Agent Observability

You'd reach for this when you need visibility into what your AI agents are actually doing in production. It gives you structured logging so you can trace agent actions and decisions, tracks API costs so you know what each agent run is costing you, and maintains compliance audit trails for regulated environments. The source doesn't specify which LLM APIs it hooks into or what the MCP tools expose, but the core use case is clear: turn your agent from a black box into something you can monitor, debug, and account for. If you're running agents at any scale or in environments where you need to show your work, this handles the observability layer.