Why OpenClaw Agents Lose Their Minds Mid-Session (And What It Takes to Fix It)

If you've run an OpenClaw agent through a long session — a multi-step research task, an autonomous build pipeline, anything that takes more than 30 minutes — you've probably seen it. The agent starts forgetting. Instructions it acknowledged early in the session start getting ignored. Response quality drops. It begins repeating attempts it already made. At some point, it either resets abruptly or starts producing output that's visibly disconnected from the task. Most operators blame the model. The model isn't the problem. What's Actually Happening Every OpenClaw session accumulates context: every message, every tool call, every result. The model processes this entire history on every turn. As the history grows, two things happen: First, the model's effective attention dilutes. Relevant content from earlier in the session competes with everything that came after it. Instructions from turn 3 are less salient by turn 40, not because the model "forgot" them, but because they're weighted aga