Here's a scenario every OpenClaw user hits eventually: your AI session is 45 minutes into a complex task, everything is going well, and then — responses start getting weird. Outputs get truncated. The assistant forgets what it did three steps ago. Eventually the session crashes and you're left SSHing into your server to figure out what happened.

This is a context overflow. And it's 100% preventable.

This guide covers why context overflow happens, how to detect it early, and the specific techniques OpenClaw provides to manage context effectively — including memory files, subagent delegation, and compaction strategies.

Why Context Windows Fill Up

Understanding how the context window fills up is the first step to preventing overflow

Every Claude session has a context window — a fixed amount of "working memory" for the conversation. Claude Sonnet supports roughly 200,000 tokens (approximately 150,000 words). That sounds enormous, but it shrinks fast when you're doing real work.

What fills context quickly:

• Screenshots — Every browser screenshot is 1,000–5,000 tokens of image data
• Large files — Reading a 500-line codebase into context costs thousands of tokens
• Long conversations — Every message (user + assistant) stays in context for the session
• Web fetches — Fetching and reading web pages adds their full content to context
• Tool outputs — Every tool call result is appended to the context chain

In a typical heavy session — browser automation, file editing, multi-step research — you can hit 60% context usage in under 30 minutes. At 75%, performance degrades. At 90%+, crashes become likely.

Detecting Context Usage Early

Monitor context usage proactively — don't wait for degraded performance to notice a problem

OpenClaw doesn't show a context usage meter by default, but there are warning signs to watch for:

Early warning signs (60-75% full):

• Responses take slightly longer than normal
• The assistant occasionally forgets recent context
• Summaries start to lose nuance

Critical warning signs (75-90% full):

• The assistant starts referencing things incorrectly
• Tool calls produce incomplete results
• Responses feel "flatter" or less detailed than expected

Imminent crash signs (90%+):

• Truncated responses mid-sentence
• Explicit token limit errors
• The assistant loses track of the task entirely

The best defense is the /status command in OpenClaw, which shows your current context usage. Check it regularly during long sessions — ideally every 15-20 minutes for heavy workloads.

Strategy 1: Use Memory Files Instead of Keeping Context In-Session

Memory files persist context between sessions, eliminating the need to reload everything into working memory

The single biggest context management improvement is shifting from "keep everything in memory" to "write important things to files."

OpenClaw's workspace is designed for this. Instead of accumulating a long conversation to maintain context, write the important state to files after each significant step:

• Daily notes: memory/YYYY-MM-DD.md — Log what happened, decisions made, current state
• Task state: memory/current-task.md — Where you are in a multi-step workflow
• Long-term memory: MEMORY.md — Curated knowledge that should persist across sessions

At the start of each new session, read only the relevant files instead of re-running all the previous work. This keeps the starting context small and focused.

Practical example:

Instead of:

"Remember that we're building a scraper for X, we decided to use Playwright, we encountered issue Y, we fixed it by doing Z..."

Do:

"Read memory/scraper-project.md for current state."

The file approach means context usage stays low, sessions don't accumulate dead weight, and you never lose state when a session crashes.

For more on OpenClaw's memory system, see the OpenClaw memory files guide.

Strategy 2: Delegate Heavy Work to Subagents

Subagents run in isolated contexts — they do the heavy lifting without filling your main session's context

Screenshots, large file processing, browser automation, and long content generation are context killers. The solution is delegation.

OpenClaw's subagent system lets you spawn a separate AI session to handle a specific task. The subagent runs in its own isolated context, does the work, and returns only the relevant output to the main session. The main session never loads the raw work.

High-context tasks that should always be delegated:

• Browser screenshots and web automation (screenshots are expensive)
• Processing large codebases or datasets
• Generating long-form content (blog posts, documentation)
• Multi-step research tasks with many web fetches

The delegation loop looks like this:

1. Main session delegates task with clear instructions
2. Subagent runs in isolation, completes the task
3. Subagent returns a compact summary/result
4. Main session continues with just the result, not all the work

See the OpenClaw subagent delegation tutorial for the full delegation workflow.

Strategy 3: Compact and Continue

Compaction summarizes the conversation history and resets context usage while preserving essential state

When context fills up mid-session and you can't or don't want to spawn a subagent, use compaction.

The /compact command in OpenClaw summarizes the conversation history into a compressed representation and continues the session with dramatically lower context usage. The tradeoff: some nuance is lost in the summary, but the essential state and progress are preserved.

When to use /compact:

• You're at 70%+ context usage with significant work remaining
• The session has accumulated a lot of historical back-and-forth that's no longer relevant
• You need to continue a session that would otherwise crash

When to use /clear instead:

• The task is complete and you're starting fresh
• The session state is no longer needed
• You want a clean slate for a new workflow

Before compacting: Always save critical state to files first. After compaction, verify the assistant still has the context it needs by asking it to summarize the current task.

Strategy 4: Chunk Large Files and Requests

Processing large files in chunks keeps context usage predictable and prevents overflow

Never load an entire large file into context in one shot. Instead, read in chunks using the offset/limit parameters of the read tool.

For a 1,000-line file, don't do: "Read the entire file."

Instead: "Read lines 1-100, extract the key patterns, then read lines 101-200, and so on."

This chunked approach keeps context usage predictable. You process the same amount of data, but each chunk is processed and summarized before loading the next — keeping peak context usage low.

Similarly, for web research tasks: fetch and summarize each source before fetching the next, rather than loading all sources simultaneously.

Frequently Asked Questions

Common questions about managing context in Claude and OpenClaw

How do I know when to spawn a subagent vs. just continue? If the task involves screenshots, large files, or will generate substantial output, use a subagent. If it's a quick decision or short calculation, stay in the main session.

Can I recover a crashed session? Partially. If you wrote memory files before the crash, you can start fresh and read those files. The conversation history is lost, but the task state is preserved.

Does /compact lose important context? Yes, some nuance is lost. The compaction creates a summary, not a perfect replica. For critical sessions, write key decisions and state to files before compacting.

What's the best token budget for a typical OpenClaw session? Aim to keep peak usage under 60% for comfortable sessions. For critical workflows, design the system so each subagent handles a focused task and never needs more than 30-40% of its context.

Does using more tools increase context usage? Yes. Every tool call and its result is appended to context. Tools with large outputs (screenshots, web fetches, file reads) are the biggest contributors.

Conclusion

Good context hygiene turns fragile AI sessions into reliable, long-running autonomous workflows

Context overflow is the most common cause of OpenClaw session failures — and the most preventable. The four strategies covered here — memory files, subagent delegation, compaction, and chunked processing — give you everything you need to run stable, long-duration AI workflows.

Think of context management as session hygiene. Write important things to files. Delegate heavy work. Monitor usage. Compact before you crash, not after.

For further reading, see Anthropic's documentation on context windows and the Anthropic prompt engineering guide. The OpenClaw memory files guide covers the full memory system in detail.