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Documentation Index

Fetch the complete documentation index at: https://docs.openclaw.ai/llms.txt

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OpenClaw builds a custom system prompt for every agent run. The prompt is OpenClaw-owned and does not use the pi-coding-agent default prompt. The prompt is assembled by OpenClaw and injected into each agent run. Prompt assembly has three layers:
  • buildAgentSystemPrompt renders the prompt from explicit inputs. It should stay a pure renderer and should not read global config directly.
  • resolveAgentSystemPromptConfig resolves config-backed prompt knobs such as owner display, TTS hints, model aliases, memory citation mode, and sub-agent delegation mode for a specific agent.
  • Runtime adapters (embedded, CLI, command/export previews, compaction) gather live facts such as tools, sandbox state, channel capabilities, context files, and provider prompt contributions, then call the configured prompt facade.
This keeps exported/debug prompt surfaces aligned with live runs without turning every runtime-specific detail into one monolithic builder. Provider plugins can contribute cache-aware prompt guidance without replacing the full OpenClaw-owned prompt. The provider runtime can:
  • replace a small set of named core sections (interaction_style, tool_call_style, execution_bias)
  • inject a stable prefix above the prompt cache boundary
  • inject a dynamic suffix below the prompt cache boundary
Use provider-owned contributions for model-family-specific tuning. Keep legacy before_prompt_build prompt mutation for compatibility or truly global prompt changes, not normal provider behavior. The OpenAI GPT-5 family overlay keeps the core execution rule small and adds model-specific guidance for persona latching, concise output, tool discipline, parallel lookup, deliverable coverage, verification, missing context, and terminal-tool hygiene.

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Structure

The prompt is intentionally compact and uses fixed sections:
  • Tooling: structured-tool source-of-truth reminder plus runtime tool-use guidance.
  • Execution Bias: compact follow-through guidance: act in-turn on actionable requests, continue until done or blocked, recover from weak tool results, check mutable state live, and verify before finalizing.
  • Safety: short guardrail reminder to avoid power-seeking behavior or bypassing oversight.
  • Skills (when available): tells the model how to load skill instructions on demand.
  • OpenClaw Control: tells the model to prefer the gateway tool for config/restart work and to avoid inventing CLI commands.
  • OpenClaw Self-Update: how to inspect config safely with config.schema.lookup, patch config with config.patch, replace the full config with config.apply, and run update.run only on explicit user request. The owner-only gateway tool also refuses to rewrite tools.exec.ask / tools.exec.security, including legacy tools.bash.* aliases that normalize to those protected exec paths.
  • Workspace: working directory (agents.defaults.workspace).
  • Documentation: local path to OpenClaw docs/source and when to read them.
  • Workspace Files (injected): indicates bootstrap files are included below.
  • Sandbox (when enabled): indicates sandboxed runtime, sandbox paths, and whether elevated exec is available.
  • Current Date & Time: time zone only (cache-stable; the live clock comes from session_status).
  • Assistant Output Directives: compact attachment, voice-note, and reply tag syntax.
  • Heartbeats: heartbeat prompt and ack behavior, when heartbeats are enabled for the default agent.
  • Runtime: host, OS, node, model, repo root (when detected), thinking level (one line).
  • Reasoning: current visibility level + /reasoning toggle hint.
OpenClaw keeps large stable content, including Project Context, above the internal prompt cache boundary. Volatile channel/session sections such as Control UI embed guidance, Messaging, Voice, Group Chat Context, Reactions, Heartbeats, and Runtime are appended below that boundary so local backends with prefix caches can reuse the stable workspace prefix across channel turns. Tool descriptions should likewise avoid embedding current channel names when the accepted schema already carries that runtime detail. The Tooling section also includes runtime guidance for long-running work:
  • use cron for future follow-up (check back later, reminders, recurring work) instead of exec sleep loops, yieldMs delay tricks, or repeated process polling
  • use exec / process only for commands that start now and continue running in the background
  • when automatic completion wake is enabled, start the command once and rely on the push-based wake path when it emits output or fails
  • use process for logs, status, input, or intervention when you need to inspect a running command
  • if the task is larger, prefer sessions_spawn; sub-agent completion is push-based and auto-announces back to the requester
  • do not poll subagents list / sessions_list in a loop just to wait for completion
agents.defaults.subagents.delegationMode can strengthen this guidance. The default suggest mode keeps the baseline nudge. prefer adds a dedicated Sub-Agent Delegation section telling the main agent to act as a responsive coordinator and push anything more involved than a direct reply through sessions_spawn. This is prompt-only; tool policy still controls whether sessions_spawn is available. When the experimental update_plan tool is enabled, Tooling also tells the model to use it only for non-trivial multi-step work, keep exactly one in_progress step, and avoid repeating the whole plan after each update. Safety guardrails in the system prompt are advisory. They guide model behavior but do not enforce policy. Use tool policy, exec approvals, sandboxing, and channel allowlists for hard enforcement; operators can disable these by design. On channels with native approval cards/buttons, the runtime prompt now tells the agent to rely on that native approval UI first. It should only include a manual /approve command when the tool result says chat approvals are unavailable or manual approval is the only path.

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Prompt modes

OpenClaw can render smaller system prompts for sub-agents. The runtime sets a promptMode for each run (not a user-facing config):
  • full (default): includes all sections above.
  • minimal: used for sub-agents; omits Memory Recall, OpenClaw Self-Update, Model Aliases, User Identity, Assistant Output Directives, Messaging, Silent Replies, and Heartbeats. Tooling, Safety, Skills when supplied, Workspace, Sandbox, Current Date & Time (when known), Runtime, and injected context stay available.
  • none: returns only the base identity line.
When promptMode=minimal, extra injected prompts are labeled Subagent Context instead of Group Chat Context. For channel auto-reply runs, OpenClaw can omit the generic Silent Replies section when the direct/group chat context already includes the resolved conversation-specific NO_REPLY behavior. This avoids repeating token mechanics in both the global system prompt and channel context.

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Prompt snapshots

OpenClaw keeps committed prompt snapshots for the Codex runtime happy path under test/fixtures/agents/prompt-snapshots/codex-runtime-happy-path/. They render selected app-server thread/turn params plus a reconstructed model-bound prompt layer stack for Telegram direct, Discord group, and heartbeat turns. That stack includes a pinned Codex gpt-5.5 model prompt fixture generated from Codex’s model catalog/cache shape, the Codex happy-path permission developer text, OpenClaw developer instructions, turn-scoped collaboration-mode instructions when OpenClaw provides them, user turn input, and references to the dynamic tool specs. Refresh the pinned Codex model prompt fixture with pnpm prompt:snapshots:sync-codex-model. By default, the script looks for Codex’s runtime cache at $CODEX_HOME/models_cache.json, then ~/.codex/models_cache.json, and only then falls back to the maintainer Codex checkout convention at ~/code/codex/codex-rs/models-manager/models.json. If none of those sources exist, the command exits without changing the committed fixture. Pass --catalog <path> to refresh from a specific models_cache.json or models.json file. These snapshots are still not a byte-for-byte raw OpenAI request capture. Codex can add runtime-owned workspace context such as AGENTS.md, environment context, memories, app/plugin instructions, and built-in Default collaboration-mode instructions inside the Codex runtime after OpenClaw sends thread and turn params. Regenerate them with pnpm prompt:snapshots:gen and verify drift with pnpm prompt:snapshots:check. CI runs the drift check in the additional boundary shard so prompt changes and snapshot updates stay attached to the same PR.

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Workspace bootstrap injection

Bootstrap files are trimmed and appended under Project Context so the model sees identity and profile context without needing explicit reads:
  • AGENTS.md
  • SOUL.md
  • TOOLS.md
  • IDENTITY.md
  • USER.md
  • HEARTBEAT.md
  • BOOTSTRAP.md (only on brand-new workspaces)
  • MEMORY.md when present
All of these files are injected into the context window on every turn unless a file-specific gate applies. HEARTBEAT.md is omitted on normal runs when heartbeats are disabled for the default agent or agents.defaults.heartbeat.includeSystemPromptSection is false. Keep injected files concise, especially MEMORY.md. MEMORY.md is intended to stay a curated long-term summary; detailed daily notes belong in memory/*.md where memory_search and memory_get can retrieve them on demand. Oversized MEMORY.md files increase prompt usage and can be partially injected because of the bootstrap file limits below. When a session runs on the native Codex harness, Codex loads AGENTS.md through its own project-doc discovery. OpenClaw still resolves the remaining bootstrap files and forwards them as Codex config instructions, so SOUL.md, TOOLS.md, IDENTITY.md, USER.md, HEARTBEAT.md, BOOTSTRAP.md, and MEMORY.md keep the same workspace-context role without duplicating AGENTS.md.
memory/*.md daily files are not part of the normal bootstrap Project Context. On ordinary turns they are accessed on demand via the memory_search and memory_get tools, so they do not count against the context window unless the model explicitly reads them. Bare /new and /reset turns are the exception: the runtime can prepend recent daily memory as a one-shot startup-context block for that first turn.
Large files are truncated with a marker. The max per-file size is controlled by agents.defaults.bootstrapMaxChars (default: 12000). Total injected bootstrap content across files is capped by agents.defaults.bootstrapTotalMaxChars (default: 60000). Missing files inject a short missing-file marker. When truncation occurs, OpenClaw can inject a concise system-prompt warning notice; control this with agents.defaults.bootstrapPromptTruncationWarning (off, once, always; default: once). Detailed raw/injected counts stay in diagnostics such as /context, /status, doctor, and logs. For memory files, truncation is not data loss: the file remains intact on disk, but the model only sees the shortened injected copy until it reads or searches memory directly. If MEMORY.md is repeatedly truncated, distill it into a shorter durable summary and move detailed history into memory/*.md, or intentionally raise the bootstrap limits. Sub-agent sessions only inject AGENTS.md and TOOLS.md (other bootstrap files are filtered out to keep the sub-agent context small). Internal hooks can intercept this step via agent:bootstrap to mutate or replace the injected bootstrap files (for example swapping SOUL.md for an alternate persona). If you want to make the agent sound less generic, start with SOUL.md Personality Guide. To inspect how much each injected file contributes (raw vs injected, truncation, plus tool schema overhead), use /context list or /context detail. See Context.

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Time handling

The system prompt includes a dedicated Current Date & Time section when the user timezone is known. To keep the prompt cache-stable, it now only includes the time zone (no dynamic clock or time format). Use session_status when the agent needs the current time; the status card includes a timestamp line. The same tool can optionally set a per-session model override (model=default clears it). Configure with:
  • agents.defaults.userTimezone
  • agents.defaults.timeFormat (auto | 12 | 24)
See Date & Time for full behavior details.

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Skills

When eligible skills exist, OpenClaw injects a compact available skills list (formatSkillsForPrompt) that includes the file path for each skill. The prompt instructs the model to use read to load the SKILL.md at the listed location (workspace, managed, or bundled). If no skills are eligible, the Skills section is omitted. Eligibility includes skill metadata gates, runtime environment/config checks, and the effective agent skill allowlist when agents.defaults.skills or agents.list[].skills is configured. Plugin-bundled skills are eligible only when their owning plugin is enabled. This lets tool plugins expose deeper operating guides without embedding all of that guidance directly in every tool description. 
<available_skills>
  <skill>
    <name>...</name>
    <description>...</description>
    <location>...</location>
  </skill>
</available_skills>
This keeps the base prompt small while still enabling targeted skill usage. The skills list budget is owned by the skills subsystem:
  • Global default: skills.limits.maxSkillsPromptChars
  • Per-agent override: agents.list[].skillsLimits.maxSkillsPromptChars
Generic bounded runtime excerpts use a different surface:
  • agents.defaults.contextLimits.*
  • agents.list[].contextLimits.*
That split keeps skills sizing separate from runtime read/injection sizing such as memory_get, live tool results, and post-compaction AGENTS.md refreshes.

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Documentation

The system prompt includes a Documentation section. When local docs are available, it points to the local OpenClaw docs directory (docs/ in a Git checkout or the bundled npm package docs). If local docs are unavailable, it falls back to https://docs.openclaw.ai. The same section also includes the OpenClaw source location. Git checkouts expose the local source root so the agent can inspect code directly. Package installs include the GitHub source URL and tell the agent to review source there whenever the docs are incomplete or stale. The prompt also notes the public docs mirror, community Discord, and ClawHub (https://clawhub.ai) for skills discovery. It tells the model to consult docs first for OpenClaw behavior, commands, configuration, or architecture, and to run openclaw status itself when possible (asking the user only when it lacks access). For configuration specifically, it points agents to the gateway tool action config.schema.lookup for exact field-level docs and constraints, then to docs/gateway/configuration.md and docs/gateway/configuration-reference.md for broader guidance.