OpenClaw Token Limit Exceeded Error — How to Fix (2026)

Understanding Token Limits

Every message to the AI model includes your system prompt, conversation history, and tool results. As conversations grow, you eventually hit the model's context window limit. Here's a rough breakdown:

GPT-4o: 128,000 tokens (~96,000 words)
Claude 3.5: 200,000 tokens (~150,000 words)
GPT-4o-mini: 128,000 tokens (~96,000 words)
Gemini 1.5: 1,000,000 tokens (~750,000 words)

Step-by-Step Fix

1. Limit Conversation History

{
  "memory": {
    "maxMessages": 20,
    "strategy": "sliding-window"
  }
}

2. Enable Automatic Summarization

{
  "memory": {
    "maxMessages": 50,
    "summarizeAfter": 20,
    "summaryModel": "gpt-4o-mini",
    "summaryMaxTokens": 500
  }
}

3. Shorten Your System Prompt

Long system prompts eat into your token budget on every request. Keep them under 500 tokens:

# Instead of a 2000-word system prompt:
"You are a helpful assistant for ACME Corp.
Respond concisely. Use tools when needed."

# Move detailed instructions to a skill or knowledge base
# that's only loaded when relevant.

4. Limit Tool Output Size

{
  "tools": {
    "maxOutputTokens": 2000,
    "truncateOutput": true
  }
}

5. Configure Per-Request Token Limits

{
  "model": {
    "maxInputTokens": 8000,
    "maxOutputTokens": 2000
  }
}

Frequently Asked Questions

What does "token limit exceeded" mean in OpenClaw?

Every AI model has a maximum context window (e.g., 128K tokens for GPT-4o, 200K for Claude). When your conversation + system prompt + tool results exceed this limit, the API returns a "token limit exceeded" error.

How do I check current token usage in OpenClaw?

Enable the token counter in openclaw.json: "logging": { "showTokenCount": true }. This will log the token count for each request and response in your OpenClaw logs.

What is the best strategy for managing tokens?

Use a combination: set maxMessages to limit conversation history, enable automatic summarization for older messages, use a shorter system prompt, and configure maxOutputTokens to prevent overly verbose responses.

Does switching to a model with a larger context window help?

It can help as a temporary fix, but larger context windows cost more per request. The better long-term solution is to manage your context efficiently with summarization, message pruning, and targeted memory.