Why Claude Is the Best LLM Backend for OpenClaw
I have been running OpenClaw with every major LLM provider since the project launched, and I keep coming back to Claude. There is something about how Anthropic's models handle multi-step reasoning and tool use that makes them a natural fit for agentic workflows. With the release of Claude Opus 4.6 in early 2026, this gap has only widened.
OpenClaw's architecture is designed to be LLM-agnostic, which means you can swap backends without rewriting your skills. But "can" and "should" are different questions. In this guide, I will walk you through setting up Claude as your OpenClaw backend, tuning it for peak performance, and keeping costs under control.
If you are new to OpenClaw, start with our complete introduction to OpenClaw before diving in here.
Prerequisites
Before you begin, make sure you have the following ready:
- OpenClaw v2.4+ installed and running (see our installation guide)
- Anthropic API key from console.anthropic.com
- Node.js 20+ or Python 3.11+ depending on your OpenClaw deployment
- At least $10 in API credits for initial testing
Step 1: Obtain Your Anthropic API Key
Head to console.anthropic.com and create an API key. I recommend creating a dedicated key for OpenClaw rather than reusing one from other projects. This makes it easier to track spending and revoke access if needed.
# Set your API key as an environment variable
export ANTHROPIC_API_KEY="sk-ant-your-key-here"
# Or add it to your OpenClaw .env file
echo 'LLM_PROVIDER=anthropic' >> ~/.openclaw/.env
echo 'ANTHROPIC_API_KEY=sk-ant-your-key-here' >> ~/.openclaw/.env
Step 2: Configure Claude as Your Backend
OpenClaw's configuration lives in ~/.openclaw/config.yaml. Here is the full Claude configuration block:
# ~/.openclaw/config.yaml
llm:
provider: anthropic
model: claude-opus-4-6
api_key: ${ANTHROPIC_API_KEY}
max_tokens: 8192
temperature: 0.3
top_p: 0.9
# Agent-specific settings
agent:
system_prompt_mode: "extended"
tool_use_strategy: "parallel"
max_tool_rounds: 15
retry_on_overload: true
retry_delay_ms: 1000
# Cost controls
budget:
daily_limit_usd: 25.00
per_task_limit_usd: 2.00
alert_threshold_pct: 80
The key settings to pay attention to are tool_use_strategy and max_tool_rounds. Setting the strategy to parallel allows Claude to call multiple tools simultaneously, which dramatically speeds up complex tasks. The max_tool_rounds of 15 gives the agent enough room to complete multi-step workflows without running away.
Step 3: Choose the Right Claude Model
Not every task needs Opus 4.6. OpenClaw supports model routing, which lets you assign different models to different skill categories:
llm:
provider: anthropic
routing:
default: claude-sonnet-4-5
coding: claude-opus-4-6
research: claude-opus-4-6
simple_queries: claude-haiku-4
summarization: claude-sonnet-4-5
Model Comparison for Agent Tasks
| Model | Reasoning | Tool Use | Speed | Cost per 1M Tokens (Input/Output) |
|---|---|---|---|---|
| Claude Opus 4.6 | Excellent | Excellent | Moderate | $15 / $75 |
| Claude Sonnet 4.5 | Very Good | Very Good | Fast | $3 / $15 |
| Claude Haiku 4 | Good | Good | Very Fast | $0.25 / $1.25 |
For most agent tasks, I run Sonnet 4.5 as the default and escalate to Opus 4.6 only for coding and deep research. This cuts my monthly costs by roughly 60% without a noticeable drop in quality.
Prompt Tuning for Agent Performance
Claude responds exceptionally well to structured system prompts. Here is the system prompt template I use for OpenClaw's core agent loop:
agent:
system_prompt: |
You are an autonomous agent running inside OpenClaw. You have access
to the following tool categories: {available_tools}.
GUIDELINES:
- Break complex tasks into discrete steps before executing
- Verify assumptions by checking data before acting
- When uncertain, prefer gathering more information over guessing
- Report progress after each major step
- If a tool call fails, try an alternative approach before giving up
CONSTRAINTS:
- Never execute destructive operations without user confirmation
- Stay within the scope of the original task
- Respect rate limits and budget thresholds
Temperature Settings by Task Type
Temperature has a big impact on agent reliability. Here is what I have found works best:
- 0.1 - 0.2: Code generation, data extraction, structured output
- 0.3 - 0.5: Research, analysis, problem solving (my default)
- 0.7 - 0.9: Creative writing, brainstorming, content generation
# Override temperature per skill category
skills:
code_generation:
temperature: 0.15
research:
temperature: 0.4
content_writing:
temperature: 0.75
Comparing Claude vs GPT-5.2 vs DeepSeek as OpenClaw Backends
I ran the same set of 50 agent tasks across all three backends. Here is how they stacked up:
Overall Benchmark Results
| Metric | Claude Opus 4.6 | GPT-5.2 | DeepSeek R2 |
|---|---|---|---|
| Task Completion Rate | 94% | 91% | 87% |
| Avg. Steps to Complete | 4.2 | 4.8 | 5.1 |
| Tool Use Accuracy | 97% | 94% | 89% |
| Cost per Task (avg) | $0.42 | $0.38 | $0.12 |
| Latency (avg first token) | 1.2s | 0.9s | 1.8s |
| Safety Refusal Rate | 3% | 2% | 1% |
Claude wins on task completion and tool use accuracy, which are the two metrics that matter most for autonomous agents. GPT-5.2 is slightly faster and cheaper. DeepSeek R2 is the budget option but stumbles on complex multi-tool chains.
Where Each Backend Excels
Claude Opus 4.6 is best for:
- Complex multi-step reasoning tasks
- Code generation and debugging
- Tasks requiring careful instruction following
- Workflows with many tool calls
GPT-5.2 is best for:
- Multimodal tasks involving image analysis
- Creative content generation
- Tasks needing very low latency
DeepSeek R2 is best for:
- Budget-conscious deployments
- Simple automation tasks
- Batch processing at scale
For a broader comparison of these models outside of OpenClaw, check out our Claude vs ChatGPT vs Gemini comparison.
Cost Optimization Strategies
Running an AI agent can get expensive fast. Here are the strategies I use to keep my Claude costs reasonable:
1. Implement Prompt Caching
Claude supports prompt caching, which can cut costs by up to 90% on repeated system prompts:
llm:
anthropic:
prompt_caching: true
cache_ttl_minutes: 60
2. Use Streaming for Long Tasks
Streaming lets you see what the agent is doing in real time and cancel early if it goes off track:
llm:
anthropic:
streaming: true
stream_tool_calls: true
3. Set Hard Budget Limits
OpenClaw's budget system is your safety net:
budget:
daily_limit_usd: 25.00
per_task_limit_usd: 2.00
monthly_limit_usd: 500.00
action_on_limit: "pause_and_notify"
4. Monitor Usage with the Dashboard
# Check your current spending
openclaw stats --period today
# View per-skill cost breakdown
openclaw stats --by-skill --period week
I recommend picking up AI Engineering by Chip Huyen if you want to go deeper on cost optimization for LLM-powered applications. It covers caching strategies, batching, and routing patterns in detail.
Troubleshooting Common Issues
"Rate limit exceeded" Errors
Claude's API has rate limits based on your tier. If you hit them frequently:
llm:
anthropic:
retry_on_overload: true
retry_delay_ms: 2000
max_retries: 5
concurrent_requests: 3 # Lower this if hitting limits
Agent Gets Stuck in Loops
If the agent keeps retrying the same failed action:
agent:
max_consecutive_failures: 3
failure_action: "escalate_to_user"
loop_detection: true
High Latency on Complex Tasks
For tasks that require many tool rounds, enable batched tool calls:
agent:
tool_use_strategy: "parallel"
batch_independent_calls: true
Advanced: Building a Hybrid Backend
For power users, OpenClaw supports routing different parts of a task to different providers:
llm:
hybrid:
planning:
provider: anthropic
model: claude-opus-4-6
execution:
provider: anthropic
model: claude-sonnet-4-5
validation:
provider: deepseek
model: deepseek-r2
This gives you the best reasoning for planning, good performance for execution, and cheap validation. My monthly costs dropped about 40% after switching to this hybrid approach.
Hardware for Self-Hosting
If you are running OpenClaw on your own hardware, consider a Raspberry Pi 5 for a lightweight, always-on deployment. Pair it with a fast SSD for caching and you have a surprisingly capable agent server. For more on self-hosting, see our OpenClaw security and self-hosting guide.
What's Next
Claude's integration with OpenClaw keeps getting better. Anthropic's recent focus on tool use and agentic capabilities suggests even tighter integrations are coming. I am particularly excited about the rumored native OpenClaw support in Claude's API, which would eliminate much of the configuration overhead we covered here.
For now, this setup gives you one of the most capable AI agent configurations available. The combination of Claude's reasoning with OpenClaw's extensible skill system is hard to beat.
If you want to take the next step, check out our guide on building custom OpenClaw skills to create workflows tailored to your needs.
Found this guide helpful? Share it with your fellow developers on X (@wikiwayne) and let me know what LLM backend you are using with OpenClaw.
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Prompt Engineering for LLMs — Great pick for anyone following this guide.
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