title: "OpenClaw + Claude API: Complete Setup Guide" date: "2026-02-26" description: "A complete guide to connecting OpenClaw with Claude API. Learn API key configuration, model selection, rate limiting, and best practices for production." category: "Guide" author: "OpenClaw Team" tags: ["claude", "api", "setup"] readTime: "10 min"
Getting OpenClaw connected to the Claude API is the first step toward building real AI-powered workflows. This guide walks you through every stage of the process — from generating your first API key to fine-tuning rate limits in a production environment.
What You'll Set Up
By the end of this guide, you'll have:
- A valid Anthropic API key scoped for OpenClaw
- Claude configured inside OpenClaw with the right model for your use case
- Rate limiting and retry logic to protect your quota
- Error handling that degrades gracefully under load
- Cost monitoring so you never get a surprise bill
Prerequisites
Before you begin, make sure you have:
- OpenClaw installed and running (installation guide)
- An Anthropic account at console.anthropic.com
- Basic familiarity with environment variables and YAML config files
- Node.js 18+ or Python 3.10+ (depending on your OpenClaw deployment)
Step 1: Generate Your Claude API Key
Log in to the Anthropic console and navigate to API Keys. Click Create Key, give it a descriptive name like openclaw-production, and copy the key immediately — you will not be able to see it again.
Keep your key out of source control. The safest pattern is to store it as an environment variable:
# Add to your shell profile (~/.zshrc or ~/.bashrc)
export ANTHROPIC_API_KEY="sk-ant-api03-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
# Or use a .env file in your project root (never commit this file)
echo "ANTHROPIC_API_KEY=sk-ant-api03-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" >> .env
echo ".env" >> .gitignore
If you're deploying to a server, use your hosting platform's secrets manager instead of a plain .env file. See the VPS deployment guide for how to manage secrets on a Linux server.
Step 2: Configure the Claude Provider in OpenClaw
Open your main OpenClaw configuration file and add the Anthropic provider block:
# openclaw.yaml
providers:
anthropic:
api_key: "${ANTHROPIC_API_KEY}" # Read from environment
base_url: "https://api.anthropic.com" # Default, can be omitted
timeout: 60s
version: "2023-06-01" # Anthropic API version header
OpenClaw resolves ${VARIABLE_NAME} placeholders at startup, so your key never lives in the config file itself. Verify the provider loads correctly:
openclaw provider test anthropic
# Expected output:
# Provider: anthropic
# Status: connected
# Latency: 342ms
# Model: claude-3-5-sonnet-20241022
Step 3: Choose the Right Model
Anthropic offers three model tiers, each with different capability and cost profiles. Picking the right one per task is the single most effective way to control costs without sacrificing quality.
| Model | Best For | Speed | Cost | |-------|----------|-------|------| | claude-3-5-haiku-20241022 | Classification, extraction, simple Q&A | Fastest | Lowest | | claude-3-5-sonnet-20241022 | Writing, coding, complex reasoning | Fast | Mid | | claude-3-opus-20240229 | Deep analysis, critical review, research | Slower | Highest |
Configure the default model for each agent in your agents file:
# agents.yaml
agents:
triage:
model: claude-3-5-haiku-20241022 # Fast and cheap for routing decisions
instructions: "Classify the incoming request and route it to the right agent."
analyst:
model: claude-3-5-sonnet-20241022 # Good balance for analysis tasks
instructions: "Analyze the data and produce a structured report."
reviewer:
model: claude-3-opus-20240229 # Best reasoning for final quality checks
instructions: "Review the output for accuracy and completeness."
You can also override the model at the workflow level for specific steps, which lets you use a cheaper model for drafting and a more capable one for final review:
# workflow.yaml
steps:
- name: draft
agent: analyst
model_override: claude-3-5-haiku-20241022 # Cheaper for first pass
- name: final_review
agent: reviewer
model_override: claude-3-opus-20240229 # Full power for final output
Step 4: Configure Rate Limiting
The Claude API enforces per-minute token and request limits. If you exceed them, you'll receive 429 Too Many Requests errors. Configure OpenClaw's built-in rate limiter to stay within your tier's boundaries:
# openclaw.yaml
providers:
anthropic:
api_key: "${ANTHROPIC_API_KEY}"
rate_limits:
requests_per_minute: 50 # Conservative — actual limit is higher
tokens_per_minute: 40000 # Adjust based on your tier
tokens_per_day: 1000000 # Daily cap to prevent runaway costs
retry:
max_retries: 3
initial_delay: 1s
backoff_factor: 2 # Exponential backoff: 1s, 2s, 4s
retry_on:
- rate_limit_exceeded
- timeout
- service_unavailable
For multi-agent systems where several agents call Claude simultaneously, enable the request queue to serialize bursts:
queue:
enabled: true
max_concurrent: 5 # Max parallel API calls
queue_timeout: 30s # Drop request if it waits longer than this
Step 5: Add Error Handling
Even well-configured integrations hit errors. The Claude API can return authentication errors, context-length errors, and occasional 500s. Handle them explicitly:
from openclaw import ClaudeProvider, APIError, RateLimitError, ContextLengthError
provider = ClaudeProvider.from_env()
def call_claude_safe(prompt: str, model: str = "claude-3-5-sonnet-20241022") -> str:
try:
response = provider.complete(
model=model,
max_tokens=2048,
messages=[{"role": "user", "content": prompt}]
)
return response.content[0].text
except RateLimitError as e:
# Log and wait — OpenClaw's retry policy handles this automatically
# if configured, but you can also handle it manually
print(f"Rate limited. Retry after {e.retry_after}s")
raise
except ContextLengthError as e:
# The prompt is too long for the model's context window
# Truncate and retry with a shorter input
truncated = prompt[:e.max_tokens // 2]
return call_claude_safe(truncated, model)
except APIError as e:
# Unexpected API error — log for debugging
print(f"API error {e.status_code}: {e.message}")
raise
For YAML-based workflows, define fallback behavior declaratively:
# workflow.yaml
steps:
- name: analyze
agent: analyst
on_error:
action: fallback
fallback_model: claude-3-5-haiku-20241022 # Cheaper fallback if Sonnet fails
max_attempts: 2
Step 6: Optimize Costs
The Claude API bills per token. A few habits dramatically reduce your monthly spend:
Use prompt caching. Anthropic supports cache-write and cache-read tokens for repeated system prompts. If your agents reuse the same system prompt across many calls, caching can cut costs by up to 90% on those tokens:
# agents.yaml
agents:
analyst:
model: claude-3-5-sonnet-20241022
cache_system_prompt: true # OpenClaw handles the cache-control headers
instructions: |
You are a senior data analyst. Your job is to...
[Long detailed system prompt that gets cached]
Set explicit max_tokens limits. Without a limit, Claude can return very long responses. Cap them per agent based on what you actually need:
generation:
max_tokens: 1024 # Enough for most structured outputs
temperature: 0.3 # Lower temperature = more focused, fewer wasted tokens
Log token usage and set alerts. Add a spend cap so you get notified before costs spiral:
# openclaw.yaml
monitoring:
token_tracking: true
alerts:
daily_token_budget: 500000
alert_at_percent: 80 # Alert when 80% of daily budget is consumed
alert_webhook: "${SLACK_WEBHOOK_URL}"
Step 7: Test the Connection End-to-End
With everything configured, run the built-in connection test suite:
# Test basic connectivity
openclaw test claude --prompt "Hello, respond with OK"
# Test model selection
openclaw test claude --model claude-3-5-haiku-20241022 --prompt "What is 2+2?"
openclaw test claude --model claude-3-5-sonnet-20241022 --prompt "Explain recursion"
# Run a full workflow dry-run (no actual execution, validates config)
openclaw workflow validate --file workflow.yaml
# Run the workflow with a test input
openclaw workflow run content_pipeline --input '{"topic": "test"}'
A successful test looks like:
[PASS] Provider: anthropic connected
[PASS] Model: claude-3-5-haiku-20241022 responded in 412ms
[PASS] Model: claude-3-5-sonnet-20241022 responded in 891ms
[PASS] Rate limiter: configured (50 req/min)
[PASS] Retry policy: 3 retries with exponential backoff
[PASS] Workflow: content_pipeline validated
Common Issues and Fixes
"Authentication error: invalid API key" — Double-check that your environment variable is exported correctly. Run echo $ANTHROPIC_API_KEY to confirm it's set in the current shell session.
"Context length exceeded" — Your prompt is larger than the model's context window. Use claude-3-5-sonnet-20241022 (200K token context) for long-context tasks, or implement chunking in your workflow.
"Rate limit exceeded" on every request — You may be on the free tier with very low limits. Check your tier in the Anthropic console and request an increase, or reduce max_concurrent in your queue config.
Slow responses — If latency is a bottleneck, switch tasks that don't need deep reasoning to claude-3-5-haiku-20241022. It's typically 3-5x faster than Sonnet.
Next Steps
With Claude connected and working, you're ready to build real workflows:
- Build a multi-agent system to coordinate multiple Claude agents
- Automate your email with AI agents for a practical real-world use case
- Scheduling AI tasks with cron jobs to run workflows on a schedule
A solid API setup is the foundation everything else builds on. Take the time to get rate limiting and error handling right now, and you'll avoid painful debugging sessions later.