How-Tos & Practical Guides

If you’ve been paying $20 a month for Claude Design and burning through your weekly token allowance in under thirty minutes, there’s now a free, self-hosted alternative that runs the same multi-agent design workflow entirely on your own machine. It’s called Open-Design, it’s Apache 2.0 licensed, and it just crossed 40,000 GitHub stars weeks after launch. What Open-Design Does Claude Design’s core value proposition is multi-agent design automation: feed it a prompt or a reference image, and a coordinated team of AI agents handles research, layout, copy, asset generation, and code — producing design artifacts that would take a human hours. Open-Design replicates that workflow but routes tasks to CLI-based AI agents running locally instead of Anthropic’s proprietary backend. ...

Every AI agent you’ve ever used follows the same basic script: you give it context, it acts. You write the system prompt, attach the documents, describe your preferences — and then the agent tries to help based on what you’ve given it in that session. OpenHuman inverts that script entirely. Instead of waiting for you to provide context, it spends time reading you first — analyzing your existing digital footprint to build a behavioral model — and only then begins acting on your behalf. ...

Multi-agent systems have a hidden cost problem that rarely appears in the demos: every time one agent communicates with another, it has to convert its internal reasoning into text, send it, and wait for the receiving agent to convert that text back into actionable computation. It’s the AI equivalent of printing a document, mailing it, and having someone retype it on arrival. RecursiveMAS proposes a different approach — and has the academic benchmarks to back it up. ...

An AI agent that fails silently is one of the most expensive debugging problems you’ll face. It picks tools, branches, retries, rewrites its own plan — and without visibility into those decisions, you’re flying blind. That’s the premise behind Motus Tracing, and it’s a compelling one. Lithos AI released Motus Tracing in May 2026 as a fully open-source observability layer for AI agents. It’s framework-agnostic, requires zero code changes to your agent, and the entire tracing stack is free to use. Here’s how it works and how to set it up. ...

If you’re building agentic AI systems for a living, there’s now a formal certification for that — and the beta window is open with a significant discount. GitHub and Microsoft launched Exam GH-600: Certified Agentic AI Developer in beta on May 13, 2026, with general availability planned for July 2026. It’s the first role-based certification focused specifically on how developers build, operate, supervise, and integrate AI agents across the software development lifecycle. ...

OpenClaw is moving fast in 2026, and the latest pre-release — 2026.5.16-beta.4 — brings a cluster of improvements that multi-agent pipeline operators have been asking for. The highlights this cycle: smarter subagent handoffs, cron scheduling refinements, Grok OAuth for SuperGrok subscribers, and a Control UI that finally shows you where your quota is going. Here’s a breakdown of what changed, why it matters, and what to check after upgrading. Cleaner Subagent Handoffs (Delegated Completions) The most significant change in beta.4 is how subagent handoffs are labeled and surfaced when they complete. Previously, completions from child agents could arrive in a way that made it unclear which task was being reported — especially in long pipelines with multiple agent depths. ...

Code extracted from Google App v17.20 reveals that Gemini Spark — Google’s forthcoming always-on Android AI agent — is built around two architectural pillars: a modular skill system and a background task scheduler. If you’re building with AI agent frameworks or evaluating the agentic AI landscape ahead of Google I/O 2026 (May 19–20), understanding this architecture will help you contextualize where Google’s platform play fits relative to what already exists. ...

When people talk about multi-agent AI development teams, they usually mean two or three agents working together on a task. Peter Steinberger means something different. Steinberger — founder of OpenClaw and now an engineer at OpenAI — runs approximately 100 Codex instances in continuous operation. They write code, review pull requests, find bugs, deduplicate GitHub issues, monitor benchmarks, and even attend meetings to draft PRs for features discussed in conversation. In 30 days, his team’s OpenAI API bill hit $1.3 million for 603 billion tokens across 7.6 million requests. The top model powering it all: GPT-5.5. ...

If you’ve been running OpenClaw with OpenAI models and paying separately for API access, that just changed. OpenClaw now routes all OpenAI model requests through the native Codex runtime harness by default, and it’s powered by your existing ChatGPT Plus, Pro, or Team subscription. No separate API key. No double billing. Sign in via OAuth, and your ChatGPT plan funds your OpenClaw agents. What Changed OpenAI announced the Codex runtime integration in early May 2026. The key shift: instead of OpenClaw calling the OpenAI API directly with a developer API key, it now uses the Codex runtime harness — the same infrastructure powering Codex in the ChatGPT interface — accessed through your existing subscription credentials. ...

OpenClaw is running two release tracks simultaneously this week: v2026.5.12 is the latest stable release, and v2026.5.16-beta.1 is the current leading beta. Together they represent the most significant package of security and performance improvements OpenClaw has shipped since earlier this year. Here’s what’s in each track, why the changes matter for production agent deployments, and how to think about upgrading. v2026.5.12 Stable — What Changed Major Memory Leak Fix The most important change in v2026.5.12 is a fix for a memory leak that caused significant RSS (Resident Set Size) growth when processing large transcripts. ...