AgentOps — Observability for AI Agents

What it is

AgentOps is a Python SDK for monitoring AI agents in production. It provides LLM cost tracking, session replay, benchmarking, and error analysis. The SDK auto-instruments LLM calls from OpenAI, Anthropic, and other providers, and integrates with agent frameworks like CrewAI, LangChain, and AutoGen.

AgentOps targets developers who build AI agents and need visibility into cost, performance, and failure patterns. It captures every LLM call, tool invocation, and agent decision in a timeline view for debugging and optimization.

How it saves time or tokens

Without observability, debugging AI agents means adding print statements and manually tracing execution paths. AgentOps auto-captures LLM calls, token usage, and costs without modifying your agent code. Session replay lets you step through an agent's decision history visually. Cost tracking surfaces expensive prompts so you can optimize token usage. Error analysis identifies patterns in agent failures across sessions.

How to use

1. Install the SDK:

pip install agentops

1. Initialize with one line at the start of your agent code:

import agentops
agentops.init(api_key='YOUR_KEY')

1. Run your existing agent code. AgentOps auto-instruments LLM calls:

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
    model='gpt-4o',
    messages=[{'role': 'user', 'content': 'Analyze this codebase.'}]
)
# AgentOps captures this call automatically

1. View sessions, costs, and replays in the AgentOps dashboard.

Example

import agentops
from crewai import Agent, Task, Crew

agentops.init(api_key='YOUR_KEY')

researcher = Agent(
    role='Researcher',
    goal='Find relevant papers on transformer architectures',
    backstory='You are an ML research assistant.'
)

task = Task(
    description='Search for papers on efficient attention mechanisms published in 2025-2026.',
    agent=researcher
)

crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()
# AgentOps tracks all LLM calls, tool uses, and costs

Related on TokRepo

• AI Agent Tools — Frameworks and tools for building AI agents
• Monitoring Tools — Observability and monitoring solutions

This tool integrates with standard development workflows and requires minimal configuration to get started. It is available as open-source software with documentation and community support through the official repository. The project follows semantic versioning for stable releases.

For teams evaluating this tool, the key advantage is reducing manual work in repetitive tasks. The automation provided by the built-in features means less custom code to maintain and fewer integration points to manage. This translates directly to lower maintenance costs and faster iteration cycles.

Common pitfalls

• AgentOps requires an API key and sends telemetry data to their dashboard; review their data handling policies if your agents process sensitive information.
• Auto-instrumentation patches LLM client libraries at import time; import agentops and call init() before importing OpenAI or Anthropic clients to ensure proper instrumentation.
• Session replay data can grow large for long-running agents; configure session limits or sampling to manage storage costs on the AgentOps platform.