Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for AI agent developers, AI engineering teams, and multi-agent application builders who need a Python SDK for AI agent monitoring, LLM cost tracking, and benchmarking that auto-instruments CrewAI, Agno, OpenAI Agents SDK, LangChain, AutoGen, AG2, and CamelAI with two lines of code (`import agentops` + `agentops.init()`), full tracing of every LLM call, tool invocation, and agent decision, r
Who should use it
Who should skip it
Skip AgentOps-AI/agentops unless the captured evidence suggests it solves a problem you are actively working on.
About this signal
AgentOps-AI/agentops is tracked by RepoRadar as a tool in the Radar section. It was first seen on 2026-07-09 and last updated on 2026-07-09. The current verdict is 'try now' with a Gold tier and easy setup difficulty. Across RepoRadar's eight signals, AgentOps-AI/agentops is strongest on momentum (9.0) and workflow potential (8.9) and weakest on maturity (6.4) — a profile worth weighing against your own priorities. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned AgentOps-AI/agentops a composite score of 8.2 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 0.0 and never affects the composite score or tier. The risk label of 'none' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to vet an AI agent or MCP server before you wire it in for the checklist behind this score.
Risk explanation
No inherent user-impacting risk is flagged from the captured evidence.