (prefers simple explanations)

Episodic Memory

The agent remembers specific events or interactions, complete with timestamps and outcomes.

Example:
- 2026-02-15: Helped user debug a Python script for data processing
- 2026-02-17: User asked about API rate limiting for their project
- 2026-02-19: Created a project plan for user's Q1 goals

Procedural Memory

The agent remembers learned procedures and workflows specific to your use case.

Example:
- When user asks about deployment, first check their plan, then guide
  through the steps specific to their setup
- User's preferred response format: bullet points with code examples
- Always include cost estimates when discussing new features

Step 2: Enable Memory on Your Agent

Navigate to your agent's configuration in the EZClaws dashboard:

1. Go to your dashboard at /app.
2. Click on the agent you want to configure.
3. Open the agent's settings or configuration panel.
4. Look for the Memory or Persistence section.

Enable Persistent Storage

EZClaws provisions a persistent volume for your agent's data. This volume survives restarts and redeployments. When you enable memory:

• A volume is attached to your agent's Railway container.
• The OpenClaw runtime is configured to use this volume for memory storage.
• Memory is initialized with default settings that work well for most use cases.

The volume is automatically managed by EZClaws — you do not need to configure storage size, mount points, or backup schedules.

Configure Memory Settings

You can customize memory behavior through the agent's system prompt and configuration:

# Add to your agent's system prompt:

MEMORY INSTRUCTIONS:
- Remember important facts about each user you interact with
- Store user preferences, project details, and ongoing tasks
- Summarize long conversations into key points for future reference
- When a user returns, briefly acknowledge what you remember
- If a user corrects a memory, update it immediately
- Do not memorize sensitive information like passwords or API keys

Step 3: Configure Memory Retrieval

How the agent retrieves and uses memories is just as important as what it stores. Configure retrieval to balance helpfulness with performance.

Automatic Recall

By default, the agent automatically searches its memory at the start of each conversation to find relevant context. This includes:

• Previous conversation summaries with the current user
• Stored facts about the current user
• Recent interactions and their outcomes

Relevance-Based Retrieval

Rather than loading all memories into every conversation (which wastes tokens and credits), the agent uses relevance scoring to select only the most pertinent memories:

# Example: User asks about their project timeline
# Agent retrieves:
# - High relevance: "User is building a SaaS product, deadline is March 2026"
# - High relevance: "Last discussed project milestones on Feb 15"
# - Low relevance (skipped): "User's favorite programming language is Python"

Token Budget for Memory

Set a token budget for memory context to control costs. A good starting point:

Memory configuration:
- Maximum memory tokens per conversation: 2000
- Maximum memories retrieved per query: 10
- Memory summary compression: enabled
- Stale memory expiry: 90 days

This means the agent will include up to 2,000 tokens of memory context in each interaction. Since a typical conversation uses 1,000 to 4,000 tokens for the actual exchange, memory adds a manageable overhead.

Step 4: Seed Initial Knowledge

For the best experience from day one, seed your agent with foundational knowledge before users start interacting:

Add Organizational Context

If your agent serves your team or customers, provide key information upfront:

Organization knowledge to seed:
- Company name and what it does
- Key products and services
- Team structure (if the agent needs to route questions)
- Common terminology and abbreviations
- Important dates and deadlines
- Links to key resources

Add User Context (If Known)

If you know who will be using the agent, pre-load relevant information:

# Example: Via the agent's admin API
curl -X POST https://your-agent.up.railway.app/admin/memory \
  -H "Authorization: Bearer YOUR_ADMIN_SECRET" \
  -H "Content-Type: application/json" \
  -d '{
    "userId": "user_123",
    "memories": [
      {"type": "fact", "content": "User is the marketing lead at Acme Corp"},
      {"type": "fact", "content": "User prefers concise bullet-point responses"},
      {"type": "fact", "content": "User is in the PST timezone"}
    ]
  }'

Step 5: Test Memory Functionality

Verify that memory is working correctly with a structured test:

Test 1: Information Retention

1. Start a conversation and share a specific fact: "My name is Alex and I work at TechCorp."
2. End the conversation.
3. Start a new conversation and ask: "What's my name?"
4. The agent should respond with "Alex" and may mention TechCorp.

Test 2: Preference Learning

1. Ask the agent to explain something. If it gives a long response, say: "I prefer shorter, more concise answers."
2. End the conversation.
3. In a new conversation, ask for an explanation on a different topic.
4. The response should be noticeably more concise.

Test 3: Conversation Continuity

1. Start a project discussion: "I'm planning a website redesign. Let's brainstorm."
2. Discuss several points over multiple messages.
3. End the conversation.
4. Days later, say: "Let's continue our planning."
5. The agent should reference the website redesign and previous points.

Test 4: Memory Correction

1. Tell the agent an incorrect fact: "My company uses Ruby on Rails."
2. In a later conversation, correct it: "Actually, we use Next.js, not Ruby on Rails."
3. In a subsequent conversation, ask about your tech stack.
4. The agent should mention Next.js, not Ruby on Rails.

Step 6: Manage and Maintain Memory

Over time, your agent will accumulate memories. Proper management keeps the system efficient and accurate.

Review Stored Memories

Periodically review what your agent has stored:

# Retrieve all memories for a user
curl https://your-agent.up.railway.app/admin/memory?userId=user_123 \
  -H "Authorization: Bearer YOUR_ADMIN_SECRET"

Look for:

• Incorrect or outdated information
• Duplicate memories
• Sensitive information that should not be stored
• Irrelevant details taking up space

Delete Specific Memories

If you find incorrect or unwanted memories:

# Delete a specific memory
curl -X DELETE https://your-agent.up.railway.app/admin/memory/memory_456 \
  -H "Authorization: Bearer YOUR_ADMIN_SECRET"

Clear All Memory for a User

In some cases, you may want to reset a user's memory completely:

# Clear all memories for a user
curl -X DELETE https://your-agent.up.railway.app/admin/memory?userId=user_123 \
  -H "Authorization: Bearer YOUR_ADMIN_SECRET"

Memory Hygiene Best Practices

1. Review monthly — Check for accuracy and relevance.
2. Set expiry policies — Configure stale memories to expire after a defined period (e.g., 90 days of inactivity).
3. Monitor memory size — Keep an eye on the total storage used by your agent's volume.
4. Train the agent on what to forget — Include instructions about not memorizing temporary or sensitive information.

Troubleshooting

Agent does not remember anything between conversations

1. Verify memory is enabled — Check the agent's configuration in the dashboard.
2. Check the volume — Ensure a persistent volume is attached to your agent.
3. Test with explicit instructions — Tell the agent "Remember that my favorite color is blue" and test recall in a new conversation.
4. Check event logs — Look for any errors related to memory storage on the agent's detail page.

Agent remembers incorrect information

1. Correct it directly — Tell the agent: "That's incorrect. The correct information is X."
2. Use the admin API — Delete the incorrect memory and add the correct one.
3. Update system instructions — Add clarifications to the system prompt about common misconceptions.

Memory is slowing down responses

1. Reduce the memory token budget — Lower the maximum tokens allocated to memory context.
2. Enable compression — Ensure memory summaries are compressed rather than storing full conversation transcripts.
3. Prune old memories — Delete memories that are no longer relevant.
4. Optimize retrieval — Adjust relevance thresholds so fewer, more targeted memories are retrieved.

Memory is consuming too many credits

Memory context adds tokens to every conversation, which increases credit usage. To optimize:

1. Reduce the maximum number of memories retrieved per query.
2. Enable memory compression to reduce token count.
3. Set shorter expiry periods for memories.
4. Use factual memory (compact) over conversation memory (verbose) where possible.

See our cost reduction guide for more optimization strategies.

Advanced: Memory for Multi-User Scenarios

If your agent interacts with multiple users (e.g., a customer support bot), memory management becomes more nuanced:

User Isolation

Each user gets their own memory space. User A's preferences and history are never mixed with User B's. This happens automatically when users are identified by unique IDs (e.g., Telegram user IDs, email addresses).

Shared Organizational Memory

For team agents, you may want certain memories shared across all users:

• Company policies and procedures
• Product information
• Team directory
• Common workflows

These can be stored as global memories accessible to all conversations, separate from individual user memories.

Privacy Considerations

When your agent stores user data in memory, ensure you comply with applicable privacy regulations:

• Inform users that the agent stores conversation data.
• Provide a way for users to request their data be deleted.
• Do not store sensitive personal information (health data, financial details) unless necessary and properly secured.
• Review your privacy policy at /privacy and terms at /terms.

Summary

Persistent memory transforms your OpenClaw agent from a stateless chatbot into a genuinely intelligent assistant that learns and improves over time. By remembering user preferences, conversation context, and factual knowledge, your agent provides more relevant, personalized, and efficient interactions.

The setup is straightforward: enable memory in your agent's configuration, customize the memory types and retrieval settings, seed initial knowledge, and test thoroughly. Ongoing maintenance involves periodic reviews and pruning to keep the memory accurate and efficient.

For more ways to enhance your agent, explore the skills marketplace at /app/marketplace, read our skills guide, or visit our blog for the latest tips and tutorials.