AgentOps
Open-source agent operations, security, and governance for observability, cost tracking, agent evaluation.
What does this agent do, and when should you use it?
The repository describes AgentOps as: Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and CamelAI. This profile is a source-based catalog entry; an independent FARS review is still pending.
Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and CamelAI.
- Evaluate controls for operating or governing agent workloads.
- Test observability, security, or deployment behavior in a sandbox.
- Integrate documented controls into an existing operations stack.
What are this agent's strengths and limitations?
- Public source and README are available for inspection.
- Focused on observability, cost tracking, agent evaluation.
- Setup, model-provider support, and maturity must be confirmed against the current release.
- No independent FARS score has been assigned yet.
How do you install or deploy this agent?
Follow the current installation instructions in the [repository README](https://github.com/AgentOps-AI/agentops#readme). Requirements and provider setup vary by release.
How do you use this agent?
Start with the examples and quickstart in the [repository documentation](https://github.com/AgentOps-AI/agentops#readme), then test the workflow with limited permissions and non-sensitive data.