rawclaw-agent

You can configure a self-improving AI agent across platforms using CLI and gateway support with persistent memory. This framework allows agent deployment, troubleshooting, and extension with reusable skills without constant reconfiguration.

Deploying an AI agent with persistent memory involves pluggable backends that retain context across sessions. The framework supports provider-agnostic models and multi-platform gateways for flexible, scalable automation.

Yes, the Rawclaw agent framework supports cron scheduling and plugins for scalable automation. It enables configuring an ecosystem of agents that spawn multiple instances, coordinate tasks, and adapt over time.

You extend an AI agent with reusable skills by creating a SKILL.md file with YAML frontmatter and a repository structure. Optional scripts, references, and assets directories hold extended resources for agent self-improvement.

Yes, you can coordinate multiple agent instances to automate complex workflows. The framework enables configuring an ecosystem where agents spawn multiple instances, coordinate tasks, and use profiles for scalable automation.

You need a SKILL.md file with YAML frontmatter specifying name and description to set up agent automation. Optional scripts, references, and assets directories provide extended resources for the framework.