hermes-agent-framework

AI agents with self-improving learning loops are built using a framework that automatically evolves capabilities over time. Hermes Agent provides this by integrating learning mechanisms that capture and apply past interactions to enhance future performance.

A three-layer memory system for AI agents organizes data into episodic conversations, semantic knowledge, and procedural skills. This architecture allows autonomous agents to manage context, recall facts, and apply learned procedures independently in production environments.

Multi-agent orchestration in production is deployed by configuring a framework that supports automatic Skill creation and tool ecosystems. Hermes Agent enables scalable orchestration by allowing customized agents to interact and manage complex workflows autonomously.

Yes, Python, a model provider, and memory configuration are required to deploy and run customized AI agents. Access to a tool ecosystem is also necessary to leverage the full plugin architecture and extensibility capabilities.

Automatic Skill creation works with built-in tool ecosystems and plugin architectures to enable complex workflow automation. The framework orchestrates newly created Skills automatically, allowing agents to expand functional capabilities dynamically.