For most teams, start with LangSmith if your agents use LangChain or LangGraph, choose Langfuse when self-hosting and detailed trace data are priorities, use Arize Phoenix for tracing tied closely to evaluation and experiments, and choose Braintrust when the main job is measuring and improving agent behavior. Add OpenTelemetry as the instrumentation layer when you want to keep the backend replaceable.
An agent trace should reconstruct the whole task, not just show a final model response. You need the model calls, tool calls, retrieval and embedding work, intermediate decisions, errors, latency, token or usage cost, and the resulting output in one navigable record. The products below differ mainly in how deeply they capture that context, how they display it, and how tightly they connect traces with evaluations, regression tests, and production operations.
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What AI agent observability has to show
Traditional application logging can tell you that a request failed. Agent observability must explain why an otherwise successful task took the wrong path. A useful trace lets an engineer follow the task chronologically and hierarchically:
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- Model operations: prompts, model responses, tool-selection decisions, token or usage data, and generation latency.
- Tool operations: the tool name, validated arguments, returned data, retries, timeouts, permission failures, and the point at which a tool result changed the plan.
- Retrieval and embedding work: query construction, retrieved document identifiers or scores, embedding calls, filters, and the context eventually sent to the model.
- Agent control flow: loops, hand-offs, planner and executor steps, sessions, and messages exchanged between agents.
- Outcome data: errors, user or evaluator feedback, final output, latency, and cost or usage estimates.
Without those spans, a trace may look healthy even when the agent used stale retrieval results, selected an inappropriate tool, repeated a failed call, or produced an answer that passed technical checks but failed a quality evaluation. The observability product should therefore make individual spans searchable, preserve parent-child relationships, and let the same trace feed dashboards, alerts, annotations, and evaluations.
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Shortlist at a glance
| Tool | Best fit | Evidence-backed strengths | Questions to verify before adoption |
|---|---|---|---|
| LangSmith | LangChain or LangGraph teams; managed workflow from traces to production operations | Trace-to-metric visibility, debugging, dashboards, alerts, feedback, online evaluations, and integrations including OpenAI, Anthropic, CrewAI, Vercel AI SDK, and Pydantic AI. Cloud, hybrid, and self-hosted deployment options are documented. | Current pricing, retention, usage limits, and deployment details for your geography and plan. |
| Langfuse | Open-source, self-hosted, or privacy-sensitive deployments | Open-source and self-hostable; captures LLM and non-LLM calls such as retrieval, embeddings, and APIs; supports sessions, agent graphs, OpenTelemetry ingestion, dashboards, alerts, prompt versioning, datasets, experiments, cost, latency, and production evaluation. | Operational cost, support expectations, and feature parity between the self-hosted and cloud editions. |
| Arize Phoenix | Teams wanting dedicated tracing connected to evaluation and experiment workflows | Documentation covers tracing, evaluation, datasets, experiments, prompts, coding agents, access control, data retention, and self-hosting. | Current license, hosted-versus-self-managed feature differences, and economics at your event volume. |
| Braintrust | Evaluation-first teams improving agents across prompt or model changes | Combines trace logging, recurring-pattern discovery, human annotation, datasets, experiments, online scoring, deployment, and production monitoring. | Trace depth and framework coverage for your specific agent stack and current plan limits. |
| OpenTelemetry | Vendor-neutral instrumentation and backend portability | Common model for traces, metrics, logs, instrumentation, semantic conventions, collectors, and sampling. | It is an instrumentation foundation, so you still need a backend and product layer for agent-specific views, evaluation, dashboards, and alerts. |
LangSmith: the natural first look for LangChain and LangGraph
LangSmith is the most straightforward choice when your application is already built around LangChain or LangGraph. Its observability workflow is designed to move from individual traces to production-wide performance metrics, then into debugging, feedback, dashboards, alerts, and online evaluations. That continuity matters when the same team owns both the agent runtime and the quality process.
What it captures and connects
The documented workflow treats traces as the evidence used to debug failures, monitor quality, and build datasets for evaluation. Integrations include OpenAI, Anthropic, CrewAI, the Vercel AI SDK, and Pydantic AI, so a stack does not have to be exclusively LangChain-based to send useful data. Dashboards and alerts turn recurring latency or error patterns into operational work, while feedback and online evaluations attach quality signals to live runs.
When it is the right fit
- Your agent uses LangChain or LangGraph and you want framework-aware traces with minimal plumbing.
- One team needs a managed path from development debugging to production monitoring and online evaluation.
- You prefer cloud, hybrid, or self-hosted deployment choices rather than committing to a single operating model immediately.
Checks before committing
Deployment availability does not establish that every feature has identical behavior in every edition. Confirm current retention, usage limits, pricing, data-region availability, and which dashboards, alerts, and evaluations are included in the plan you will operate.
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Langfuse is the leading option in this shortlist when data control, open-source availability, or self-hosting is a primary requirement. Its documented trace model includes both LLM and non-LLM activity: retrieval, embedding calls, API calls, and other application steps can appear alongside generations instead of being hidden in separate logs.
Why its trace model is useful for agents
Sessions and agent graphs preserve the larger task structure, while cost and latency views help identify expensive or slow branches. Prompt versioning, datasets, experiments, alerts, and production evaluation connect an observed failure to a repeatable improvement cycle. OpenTelemetry ingestion gives teams a way to bring in instrumentation from components that are not native to the platform.
When to choose it
- You need to run the observability service in your own environment or want an open-source option to inspect and modify.
- Privacy policies require tighter control over prompts, retrieved context, tool arguments, or user identifiers.
- Retrieval, embeddings, and API calls are as important to debugging as the model generation itself.
Operational trade-offs
Self-hosting transfers responsibility for upgrades, storage, backups, access control, scaling, and incident response to your team. Compare those responsibilities with the managed edition, and verify that the features you depend on remain available and supported in the selected deployment.
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Arize Phoenix: tracing aligned with evaluation and experiments
Arize Phoenix is a strong choice for teams that want tracing to lead directly into evaluation work rather than remain an isolated debugging screen. Its documentation has first-class areas for tracing, evaluation, datasets and experiments, prompts, coding agents, access control, data retention, and self-hosting.
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How it supports an improvement loop
Engineers can inspect a failing trace, collect representative examples into a dataset, run an experiment against a prompt or model change, and compare the resulting evaluations. That workflow is useful when the difficult question is not merely “which span failed?” but “does this change improve the class of tasks that users care about?”
When Phoenix fits
- Your team already treats datasets and experiments as part of release engineering.
- You need trace-level evidence for retrieval or tool behavior and a place to evaluate the resulting outputs.
- You want self-hosting documented as part of the deployment choices.
License terms, hosted-versus-self-managed differences, and scale economics can change. Verify them for the version and operating model you intend to use rather than assuming that documentation for one edition applies to another.
Braintrust: an evaluation-first observability workflow
Braintrust is aimed at teams whose central bottleneck is understanding and improving agent behavior across prompt, model, or tool changes. Its documented workflow is instrument, observe, annotate, evaluate, deploy, and monitor.
From traces to patterns
Trace logging supplies the raw execution record. Pattern discovery helps find recurring failure modes across runs, while human annotation adds judgments that automated checks may miss. Datasets and experiments make those examples reusable for comparison, and online scoring and production monitoring show whether an improvement survives contact with live traffic.
When it is the best match
- Evaluation criteria are still evolving and subject-matter experts need to annotate examples.
- You routinely compare prompts, models, or agent policies and want the comparisons attached to production evidence.
- Deployment decisions depend on quality scores and recurring patterns, not only uptime and latency.
Before adoption, verify that the SDKs and frameworks used by your agent expose the depth of spans you need and that current plan limits cover your trace and evaluation volume.
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OpenTelemetry: the portability layer, not the whole product
OpenTelemetry provides a common instrumentation contract for traces, metrics, logs, semantic conventions, collectors, and sampling. Instrumenting your agent with that model can make the backend replaceable: you can route the same telemetry to a different storage or analysis product later instead of rewriting every application integration.
What it does well
- Defines consistent trace and span context across model calls, tools, retrieval, and application services.
- Provides collectors that can receive, process, sample, and export telemetry.
- Separates instrumentation from the storage or user interface chosen for analysis.
What it does not provide by itself
OpenTelemetry alone is not a hosted agent-debugging and evaluation product. It does not supply the complete set of agent-specific dashboards, prompt workflows, evaluators, annotation queues, production alerts, or regression-test management that the products above provide. Plan for a backend and a product layer, whether that layer is one of the shortlisted platforms or an internal system.
How to compare tools for your workload
Do not select an observability platform by counting screenshots or generic integrations. Run each candidate against the same representative tasks and inspect the evidence it preserves.
1. Trace depth
Follow one task from the initial request through planning, model calls, tool arguments, retrieval, embeddings, retries, errors, and final output. Check whether each operation is a child span of the correct parent and whether you can search by tool, model, session, user-safe identifier, or error.
2. Framework and SDK coverage
List every runtime component: agent framework, model providers, retrieval layer, tool server, background workers, and evaluation jobs. A product that integrates with the framework but drops custom tool or retrieval spans will leave the most important part of your failure invisible.
3. Multi-agent representation
For multi-agent systems, verify that sessions, agent graphs, inter-agent messages, and tool failures remain searchable in one trace. If a hand-off creates a disconnected trace, debugging a wrong delegation becomes a correlation exercise across separate screens.
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4. Evaluation and annotation
Ask how a production trace becomes a dataset example, how human feedback is stored, whether evaluators can run online or offline, and how a prompt or model change is compared with a baseline. Braintrust emphasizes this workflow; Phoenix and Langfuse document datasets and experiments; LangSmith connects traces with feedback and online evaluations.
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Prompts, retrieved documents, tool arguments, and user identifiers can contain sensitive data. Compare redaction controls, access roles, retention settings, hosting location, and deletion behavior. For self-hosted options, include storage, upgrades, backups, and on-call work in the cost calculation.
6. Production operations
Check dashboards, alert conditions, sampling controls, incident links, and the ability to compare latency, errors, cost, and evaluation scores by release or model. A trace viewer that cannot surface regressions in production will force engineers to discover them through user complaints.
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- Define the task boundary. Create one root trace per user request, job, or scheduled task. Attach a stable, privacy-safe identifier and record the deployment, agent version, model, and environment.
- Instrument every meaningful step. Create child spans for model generations, tool calls, retrieval queries, embedding requests, API calls, validation, retries, and final response assembly. Record start and end times, status, and bounded error details.
- Preserve decision context. Store the selected tool, validated arguments, retrieved-item identifiers, and the model output that led to the next step. Redact secrets and unnecessary personal data before export.
- Propagate context across services. Carry trace context through queues, workers, tool servers, and agent hand-offs. Without propagation, a single task fragments into unrelated traces.
- Choose a sampling policy. Keep complete traces for failures, evaluator examples, and new releases while sampling routine successes according to your storage and privacy budget. Document the policy so a missing span is not mistaken for a successful no-op.
- Connect traces to quality signals. Attach user feedback, human labels, automated evaluator scores, latency, and cost or usage data to the relevant run. Store the evaluator version and dataset or experiment identifier.
- Build operational views. Create dashboards for error rate, p95 or equivalent latency views, tool failure counts, retrieval misses, model usage, and evaluation scores. Alerts should identify a threshold, release, model, or tool dimension that an engineer can act on.
- Turn failures into regression tests. Save representative traces and annotations as a dataset. Re-run them after prompt, model, retrieval, or tool changes and compare quality as well as latency and cost.
- Test the failure path. Deliberately trigger tool timeouts, malformed arguments, empty retrieval results, provider errors, and evaluator failures. Confirm that the trace remains complete and that sensitive payloads are not exposed.
Debugging a wrong agent step
Start at the final outcome and walk upward through the trace rather than reading logs chronologically in separate systems.
- Classify the failure. Decide whether the output is factually wrong, incomplete, unsafe, too slow, too expensive, or operationally failed. This determines which spans and evaluator scores matter.
- Find the first divergence. Compare the failing trace with a successful trace or a baseline dataset example. Identify the earliest point where the retrieved context, tool choice, argument, or model instruction differs.
- Check inputs before blaming the model. Inspect retrieval filters, embedding responses, stale records, tool schemas, authorization results, and truncated context. An incorrect input can make a later model decision appear irrational.
- Inspect control flow. Look for retries, loops, hand-offs, and fallback branches. Confirm that a timeout or partial tool result was marked as an error instead of being silently passed to the next model call.
- Use evaluation evidence. Apply the relevant automated evaluator or human annotation to determine whether the change fixes the class of failure, not just the single trace.
- Ship a guarded change. Compare the revised prompt, model, retrieval setting, or tool schema on a fixed dataset, then monitor production traces and alerts after release.
Common pitfalls and remedies
Only logging the final answer
A final response cannot explain a bad tool choice or missing retrieval result. Add spans for every model, tool, retrieval, embedding, and API operation.
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Recording unstructured blobs
Large undifferentiated logs are hard to query and expensive to retain. Use consistent fields for operation name, status, latency, model, tool, session, release, and evaluator metadata, while keeping full payloads subject to redaction and size limits.
Sampling away the evidence
Uniform sampling can discard rare but serious failures. Bias retention toward errors, low evaluation scores, new releases, and traces selected by users or evaluators.
Ignoring non-LLM latency
Retrieval, embedding, network, and tool delays often dominate end-to-end time. Measure them as first-class spans so optimization work targets the real bottleneck.
Conflating observability with evaluation
A trace tells you what happened; an evaluation tells you whether the result met a quality criterion. Select a product or architecture that supports both when regression prevention matters.
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Self-hosting can satisfy data-control requirements but adds upgrades, backups, scaling, support, and incident response. Assign those responsibilities before approving the architecture.
Which tool should you choose?
| Your priority | Best starting point | Reason |
|---|---|---|
| LangChain or LangGraph application | LangSmith | Framework-aligned tracing with dashboards, alerts, feedback, and online evaluations. |
| Self-hosting, open source, and detailed cost or latency inspection | Langfuse | Self-hostable platform that traces LLM and non-LLM calls and includes sessions, graphs, prompts, datasets, and evaluations. |
| Tracing connected to datasets and controlled experiments | Arize Phoenix | Documentation places tracing, evaluation, experiments, prompts, access control, retention, and self-hosting in one platform. |
| Human annotation and continuous behavior improvement | Braintrust | Combines trace logging, pattern discovery, annotation, datasets, experiments, online scoring, deployment, and monitoring. |
| Freedom to change analysis backends | OpenTelemetry plus a backend | Common instrumentation and collector model reduces vendor lock-in, while a separate product supplies agent-specific analysis. |
There is no universal winner independent of framework, data policy, evaluation maturity, and production scale. Instrument a representative workflow, verify the depth of the resulting trace, and make the final decision on the evidence your own failure modes require.
Quick Recap
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