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.
Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.
Decision playbook
Signals are present but mixed. Use the checklist below to confirm the source and operational safety for your environment.
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78
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No baseline selected
No major trust-signal divergence detected in the current selection.
Confirm ownership and provenance before trusting install instructions.
Source link availableRequired
Open the canonical repository and verify ownership.
Source provenance statusRequired
Marked as source-backed.
Metadata reviewed
Registry metadata indicates a reviewed listing.
Validate risk disclosures before installation or API wiring.
Safety notes presentRequired
Review the listed safety guidance before running commands.
Privacy notes presentRequired
Review data handling notes before connecting accounts or secrets.
Trust level risk gateRequired
Trust level does not block evaluation.
Check package metadata and artifact integrity signals.
Install payload available
Install or copy payload is available for review.
Package verification flag
No package verification flag provided.
Checksum metadata
No checksum provided for downloaded artifact.
Use compare context to validate trade-offs before adoption.
Compare tray has multiple entries
Add at least one more entry to compare trust differences.
Baseline comparison available
No baseline peer selected yet.
Diverging trust signals identified
No major trust-signal divergence found.
Setup at a glance
Copy-ready — paste the snippet to get started.
Adoption plan
Current risk score 16/100. Use staged verification before broader rollout.
Validate source and review signals before any execution.
Confirm source provenanceRequired
Source URL/provenance metadata is present.
Confirm metadata review state
Listing has review metadata.
Verify install payload
Install/config payload exists and can be inspected.
Confirm safety, privacy, and package integrity signals.
Review safety notesRequired
Safety notes are present.
Review privacy notesRequired
Privacy notes are present.
Verify package integrity metadata
No package verification/checksum metadata.
Adopt in controlled steps based on the selected plan.
Run in isolated sandbox firstRequired
Use a constrained sandbox and observe behavior across multiple tasks.
Roll out graduallyRequired
Roll out to a small cohort before wider usage.
Set monitoring and fallback
Define rollback path and monitor errors after adoption.
Evidence readiness
Required evidence gates are covered (5/6 signals complete).
Source repository/provenance is listed.
Required in this preset
Review metadata is present.
Required in this preset
Safety notes are present.
Required in this preset
Privacy notes are present.
Optional in this preset
Package integrity metadata is missing.
Optional in this preset
Install payload is available.
Required in this preset
Required evidence gates are covered for this preset.
Decision timeline
5/6 steps complete with no blocking gaps for this preset.
triage
Source/provenance metadata is available.
triage
Review metadata is available.
verify
Safety notes are available.
verify
Privacy notes are available.
verify
Package integrity metadata is missing.
rollout
Install payload is available.
No required blockers for this timeline preset.
Prerequisite readiness
3 prerequisites to line up before setup. Have accounts and credentials ready first.
Safety & privacy surface
3 safety and 3 privacy notes across 3 risk areas. Review closely: credentials & tokens.
Disclosure: editorial
## 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.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.
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.
Editorial listing. No paid placement or affiliate link is used.
Show that AgentOps is listed on HeyClaude. Paste this Markdown into your README — it renders the badge and links back to this page.
[](https://heyclau.de/entry/tools/agentops)AgentOps side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.
1 trust signal differ across this comparison (Submitter).
| Field | Open-source observability platform and SDK for tracing, debugging, replaying, and cost-monitoring AI agent and LLM application runs. Open dossier | Open-source LLM observability platform for logging, metrics, cost tracking, feedback, and gateway workflows. Open dossier | Open-source LLM engineering platform for tracing, prompt management, evaluation, metrics, and observability. Open dossier | Open-source observability and evaluation tooling for LLM applications, traces, datasets, and experiments. Open dossier |
|---|---|---|---|---|
| Next steps | ||||
| Trust | ||||
| Review status | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed |
| Package trust | Package not verified | Package not verified | Package not verified | Package not verified |
| Source provenance | Source-backed | Source-backed | Source-backed | Source-backed |
| SubmitterDiffers | oktofeesh1 | — | — | — |
| Install risk | Review first | Review first | Review first | Review first |
| Notes | Safety ✓ Privacy ✓ | Safety · Privacy ✓ | Safety · Privacy ✓ | Safety · Privacy · |
| Brand | ||||
| Category | tools | tools | tools | tools |
| Source | Source-backed | Source-backed | Source-backed | Source-backed |
| Author | AgentOps | Helicone | Langfuse | Arize AI |
| Added | 2026-06-03 | 2026-04-27 | 2026-04-27 | 2026-04-27 |
| Platforms | ||||
| Harness | ||||
| Source repo | — | — | — | — |
| 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. | — missing | — missing | — missing |
| 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. | ✓When used as a proxy, Helicone sits in the request path and logs your LLM prompts, responses, and metadata (Helicone cloud or your self-hosted instance); review what request data is captured, keep secrets out of logged payloads, or use the self-hosted/async logging options. | ✓Langfuse receives traces of your LLM/agent runs — prompts, outputs, and metadata — sent to Langfuse Cloud or your self-hosted instance; review what trace data leaves your environment and keep secrets out of logged inputs. | — missing |
| Prerequisites |
| — none listed | — none listed | — none listed |
| Install | — | — | — | — |
| Config | — | — | — | — |
| Citations | ||||
| Claim | Unclaimed | Unclaimed | Unclaimed | Unclaimed |
Open-source LLM observability and evaluation tooling.
Source-backed guides for putting this to work.
Instrument LLM and agent apps with traces, metrics, logs, and redaction.
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