OpenClaw memory loss fix using Mem0 plugin

The memory problem in OpenClaw

OpenClaw agents are stateless between sessions by default. Memory lives in files that get loaded at startup, but context compaction—which summarizes old context to save tokens—rewrites or drops those files mid-conversation. This causes agents to gradually forget information across sessions, particularly affecting files like MEMORY.md that users create to preserve knowledge.

The architectural solution

The core issue is that any memory stored inside the context window can be destroyed by context management. The fix is to move memory outside the context window entirely, so compaction cannot touch it.

Mem0 + OpenClaw integration

Install the plugin with one command:

openclaw plugins install @ mem0/openclaw-mem0

Get an API key at mem0, drop it in your openclaw.json, and setup is complete in under 30 seconds.

How it works

• Auto-Recall: Runs on every turn before your agent responds. It searches for relevant memories (preferences, project structure, past decisions) and injects them fresh into working context. This survives compaction because memories aren't stored in the context window—they're pulled in new each turn.
• Auto-Capture: Runs after each response. It extracts what's worth remembering, deduplicates it, updates outdated facts, and stores it externally. No rules to configure.

Memory scopes

• Long-term (user-scoped): Your name, tech stack, project structure, decisions. Persists across every session forever.
• Short-term (session-scoped): What you're actively working on right now. Doesn't pollute the long-term store.

Both get searched on recall, with long-term memories searched first.

Self-hosted option

For those who don't want data leaving their machine: set "mode": "open-source" in your config. This lets you bring your own stack—Ollama for embeddings, Qdrant for vectors, and your choice of LLM (Anthropic or others). No Mem0 API key needed, nothing leaves your machine.

Practical impact

Before: Starting a new session requires re-explaining your stack and preferences, wasting about 20 minutes reconstructing context before actually working.

After: Starting a new session—the agent immediately knows your name, your stack, and where you left off, allowing you to start working immediately.

The difference becomes particularly significant when running multiple agents.