agentops

Overview

Provides a Python SDK for monitoring AI agents, tracking LLM costs, and running benchmarks across agent frameworks. Collects interaction logs, metrics, and cost data from multiple providers and agent runtimes to give unified visibility. Includes built-in evaluation metrics and adapters for popular agent frameworks to standardize agent observability and benchmarking. For example, adapters for popular agent frameworks can leverage the Agent Registry Pattern to register and track components, while Human-in-the-Loop concepts can guide evaluation, and planning-oriented Planning Pattern can structure benchmark hooks.

Key Benefits

Ideal For

Teams running multiple agent frameworks who need centralized observability, cost attribution, and repeatable evaluation before production. This can be complemented by a Planning Pattern approach for structured benchmarking, and a Agent Registry Pattern to keep track of agents and runtimes across environments.

Real-World Examples

• Centralize interaction logs and metrics from different agent frameworks for unified analysis
• Attribute LLM costs to individual agents and workflows for budget and optimization
• Run repeatable benchmarks and evaluation metrics to compare agent reliability and regressions
• Integrate agent observability into pre-production checks and CI pipelines