Observability and evaluation guide
The best AI agent observability and evaluation tools for 2026, by layer
A trace tells you what ran. An evaluation tells you whether behavior met a criterion. A human decision record explains what the team accepted and why. Production teams usually need more than one of those layers.
Start with the method, then choose the product layer that matches the work.
Direct answer
Choose LangSmith for integrated tracing and evaluation across agent frameworks; Langfuse for open-source, OpenTelemetry-based LLM observability; Arize Phoenix for open-source tracing, experiments, and evaluations; Braintrust for dataset-centered evaluation and regression workflows; OpenAI Agents SDK tracing for native visibility inside SDK applications; and Google ADK when evaluation belongs in an ADK development lifecycle. Raltic complements these tools by preserving the human-facing brief, evidence, tasks, corrections, and decision. It is not a replacement for span-level tracing or automated evals.
Useful telemetry becomes a test, then returns to production as a verified change.
- 01
Instrument
Capture model calls, tools, retrieval, handoffs, timing, and errors.
- 02
Inspect
Find the exact step where behavior, cost, latency, or evidence broke.
- 03
Evaluate
Score repeatable cases with code, human labels, or a documented rubric.
- 04
Regress
Promote failures into datasets and compare the next version on the same cases.
How we chose
The method is part of the answer.
Raltic publishes this guide and appears in it. No company paid for inclusion. We checked current official documentation and matched each product to a specific job instead of assigning a universal score.
- 01We separate application tracing, offline evaluation, online monitoring, datasets, and human workflow evidence.
- 02Each external capability is tied to current first-party documentation rather than a vendor-neutral feature spreadsheet.
- 03Open-source and self-hosted options are described as deployment choices, not automatic privacy guarantees.
- 04Native framework tracing is compared with independent platforms without assuming that one replaces the other.
- 05Raltic publishes this guide and appears only as a complementary workflow-evidence layer, not as a trace or eval platform.
Quick comparison
What each platform is built to do.
| Platform | Product type | Best for | Lift | Review model |
|---|---|---|---|---|
| LangSmith | Agent observability and evaluation platform | Teams wanting tracing, datasets, feedback, and evals across common agent frameworks | Moderate | Human feedback plus code, LLM, pairwise, offline, and online evaluators |
| Langfuse | Open-source LLM observability platform | Teams wanting OpenTelemetry-based tracing with self-hosting and LLM-specific analytics | Moderate | Scores from users, humans, code, or model-based evaluators attached to traces |
| Arize Phoenix | Open-source AI observability and evaluation | AI engineers who want OpenTelemetry traces, experiments, evaluators, and local or hosted deployment | Moderate | Code, LLM, and human evaluations over traces, spans, experiments, or datasets |
| Braintrust | Evaluation and experiment platform | Teams treating datasets and repeatable regression tests as the center of AI quality | Moderate | Versioned datasets, code or model scorers, experiments, online scoring, and human review |
| OpenAI Agents SDK tracing | Framework-native tracing | Applications already built on OpenAI Agents SDK | Low | SDK run traces plus application-defined evaluation and approval surfaces |
| Google ADK evaluation | Framework-native agent evaluation | Teams building and testing agent trajectories inside the ADK lifecycle | Moderate | Expected tool trajectories, response criteria, datasets, UI, CLI, and CI tests |
| Raltic | Human-facing workflow evidence layer | Teams preserving the brief, agent updates, tasks, reviewer corrections, and accepted decision | Low | Shared room, visible tasks, posted evidence, feedback, and human-owned decision |
Detailed picks
Choose the native operating model.
Best for
Teams wanting tracing, datasets, feedback, and evals across common agent frameworks
Agent observability and evaluation platform
LangSmith
LangSmith models an application run as a trace made of runs and can link multi-turn traces into threads. Official integrations cover LangChain, LangGraph, OpenAI, Anthropic, CrewAI, and other paths, while manual instrumentation handles custom code. Its evaluation layer connects datasets, experiments, production runs, feedback, and reusable evaluators. This is a practical choice when a team wants one commercial workflow from debugging a run to testing the fix.
- 01Broad framework tracing and manual instrumentation
- 02Offline datasets and comparable experiments
- 03Online run or thread evaluation with shared evaluators
Watch for: Define retention, sampling, sensitive-data handling, and cost before tracing high-volume production traffic. Framework convenience does not remove telemetry governance.
Best for
Teams wanting OpenTelemetry-based tracing with self-hosting and LLM-specific analytics
Open-source LLM observability platform
Langfuse
Langfuse organizes individual operations as observations, groups them into traces, and connects related traces through sessions. It is built on OpenTelemetry and adds LLM-specific concepts such as generations, tool calls, retrieval, token usage, cost, prompts, scores, datasets, and experiments. The open-source and self-hosted path is attractive for teams that need deployment control, but they still own instrumentation quality and platform operations.
- 01OpenTelemetry foundation and open-source deployment
- 02LLM-specific traces, sessions, costs, and scores
- 03Datasets, experiments, prompts, and dashboards
Watch for: A trace is only as useful as its boundaries and names. Establish stable trace, observation, environment, release, and user metadata before dashboards or evaluators depend on them.
Best for
AI engineers who want OpenTelemetry traces, experiments, evaluators, and local or hosted deployment
Open-source AI observability and evaluation
Arize Phoenix
Phoenix combines OpenTelemetry and OpenInference tracing with prompt work, datasets, experiments, replay, human annotations, and code or model-based evaluators. It is especially useful when the team wants to inspect retrieval and tool behavior, turn production traces into datasets, and compare a candidate change against the same inputs. The open-source project can run locally or on team infrastructure, while Arize offers a broader hosted monitoring path.
- 01OpenTelemetry and OpenInference instrumentation
- 02Trace-to-dataset and experiment workflow
- 03Deterministic, LLM, and human evaluation options
Watch for: Phoenix and the broader Arize commercial platform have different scopes. Confirm which alerting, online evaluation, retention, and operations features belong to the deployment you plan to use.
Best for
Teams treating datasets and repeatable regression tests as the center of AI quality
Evaluation and experiment platform
Braintrust
Braintrust's strongest organizing object is the evaluation dataset. Test cases hold inputs, optional expected outputs, and metadata; experiments create comparable records of a task and its scores. Production traces and reviewed examples can feed new cases back into the dataset. This makes Braintrust a good fit for teams that want evals in development and CI, then online scoring to surface new failures after release.
- 01Versioned datasets and immutable experiments
- 02Code, autoeval, and model-based scores
- 03Production-to-dataset feedback and CI regression paths
Watch for: A large scorer library does not define product quality for you. Start with a small, reviewed set of consequential examples and measure evaluator disagreement before scaling automated judges.
Best for
Applications already built on OpenAI Agents SDK
Framework-native tracing
OpenAI Agents SDK tracing
The Agents SDK automatically traces runner calls, model generations, tools, guardrails, handoffs, and custom spans. That native structure is a sensible first choice for an application already using the SDK because it follows the runtime's own concepts with little extra work. It is not a vendor-neutral observability strategy by itself, and sensitive trace data, external processors, retention, and eval design still require explicit decisions.
- 01Automatic traces aligned to SDK runtime concepts
- 02Generations, tools, guardrails, handoffs, and custom spans
- 03External trace processor integrations
Watch for: Review the sensitive-data configuration before production. Native tracing can include inputs and outputs that should not be exported without a clear data policy.
Best for
Teams building and testing agent trajectories inside the ADK lifecycle
Framework-native agent evaluation
Google ADK evaluation
Google ADK treats agent evaluation as more than final-answer scoring. Its guidance separates tool trajectory from final response quality, supports expected intermediate tool use and sub-agent responses, and offers web, command-line, programmatic, and conformance paths. This is a strong native choice when ADK is already the framework and the team wants evaluation artifacts to live beside agent code.
- 01Trajectory and tool-use evaluation
- 02Test files, eval sets, criteria, and custom metrics
- 03Web, CLI, programmatic, and CI-oriented execution
Watch for: Some documented evaluation capabilities vary by language or service dependency. Confirm support for the language, metric, and deployment path in the current ADK release.
Best for
Teams preserving the brief, agent updates, tasks, reviewer corrections, and accepted decision
Human-facing workflow evidence layer
Raltic
Raltic answers a question that technical telemetry usually leaves open: what did the team ask for, what evidence did people actually consider, who requested a correction, and which result became the accepted decision? Local and cloud agents can post work into the same room as human owners. This is useful operational memory, but it does not replace span-level traces, latency and cost telemetry, automated eval datasets, or runtime alerts.
- 01Shared context across local agents, cloud agents, and people
- 02Tasks and reviewer feedback beside posted evidence
- 03Human-readable outcome and decision history
Watch for: Pair Raltic with framework or observability telemetry when engineers need step-level model, tool, latency, token, or error analysis. Do not treat room history as an automated evaluation system.
Decision path
Turn the shortlist into a real test.
- 01
Write the failure taxonomy
Separate wrong final answers, wrong tool choice, bad arguments, missing evidence, latency, cost, permission errors, and reviewer disagreement.
- 02
Instrument one complete run
Capture the request, retrieval, model steps, tools, handoffs, errors, final output, and version metadata without collecting unnecessary secrets.
- 03
Curate the first dataset
Start with a small set of important successes, failures, and edge cases reviewed by people who understand the task.
- 04
Close the loop
Turn production failures into regression cases, compare the candidate fix, and keep monitoring after release.
FAQ