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ScriptsMar 31, 2026·2 min de lecture

AgentOps — Observability for AI Agents

Python SDK for AI agent monitoring. LLM cost tracking, session replay, benchmarking, and error analysis. Integrates with CrewAI, LangChain, AutoGen, and more. 5.4K+ stars.

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Introduction

AgentOps is a Python SDK for monitoring and observing AI agents in production. It auto-instruments LLM calls to track costs, latency, token usage, errors, and session replays. Integrates with CrewAI, LangChain, AutoGen, OpenAI Agents SDK, Agno, and CamelAI with one line of code. 5,400+ GitHub stars, MIT licensed.

Best for: Teams running AI agents in production who need cost control and debugging Works with: OpenAI, Anthropic, CrewAI, LangChain, AutoGen, Agno, CamelAI

Key Features

Auto-Instrumentation

Add agentops.init() and all LLM calls are automatically tracked — no code changes needed.

Cost Tracking

Real-time cost per session, per agent, per model. Set budget alerts.

Session Replay

Replay entire agent sessions step-by-step: prompts, responses, tool calls, errors.

Error Analysis

Automatic detection of hallucinations, infinite loops, and failed tool calls.

Benchmarking

Compare agent performance across models, prompts, and configurations.

Dashboard

Web dashboard for team-wide monitoring, analytics, and alerting.

FAQ

Q: What is AgentOps? A: A Python SDK for AI agent observability — cost tracking, session replay, benchmarking, and error analysis. Auto-instruments LLM calls. 5.4K+ stars.

Q: Does AgentOps add latency? A: Minimal — instrumentation is async and non-blocking. Typically <5ms overhead per LLM call.

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Source et remerciements

Created by AgentOps AI. Licensed under MIT. AgentOps-AI/agentops — 5,400+ GitHub stars

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