Build custom skills to extend OpenClaw. Create specialized capabilities for your assistant. Skill Structure – SKILL.md: Skill description – scripts/: Executable scripts – references/: Documentation Example Skill Create a skill that integrates with your favorite API. Best Practices ✅ Clear documentation ✅ Error handling ✅ Input validation Conclusion Skills make OpenClaw infinitely extensible!
OpenClaw is a powerful AI assistant framework. Build custom AI assistants with OpenClaw. Features ✅ Multi-platform support ✅ Custom skills ✅ Memory management ✅ Tool integration Getting Started 1. Install OpenClaw 2. Configure your API keys 3. Create custom skills 4. Deploy your assistant Skills Extend OpenClaw with custom skills for your needs. Conclusion OpenClaw … Read more
Build a chatbot API with conversation memory. Create contextual chatbot services. Architecture API Endpoint → Memory Store → LLM Implementation class ChatbotAPI: def __init__(self): self.memories = {} def chat(self, session_id, message): if session_id not in self.memories: self.memories[session_id] = [] self.memories[session_id].append({ “role”: “user”, “content”: message }) response = client.chat.completions.create( model=”gpt-4″, messages=self.memories[session_id] ) return response.choices[0].message.content Conclusion Memory … Read more
Generate embeddings for text at scale. Create text vectors for semantic search. Example response = client.embeddings.create( model=”text-embedding-3-small”, input=[“Hello world”, “How are you?”] ) embeddings = [e.embedding for e in response.data] Models ✅ text-embedding-3-small ✅ text-embedding-3-large Pricing $0.02/1M tokens (small) $0.13/1M tokens (large) Conclusion Embeddings enable semantic search!
Use vision models for image understanding. Process images with LLMs. GPT-4 Vision Example response = client.chat.completions.create( model=”gpt-4-vision-preview”, messages=[{ “role”: “user”, “content”: [ {“type”: “text”, “text”: “What’s in this image?”}, {“type”: “image_url”, “image_url”: {“url”: “image_url”}} ] }] ) Use Cases ✅ Image analysis ✅ Document OCR ✅ Visual Q&A Conclusion Vision APIs enable image understanding!
Use function calling for structured outputs. Make LLMs call your functions. Example tools = [{ “type”: “function”, “function”: { “name”: “get_weather”, “parameters”: { “type”: “object”, “properties”: {“location”: {“type”: “string”}} } } }] response = client.chat.completions.create( model=”gpt-4″, messages=[…], tools=tools ) Conclusion Function calling enables structured interactions!
Use async calls to improve performance. Make concurrent API requests efficiently. Async Example import asyncio from openai import AsyncOpenAI client = AsyncOpenAI() async def generate(prompt): return await client.chat.completions.create(…) async def main(): tasks = [generate(f”Task {i}”) for i in range(10)] results = await asyncio.gather(*tasks) Benefits ✅ Faster processing ✅ Better throughput ✅ Resource efficient Conclusion Async … Read more
Create your own AI-powered API. Build APIs that use LLMs for processing. Architecture FastAPI → AI Service → LLM Provider FastAPI Example from fastapi import FastAPI app = FastAPI() @app.post(“/generate”) async def generate_text(prompt: str): response = client.chat.completions.create(…) return {“text”: response.choices[0].message.content} Conclusion Build your own AI APIs easily!
Monitor and optimize API costs. Track spending and reduce costs. Monitoring Tools ✅ Built-in dashboards ✅ Custom tracking ✅ Alerts Cost Tracking class CostTracker: def __init__(self): self.total_tokens = 0 self.total_cost = 0 def track(self, input_tokens, output_tokens): self.total_tokens += input_tokens + output_tokens self.total_cost += calculate_cost(…) Conclusion Monitoring prevents bill surprises!
Choose between streaming and batch responses. Understand when to use each approach. Streaming ✅ Real-time output ✅ Better UX ✅ Early stopping Batch ✅ Simpler code ✅ Full response ✅ Easier testing When to Use Streaming: Chat apps, long responses Batch: Processing, batch jobs Conclusion Choose based on your use case!