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Langfuse, Comet Opik, and Arize Phoenix are the strongest open-source-oriented alternatives for tracing AI agents. Each can expose the chain of model calls, tool executions, retrieval, and application logic that turns an agent failure into something diagnosable. They differ in trace model, instrumentation approach, deployment choices, evaluation features, and license boundaries. Choose by the workflow you need to inspect and the infrastructure your team can operate—not by the phrase “open source” alone.
The short list
The three projects below are credible starting points for an evaluation. Their capabilities and legal terms can change, so read the current repositories and license files before committing to a production architecture.
| Project | Best fit | Tracing and workflow coverage | Deployment and licensing notes |
|---|---|---|---|
| Langfuse | LLM engineering and application observability | Traces model calls and related logic, including retrieval and agent actions. | Self-hosting documentation covers Docker Compose for local use and Kubernetes or other infrastructure for production. The repository states that it is MIT licensed except for ee folders. |
| Comet Opik | Agent tracing combined with evaluation and production monitoring | Multi-step traces can include LLM calls, tool executions, and retrieval; the project also presents prompt management, evaluations, and monitoring. | The README describes the platform as Apache-2.0 licensed and self-hostable. Confirm the current repository and license before adoption. |
| Arize Phoenix | OpenTelemetry-based tracing and experiment-oriented workflows | Runtime tracing, evaluations, datasets, experiments, prompt management, and integrations with many agent frameworks and model providers. | The project describes itself as open source and self-hostable and can run locally, in containers, or in the cloud. Check the repository’s actual LICENSE and component terms for legal decisions. |
These are not interchangeable dashboards. A useful comparison asks how each system represents parent and child spans, how you instrument your framework, what can be exported, which evaluation workflows are available, and who will run the storage, upgrades, access controls, and backups.
Why agent tracing is different from ordinary application logging
An AI agent usually performs a sequence rather than one request-response operation. It may call a language model, select a tool, retrieve documents, call the model again, retry after an error, and finally produce an answer. A single “request failed” line cannot show which step caused the failure or whether the final answer was based on the intended evidence.
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Agent observability records that sequence as a trace. A trace normally has a root operation for the user request and nested spans for model calls, tool executions, retrieval, prompts, application functions, retries, and errors. The important property is the relationship between those spans: investigators should be able to move from the final response to the exact child operation, input, output, timing, and exception that shaped it.
- Model calls: capture which invocation occurred, its role in the workflow, and the resulting output or error.
- Tool executions: show the selected tool, arguments, return value, and whether the tool failed or timed out.
- Retrieval: preserve the retrieval step and the context passed onward, subject to your privacy and redaction policy.
- Control flow: make retries, branches, handoffs, and nested agent calls visible instead of flattening them into unrelated log lines.
- Evaluation context: connect a trace to a score, dataset item, experiment, or human review when you are testing quality.
Without this context, teams tend to debug symptoms: a slow endpoint, a hallucinated answer, or a tool error. With it, they can ask whether the wrong tool was selected, retrieval returned irrelevant material, a retry changed the prompt, or a provider response was malformed.
Langfuse: a self-hostable LLM observability option
What it covers
Langfuse’s repository presents it as an open-source LLM engineering and observability platform. Its tracing model covers LLM calls and related logic such as retrieval and agent actions. That makes it a natural candidate when your main need is to inspect application behavior around model invocations rather than only collect infrastructure metrics.
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Self-hosting routes
The Langfuse self-hosting documentation describes Docker Compose as a local-use and testing route, with Kubernetes or other infrastructure options for production. This gives a team a practical progression: start with a local instance, instrument a representative agent, then design a production deployment around durable storage, secrets, network access, backups, and upgrades.
Self-hosting is not the same as delegating operations to a hosted service. The documentation explicitly places infrastructure responsibility on the operator for production self-hosted deployments. Your runbook therefore needs to cover database and object-storage capacity, monitoring of the observability stack itself, recovery from a failed upgrade, retention, and access to sensitive prompts and tool data.
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License boundary to inspect
The repository says the MIT license applies except to ee folders. Treat that as a repository-scope detail, not as a blanket statement that every feature is governed identically. Identify the exact components and features you intend to deploy, inspect their current terms, and involve legal or procurement reviewers when license obligations matter.
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- Your team wants an LLM-focused trace view for calls and adjacent application logic.
- You need a documented path from local Docker Compose testing to a Kubernetes-style production deployment.
- You can operate the infrastructure and want control over where prompts, retrieval context, and outputs are stored.
- You are prepared to separate open-source-core capabilities from any
eecomponents during architecture and licensing review.
Comet Opik: multi-step traces plus evaluation workflows
What it covers
Comet Opik’s README describes tracing that includes LLM calls, tool executions, and retrieval in multi-step traces. It also presents evaluation, prompt management, and production monitoring as parts of the platform. That combination is relevant when the observability question is not only “what happened?” but also “how did this version perform across a repeatable test set?”
For an agent, evaluate whether a trace keeps the sequence intact when the workflow branches, retries, or invokes several tools. Check whether attributes that matter to your debugging process—tool name, arguments, retrieved context identifiers, model settings, status, and error details—remain attached to the correct span. The project description establishes the multi-step tracing goal; your own integration test must establish how completely your particular framework and code path are represented.
Self-hosting and platform scope
Opik describes itself as self-hostable across its full platform. The README also states an Apache-2.0 license. Those are useful starting points for an evaluation, but project claims and repository contents can change. Read the current README, LICENSE, dependency notices, and release documentation before making a compliance or redistribution decision.
Local and self-hosted deployment can be attractive for teams that cannot send prompts, retrieved documents, customer identifiers, or tool payloads to an external service. It also means you must plan the operational details yourself: persistent storage, scaling, authentication, retention, backups, upgrade testing, and incident access. Ask for a deployment diagram and a data-flow inventory before exposing production traffic.
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- Your roadmap treats evaluation and production monitoring as part of the same agent lifecycle as tracing.
- You need traces that explicitly represent tools and retrieval, not merely a model request and a final response.
- You want to compare prompt or agent changes against datasets and experiments, subject to what your chosen version supports.
- You prefer a platform whose README describes Apache-2.0 coverage and full-platform self-hosting, while still verifying the current terms.
Arize Phoenix: OpenTelemetry-oriented tracing and experiments
What it covers
The Arize Phoenix repository describes runtime tracing based on OpenTelemetry and includes evaluation, datasets, experiments, prompt management, and integrations with popular agent frameworks and model providers. This makes Phoenix especially relevant when your organization already favors OpenTelemetry concepts or wants an instrumentation layer that can span several frameworks.
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OpenTelemetry-based instrumentation can be a strong fit for teams that want a standard span model and a clearer boundary between application code and the observability backend. The practical test is still yours: instrument one complete agent flow and inspect whether the emitted spans preserve parent-child relationships, attributes, errors, and timing across model calls, retrieval, tools, and retries.
Deployment choices
Phoenix documents local, container, and cloud operation. A local run is useful for validating instrumentation without changing production traffic. A container deployment can fit an existing platform team, while a cloud arrangement may reduce the amount of infrastructure you operate. Whichever route you select, decide where trace payloads are stored, how long they are retained, who can view them, and how credentials are rotated.
License verification
The repository presents Phoenix as open source and self-hostable, but that description should not be turned into an automatic OSI-approved classification or a legal conclusion. Consult the repository’s actual LICENSE and the terms for individual components before approving it for a regulated or redistributed product. Keep this verification separate from the technical evaluation so a promising trace workflow is not mistaken for a completed legal review.
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- Your instrumentation strategy is already centered on OpenTelemetry or OpenInference-style concepts.
- You need runtime traces alongside datasets, experiments, and evaluations.
- Your agents use several supported frameworks or providers and you want to assess the breadth of available integrations.
- You need a deployment that can begin locally and later move to containers or cloud infrastructure.
Feature and operating comparison
The table highlights the differences that should shape a proof of concept. “Not stated” means the supplied project evidence does not establish a comparable detail; it is not a claim that the feature is absent.
| Decision area | Langfuse | Comet Opik | Arize Phoenix |
|---|---|---|---|
| Primary orientation | LLM engineering and observability | Agent tracing, evaluation, prompt management, and production monitoring | OpenTelemetry-based tracing, evaluation, datasets, experiments, and prompt management |
| Trace content described by project evidence | LLM calls plus related logic such as retrieval and agent actions | Multi-step LLM calls, tool executions, and retrieval | Runtime traces; exact span coverage should be validated in your instrumentation test |
| Evaluation and experiment emphasis | Not stated in the supplied project description | Evaluations are presented as part of the platform | Evaluations, datasets, and experiments are presented as platform capabilities |
| Local or self-hosted path | Docker Compose for local/testing; Kubernetes or other infrastructure for production | README describes local and self-host deployment options | Can run locally, in containers, or in the cloud |
| License statement in project evidence | MIT except for ee folders, according to the repository |
Apache-2.0, according to the README | Repository describes it as open source; verify LICENSE and component terms |
| Operational responsibility | Operator responsibility for production self-hosting is explicitly noted | Self-hosting requires your team to operate storage, security, and upgrades | Responsibility depends on the selected local, container, or cloud arrangement |
How to run a fair evaluation
Do not choose a platform from a feature checklist alone. Use the same agent workflow, data-handling rules, and success criteria for every candidate.
- Define the workflow. Select a representative path that includes a model call, at least one tool, retrieval, a retry, and a deliberate failure. Include the final response so you can follow causality from input to outcome.
- Instrument every meaningful step. Create a root trace for the user request and child spans for planning, model calls, retrieval, tool execution, retries, and final synthesis. Record only attributes your privacy policy permits.
- Inspect trace semantics. Verify parent-child relationships, ordering, timestamps, status and error fields, and whether a failed child span remains visible when the application recovers.
- Check payload usefulness. Confirm that the trace contains the identifiers and metadata your team actually needs: model or provider identity, tool name, retrieval reference, prompt version, and application correlation ID. Do not assume every integration captures all of them.
- Test export and ingestion. Determine whether traces can be exported in a format your data warehouse, incident tooling, or analytics pipeline can consume. Test both a successful trace and one containing errors and nested retries.
- Exercise evaluation workflows. If quality measurement matters, attach representative examples to evaluations, datasets, or experiments where the platform supports them. Check how reviewers find a failing example and open its underlying trace.
- Measure operational effort. Deploy each candidate in the environment you would actually operate. Record configuration steps, secret management, storage dependencies, upgrade procedure, backup and restore behavior, and the work required to restrict access.
- Run privacy checks. Trace prompts, outputs, retrieved documents, tool arguments, and user identifiers through the complete data flow. Test redaction, retention, deletion, and role-based access before sending realistic sensitive data.
- Document the decision. Keep a scorecard with evidence from the same workflow. Separate observed behavior from project documentation, and record the version and license files reviewed.
Deployment planning for a self-hosted stack
Local proof of concept
Start with a non-sensitive agent and a local deployment. Langfuse documents Docker Compose for this purpose; Opik documents local options; Phoenix documents local operation. The goal is not to estimate production performance. It is to confirm that your instrumentation emits the trace shape you need and that developers can find a failure quickly.
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Production topology
For production, separate the agent service from the observability service and its persistent storage. Define network paths, authentication, encryption, retention, backups, and upgrade windows. Decide whether traces need high availability or whether a temporary observability outage may degrade gracefully while the agent continues serving requests.
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Data minimization
Agent traces can contain the most sensitive material in your system: user prompts, model outputs, retrieved documents, credentials accidentally passed to tools, and personal data. Instrumentation should support a deliberate choice about which fields are recorded. Redact secrets before span export, use identifiers instead of raw documents where possible, and set retention by data class rather than keeping everything indefinitely.
Capacity and cost control
Trace volume grows with agent steps, retries, and payload size. Establish sampling or payload limits only after confirming that they do not remove the evidence needed for incident investigation. Keep a full-fidelity path for controlled debugging and a lower-volume policy for routine traffic if your platform and compliance rules allow it.
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Logging only the final answer
A final response cannot explain a bad retrieval result or an incorrect tool call. Add spans around each model, retrieval, and tool boundary, then verify the nesting with a forced failure.
Flattening retries into one event
When retries overwrite the original error, you lose the reason the agent changed course. Preserve each attempt as a child span and mark the final outcome separately.
Assuming an integration captures custom code
Framework support does not guarantee visibility into your wrappers, business rules, or proprietary tools. Add explicit instrumentation around custom functions and inspect a trace generated by the real application path.
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Treating open source as a single legal category
Repository scope, enterprise directories, dependencies, and component licenses can differ. Review the exact commit or release, the LICENSE files, and the features you plan to deploy. Langfuse’s stated exception for ee folders and Phoenix’s need for direct LICENSE verification illustrate why a name alone is insufficient.
Sending sensitive payloads by default
Unfiltered prompts and tool arguments can expose secrets. Begin with synthetic or redacted data, define field-level policies, and test that redaction occurs before export rather than only in the user interface.
Evaluating with a happy-path demo
A single successful question does not test agent observability. Include a tool timeout, irrelevant retrieval, malformed provider output, retry, and partial failure. The winning system is the one that leaves an actionable trace for those cases, not merely an attractive success screen.
Which option should you choose?
Choose Langfuse when
Your immediate problem is understanding LLM-centric application behavior and you want a documented self-hosting route that begins with Docker Compose. Give extra attention to which capabilities are in the MIT-covered core and which are in ee folders.
Choose Opik when
You want multi-step agent traces alongside evaluation, prompt management, and production monitoring, and you value the project’s stated Apache-2.0, full-platform self-hosting position. Validate the current release, integrations, and operating requirements with your workflow.
Choose Phoenix when
Your team prefers OpenTelemetry-oriented instrumentation or needs tracing tied to datasets, experiments, evaluations, and broad framework/provider integrations. Confirm the repository license and component terms before making a legal classification.
Run a three-way proof of concept when
Your architecture is still evolving, multiple teams own different agent frameworks, or license and data-residency requirements are material. A short, controlled comparison using the same traces and failure cases will reveal more than a generic feature matrix.
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Practical selection checklist
- Can the system show the complete parent-child sequence for model calls, tools, retrieval, retries, and failures?
- Can your framework and custom code emit the attributes needed for debugging?
- Can you export or ingest traces in the format required by your data and incident systems?
- Do evaluations, datasets, experiments, or prompt workflows match the way your team improves agents?
- Is local, container, cloud, or Kubernetes deployment compatible with your data-residency policy?
- Who owns storage sizing, backups, upgrades, authentication, retention, and incident response?
- Have you reviewed the exact repository LICENSE files and any enterprise or component boundaries?
- Can you redact prompts, outputs, retrieved content, tool arguments, and identifiers before export?
- Does the system remain useful when a tool times out, retrieval is irrelevant, a provider returns an error, or a retry changes the path?
Bottom line
Start with Langfuse, Comet Opik, and Arize Phoenix, then instrument the same failure-prone agent workflow in each. Langfuse is oriented toward LLM engineering and offers documented self-hosting paths; Opik emphasizes multi-step agent traces with evaluation and monitoring; Phoenix centers OpenTelemetry-style tracing with evaluation and experiment capabilities. The best choice is the one that preserves actionable trace context, satisfies your data and license requirements, and fits the team’s capacity to run it in production.
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