Best LLM observability tools
Observability and tracing platforms for LLM and agent applications — traces, metrics, prompts, and evaluation.
Observability and tracing platforms for LLM and agent applications — traces, metrics, prompts, and evaluation.
Compared at a glance
The top 5 picks side by side on trust, install, platform support, and disclosed notes — full rationale for each below.
| Field | LangSmith Observability, evaluation, tracing, and testing platform for LLM applications and agent workflows. Open dossier | Evidently Open-source ML and LLM observability framework for evaluating, testing, and monitoring data quality, drift, model behavior, and AI application outputs. Open dossier | Arize Phoenix Open-source observability and evaluation tooling for LLM applications, traces, datasets, and experiments. Open dossier | AgentOps Open-source observability platform and SDK for tracing, debugging, replaying, and cost-monitoring AI agent and LLM application runs. Open dossier | DeepEval Open-source Python framework for unit-testing LLM applications, agents, RAG pipelines, metrics, regression suites, and traces. Open dossier |
|---|---|---|---|---|---|
| Trust | |||||
| Install risk | Review first | Review first | Review first | Review first | Review first |
| Notes | Safety · Privacy ✓ | Safety ✓ Privacy ✓ | Safety · Privacy · | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ |
| Category | tools | tools | tools | tools | tools |
| Source | source-backed | source-backed | source-backed | source-backed | source-backed |
| Author | LangChain | Evidently AI | Arize AI | AgentOps | Confident AI |
| Added | 2026-04-27 | 2026-06-03 | 2026-04-27 | 2026-06-03 | 2026-06-03 |
| Platforms | CLI | CLI | CLI | CLI | CLI |
| Source repo | — | — | — | — | — |
| Safety notes | — missing | ✓Evidently metrics and tests are decision support, not proof that a model, dataset, prompt, or LLM application is correct, fair, safe, or production-ready. Drift, data quality, and LLM judge results can be noisy or context-dependent, so thresholds should be calibrated on representative data before blocking releases or triggering alerts. Reports, test suites, and dashboards can influence deployment and incident workflows, so review generated conditions before wiring them into CI, monitoring, or agent-managed remediation. Synthetic data generation, prompt optimization, LLM-as-judge evaluations, and provider-backed metrics can call configured model services and should be scoped for cost and data handling. Self-hosted dashboards, local reports, and exported artifacts need normal access controls because they can become a shared source of operational decisions. | — missing | ✓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. | ✓DeepEval metrics should be treated as regression and review signals, not proof that an LLM application is safe, correct, or production-ready. LLM-as-a-judge metrics can call configured model providers, consume quota, hit rate limits, and produce judge-model errors that need separate handling. Evaluation thresholds should be calibrated on real examples before they block deployments or trigger automated rollback, ranking, billing, or moderation decisions. Tracing instrumentation can wrap live application code, agents, retrievers, tools, and model calls; keep eval and production environments clearly separated. |
| Privacy notes | ✓LangSmith receives traces of your LLM and agent runs — prompts, outputs, tool calls, and metadata — sent to LangSmith's cloud (or your self-hosted instance); review what trace data leaves your environment and keep secrets out of logged inputs. | ✓Evidently can process dataset columns, feature values, predictions, labels, model metadata, prompts, retrieved context, responses, traces, evaluation scores, and custom metric outputs. HTML, JSON, and Python dictionary reports can contain samples, column names, feature distributions, prompt text, generated answers, labels, or other sensitive operational data. Evidently Platform and Cloud workflows add hosted storage, dashboards, dataset management, tracing, user management, and alerting that should be reviewed against team data-retention and access-control policies. LLM-based evaluations may send prompts, responses, references, or scoring context to configured model providers unless a local evaluation path is used. Local report files and dashboard exports should be kept out of public repositories and shared workspaces unless reviewed for sensitive data. | — missing | ✓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. | ✓Test cases, traces, spans, prompts, actual outputs, expected outputs, retrieval context, tool arguments, metadata, and evaluation results may contain sensitive user or business data. LLM-based metrics can send evaluation payloads to the configured model provider unless a reviewed local model path is used. DeepEval documentation says evaluations run locally by default, while Confident AI login and cloud reporting are optional paths for centralized results. The official data privacy docs say DeepEval collects basic PostHog telemetry by default, including event names, metric names, notebook usage, an anonymous UUID, and public IP, with `DEEPEVAL_TELEMETRY_OPT_OUT=1` available for opt-out. |
| Prerequisites | — none listed |
| — none listed |
|
|
| Install | — | — | — | — | — |
| Config | — | — | — | — | — |
| Citations | |||||
| Claim | Unclaimed | Unclaimed | Unclaimed | Unclaimed | Unclaimed |
- 01Why it made the cut
LangSmith is included because it has privacy notes present, source-backed source posture.
Reach for insteadIf this will touch credentials, local files, or production systems, inspect the upstream source first.
- 02Why it made the cut
Evidently is included because it has safety notes present, privacy notes present, source-backed source posture.
Reach for insteadIf this will touch credentials, local files, or production systems, inspect the upstream source first.
- 03Why it made the cut
Arize Phoenix is included because it has source-backed source posture.
Reach for insteadIf this will touch credentials, local files, or production systems, inspect the upstream source first.
- 04Why it made the cut
AgentOps is included because it has safety notes present, privacy notes present, source-backed source posture.
Reach for insteadIf this will touch credentials, local files, or production systems, inspect the upstream source first.
- 05Why it made the cut
DeepEval is included because it has safety notes present, privacy notes present, source-backed source posture.
Reach for insteadIf this will touch credentials, local files, or production systems, inspect the upstream source first.
- 06Why it made the cut
MLflow is included because it has safety notes present, privacy notes present, source-backed source posture.
Reach for insteadIf this will touch credentials, local files, or production systems, inspect the upstream source first.
- 07Why it made the cut
TruLens is included because it has safety notes present, privacy notes present, source-backed source posture.
Reach for insteadIf this will touch credentials, local files, or production systems, inspect the upstream source first.
- 08Why it made the cut
Langfuse is included because it has privacy notes present, source-backed source posture.
Reach for insteadIf this will touch credentials, local files, or production systems, inspect the upstream source first.
- 09Why it made the cut
Helicone is included because it has privacy notes present, source-backed source posture.
Reach for insteadIf this will touch credentials, local files, or production systems, inspect the upstream source first.
- 10Why it made the cut
Weave is included because it has source-backed source posture.
Reach for insteadIf this will touch credentials, local files, or production systems, inspect the upstream source first.
- 11Why it made the cut
Agno is included because it has safety notes present, privacy notes present, source-backed source posture.
Reach for insteadIf this will touch credentials, local files, or production systems, inspect the upstream source first.
- 12Why it made the cut
Dagster is included because it has safety notes present, privacy notes present, source-backed source posture.
Reach for insteadIf this will touch credentials, local files, or production systems, inspect the upstream source first.
- 13Why it made the cut
Hugging Face Evaluate is included because it has safety notes present, privacy notes present, source-backed source posture.
Reach for insteadIf this will touch credentials, local files, or production systems, inspect the upstream source first.
- 14Why it made the cut
Label Studio is included because it has safety notes present, privacy notes present, source-backed source posture.
Reach for insteadIf this will touch credentials, local files, or production systems, inspect the upstream source first.
Missing a pick? Propose an edit to this list — every change goes through the same review queue as new entries.
Suggest a pickGet the weekly brief
One calm read on Claude workflows. Sundays. No tracking pixels.
Unsubscribe any time. No tracking pixels. No partner blasts.