Tutorials

Develop plugins to extend OpenClaw. Create powerful integrations with plugins. Plugin Architecture Plugins add new capabilities to OpenClaw. Creating a Plugin 1. Define plugin structure 2. Implement plugin logic 3. Add configuration 4. Test thoroughly Conclusion Plugins enable custom integrations!

Deploy OpenClaw in production environments. Run your AI assistant reliably at scale. Deployment Options ✅ Docker ✅ Kubernetes ✅ Cloud platforms Configuration Use environment variables for configuration. Monitoring Set up logging and monitoring for production. Conclusion Production deployment ensures reliability!

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!

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!

Use multiple AI models in one application. Combine the strengths of different models. Model Selection Strategy Simple tasks: Use cheaper models Complex reasoning: Use GPT-4 or Claude Code: Use DeepSeek Architecture class MultiModelLLM: def __init__(self): self.models = { “fast”: OpenAI(model=”gpt-3.5-turbo”), “smart”: OpenAI(model=”gpt-4″), “cheap”: DeepSeek() } Conclusion Multi-model apps optimize cost and quality!