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There is no universal winner. OpenSearch and Elasticsearch (Elastic Observability) add AI-agent telemetry to broad search and observability platforms, while specialist products such as Langfuse and Arize Phoenix center their workflows on traces, evaluation and experimentation. Choose among them by checking instrumentation coverage, trace detail, evaluation needs, deployment and data controls, operating model and total cost at your expected volume—not by counting feature names.
What AI-agent observability must show
Monitoring an agent with request logs alone hides the chain of work that produced an answer. A useful trace follows one execution from start to finish and relates the individual spans inside it. Those spans can represent model calls, tool executions, retrieval operations, application code, errors and other steps.
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The fields you see depend on the instrumentation path. Well-instrumented applications can expose prompts and responses, model identifiers, latency, token consumption, tool arguments and results, retrieved documents, status codes and parent-child relationships. Sensitive prompts, responses and retrieved data may require redaction or access controls before they are exported.
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- Span: an operation within that flow, such as an LLM call, tool call or retrieval step.
- Context: identifiers and relationships that let an engineer move from an answer to the exact calls and inputs that produced it.
That structure supports questions ordinary logs cannot answer reliably: Which model call consumed the tokens? Did retrieval return irrelevant documents? Which tool added most of the latency? Where did an error first appear? Did a prompt change alter the final answer?
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OpenSearch Agent Traces
What the feature is
OpenSearch documentation describes Agent Traces as an interface for exploring, debugging and monitoring generative-AI and large-language-model agent execution. The feature was introduced in OpenSearch 3.6. Its documented data path uses OpenTelemetry GenAI semantic conventions, Data Prepper for ingestion, OpenSearch indices for storage and OpenSearch Dashboards for visualization.
The interface is designed around hierarchical trace trees, directed-acyclic-graph views, timelines, span details and metrics. These views help an engineer move between the overall agent run and the operation that caused a delay, failure or unexpected result.
Components you need
The documented workflow assumes an OpenSearch cluster, OpenSearch Dashboards and Data Prepper. Confirm that the deployment you select includes compatible versions of all three components and that the Agent Traces capability is enabled. A managed service may package some components differently from a self-managed installation; verify the exact offering before planning the rollout.
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- Ingestion: Data Prepper receives and routes telemetry into OpenSearch.
- Storage: trace and span documents are retained in OpenSearch indices, subject to your index, retention and access policies.
- Analysis: Dashboards provides the Agent Traces exploration views and the surrounding logs, metrics and trace context.
What an OpenSearch implementation is good at
OpenSearch is a natural candidate when your operations team already runs OpenSearch for search, logs, metrics or application monitoring. Agent traces can live beside those signals, allowing one platform to correlate an agent run with infrastructure and service telemetry. The OpenSearch Observability Stack is positioned as an open-source, OpenTelemetry-native stack that includes traces, logs, metrics, application monitoring, dashboards and AI-agent observability.
This is a platform-fit advantage, not proof of lower cost or better performance. You still have to operate or procure the cluster, ingestion path and dashboards, size storage for trace volume and establish retention and access controls.
OpenSearch setup checklist
- Confirm versions. Use an OpenSearch release that includes Agent Traces (the documentation identifies 3.6 as the introduction) and pair it with compatible Dashboards and Data Prepper versions.
- Map your agent operations. Decide which model calls, tools, retrieval steps and application boundaries must appear as spans. Do not assume a framework exporter captures every field automatically.
- Configure OpenTelemetry export. Emit GenAI semantic-convention attributes through the supported exporter or collector path, keeping secrets and unnecessary prompt content out of telemetry.
- Route through Data Prepper. Validate that spans arrive, preserve their parent-child relationships and are written to the intended OpenSearch indices.
- Inspect a complete run. Use the tree, DAG and timeline views to verify that model, tool and retrieval operations are connected and that duration, errors and token fields are populated where your instrumentation provides them.
- Set operations policies. Apply index lifecycle, sampling, retention, role-based access and redaction rules before sending production prompts or responses.
Elasticsearch and Elastic Observability
Two documented telemetry paths
Elastic documents LLM observability through integrations that ingest provider logs and metrics, and through APM tracing based on OpenTelemetry. The tracing path can capture the model involved, duration, errors, token consumption and prompt/response interaction when the instrumentation emits those details. Provider and framework availability varies, so support must be checked for the exact SDK, language, provider and product version you intend to use.
The integrations path can be useful when a model provider already exposes operational logs or usage metrics and you want them in an existing Elastic deployment. It may not provide the same parent-child execution graph as application-level tracing unless the necessary correlation identifiers and spans are also emitted.
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The technical-preview qualification
The cited Elastic EDOT LLM use-case documentation labels its described LLM instrumentation as a technical preview. That status matters: a page listing a provider or language is not a promise of general availability, long-term compatibility or enterprise support. Treat preview instrumentation as something to validate in a non-production environment, and confirm its status again for the release you will deploy.
Elastic’s broader OpenTelemetry documentation describes OTLP intake and Elastic distributions. It also warns that some contribution-based collector and SDK approaches do not provide Elastic enterprise APM features or implementation support. An exporter that technically sends OTLP data may therefore deliver a different experience from an Elastic-supported distribution.
When Elastic is the practical fit
Elastic is a strong candidate for teams already using Elastic logs, metrics, APM and security workflows and wanting AI telemetry correlated with those signals. Existing dashboards, alerting, identity and retention practices can reduce platform sprawl. The decision still depends on whether your required provider and language combination is supported, whether the relevant instrumentation is generally available or preview, and whether the resulting schema exposes the fields your developers need.
Elastic validation steps
- List the exact combination. Record the model provider, framework, programming language, SDK version and Elastic distribution you will use.
- Check status. Determine whether the required integration or EDOT instrumentation is generally available, technical preview or supplied by a community contributor.
- Choose signals. Decide whether provider logs and metrics are sufficient or whether application-level APM spans are required to connect model, tool and retrieval operations.
- Test schema fidelity. Send representative traces and verify that token use, duration, errors, prompts/responses and parent-child relationships appear as expected.
- Confirm support boundaries. Establish which collector, SDK and dashboard components receive vendor support and which do not.
Purpose-built platforms: Langfuse and Arize Phoenix
Langfuse
Langfuse presents tracing as the debugging foundation for generative-AI applications. Its product description includes prompts, responses, token use, latency, tools and retrieval steps in traces, then connects those traces to LLM-as-a-Judge evaluation, prompt management, experiments, datasets and custom dashboards.
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Arize Phoenix
Arize describes Phoenix as an open-source tool for experimentation, evaluation and troubleshooting of AI and LLM applications. It supports OpenTelemetry and OpenInference instrumentation, which can help teams instrument applications with vendor-neutral or AI-specific conventions and inspect the resulting traces in an AI-focused interface.
The documentation result describing Phoenix was marked as an older crawl. Confirm the current release, hosting options and licensing before treating those details as current. As with any specialist tool, test the exact framework and provider adapters you need rather than assuming that OpenTelemetry support guarantees complete GenAI fields.
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Where specialists differ from broad platforms
Specialist products foreground prompt iteration, evaluation datasets, experiments and AI-quality analysis. OpenSearch and Elastic foreground a broader operational estate in which AI traces are another signal alongside infrastructure, service and application telemetry. Neither approach is automatically superior: a research-and-development team may value rapid evaluation workflows, while a production operations team may prioritize one access model, one retention policy and one incident view.
Platform comparison at a glance
| Option | Documented focus | Telemetry and workflow | Important qualification |
|---|---|---|---|
| OpenSearch Agent Traces | AI-agent trace exploration inside OpenSearch | OpenTelemetry GenAI semantic conventions; Data Prepper ingestion; OpenSearch index storage; Dashboards with trace trees, DAGs, timelines, span details and metrics | Agent Traces introduced in OpenSearch 3.6; cluster, Dashboards and Data Prepper are documented prerequisites |
| Elasticsearch / Elastic Observability | LLM logs and metrics plus APM tracing in a broader observability stack | Provider integrations and OpenTelemetry-based tracing; documented fields include model, duration, errors, token consumption and prompt/response interaction when emitted | Provider and language coverage varies; the cited EDOT LLM use-case documentation labels its instrumentation technical preview |
| Langfuse | Generative-AI tracing and development workflow | Traces for prompts, responses, tokens, latency, tools and retrieval, with evaluation, prompt management, experiments, datasets and dashboards | Capabilities described in the product overview; compare interoperability, hosting and operational controls with your existing stack |
| Arize Phoenix | AI/LLM experimentation, evaluation and troubleshooting | OpenTelemetry and OpenInference instrumentation with an AI-focused analysis experience | The documentation result was an older crawl; verify current release, hosting and licensing details |
How to choose without a misleading feature checklist
1. Start with instrumentation coverage
Write down every provider, agent framework, language and version in the proposed system. Ask whether each emits the spans and attributes you require, whether support is generally available, preview or third-party, and how upgrades are handled. A platform cannot display data that the application never sends.
2. Define the minimum useful trace
For each production request, decide whether engineers need model calls, tool calls, retrieval, errors, timing, token usage, prompts and responses. Mark which fields are mandatory, which are optional and which must be redacted. Then test a real multi-step run instead of a single model request.
3. Separate observability from evaluation
Operational observability answers whether the system is slow, failing or expensive. Evaluation asks whether the answer is correct, grounded, safe or otherwise useful. Langfuse explicitly combines traces with evaluation, prompts, experiments and datasets. With OpenSearch or Elastic, determine which evaluation functions are native, which require a separate specialist tool and which require a custom pipeline.
4. Match the existing platform
If your organization already operates OpenSearch or Elastic, shared identity, alerting, retention and on-call practices may outweigh the appeal of a separate interface. If developers spend most of their time iterating on prompts, retrieval and model quality, an AI-focused workflow may reduce friction. Validate both groups’ daily tasks with representative traces.
5. Decide where data may live
Prompts, responses, tool arguments and retrieved passages can contain personal, confidential or regulated information. Confirm whether the selected edition is self-managed or hosted, where data is stored, how access is controlled, how long traces are retained and how deletion works. These terms vary by deployment and must be checked in current vendor documentation.
6. Model the real cost
Compare ingestion and retention at your expected trace volume, plus compute, storage, sampling, support, staffing and any paid capabilities required for evaluation or enterprise controls. The available documentation does not provide aligned prices, retention rates or a defensible cross-vendor cost ranking.
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7. Weigh maturity and support
Record the release and support status of every component. In particular, do not treat the Elastic EDOT LLM page’s technical-preview instrumentation as equivalent to a generally available, fully supported integration. Apply the same scrutiny to community exporters and older documentation pages.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical rollout plan
- Choose a narrow pilot. Instrument one agent workflow that includes at least one model call, one tool and one retrieval operation, so you can test parent-child relationships rather than a flat request log.
- Capture representative cases. Include successful, slow, failed and low-quality runs. Test long prompts, tool errors, retrieval misses and model retries.
- Compare trace completeness. For each candidate, verify the same required fields, navigation from root trace to span, latency breakdown, token reporting and error context.
- Run an evaluation loop. Keep a small dataset of known cases, change one prompt, retriever or model setting and record how quality scores and operational metrics change.
- Apply data controls. Redact secrets, restrict sensitive attributes, configure retention and document who can view prompt and response content.
- Estimate production load. Measure span volume per request, sampling choices, index or storage growth and query behavior before committing to retention periods.
- Define support ownership. Assign responsibility for instrumentation upgrades, collector failures, schema changes, dashboards and incident response.
- Recheck volatile details. Before launch, confirm versions, provider coverage, preview status, hosting terms, licensing and current pricing for the exact deployment.
Common failure modes and fixes
Only the final request appears
Cause: the application exports an HTTP or framework request but does not create child spans for model, tool and retrieval operations.
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Traces exist but contain no useful GenAI fields
Cause: an exporter sends generic spans, or the provider/framework adapter does not emit token, model or prompt attributes.
Fix: check the exact semantic-convention support and adapter version; add carefully reviewed manual attributes where automatic instrumentation cannot provide them.
Parent-child relationships break across services
Cause: trace context is lost when work crosses queues, workers, tool servers or retrieval services.
Fix: propagate context through every transport and test retries and asynchronous jobs, not just synchronous calls.
Costs grow faster than expected
Cause: full prompt and response capture, high-cardinality attributes, unbounded retention or unsampled spans at production volume.
Fix: classify sensitive fields, sample deliberately, retain detailed traces for the period needed for debugging and keep aggregate metrics longer when appropriate.
A preview integration fails after an upgrade
Cause: preview or community instrumentation changes schema, provider support or compatibility without the guarantees of a generally available integration.
Fix: pin tested versions, maintain a representative telemetry test, monitor release notes and confirm support status before upgrading production.
Operations and AI teams use different systems
Cause: an AI specialist tool contains evaluation context while the incident team works in a general observability platform, with no shared identifiers.
Fix: standardize trace IDs and key deployment metadata, define which system is authoritative for incidents and link evaluation records to the corresponding production traces.
Bottom-line decision framework
Choose OpenSearch Agent Traces when you want documented agent-trace views in an OpenSearch-centered architecture and are prepared to run the required cluster, Dashboards and Data Prepper components. Choose Elastic Observability when existing Elastic operations, APM and provider integrations are valuable and the exact instrumentation you need is supported at an acceptable maturity level. Choose a purpose-built platform when prompt iteration, evaluation, datasets and experimentation are the center of the team’s work, while confirming interoperability with production operations.
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Whichever route you take, make the decision with a pilot that exercises real model, tool and retrieval chains. Verify trace completeness, evaluation workflow, data handling, support status and production economics at your own volume. Documentation establishes the available product paths; it does not establish a universal performance, quality or price winner.
Quick Recap
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