Learn how to fix OpenClaw context limit exceeded errors by tuning reserveTokensFloor, managing token budgets, and matching LM Studio to real usable context.
If you are running OpenClaw with local LLMs through LM Studio and suddenly hit the dreaded “Context limit exceeded” error, you are not alone.
This is one of the most frustrating issues users face when building serious local AI workflows.
The error often appears unexpectedly, even when your model claims to support massive context windows like 32K, 64K, or even 128K tokens.
Instead of producing a response, OpenClaw fails with messages like:
Context limit exceeded. Increase agents.defaults.compaction.reserveTokensFloor to 20000 or higherAt first glance, this looks like a simple configuration problem.
In reality, it is usually the result of a mismatch between your actual usable context window, OpenClaw’s token reservation strategy, and the context metadata exposed by your local inference backend.
In this guide, we will break down exactly why this happens and how to fix it properly.
A very common scenario looks like this:
LM Studio UI reports:
128K context supportedOpenClaw behaves as if only:
32K context is availableThis happens because:
As a result, OpenClaw reserves tokens based on expectations that exceed what the model can realistically process.
This triggers context overflow.
---
The most important setting involved is:
agents.defaults.compaction.reserveTokensFloorThis value tells OpenClaw how many tokens to reserve for safe completion generation.
Think of it as protected output space.
If your reserve is too small:
OpenClaw risks truncating completions.
If your reserve is too large:
You reduce available prompt context and trigger overflow earlier.
Finding the correct balance is critical.
---
Use these practical starting points:
| Context Window | Recommended reserveTokensFloor |
| -------------- | ------------------------------ |
| 8K | 2000 |
| 16K | 4000 |
| 32K | 6000–8000 |
| 64K | 12000 |
| 128K | 18000–22000 |
If you are unsure, start conservatively.
For most local OpenClaw + LM Studio setups:
6000 to 8000is the sweet spot.
---
Edit your OpenClaw configuration file.
Locate:
agents.defaults.compactionUpdate it:
{
"agents": {
"defaults": {
"compaction": {
"reserveTokensFloor": 8000
}
}
}
}Restart OpenClaw after saving.
This gives the agent enough safe completion headroom.
---
Do not trust the UI-reported context size blindly.
Verify what your loaded model is actually using.
Check:
If your model claims 128K but practical behavior collapses near 32K, configure OpenClaw around 32K.
Stability beats theoretical maximums.
---
Long-running sessions are the biggest hidden context killer.
Every interaction adds:
Eventually your token budget fills up.
Best practice:
Start fresh sessions for new tasks.
Do not keep unrelated workflows in a single conversation.
---
If compaction happens too late, OpenClaw reaches overflow before cleanup occurs.
Adjust compaction thresholds to trigger summarization earlier.
Earlier compaction reduces token pressure and improves stability during long agent sessions.
---
Large tool responses consume enormous token space.
Common offenders:
Instead of passing everything, trim outputs to relevant sections.
Smaller context = fewer overflow errors.
---
A local setup using:
was repeatedly failing with:
Context limit exceededAfter tuning:
the issue disappeared completely.
This is one of the clearest examples of why advertised context size is not always usable context.
---
For production-grade local agent workflows:
Avoid mixing unrelated workflows.
Fresh sessions reduce token bloat.
Watch long-running tool-heavy interactions.
Leave enough completion headroom.
Always test practical context boundaries.
---
Context overflow in OpenClaw is rarely a bug.
It is usually a configuration mismatch between:
Once you understand how token budgeting works, these errors become predictable and easy to fix.
For most local AI builders, the fix comes down to one simple principle:
Configure for real-world usable context, not theoretical maximum context.That single adjustment makes OpenClaw dramatically more stable for serious local agent workflows.