9 articles
The three most important agent architectures — ReAct, Plan-and-Execute, and Reflexion — each solve different problems. Learn when to use which and how they work in practice.
OpenClaw's browser relay lets your AI agent control a real browser — taking screenshots, clicking elements, filling forms, and navigating pages. Here's how it works and when to use it.
OpenClaw's hooks system lets you trigger shell commands, scripts, or API calls on specific events — messages received, actions taken, or scheduled times. This guide covers every hook type with practical examples.
How to run multiple OpenClaw instances or use the orchestration layer to parallelise tasks, assign specialised agents, and build reliable multi-step AI workflows.
OpenClaw's skill system lets you add any capability your AI doesn't have by default. This guide covers building skills from scratch — REST API calls, database lookups, shell commands, and publishing to the community.
LangChain handles linear pipelines. LangGraph handles everything that needs loops, branching, or persistent state. Here's the exact decision framework — with code showing when each breaks and why.
How to reliably get JSON, typed objects, and formatted data from ChatGPT, Claude, and Gemini APIs. Covers response_format, tool use, Pydantic, and every technique that actually works in production.
LangGraph extends LangChain with graph-based agent architecture — nodes, edges, state, and cycles. Learn how to build reliable multi-step AI agents with real Python code examples.
