openclaw-openclaw/docs/reference/token-use.md

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How OpenClaw builds prompt context and reports token usage + costs	Explaining token usage, costs, or context windows Debugging context growth or compaction behavior	Token use and costs

OpenClaw tracks tokens, not characters. Tokens are model-specific, but most OpenAI-style models average ~4 characters per token for English text.

How the system prompt is built

OpenClaw assembles its own system prompt on every run. It includes:

• Tool list + short descriptions
• Skills list (metadata only; instructions load on demand with read). Native Codex turns get the compact skills block as turn-scoped collaboration developer instructions; other harnesses get it in the normal prompt surface. Bounded by skills.limits.maxSkillsPromptChars, with optional per-agent override at agents.list[].skillsLimits.maxSkillsPromptChars.
• Self-update instructions
• Workspace + bootstrap files (AGENTS.md, SOUL.md, TOOLS.md, IDENTITY.md, USER.md, HEARTBEAT.md, BOOTSTRAP.md when new, plus MEMORY.md when present). Large injected files are truncated by agents.defaults.bootstrapMaxChars (default: 20000); total bootstrap injection is capped by agents.defaults.bootstrapTotalMaxChars (default: 60000).
  • Native Codex turns do not paste raw MEMORY.md when memory tools are available for that workspace; they get a small memory pointer in turn-scoped collaboration developer instructions instead and use memory tools on demand. If tools are disabled, memory search is unavailable, or the active workspace differs from the agent memory workspace, MEMORY.md falls back to the normal bounded turn-context path.
  • Lowercase root memory.md is never injected. It is legacy repair input for openclaw doctor --fix, which migrates it into MEMORY.md.
  • memory/*.md daily files are not part of the normal bootstrap prompt; they stay on-demand via memory tools on ordinary turns. Reset/startup model runs can prepend a one-shot startup-context block with recent daily memory for that first turn, controlled by agents.defaults.startupContext. Bare chat /new and /reset are acknowledged without invoking the model.
  • Post-compaction AGENTS.md excerpts are separate and require explicit agents.defaults.compaction.postCompactionSections opt-in.
• Time (UTC + user timezone)
• Reply tags + heartbeat behavior
• Runtime metadata (host/OS/model/thinking)

See the full breakdown in System Prompt.

When documenting credentials or auth snippets, use the Secret Placeholder Conventions to avoid secret-scanner false positives in docs-only changes.

What counts in the context window

Everything the model receives counts toward the context limit:

• System prompt (all sections above)
• Conversation history (user + assistant messages)
• Tool calls and tool results
• Attachments/transcripts (images, audio, files)
• Compaction summaries and pruning artifacts
• Provider wrappers or safety headers (not visible, but still counted)

Runtime-heavy surfaces have their own explicit caps under agents.defaults.contextLimits (per-agent overrides under agents.list[].contextLimits):

Key	Purpose
memoryGetMaxChars	Max characters memory_get returns before truncation.
memoryGetDefaultLines	Default memory_get line window when a request omits lines.
toolResultMaxChars	Advanced ceiling for a single live tool result (up to 1000000 chars).
postCompactionMaxChars	Max characters retained from AGENTS.md during post-compaction refresh.

These are bounded runtime excerpts and injected runtime-owned blocks, separate from bootstrap limits, startup-context limits, and skills prompt limits.

toolResultMaxChars is unset by default, so OpenClaw derives the live tool-result cap from the effective model context window: 16000 chars below 100K tokens, 32000 chars at 100K+ tokens, 64000 chars at 200K+ tokens. The runtime context-share guard still caps a single tool result at 30% of the context window even when a larger explicit ceiling is configured.

For images, OpenClaw downscales transcript/tool image payloads before provider calls. Tune with agents.defaults.imageMaxDimensionPx (default: 1200):

• Lower values reduce vision-token usage and payload size.
• Higher values preserve more visual detail for OCR/UI-heavy screenshots.

For a practical breakdown (per injected file, tools, skills, and system prompt size), use /context list or /context detail. See Context.

How to see current token usage

In chat:

• /status -> emoji-rich status card with the session model, context usage, last response input/output tokens, and estimated cost when local pricing is configured for the active model.
• /usage off|tokens|full -> appends a per-response usage footer to every reply. Persists per session (stored as responseUsage).
  • /usage reset (aliases: inherit, clear, default) clears the session override so it re-inherits the configured default.
  • /usage tokens shows turn token/cache details.
  • /usage full shows compact model/context/cost details; estimated cost appears only when OpenClaw has usage metadata and local pricing for the active model. Custom messages.usageTemplate layouts can include token/cache fields.
• /usage cost -> local cost summary from OpenClaw session logs.

Other surfaces:

• TUI/Web TUI: /status and /usage are supported.
• CLI: openclaw status --usage and openclaw channels list show normalized provider quota windows (X% left, not per-response costs). Current usage-window providers: Claude (Anthropic), ClawRouter, Copilot (GitHub), DeepSeek, Gemini (Google Gemini CLI), MiniMax, OpenAI, Xiaomi, Xiaomi Token Plan, and z.ai.

Usage surfaces normalize common provider-native field aliases before display. For OpenAI-family Responses traffic, that includes both input_tokens/output_tokens and prompt_tokens/completion_tokens, so transport-specific field names do not change /status, /usage, or session summaries. Gemini CLI usage is normalized too: the default stream-json parser reads assistant message events, and stats.cached maps to cacheRead, with stats.input_tokens - stats.cached used when the CLI omits an explicit stats.input field. Legacy JSON overrides still read reply text from response.

For native OpenAI-family Responses traffic, WebSocket/SSE usage aliases normalize the same way, and totals fall back to normalized input + output when total_tokens is missing or 0.

When the current session snapshot is sparse, /status and session_status can recover token/cache counters and the active runtime model label from the most recent transcript usage log. Existing nonzero live values still take precedence over transcript fallback values, and larger prompt-oriented transcript totals can win when stored totals are missing or smaller.

Usage auth for provider quota windows comes from provider-specific hooks first; if a provider has no hook (or the hook does not resolve a token), OpenClaw falls back to matching OAuth/API-key credentials from auth profiles, env, or config.

Assistant transcript entries persist the same normalized usage shape, including usage.cost when the active model has pricing configured and the provider returns usage metadata. This gives /usage cost and transcript-backed session status a stable source even after the live runtime state is gone.

OpenClaw keeps provider usage accounting separate from the current context snapshot. Provider usage.total can include cached input, output, and multiple tool-loop model calls, so it is useful for cost and telemetry but can overstate the live context window. Context displays and diagnostics use the latest prompt snapshot (promptTokens, or the last model call when no prompt snapshot is available) for context.used.

Cost estimation (when shown)

Costs are estimated from your model pricing config:

models.providers.<provider>.models[].cost

These are USD per 1M tokens for input, output, cacheRead, and cacheWrite. If pricing is missing, /usage full omits cost; use /usage tokens or a custom messages.usageTemplate when you need token/cache details in every reply. Cost display is not limited to API-key auth: non-API-key providers such as aws-sdk can show estimated cost when their configured model entry includes local pricing and the provider returns usage metadata.

After sidecars and channels reach the Gateway ready path, OpenClaw starts an optional background pricing bootstrap for configured model refs that do not already have local pricing. That bootstrap fetches remote OpenRouter and LiteLLM pricing catalogs. Set models.pricing.enabled: false to skip those catalog fetches on offline or restricted networks; explicit models.providers.*.models[].cost entries still drive local cost estimates.

Cache TTL and pruning impact

Provider prompt caching only applies within the cache TTL window. OpenClaw can optionally run cache-ttl pruning: it prunes the session once the cache TTL has expired, then resets the cache window so subsequent requests re-use the freshly cached context instead of re-caching the full history. This keeps cache write costs lower when a session goes idle past the TTL.

Configure it in Gateway configuration and see the behavior details in Session pruning.

Heartbeat can keep the cache warm across idle gaps. If your model cache TTL is 1h, setting the heartbeat interval just under that (e.g., 55m) can avoid re-caching the full prompt, reducing cache write costs.

In multi-agent setups, you can keep one shared model config and tune cache behavior per agent with agents.list[].params.cacheRetention.

For a full knob-by-knob guide, see Prompt Caching.

For Anthropic API pricing, cache reads are significantly cheaper than input tokens, while cache writes are billed at a higher multiplier. See Anthropic's prompt caching pricing for the latest rates and TTL multipliers: https://docs.anthropic.com/docs/build-with-claude/prompt-caching

Example: keep 1h cache warm with heartbeat

agents:
  defaults:
    model:
      primary: "anthropic/claude-opus-4-6"
    models:
      "anthropic/claude-opus-4-6":
        params:
          cacheRetention: "long"
    heartbeat:
      every: "55m"

Example: mixed traffic with per-agent cache strategy

agents:
  defaults:
    model:
      primary: "anthropic/claude-opus-4-6"
    models:
      "anthropic/claude-opus-4-6":
        params:
          cacheRetention: "long" # default baseline for most agents
  list:
    - id: "research"
      default: true
      heartbeat:
        every: "55m" # keep long cache warm for deep sessions
    - id: "alerts"
      params:
        cacheRetention: "none" # avoid cache writes for bursty notifications

agents.list[].params merges on top of the selected model's params, so you can override only cacheRetention and inherit other model defaults unchanged.

Anthropic 1M context

OpenClaw sizes GA-capable Claude 4.x models such as Opus 4.8, Opus 4.7, Opus 4.6, and Sonnet 4.6 with Anthropic's 1M context window. You do not need params.context1m: true for those models.

agents:
  defaults:
    models:
      "anthropic/claude-opus-4-6":
        alias: opus

Older configs can keep context1m: true, but OpenClaw no longer sends Anthropic's retired context-1m-2025-08-07 beta header for this setting and does not expand unsupported older Claude models to 1M.

Requirement: the credential must be eligible for long-context usage. If not, Anthropic responds with a provider-side rate limit error for that request.

If you authenticate Anthropic with OAuth/subscription tokens (sk-ant-oat-*), OpenClaw preserves the OAuth-required Anthropic beta headers while stripping the retired context-1m-* beta if it remains in older config.

Tips for reducing token pressure

• Use /compact to summarize long sessions.
• Trim large tool outputs in your workflows.
• Lower agents.defaults.imageMaxDimensionPx for screenshot-heavy sessions.
• Keep skill descriptions short (skill list is injected into the prompt).
• Prefer smaller models for verbose, exploratory work.

See Skills for the exact skill list overhead formula.

Related

• API usage and costs
• Prompt caching
• Usage tracking