How to Configure OpenClaw Thinking Levels for Better Agent Reasoning

OpenClaw maps its eight thinking levels across five major LLM provider APIs, each with a different subset of supported levels and fallback rules (official docs). Ollama supports four of the eight levels. Z.AI reduces the full range to a binary toggle. Anthropic's Claude 4.6 defaults to adaptive reasoning when no explicit level is set, while earlier Claude models default to off. This means that switching providers, or even upgrading a model version within the same provider, can silently change your agent's reasoning depth without any configuration change on your part.

The reason is straightforward: LLM providers don't agree on how to expose reasoning control. Anthropic uses an effort parameter and adaptive budget. OpenAI routes through a Responses API effort field. Google maps to a thinkingLevel value or a budget integer. DeepSeek accepts a reasoning_effort string. Each API has different supported values, different defaults, and different behavior when you request a level it doesn't natively support.

OpenClaw's thinking system sits on top of this. It gives you a single scale from off to max, translates your choice into the correct provider-specific API call, and falls back to the nearest supported level when your request doesn't map cleanly. The result is a unified interface for reasoning control regardless of which model you're running.

Consistent reasoning requires understanding what each level does, how the resolution hierarchy picks which level applies, and where the mapping breaks down. Getting this right matters because reasoning depth directly affects both output quality and cost. A planning agent that defaults to minimal thinking produces shallow task breakdowns. A formatting agent stuck on high burns tokens on unnecessary deliberation. The eight levels exist so you can match reasoning effort to task complexity.

Here are all eight levels, from least to most reasoning effort:

1. off disables reasoning entirely. The model responds immediately without any internal deliberation. Use this for simple formatting tasks, status checks, and data movement where thinking adds latency without improving output.

2. minimal activates the lightest reasoning pass. The model does basic sanity checking but won't trace through multi-step logic. Good for message routing, template filling, and tasks that need a quick judgment call rather than analysis.

3. low provides enhanced reasoning beyond the minimum. The model traces through basic logical steps and catches simple errors. Suitable for documentation lookups, straightforward debugging, and tasks where the answer is mostly in the prompt.

4. medium is the default for most reasoning-capable models when no explicit level is configured. The model works through multi-step logic chains and considers alternative approaches. This covers the majority of coding tasks, writing with judgment, and technical Q&A.

5. high engages full reasoning capacity for genuinely complex problems. The model explores multiple solution paths, evaluates tradeoffs, and double-checks its work. Reserve this for architecture decisions, regression analysis, and multi-file change planning.

6. xhigh pushes beyond standard reasoning for the hardest problems. Not every provider supports this level, so OpenClaw falls back to the nearest available option when xhigh isn't recognized. This should remain an exception reserved for specific tasks, not a session default.

7. adaptive lets the provider decide how much reasoning each request needs. Anthropic's Claude 4.6 family supports this natively, allocating reasoning budget dynamically based on task complexity. Simpler requests get less thinking; harder ones get more.

8. max requests the deepest reasoning the provider supports. On providers with a narrower range, max maps to the highest native level available. On Ollama, for example, max resolves to the equivalent of high because that's the ceiling of Ollama's reasoning API.

The practical rule: start at medium as your session default, bump to high for specific complex tasks, and only reach for xhigh or max when you've confirmed the model supports it and the task justifies the cost.

OpenClaw also recognizes several aliases that can trip you up. x-high, extra-high, and extra_high all resolve to xhigh. But highest resolves to high, not max. If you type /think highest expecting maximum reasoning, you'll get one level below what you intended.

One distinction worth understanding early: thinking level and reasoning visibility are independent settings. Thinking level controls how much reasoning the model performs internally. Reasoning visibility controls whether that internal reasoning appears in the response. You can pair deep reasoning with hidden internals, or lighter reasoning with a visible thought process, depending on your debugging needs.

When you send a message to an OpenClaw agent, the system determines which thinking level to use by checking five configuration sources in priority order:

1. Inline directive. A thinking-level command included in a normal message applies to that single request only. The next message reverts to whatever default is active.

2. Session override. Send a directive-only message (just /think:medium with no other text) and it persists as the session default. Every subsequent message in that session uses medium unless overridden by an inline directive.

3. Per-agent default. Set thinkingDefault in the agent's configuration to give a specific agent its own baseline. A planning agent might default to high while a messaging agent defaults to off.

4. Global default. Set thinkingDefault at the global defaults level for a system-wide fallback. This catches any agent or session that doesn't specify its own level.

5. Provider fallback. If nothing is configured anywhere, reasoning-capable models default to medium or their nearest supported level. Non-reasoning models stay off.

This hierarchy means you can set a conservative global default like medium, override it for specific agents that need more or less reasoning, further override it per session when working on something complex, and still target individual messages when a single request needs different treatment.

To check what level is currently active, send /think with no argument. To clear a session override and fall back to the agent or global default, use /think default. OpenClaw also accepts inherit, clear, reset, and unpin as aliases for clearing the override.

Per-agent defaults are more permanent than session overrides. When you start a new session with an agent that has thinkingDefault: high, that default persists across every session until you change the configuration. Session overrides disappear when the session ends.

OpenClaw provides three equivalent command formats for setting thinking levels during a conversation:

• /t <level> (shortest form)
• /think:<level> (colon-separated)
• /thinking <level> (space-separated)

All three accept the same level values: off, minimal, low, medium, high, xhigh, adaptive, or max.

Message-level overrides work by including the command in a regular message. The directive applies to just that message. For example, /t high analyze the security implications of this API change sends the analysis request with high reasoning, and the next message returns to the session default.

Session overrides work by sending the command as a standalone message with no other content. Sending just /think:medium sets the default for all subsequent messages in that session.

Checking and clearing levels. Send /think with no argument to see the current active level, including which layer of the hierarchy it comes from. Use /think default to clear any session override and fall back to the configured default.

Persistent configuration lets you set defaults that survive across sessions. The thinkingDefault property in your OpenClaw configuration controls both per-agent and global defaults. Per-agent settings go under the individual agent's entry, while the global fallback goes under the agents defaults section.

For teams running multiple agents, per-agent defaults are the most practical approach. A code review agent benefits from high reasoning by default. A notification agent that sends status updates doesn't need any reasoning. Setting these at the agent level means nobody has to remember to adjust thinking levels per session.

One pattern that works well for complex projects: set a medium global default, bump specific agents to high, and use inline /t max for particularly thorny problems. This keeps costs reasonable for routine tasks while ensuring complex reasoning gets the budget it needs.

When agents store their outputs in persistent workspaces like Fastio, consistent thinking configuration matters because quality compounds through the pipeline. If a research agent runs at minimal and feeds shallow findings to a writing agent at high, the writer receives bad inputs regardless of its own reasoning depth. Matching thinking levels to each agent's role in a multi-agent workflow improves the end result that lands in the workspace.

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Here's where the abstraction shows its seams. Each LLM provider implements thinking differently, and OpenClaw's mapping layer handles the translation.

Anthropic Claude defaults to adaptive when no level is set on supported models. Adaptive mode lets Claude allocate its own reasoning budget per request. Fixed levels from low through high map to Anthropic's effort parameter. Newer Claude models support xhigh and max through extended effort configurations.

OpenAI GPT maps thinking levels through the Responses API. Turning thinking off sends the appropriate "no reasoning" signal on models that support it. On models without reasoning controls, the parameter is omitted entirely. Custom OpenAI-compatible providers can opt into extended level support through their model definition.

Google Gemini maps adaptive to Gemini's native dynamic thinking, where the model allocates its own reasoning budget. Fixed levels map to the nearest supported Gemini thinking level or budget value. The specifics vary between Gemini model generations.

DeepSeek collapses xhigh and max to the same highest reasoning effort on direct connections. Lower non-off levels also compress into a narrower range. Through proxy providers like OpenRouter, the mapping narrows further since the intermediary may not distinguish between the top two levels.

Ollama supports only four levels: low, medium, high, and max. Max maps to Ollama's native high because the Ollama API tops out there. If you request xhigh or adaptive on an Ollama-backed agent, OpenClaw falls back to the nearest supported level.

Binary providers like Z.AI and Moonshot support only on/off thinking. Any non-off level maps to on. If you need granular reasoning control, binary providers aren't the right backend for reasoning-heavy agents.

The practical takeaway: if you switch providers, check that your thinking levels still resolve to what you expect. Run /think with no argument after connecting a new model to see the active level and confirm it matches your intent.

Different agent roles need different reasoning depths. Here's a starting framework based on task complexity:

Set off or minimal for message routing and notification agents, simple data reformatting and templating, status checks and heartbeat agents, and any task where speed matters more than depth.

Set low or medium for general coding assistance and debugging, documentation-based answers, task triage and prioritization, and technical writing where some judgment is required.

Set high for architecture and design reviews, multi-file refactoring analysis, security audits and risk assessments, and incident investigation where competing hypotheses need evaluation.

Set adaptive for Claude 4.6 agents that handle mixed workloads. Adaptive thinking allocates reasoning dynamically: simple requests get minimal thinking, complex ones get more. The tradeoff is that you lose explicit control over the budget, which can make costs less predictable.

Avoid xhigh and max as defaults. These levels exist for specific hard problems, not as baselines. Using them across the board wastes tokens on routine tasks and increases response latency for every request.

For pipelines where multiple agents collaborate, match thinking levels to each agent's position in the chain. A research agent that gathers information might run at medium. An analysis agent that synthesizes findings needs high. A formatting agent that structures the output into a deliverable runs at low or off.

When those agents write their outputs to shared workspaces, tools like Fastio give you persistence and version history across the pipeline. You can review what each agent produced, trace output quality back to configuration, and adjust levels based on actual results. Intelligence Mode indexes agent output for semantic search and chat, which means you can query across everything your agents have produced without reading each file individually.

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