AgentOps
Open-source observability platform and SDK for tracing, debugging, replaying, and cost-monitoring AI agent and LLM application runs.
Open the source and read safety notes before installing.
Safety notes
- AgentOps instruments LLM calls, tools, operations, and agent workflows, so enable it intentionally in environments where captured traces are allowed.
- Cost and latency dashboards are useful for operations, but alerting and budget decisions still need human-reviewed thresholds.
- Self-hosted deployments require normal backend hardening for database access, secrets, authentication, and retained trace data.
Privacy notes
- Traces can include prompts, completions, tool inputs, tool outputs, errors, costs, tokens, tags, and application metadata.
- The docs say AgentOps automatically collects basic host environment details such as OS, Python version, anonymized hostname, and SDK version.
- Hosted dashboard use sends telemetry to AgentOps infrastructure; self-hosted use still requires retention, access-control, and log-review policies.
Prerequisites
- Python or TypeScript/JavaScript application using a supported LLM provider or agent framework.
- AgentOps project/API key for hosted dashboard use, or a reviewed self-hosted deployment plan.
- A telemetry policy for which prompts, responses, tool calls, metadata, and host details may be captured.
Schema details
- Install type
- copy
- Troubleshooting
- No
- Scope
- Source repo
- Website
- https://agentops.ai
- Pricing
- open-source
- Disclosure
- editorial
- Application category
- DeveloperApplication
- Operating system
- macOS, Windows, Linux, Web, Self-hosted
Full copyable content
## Editorial notes
AgentOps is a practical fit for teams running Claude-adjacent agents in production because it focuses on the parts that are hard to reconstruct after a failure: traces, spans, session replays, LLM calls, tool activity, errors, latency, and cost. It also has integrations for common agent stacks and providers, including OpenAI Agents SDK, CrewAI, AG2/AutoGen, Agno, LangChain, LangGraph, Anthropic, OpenAI, LiteLLM, LlamaIndex, and others.
## Source notes
- The official introduction describes AgentOps as a platform for testing, debugging, and deploying AI agents and LLM apps, with automatic tracking after initialization.
- The quickstart documents SDK installation, API-key setup, automatic instrumentation, decorators for custom tracing, and dashboard trace viewing.
- The core concepts docs describe sessions, agents, workflows, operations, LLM spans, tool spans, OpenTelemetry foundations, dashboard views, and host-environment metadata.
- The GitHub README describes replay analytics, debugging, LLM cost management, framework integrations, and self-hosting.
## Duplicate check
Checked current `content/tools/`, open pull requests, live HeyClaude search results, and repository-wide content for `AgentOps`, `agentops.ai`, `github.com/AgentOps-AI/agentops`, `agent observability`, `LLM tracing`, and `agent cost tracking`. Existing content includes a generic observability skill, but no dedicated AgentOps tools entry or open duplicate PR was found.
## Disclosure
Editorial listing. No paid placement or affiliate link is used.About this resource
Editorial notes
AgentOps is a practical fit for teams running Claude-adjacent agents in production because it focuses on the parts that are hard to reconstruct after a failure: traces, spans, session replays, LLM calls, tool activity, errors, latency, and cost. It also has integrations for common agent stacks and providers, including OpenAI Agents SDK, CrewAI, AG2/AutoGen, Agno, LangChain, LangGraph, Anthropic, OpenAI, LiteLLM, LlamaIndex, and others.
Source notes
- The official introduction describes AgentOps as a platform for testing, debugging, and deploying AI agents and LLM apps, with automatic tracking after initialization.
- The quickstart documents SDK installation, API-key setup, automatic instrumentation, decorators for custom tracing, and dashboard trace viewing.
- The core concepts docs describe sessions, agents, workflows, operations, LLM spans, tool spans, OpenTelemetry foundations, dashboard views, and host-environment metadata.
- The GitHub README describes replay analytics, debugging, LLM cost management, framework integrations, and self-hosting.
Duplicate check
Checked current content/tools/, open pull requests, live HeyClaude search results, and repository-wide content for AgentOps, agentops.ai, github.com/AgentOps-AI/agentops, agent observability, LLM tracing, and agent cost tracking. Existing content includes a generic observability skill, but no dedicated AgentOps tools entry or open duplicate PR was found.
Disclosure
Editorial listing. No paid placement or affiliate link is used.
Source citations
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