OpenClaw Memory Problem SOLVED | Stop Wasting Time Explaining

OpenClaw's three memory layers: embeddings, QMD, and skills — pick at least one or your agent resets every session.

## Why Memory Breaks OpenClaw agents start a new session each time, effectively "waking up" with no context. Feed it your life story and it condenses the chat into short notes plus embedding vectors. Once you talk to it the next day, it has to re-read its notes to recall who you are. The context window makes dumping full transcripts into OpenAI too expensive at scale, which is why blind-search memory fails after a month of use.

## Enable Semantic Search First Turn on semantic search with embeddings. OpenClaw converts your conversation history into numerical vectors and pulls relevant chunks on demand. The latest OpenClaw update now ships Mistral as an embedding backend alongside OpenAI, and Mistral is cheaper to run at scale.

## Add QMD as a Cost-Cut Layer QMD is the second memory path. It sits alongside semantic search as a cheaper retrieval option the Boxmining team is still testing, useful when OpenAI embedding costs grow with chat history.

## Turn Repeats into Skills For workflows that recur every day, write them as a skill in the OpenClaw skills directory. The file is plain English, readable from Termius or any SSH client, and editable. Tell the agent explicitly: refine that skill, and it updates the file rather than re-deriving the rule every session.

## The Obsidian Safety Net Beyond the three built-in layers, have your OpenClaw agent write a plain-text summary of each session, then push that file to Obsidian. This gives you a durable, human-readable log independent of the embedding index — useful when you don't trust a vector search to surface a specific preference.

## How Users Actually Differ The host treats memory lightly because his work is project-based and non-repetitive. His co-host is the opposite — he saves full daily chat summaries because small forgotten details (call them preferences) break his workflow. The channel is explicitly asking which approach you want more videos on: QMD deep-dives, Obsidian workflows, or general OpenClaw troubleshooting.