anthropic-api
Purpose
This skill integrates with Anthropic's Claude API to leverage advanced AI models for tasks requiring tool use and extended context, enabling complex interactions like function calling and long-form processing.
When to Use
Use this skill for scenarios involving sophisticated AI reasoning, such as analyzing large documents, generating code with tools, or handling multi-step workflows. It's ideal when standard models fall short, like in research, automation, or interactive applications.
Key Capabilities
- Access Claude models (e.g., claude-3-haiku, claude-3-sonnet) via API for text generation and completion.
- Support tool use, allowing the AI to call external functions based on JSON-defined tools.
- Handle extended contexts up to 200K tokens for processing long inputs.
- Real-time streaming responses for interactive sessions.
- Fine-grained control over parameters like temperature (0.0-1.0) and top_p (0-1).
Usage Patterns
To use this skill in OpenClaw, first set the API key as an environment variable (e.g., export ANTHROPIC_API_KEY=your_key). Invoke the skill via OpenClaw's execute command, passing parameters like model and prompt. For HTTP-based integration, make POST requests to the API endpoint. Always include authentication headers. Structure requests with JSON bodies containing "messages" array for conversation history and "tools" for function definitions.
Common Commands/API
- Endpoint: POST https://api.anthropic.com/v1/messages
- Authentication: Set header 'Authorization: Bearer $ANTHROPIC_API_KEY' (use env var for security).
- Key Parameters: In request body, include "model" (e.g., "claude-3-5-sonnet-20241001"), "max_tokens" (e.g., 1024), "temperature" (e.g., 0.7), and "tools" as an array of JSON objects (e.g., [{"name": "get_weather", "description": "Fetch weather data"}]).
- CLI Example: Use curl for testing: curl -X POST https://api.anthropic.com/v1/messages -H "Authorization: Bearer $ANTHROPIC_API_KEY" -H "Content-Type: application/json" -d '{"model": "claude-3-haiku", "messages": [{"role": "user", "content": "Hello"}]}'
- Code Snippet (Python): import requests response = requests.post('https://api.anthropic.com/v1/messages', headers={'Authorization': f'Bearer {os.environ["ANTHROPIC_API_KEY"]}'}, json={'model': 'claude-3-haiku', 'messages': [{'role': 'user', 'content': 'Summarize this.'}]}) print(response.json())
- Config Format: In OpenClaw, configure via skill settings with YAML like: api_key: $ANTHROPIC_API_KEY, default_model: claude-3-sonnet.
Integration Notes
In OpenClaw, load this skill with openclaw load anthropic-api. Set up environment variables for keys (e.g., ANTHROPIC_API_KEY) to avoid hardcoding. For tool integration, define tools in your prompt as JSON arrays and handle responses in a loop to process AI-generated actions. Ensure your application handles rate limits (e.g., 10 requests per minute) by implementing retry logic with exponential backoff. Test integrations in a sandbox environment before production use.
Error Handling
Common errors include 401 (unauthorized) for invalid API keys—check if $ANTHROPIC_API_KEY is set correctly. Handle 429 (rate limit) by waiting and retrying with a delay (e.g., time.sleep(60)). For 400 (bad request), validate your JSON payload, especially the "messages" structure. In code, use try-except blocks: try: response = requests.post(...) except requests.exceptions.HTTPError as e: print(f"Error: {e.response.status_code} - {e.response.text}"); if e.response.status_code == 429: retry_after = int(e.response.headers.get('Retry-After', 60)); time.sleep(retry_after).
Usage Examples
- Text Completion Task: To generate a summary, use OpenClaw with: openclaw execute anthropic-api --prompt "Summarize the key points of quantum computing" --model claude-3-haiku. This sends a POST request and returns the AI response directly.
- Tool Use Example: For fetching data, define a tool and prompt: openclaw execute anthropic-api --prompt "Get the current weather for New York" --tools '[{"name": "get_weather", "description": "Fetch weather", "parameters": {"location": "string"}}]'. The AI will respond with a tool call, which you handle by executing the function and feeding back the result.
Graph Relationships
- Related to cluster: ai-apis
- Tagged with: ai-apis, api
- Connected skills: openai-api (for comparative AI capabilities), function-caller (for enhanced tool integration)