If you’ve been running an OpenClaw agent and noticed it getting progressively dumber throughout the day, you’re not alone. In this video, we break down exactly why this happens and what you can do about it. It all comes down to one thing: the context window.
What Is the Context Window?
Think of the context window as your AI agent’s short-term memory — its working brain. Every message you send, every file it reads, every task it processes takes up space in that window. It’s measured in tokens (roughly 4 characters per token), and every model has a hard limit.
The best analogy is a human assistant who’s been given too many tasks at once. Tell them to handle your car, your house, your parents visiting, your dinner reservations — at some point they get overloaded and start dropping balls. That’s exactly what happens to your AI agent when the context window fills up.
Research backs this up too. A 2025 study by Chroma Research called “Context Rot” tested 18 different LLMs and found that models do not use their context uniformly — their performance grows increasingly unreliable as input length grows. Even for simple tasks, LLMs exhibit inconsistent performance across different context lengths. The longer the context, the worse the reasoning gets, especially for multi-step problems.
Why Your Agent Wakes Up Already Loaded
Here’s something that surprised us. Every day, OpenClaw essentially kills your agent and restarts it fresh. It wakes up, reads its long-term memory files (your SOUL.md, MEMORY.md, AGENTS.md, and other config files), and loads all of that into the context window. It’s like an assistant coming to work, reading their briefing notes, and getting up to speed.
The problem? If you’ve stuffed those files with your life story, your preferences, your childhood memories, and every random thought you’ve ever had — your agent wakes up with a context window that’s already half full before it’s done a single task.
In our test, Jeff (running on MiniMax 2.5) woke up at the start of the day already at 136K tokens. That’s because in the early days, the common advice was to “blast your agent with your life story so it understands you better.” Turns out, that’s actually counterproductive. All that irrelevant context is eating into the space your agent needs for actual work.
Cheap Models Get Hit Harder
Not all models handle large context equally. We compared two setups side by side:
Stark running on Claude Opus — woke up at around 100K out of 200K capacity, and still performed fluidly. Opus is genuinely good at working with large context windows and maintaining quality throughout.
Jeff running on MiniMax 2.5 — started struggling almost immediately. As one of our viewers, Note, put it: “The moment you go above 120K context window, it feels like I’m talking to ChatGPT 3.5.”
There’s a hidden reason for this beyond just model quality. To save costs, cheaper models like MiniMax aggressively dump parts of the context they consider unimportant. This is an internal optimization to reduce compute costs — but sometimes what they dump is actually critical to your task. You might ask it to make a presentation and halfway through it forgets what the presentation is even about.
This aligns with what researchers have found: relevant information buried in the middle of longer contexts gets degraded considerably, and lower similarity between questions and stored context accelerates that degradation.
How to Keep Your Agent Smart
Based on our testing, here are the practical tips that actually work:
1. Trim your memory files. Go through your SOUL.md, USER.md, and other long-term storage files. Remove anything that isn’t directly relevant to the tasks you need your agent to do. Your agent doesn’t need to know your life story — it needs to know how to do its job.
2. Specialize your agent. AI models actually gravitate toward specialization. Instead of making your agent a general-purpose assistant that handles everything from dinner reservations to research reports, train it for specific tasks. In our test, Stark was trained specifically for making presentations and research — and it delivered significantly better results than Jeff, who was loaded with general life context.
3. Monitor your context usage. You can simply ask your agent “How much context are you using?” and it’ll tell you. On the OpenClaw terminal, it sometimes displays this automatically. Keep an eye on it throughout the day.
4. Clear context when needed. If you feel your agent getting dumber, start a new session. This kills the current context and lets the agent restart fresh. There’s also a natural compacting stage where the agent automatically summarizes and compresses older context — similar to how your own brain forgets the details of brushing your teeth but remembers the important meeting you had.
5. Choose your model wisely. If you’re on a budget with MiniMax or other Chinese models, context management becomes even more critical. These models aggressively optimize to save compute, which means they’ll cut corners on context retention. If you can afford it, models like Claude Opus handle large context windows much more gracefully.
The Bottom Line
Context window management is probably the single most impactful thing you can do to improve your OpenClaw agent’s performance. It’s not about giving your agent more information — it’s about giving it the right information and keeping that working memory clean.
The takeaway is simple: less irrelevant context equals a smarter agent. Trim the fat from your memory files, specialize your agent’s purpose, and don’t be afraid to restart sessions when things get sluggish. Your agent will thank you — by actually being useful.
Michael Gu
Michael Gu, Creator of Boxmining, stared in the Blockchain space as a Bitcoin miner in 2012. Something he immediately noticed was that accurate information is hard to come by in this space. He started Boxmining in 2017 mainly as a passion project, to educate people on digital assets and share his experiences. Being based in Asia, Michael also found a huge discrepancy between digital asset trends and knowledge gap in the West and China.