Using Claude with OpenClaw

OpenClaw can use Claude (Anthropic) as its LLM for planning and replying in the US. Set your API key, pick a model (e.g., Claude 3.5 Sonnet for balance of speed and quality), and optionally tune system prompts. This post covers setup, model choice, and how to measure that Claude-powered tasks are succeeding when you track events with a platform like SingleAnalytics."

If you're in the US and want OpenClaw to use Claude for reasoning and language, you're in good company. Claude models are strong at following instructions, tool use, and long context: all useful for an agent that has to plan steps, call skills, and respond clearly. This guide covers how to use Claude with OpenClaw: what to configure, which model to choose, and how to get reliable task execution and measure it.

Why Claude fits OpenClaw

• Instruction following: Claude is tuned to follow system prompts and user intent, which helps the agent stay on task and use the right skill.
• Tool use: good at choosing when and how to call tools (skills), with clear arguments and handling of results.
• Long context: large context windows let you send more conversation and memory without truncation, so the agent has full context for complex requests.
• Safety and clarity: Anthropic’s focus on helpfulness and clarity can mean fewer off-the-rails replies and clearer error handling.

For US users, Claude is available via API with clear data-processing terms; enterprise and opt-out options exist if you need stricter control over how prompts are used.

How to set Claude as OpenClaw’s model

OpenClaw’s exact config varies by version. Typically you:

1. Get an API key: sign up at Anthropic, create an API key, and (for US production) review rate limits and terms.
2. Set the key: in .env or your config, set the variable OpenClaw expects (e.g., ANTHROPIC_API_KEY). Never commit the key; use a secrets manager in production.
3. Select the model: in config, set the model name (e.g., claude-3-5-sonnet-20241022 or whatever the current default is). Check Anthropic’s docs for the latest model IDs and regions.
4. Restart OpenClaw: so it loads the new key and model. Send a test message and confirm the agent replies using Claude (you may see the model name in logs or in a debug response).

If OpenClaw supports multiple providers, you may have a config like LLM_PROVIDER=anthropic and LLM_MODEL=claude-3-5-sonnet-20241022. Set those and, if needed, any model-specific options (temperature, max tokens).

Choosing a Claude model for OpenClaw

| Model | Best for | Tradeoff | |-------|-----------|----------| | Claude 3.5 Sonnet | Most tasks: planning, tool use, replies | Balance of speed, cost, quality | | Claude 3 Opus | Hard reasoning, long documents, low error tolerance | Slower, higher cost | | Claude 3 Haiku | High volume, simple tasks, low latency | Less capable on complex multi-step work |

For most US users, Claude 3.5 Sonnet is the default: fast enough for chat, strong at tool use, and affordable at scale. Use Opus when a task is critical and you want maximum reliability; use Haiku for high-volume, simple flows (e.g., FAQ-style replies or simple routing). You can also configure model fallback (e.g., try Sonnet, then Opus on retry) if your OpenClaw version supports it.

Getting the best results

• System prompt: the system prompt tells Claude how to behave as an agent: use tools, ask for clarification when needed, return short confirmations. Tune it for your use case (e.g., "Always confirm before sending email" or "Use the work calendar by default"). US teams that measure task success rate often A/B test prompt changes and track completion and override rate. SingleAnalytics can ingest those events so you see which configs perform best.
• Context and memory: Claude benefits from clear context. Feed the agent the relevant memory (preferences, recent context) so it doesn’t guess. OpenClaw’s persistent memory layer is built for that.
• Temperature: for task execution, lower temperature (e.g., 0.2–0.4) often gives more consistent tool choices and fewer creative but wrong moves. Save higher temperature for drafting or brainstorming skills.
• Retries and fallbacks: if a request fails (rate limit, timeout), retry with backoff. Optionally fall back to another model or a cached response so the user still gets an answer. Emit task_failed and task_retried so you can monitor reliability in your analytics platform.

Cost and rate limits (US)

• Cost: Anthropic charges per input and output token. Sonnet is cheaper than Opus; Haiku is cheapest. Monitor usage (tokens per task or per user) so you can project cost at scale. Some OpenClaw setups log token usage; you can emit aggregate stats (e.g., tokens per workflow) to your analytics if you want to correlate cost with success. SingleAnalytics supports custom events for that.
• Rate limits: US API tiers have different limits. If you hit them, use backoff and consider batching or queueing. Track rate-limit errors in your events so you know when to upgrade or optimize.

Measuring Claude-powered OpenClaw

Once Claude is wired in, you want to know: are tasks succeeding? Which workflows fail most? Does model or prompt change improve success rate? Emit events (e.g., task_started, task_completed, task_failed) with properties like model, workflow_id, and user_id. Send them to a single analytics platform so you can segment by model and workflow and tie agent usage to product and revenue. SingleAnalytics gives US teams one place for traffic, product, and agent events, so you can see the full picture and iterate on Claude’s role in your stack.

Summary

Using Claude with OpenClaw in the US means setting your Anthropic API key and model (e.g., Claude 3.5 Sonnet), tuning the system prompt and temperature for task execution, and monitoring success and cost. Choose Sonnet for most workloads, Opus for the hardest tasks, and Haiku for high-volume simple ones. To see how Claude-powered tasks perform over time and how they tie to business outcomes, send agent events to a unified analytics platform like SingleAnalytics. so you can improve what matters.