How do I know if my agent is hitting context limits?

The analytics panel shows a context usage graph. If the line frequently touches the model's maximum, your agent is hitting the limit. You may also notice symptoms like the agent forgetting earlier parts of the conversation or producing shorter, less detailed responses.

Does summarization lose important details?

Well-configured summarization preserves key facts, decisions, and user requirements. However, fine details like exact wording or timestamps may be lost. For workflows where every detail matters, use a persistent memory store alongside summarization.

Can I manually clear the agent's context?

Yes. You can trigger a context reset from the dashboard or by sending a reset command in the conversation. The agent starts with a clean context window while retaining access to persistent memory stores.

Troubleshooting

Memory Issues: Context Cleanup and Optimization

Diagnose and fix context window problems that cause your OpenClaw agent to lose track of conversations, repeat itself, or produce degraded responses.

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What You Will Get

By the end of this guide, you will know how to diagnose and fix memory-related issues that degrade your OpenClaw agent's performance. Your agent will manage its context window efficiently, avoid losing important conversation details, and stop repeating information unnecessarily.

Context window issues manifest in several ways: the agent forgets what was discussed earlier, gives contradictory answers, or suddenly produces incoherent responses. These problems typically occur when the conversation grows long enough to exceed the model's context limit.

You will learn to monitor context usage, configure automatic summarization, prune irrelevant messages, and set up alerts for approaching context limits. The result is an agent that maintains coherent, high-quality conversations regardless of length.

Step-by-Step Troubleshooting

Follow these steps to diagnose and fix memory issues.

Check Context Usage Metrics

Open your agent's analytics panel and look at the context usage graph. This shows how many tokens of context are used per message over time. If usage consistently approaches the model's limit, that explains the degraded responses. Note which conversations hit the ceiling.

Enable Automatic Summarization

Turn on automatic context summarization in your agent's memory settings. When the conversation exceeds a configurable threshold, older messages are summarized into a compact paragraph that preserves key facts. This frees up token space for new messages while retaining essential context.

Configure Message Pruning

Set up message pruning rules that remove low-value messages from context. System messages, greetings, and confirmations can be pruned first since they rarely contain information the agent needs later. Configure the pruning priority so important messages like user requirements are kept longest.

Audit Knowledge Base Injection

If you use RAG, check how many knowledge base chunks are injected per message. Too many chunks crowd out conversation history. Reduce the retrieval count or increase the similarity threshold to inject only the most relevant chunks.

Split Long Conversations

For workflows that naturally span many messages, consider splitting the conversation at logical breakpoints. The agent can summarize the current state and start a fresh context window. This is especially effective for multi-step processes that take dozens of exchanges.

Upgrade the Model's Context Window

If your use case genuinely requires long context, switch to a model with a larger context window. Check the Model Configuration panel for available options and their token limits. Larger context windows cost more per token, so balance the need against your budget.

Monitor After Changes

After applying fixes, monitor the context usage metrics for at least a week. Verify that the agent no longer hits the context limit and that response quality has improved. If issues persist, review the summarization and pruning settings for further tuning.

Tips and Best Practices

Set Context Usage Alerts

Configure an alert that triggers when context usage exceeds 80% of the model's limit. This gives you a heads-up before users experience degraded responses and allows you to intervene proactively.

Review Summarization Quality

Periodically check the summaries generated by the automatic summarization feature. Poor summaries that drop important details can cause the same issues as a full context window. Adjust the summarization prompt if needed.

Use Separate Memory Stores

For facts the agent needs across all conversations, use a persistent memory store rather than keeping them in the conversation context. This reserves the context window for active dialogue.

Educate Users on Context Limits

If your agent serves technical users, consider having it mention when a conversation is getting long. A message like 'This conversation is getting lengthy. Would you like me to summarize and start fresh?' empowers users to help manage the issue.

Frequently Asked Questions

Memory Management and RAG Performance Optimization Model Switching Strategies

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