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Agent Evaluation & Observability

AgentOps

AgentOps is a developer platform for observing AI-agent and LLM application runs. It tracks LLM calls, tool use and multi-agent interactions, and provides logs, error records and run replay for debugging.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

AgentOps — OpenViST project profile in Agent Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use AgentOps when you need to inspect the events, calls, tools or errors within an AI-agent run, or replay a run as part of debugging.

Who it is for

Developers building or maintaining AI-agent and LLM applications.

Documented uses

  • Trace LLM calls, tool use and interactions among agents.
  • Review run logs and errors while debugging.
  • Replay agent runs to investigate unexpected behavior.
  • Inspect tracked prompt-injection events.
Before you choose

Requirements & limits

  • The documented workflow centers on tracing and replaying runs, rather than building the agent itself.
  • Named ecosystem connections include OpenAI, CrewAI and AutoGen.
  • Its stated event coverage includes LLM calls, tools, errors and multi-agent interactions.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Explore AgentOps example notebooks

Use the listed sample notebooks to examine a simple agent, a multi-agent design, or concurrent traces.

Before you start

  • Access to the AgentOps examples documentation.
  • A Jupyter Notebook environment for examples identified as notebooks.

Steps

  1. Open the AgentOps examples page.

  2. Choose a listed example, such as Simple Agent, Multi-Agent, or Concurrent Traces.

  3. Use the notebook sample code for the chosen example.

Expected outcome

A selected AgentOps example notebook is available for exploring agent runs, multi-agent workflows, or concurrent traces.

Guide limitations

  • The examples page lists sample notebooks and integrations but does not provide the notebook execution steps or environment setup.
  • Examples and integrations vary; the page does not establish compatibility or successful execution for a particular setup.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
AgentOpsTrace LLM calls, tool use and interactions among agents.Web
LangwatchTrace and inspect LLM or agent activity in production.CLI, Self-hosted, Server, WebThe project documents a hosted LangWatch Cloud option and self-hosting. The repository describes the project as open source under Apache 2.0.
Evidence and transparency

Sources & review

agentops.ai ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
7 October 2026
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