What is OpenClaw, and what came after it?

The problem it solves

Most AI tools are isolated. You use ChatGPT for one thing, a separate tool for research, another for writing. They don't talk to each other, your data doesn't flow between them, and you end up doing the integration manually every time.

Agent harnesses fix this by creating a shared layer that lets skills, models, APIs, memory, approvals, and workflows connect. A research skill can pass its output directly into a writing skill. A monitoring skill can trigger an action skill when something changes. The connections happen automatically, without you having to build the plumbing.

The key pieces

Skills are pre-built capabilities — things like 'search the web', 'summarise a document', 'monitor a data source'. You install them like apps.

Models are the AI brains behind specific tasks — reasoning, generating text, analysing images. Different models are better at different things.

APIs are connection points to external services — databases, third-party tools, live data sources. They let your skills pull in real-world information.

Datasets are the training and reference data that make models smarter for specific domains.

Why it matters now

AI capabilities are growing faster than most people can track. New skills, models, and APIs appear every week. Without a curated, trusted source of what's actually available and what actually works, you end up either missing the best tools or installing unreliable ones.

That's what ClawCurrent exists to fix. We track the ecosystem, review every listing independently, and give you a trusted map of what to use and when.