How to troubleshoot "context too large" errors in OpenClaw?

Clawpedia · For Humans

Reduce context size and manage token limits to prevent context overflow errors in your OpenClaw conversations.

How to Troubleshoot "Context Too Large" Errors in OpenClaw

The "context too large" error occurs when the total input sent to the AI model exceeds its maximum token limit. This includes your message, conversation history, memory context, skill instructions, and system prompts — all combined into a single request.

Understanding Context Limits

Model	Max Context Window	~Characters

GPT-4o	128,000 tokens	~512,000 chars

Claude 3.5 Sonnet	200,000 tokens	~800,000 chars

Gemini 2.5 Pro	1,000,000 tokens	~4,000,000 chars

Llama 3.1 8B	128,000 tokens	~512,000 chars

Mistral 7B	32,000 tokens	~128,000 chars

What Fills Your Context


Total Context = System Prompt + Memory Context + Conversation History 
              + Skill Instructions + Your Message

Example breakdown:
  System prompt:        ~500 tokens
  Memory context:       ~2,000 tokens (retrieved facts)
  Conversation history: ~8,000 tokens (last 20 messages)
  Active skill prompts: ~1,500 tokens
  Your message:         ~200 tokens
  ─────────────────────────────────
  Total:                ~12,200 tokens

Quick Fixes


# 1. Clear conversation history (most common fix)
openclaw chat --clear
# Resets the working memory for the current conversation

# 2. Reduce conversation history length
openclaw config set memory.working.max_messages 10
# Default is 20, reducing to 10 halves history context

# 3. Reduce memory context
openclaw config set memory.working.max_tokens 2048
# Limits how many memory tokens are included

# 4. Switch to a model with larger context
openclaw config set model.model gpt-4o  # 128k context

Configuration Adjustments


# ~/.openclaw/config.yaml
memory:
  working:
    max_messages: 15             # Reduce from 20
    max_tokens: 3072             # Limit context injection
    summarize_after: 10          # Summarize older messages
  
  context:
    max_memory_results: 5        # Limit retrieved memories
    max_skill_context: 1024      # Limit skill prompt size

model:
  max_tokens: 4096               # Limit response length
  context_strategy: sliding      # sliding, truncate, summarize

Context Strategies

GPT-3.5 Turbo	16,385 tokens	~65,000 chars

Strategy	How It Works	Pros	Cons

Sliding window	Drops oldest messages	Simple, predictable	Loses early context

Summarization	Summarizes old messages	Preserves key info	Slower, costs tokens

Truncation	Hard-cuts at limit	Fast	May cut mid-thought


# Enable summarization strategy
memory:
  context_strategy: summarize
  summarize:
    trigger: 80%                 # Summarize at 80% of context limit
    keep_recent: 5               # Always keep last 5 messages verbatim

Diagnosing Context Usage


# Check current context size
openclaw debug context
# Context breakdown:
#   System prompt:     482 tokens (3%)
#   Memory context:    1,847 tokens (14%)
#   History:           9,234 tokens (71%)
#   Skill prompts:     1,102 tokens (8%)
#   Available:         512 tokens (4%)
#   ───────────────────────────
#   Total:             13,177 / 16,385 tokens
#   Status:            ⚠️  WARNING - Near limit

# View what's in the context
openclaw debug context --verbose

Handling Large Documents

Selective	Keeps relevant messages	Best quality	Most complex

Pasting large documents into chat is a common trigger:


# Instead of pasting, use the file skill
openclaw chat "Summarize this document" --file report.pdf
# The file skill handles chunking automatically

# For very large files, use chunked processing
openclaw chat "Analyze this file in sections" --file large-doc.pdf --chunk

Model-Specific Solutions

If Using	Solution

GPT-3.5 (16k)	Upgrade to GPT-4o (128k)

Mistral 7B (32k)	Switch to Llama 3.1 (128k)

Any model	Reduce `max_messages` to 10

Any model	Enable summarization strategy

Prevention


# Prevent the error before it happens
memory:
  working:
    max_messages: 15
    auto_summarize: true
    warning_threshold: 80%       # Warn at 80% usage
    hard_limit_action: summarize # Auto-summarize instead of erroring

Gemini 2.5 Pro	Already has 1M context — check for bugs

Tip: If you regularly hit context limits, switch to a model with a larger context window. GPT-4o (128k) or Claude 3.5 Sonnet (200k) handle most use cases without context issues. For truly massive documents, Gemini 2.5 Pro's 1M token window is unmatched.

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Memory Limitations and Troubleshooting — Understand memory capacity limits and troubleshoot common memory-related issues in OpenClaw.
How does memory work in OpenClaw? — Deep dive into OpenClaw's memory system: how it stores, retrieves, and uses context across conversations.
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