When building autonomous AI agents with frameworks like OpenClaw, one of the biggest challenges is memory management. As the agent runs for days or weeks, the context window grows, leading to hallucination and high token costs. OpenLTM introduces a self-decaying memory system that solves this natively.
The Context Window Problem
Traditional local agents append every thought, action, and observation to their context window. When it gets full, they summarize it or abruptly clear parts of it. Both approaches lose critical nuances or break task continuity.
OpenLTM provides a biological-inspired approach: memories have an activation weight that decays over time unless re-accessed.
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Setting Up OpenLTM with OpenClaw
1. Installation
First, install the OpenLTM MCP server and its dependencies.
npm install -g @openclaw/openltm-mcp2. Configuration
Next, configure your OpenClaw agent to use the OpenLTM server in your openclaw.json.
{
"mcpServers": {
"openltm": {
"command": "openltm-mcp",
"args": ["--decay-rate=0.05", "--storage-dir=~/.openltm"]
}
}
}How Self-Decaying Memory Works
Whenever the agent stores an observation, OpenLTM assigns it an initial weight of 1.0. Every time the agent queries its memory, retrieved memories get a weight boost, simulating rehearsal. Memories that drop below a threshold (e.g., 0.1) are offloaded to cold storage and excluded from the active context.
FAQ
Does OpenLTM work entirely offline?
Yes. OpenLTM is a local storage layer and requires zero internet connection to function.
Is OpenLTM free?
Yes, it is open-source and free to use alongside OpenClaw.
Can I use it with Ollama?
Absolutely. By pairing OpenLTM with an Ollama backend for OpenClaw, your entire agentic workflow remains local, private, and cost-free.
Wrap-up
Adding OpenLTM to your OpenClaw agents provides the long-term context retention necessary for complex autonomous workflows. Give it a try!
Written by Matteo Giardino, CTO and founder. I build AI agents for SMEs in Italy. My projects.