The fastest way to reduce OpenClaw token usage is to measure the context first, then apply the smallest fix that addresses the largest source. Run /usage tokens, /context detail, and /context map; compact old conversation turns with /compact; enable cache-TTL pruning for stale tool results; improve prompt-cache reuse; and trim oversized tool, skill, image, and model payloads. These controls reduce different parts of a request, so using the wrong one can save little or remove useful detail.
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There is no dependable, universal percentage saving. Token counts depend on the model, provider route, prompt shape, tool output, cache behavior, and workload. The practical goal is to remove unnecessary input while preserving the context your task actually needs.
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What OpenClaw tokens represent
Every OpenClaw request is built from input context and a generated reply. Input can include the system prompt, conversation transcript, tool schemas, tool results, skills, files, and images. The model then produces output tokens for its response. A long-running session can therefore become expensive even when your latest question is short: the latest request may still carry a large amount of earlier context.
OpenClaw’s own compaction reference states: “Every model has a context window: the maximum number of tokens it can process.” That limit is separate from price, but the same growing context can increase processing and billing. Tokenization is model-specific; the rough rule of four English characters per token is only an average and is not a precise way to estimate a bill.
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Token growth usually comes from one or more of these sources:
- Older conversational turns that are still relevant to the active context only in a limited way.
- Large search, file, browser, or execution results pasted into later requests.
- Verbose tool schemas or long skill descriptions included on every call.
- High-resolution screenshots or other image inputs.
- Using a larger model for exploratory work that does not require its full capability.
- A stable prompt prefix being reprocessed instead of read from a provider cache.
These causes need different remedies. Compaction changes how older conversation is represented, pruning removes eligible tool-result context from the active request, caching avoids reprocessing an unchanged prefix, and payload trimming prevents excess data from entering the request at all.
Measure the baseline before changing settings
Do not start by turning on every reduction feature. First identify whether conversation text, tool output, image data, skills, or model choice dominates your requests.
Check reply usage
Run:
/usage tokensThis exposes token usage on replies, giving you a before-and-after reference for later changes. Capture the result during a normal workload rather than judging the system from one unusually short or long prompt.
Inspect the context breakdown
Run:
/context detailThe detail view separates major contributors such as files, tool schemas, skills, the system prompt, and the transcript. Use it to answer a specific question: what is consuming the active context right now?
Use a visual map for large sessions
Run:
/context mapThe map presents the same kind of information as a visual treemap. Large blocks make an oversized tool result or an unexpectedly heavy skill definition easier to spot than a text-only listing.
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Turn the measurement into a decision
| Largest contributor | First control to try | What to watch |
|---|---|---|
| Old conversational turns | /compact |
Whether the resulting summary retains the details you still need |
| Old search, file, or execution results | Cache-TTL context pruning | Whether the results have passed the TTL and context-size conditions |
| Repeated, unchanged prompt prefix | Prompt-cache retention and a suitably timed heartbeat | Provider support and cache-read activity |
| Very large tool responses | Trim the tool output at its source | Whether the retained fields still answer the task |
| Screenshot-heavy requests | Lower agents.defaults.imageMaxDimensionPx |
Whether visual detail remains sufficient |
| Verbose exploratory replies | Select a smaller model where acceptable | Answer quality, capability, and error rate |
Use /compact to control accumulated conversation history
/compact is the appropriate tool when the transcript itself has become the main source of growth. OpenClaw summarizes older turns into a persisted compaction entry and keeps a recent tail of messages intact. The model receives the summary rather than every old conversational turn in its original form.
What compaction preserves and changes
- Recent messages remain available as the recent tail.
- Older turns are represented by a summary instead of their full wording.
- OpenClaw keeps assistant tool calls paired with their matching tool results when selecting a compaction boundary.
- Compaction is persistent; it is not merely a temporary projection for one request.
The trade-off is information density. A summary can preserve decisions, goals, and conclusions while omitting an exact quotation, an intermediate calculation, or a rarely used parameter. If a later task depends on such detail, keep it in a concise file or another deliberately retained artifact before compacting.
When to compact
- Use it after a long planning or debugging session when old dialogue is no longer needed verbatim.
- Use it when
/context detailshows the transcript, rather than tool results, is the dominant block. - Do not use it as the first response to a single huge file or search result; pruning or output trimming addresses that more directly.
A safe compaction procedure
- Run
/context detailand note the transcript size and the details that must survive. - Record any exact values, decisions, file names, or pending actions that a summary could obscure.
- Run
/compact. - Continue with a representative task and check that the assistant can still use the required context.
- Run
/usage tokensagain to compare the active request with the baseline.
Prune stale tool results with cache-TTL mode
Session pruning is different from compaction. Pruning removes eligible old tool-result content from the projected in-memory context used for a request while leaving the full session transcript inspectable. It is designed for sessions where search results, file dumps, logs, or command output are the source of bloat.
How cache-TTL pruning behaves
With contextPruning.mode: "cache-ttl", OpenClaw waits for the configured cache time-to-live and for context-size conditions before pruning. A result that is still inside the TTL, or a session that has not reached the relevant context condition, may remain in the request. This deliberate delay protects recently useful results and avoids repeatedly rebuilding context that may still be needed.
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agents:
defaults:
contextPruning:
mode: "cache-ttl"
ttl: "1h"The one-hour value is an example configuration, not a universal recommendation. Choose a TTL that matches how long tool results remain useful in your workflow. A short investigative loop may need recent results for longer than a repetitive background task.
When pruning is the better choice
- Use pruning when
/context detailidentifies tool results as the largest block. - Use it for long sessions that repeatedly call search, file, or execution tools.
- Use it when you want the active request to become smaller without rewriting the conversational history.
Pruning does not make old output disappear from the session transcript. It changes what is projected into the model context for eligible requests. If the assistant needs a pruned result later, retrieve or regenerate the relevant information instead of assuming it remains in the active prompt.
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Increase prompt-cache reuse
Prompt caching can reduce repeated processing of an unchanged prompt prefix. Rather than sending every stable prefix through full processing on each request, a supported provider route can read the cached portion and process only what changed. This is different from deleting context: the prefix remains available through the cache mechanism.
OpenClaw cache controls
OpenClaw exposes cacheRead and cacheWrite usage and supports cacheRetention values such as none, short, and long. Retention can be set globally, per model, or per agent. The exact effect depends on the provider route and model, so verify support for the route you actually use.
Keep a stable prefix warm
A heartbeat interval just below the provider’s cache window can keep an otherwise idle cache warm. The official example uses a 55-minute heartbeat for a one-hour cache window:
agents:
defaults:
params:
cacheRetention: "long"
contextPruning:
mode: "cache-ttl"
ttl: "1h"
heartbeat:
every: "55m"This example combines cache retention, pruning, and a heartbeat, but they solve separate problems. The heartbeat does not shrink a transcript; it helps preserve a reusable prefix across idle gaps. A heartbeat that runs more often than necessary can itself create additional requests, so match the interval to the provider’s cache window and your operational needs.
Conditions for useful cache reads
- The beginning of the request must remain stable enough for the provider to recognize the prefix.
- The selected provider route and model must support the relevant cache behavior.
- Changing system instructions, tool schemas, skills, or other early content can reduce cache reuse.
- Inspect cache-read and cache-write counters rather than assuming that a retention setting produced a hit.
Reduce the payload before it enters context
Removing unnecessary input is the most direct way to lower active context. Make each tool, skill, image, and model choice proportional to the task.
Trim tool outputs at the source
Large raw outputs are expensive when every later turn carries them forward. Configure searches, file inspection, and execution tools to return only the fields, lines, records, or error sections needed for the next decision. Prefer a short structured result over an entire log when the task only needs a status, identifier, or exception.
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Keep enough provenance to act safely: a filename, command, timestamp, or record key can be more useful than a huge unlabelled excerpt. If the complete output may be needed later, save it outside the active context and pass a focused extract into the conversation.
Lower screenshot dimensions when images dominate
For screenshot-heavy sessions, lower agents.defaults.imageMaxDimensionPx. A smaller maximum dimension reduces image payload while retaining the overall layout needed for many navigation and visual-check tasks. Do not lower it so far that text or small interface controls become unreadable; test the setting against the visual work you actually perform.
Keep skill descriptions concise
Skill descriptions and instructions can be included repeatedly. Remove duplicate prose, move long explanations into on-demand references where your setup supports that pattern, and state only the trigger, inputs, constraints, and expected output needed for routing. A concise description improves both context size and the chance that the model notices the instruction that matters.
Use a smaller model for suitable work
Smaller models can reduce the cost of verbose exploratory turns when the task does not require the larger model’s reasoning depth, context capacity, or tool competence. Evaluate the result, not just the price: compare factual accuracy, formatting, tool selection, and the amount of rework required. Keep a more capable model for tasks where an incorrect action costs more than the saved tokens.
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Do not confuse output length with input reduction
Asking for concise replies can reduce generated output tokens, but it does not remove a large transcript or tool payload from the input. Use response-length guidance alongside, not instead of, context measurement and payload trimming.
Choose the right technique for the problem
| Technique | Main reduction target | What remains | Primary trade-off | Compatibility concern |
|---|---|---|---|---|
/compact |
Older conversation text | A summary plus the recent tail | Some detail is represented only in the summary | Available as an OpenClaw session operation |
| Cache-TTL pruning | Old tool-result context | The full transcript on disk | Pruning waits for TTL and context-size conditions | Controlled by OpenClaw context-pruning settings |
| Prompt caching | Repeated processing of a stable prefix and cache writes | The cached prefix and usage counters | It depends on a supported provider route and stable request history | Provider- and model-dependent |
| Tool and output trimming | Oversized tool responses | Only the retained output | Raw detail is less immediately available | Depends on each tool’s filtering controls |
| Smaller model selection | Cost of verbose exploratory turns | The selected model’s task output | Quality and capability may differ | Depends on model availability and task fit |
An implementation workflow that minimizes risk
- Baseline a normal session. Run
/usage tokens,/context detail, and, when useful,/context map. Save the observations, not just a single total. - Fix the largest avoidable payload. Trim a tool response, shorten a skill description, or reduce screenshot dimensions if one of those dominates.
- Separate transcript growth from tool-result growth. Choose
/compactfor old dialogue and cache-TTL pruning for stale tool output. - Configure caching only after checking stability. Set an appropriate retention value and consider a heartbeat below the provider cache TTL when the prefix is stable.
- Test model substitution on low-risk work. Compare a smaller model on exploratory tasks before making it the default.
- Re-measure the same workload. Use the same type of session and inspect usage, context composition, answer quality, and any extra operational activity from heartbeats.
- Keep one change reversible. If quality drops or cache reads do not appear, restore the previous setting and test the next intervention separately.
Troubleshooting common outcomes
Compaction saved little
Check whether the transcript was actually the largest contributor. If tool results or images dominate /context detail, compaction cannot remove most of that payload. Trim outputs or enable pruning instead.
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Pruning has not removed anything
Confirm that the mode is exactly cache-ttl, that the configured TTL has elapsed, and that the context-size conditions for pruning have been reached. Recent results are intentionally protected until those conditions are met.
Cache retention produces no visible benefit
Verify that the provider route and model support caching, then check whether the early prompt remains unchanged. A modified system prompt, skill list, tool schema, or other prefix content can prevent reuse. Inspect cache-read counters rather than relying on the configuration value alone.
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Raise agents.defaults.imageMaxDimensionPx incrementally until text and controls are readable, then keep the lowest value that still supports the task. Image reduction is a trade-off, not a universal setting.
A smaller model gives shorter but worse results
Limit the smaller model to exploratory or repetitive work and keep a more capable model for tasks requiring nuanced reasoning, reliable tool use, or high-cost decisions. Compare the total workflow cost, including retries and corrections.
How to judge savings without misleading percentages
OpenClaw’s official references do not publish a universal reduction figure. A session dominated by a repeated stable prefix may benefit mainly from cache reads; a log-analysis session may benefit from output trimming and pruning; a long design conversation may benefit from compaction. The same setting can therefore produce very different results across users.
Track a representative workload with the same model and provider route. Record input and output usage, cache reads and writes where exposed, context composition, response quality, and any additional heartbeat requests. Treat the result as a workload-specific measurement rather than a promise that applies to every OpenClaw deployment.
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