How to Give OpenClaw Persistent Memory with Agent-Memo
OpenClaw is the fastest-growing open-source AI agent in 2026, but it has no built-in memory. Here's how to add persistent cloud memory via MCP in under 2 minutes.
TL;DR:OpenClaw supports MCP natively. Add a single config to connect Agent-Memo.AI and your OpenClaw agent gets persistent cloud memory — store decisions, recall context across sessions, and build a knowledge graph of your project.
What is OpenClaw?
OpenClaw is the open-source AI agent that crossed 100,000 GitHub stars in its first week. Created by Peter Steinberger, it connects your favorite messaging platforms (Slack, Discord, Telegram, WhatsApp, and 15+ more) to AI models like Claude, GPT, and DeepSeek.
With 100+ built-in skills and full MCP support, OpenClaw is one of the most versatile AI agents available. But like most agents, it has one major gap: no persistent memory. Every conversation starts from scratch.
The problem: OpenClaw forgets everything
OpenClaw processes messages through LLMs, but it doesn't store context between sessions. This means:
- Your agent doesn't remember previous conversations
- Preferences and instructions need to be repeated
- Project context is lost when the session ends
- Team members can't share knowledge through the agent
SOUL.md files help with static instructions, but they can't capture dynamic context — decisions made during conversations, evolving preferences, or relationships between entities in your project.
The solution: Agent-Memo via MCP
Since OpenClaw supports the Model Context Protocol (MCP), you can connect Agent-Memo.AI to give it persistent cloud memory. The agent gets 14 tools for storing, recalling, searching, and organizing memories — plus a knowledge graph for entity relationships.
Setup (under 2 minutes)
Step 1: Get your API token
Create a free account at agent-memo.ai and copy your API token from the dashboard.
Step 2: Add the MCP server config
Add Agent-Memo to your OpenClaw MCP configuration:
{
"mcpServers": {
"agent-memo": {
"command": "npx",
"args": ["-y", "@agent-memo/mcp-client"],
"env": {
"AGENTMEMO_TOKEN": "your-token-here"
}
}
}
}OpenClaw will start the MCP server and discover the 14 memory tools automatically.
What your OpenClaw agent can now do
With Agent-Memo connected, your agent has access to:
- memory_store / memory_recall — Save and search memories using semantic similarity (BGE-M3, multilingual)
- memory_overview — Load high-importance memories at conversation start
- memory_update / memory_delete — Keep memories current
- memory_check_duplicate — Avoid redundant storage
- kg_add / kg_query — Build a knowledge graph of entity relationships
- kg_timeline — Track how relationships change over time
Example: OpenClaw as a team assistant
Imagine your team uses OpenClaw on Slack. With Agent-Memo connected:
- A developer asks the agent about the project architecture — it recalls from memory
- Someone makes a decision in a channel — the agent stores it automatically
- A new team member asks "what tech stack do we use?" — the agent queries the knowledge graph
- Weeks later, someone asks "why did we choose PostgreSQL?" — the agent finds the original decision
The agent becomes a team knowledge base that learns from every conversation.
OpenClaw + SOUL.md + Agent-Memo
These three work together:
- SOUL.md — Static personality and base instructions (who the agent is)
- Agent-Memo memories — Dynamic context that evolves (what the agent has learned)
- Agent-Memo KG — Structured relationships (how things connect)
SOUL.md defines the agent's character. Agent-Memo gives it a growing brain.
Pricing
Agent-Memo.AI's free tier includes 1,000 memories, 100 knowledge graph facts, and 1,000 API calls per month — enough for personal use. Teams can upgrade to Pro ($9.9/mo) or Team ($49/mo + $7/seat) for higher limits and shared memories.