---
id: agentops
version: "0.4.21"
license: unclear
license_treatment: permissive
maintenance: active
---

# agentops — Observability and DevTool Platform for AI Agents

License: permissive · Maintenance: active · Downloads: 299.8K/mo

## What it is and what it does

AgentOps is an observability platform designed to instrument AI agent applications with minimal code overhead. It captures execution traces, LLM API calls, costs, and errors, then replays them in a web dashboard for debugging and analysis. The package provides decorators (@session, @agent, @operation, @task, @workflow) to mark code boundaries and automatically record inputs, outputs, and exceptions. It integrates natively with popular agent frameworks (CrewAI, AG2, LangGraph, Camel, Langchain, Cohere, OpenAI Agents SDK) and can be self-hosted on your own infrastructure.

The core use case is reducing the time to debug and optimize AI agents by providing step-by-step execution graphs, LLM spend tracking across foundation model providers, and session replay. It depends on OpenTelemetry for instrumentation, httpx and requests for HTTP communication, and pyyaml for configuration. The package supports Python 3.9 through 3.13 and has no known security vulnerabilities.

Use it for:

- Debug multi-step agent workflows by replaying execution traces and inspecting LLM calls step-by-step.
- Track and optimize LLM costs across different foundation model providers in production agent systems.
- Monitor CrewAI or AG2 agents with automatic telemetry by setting AGENTOPS_API_KEY and calling init().
- Instrument custom agent code with decorators to capture operation boundaries and exception handling.
- Self-host the AgentOps dashboard and API backend on your own cloud for air-gapped or compliance-sensitive deployments.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

AgentOps provides observability and monitoring for AI agents, capturing execution traces, LLM costs, and session replays with minimal code integration.

Yes. AgentOps is actively maintained, has no security vulnerabilities, and installs with low friction. The MIT license poses no restrictions. Install it if you are building or debugging AI agents and want production observability without writing custom instrumentation—the decorator-based API is lightweight and the framework integrations are well-established. Skip it only if you have no need for agent monitoring or prefer a fully offline debugging workflow.

## Install

pip install agentops
uv add agentops
poetry add agentops

## Installing agentops

Before you install: Low friction installation with a pure Python wheel. Actively maintained with recent releases; last commit 2026-06-25 and 5775 GitHub stars indicate ongoing development and community adoption.

License in practice: MIT license (permissive) means you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.

Quickstart:

pip install agentops

import agentops

agentops.init("<YOUR_API_KEY>")
# ... your agent code ...
agentops.end_session('Success')

Requires an API key from the AgentOps dashboard (https://app.agentops.ai/settings/projects) to send telemetry; local-only usage without an API key is not supported by the basic integration.

Verify before relying:

- Whether the package can operate in offline mode or with a self-hosted backend without requiring the cloud API key
- Performance overhead of instrumentation on agent execution speed and latency
- Data retention and privacy policies for telemetry sent to AgentOps servers

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 299.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

ai agent monitoring, llm observability platform, agent execution tracing, ai agent debugging, session replay for agents, llm cost tracking, agent analytics dashboard, ai-agent-observability, llm-monitoring, opentelemetry-based

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