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Langfuse helps you investigate an LLM application by recording the steps behind a response—not just the response itself. A trace can group model generations, tool calls, retrieval, and other work into one inspectable request, so you can see where a run went, how its steps relate, and where to look when the outcome is unexpected. For multi-turn interactions, a session can group related traces together.
The practical starting point is to instrument the parts of your workflow that explain its behavior: model calls, agent decisions, tools, retrieval, and important custom steps. Choose a capture route that fits your existing stack, then inspect an individual trace to connect the final answer to the work that produced it.
Why trace an agent run instead of inspecting only its answer?
An LLM response is an outcome, not an explanation. In an agent workflow, the same unexpected answer might follow from a model generation, a tool result, retrieved context, application logic, or a sequence of those steps. If you record only the final text, those possibilities are difficult to distinguish.
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Langfuse describes generative AI systems as “inherently non-deterministic.” That matters operationally: a prompt and input that once produced a good result may not be enough to explain a later run. A trace provides a structured record of the steps and their relationships, giving you evidence to inspect rather than requiring you to infer the entire execution from the answer.
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Observability is the broader ability to understand what a system is doing from its recorded data; tracing is one way to follow a particular request through its work. In Langfuse, the record can include prompts, model output, token usage, latency, tool calls, retrieval, and links between steps. The value depends on instrumenting the steps that matter to your application: a model-only record cannot explain an unrecorded tool or custom operation.
What are traces, observations, and sessions?
Langfuse uses three related concepts: observations describe individual units of work, traces group work for a request or operation, and sessions connect related traces across a user interaction. Keeping those levels distinct makes it easier to understand both one run and a longer conversation.
Observations: the individual steps
An observation is a recorded step. It can represent a model call, a tool action, retrieval, or a generic piece of application work. Observations can be nested, which lets a trace represent a workflow with parent and child steps rather than a flat list that loses structure.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors| Observation type | What it represents | Example in a workflow |
|---|---|---|
generation |
An AI model generation. | A model produces a response or decides what to do next. |
agent |
A component that decides application flow and may use tools. | An agent selects an action based on the conversation and available tools. |
tool |
An action such as a function or API call. | A function runs after the agent selects it. |
retriever |
A retrieval operation. | The application looks up material to provide context. |
chain |
A link between steps. | A workflow component connects stages of processing. |
span |
A generic unit of work with a duration. | A custom operation that does not fit a more specific type. |
event |
A discrete event. | An occurrence worth recording that is not itself a duration-bearing operation. |
Use the type that best describes what the component does. For example, record an API action as a tool observation, rather than treating the tool’s work as if it were another model generation. This vocabulary helps a reader distinguish what the model did from what the surrounding application did.
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Traces: the work for one request
A trace groups observations for a request or operation. Depending on how you instrument the application, it can show a chain of related steps—such as an agent operation, a retrieval operation, a generation, and a tool action—instead of leaving those records disconnected. A trace is useful when investigating a particular run because it gives the steps a shared context.
Sessions: related traces across an interaction
A session can group multiple traces belonging to one user interaction, such as a multi-turn chat. Propagate a stable sessionId across the observations you want associated with that interaction. Langfuse documents session identifiers as US-ASCII strings shorter than 200 characters; identifiers longer than that are dropped.
A session and a trace answer different questions. The trace helps you follow one request through its steps; the session helps you connect that request with other traces from the same interaction. A stable session identifier is particularly useful when a conversation spans several turns, each of which generates its own trace.
How do I trace agent calls instead of guessing?
Start by mapping the execution path you need to understand, then record its meaningful components using the capture method suited to your stack. The following workflow is a practical way to plan instrumentation without assuming every application uses the same framework or deployment model.
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- Choose the unit you want to investigate. For a single request, identify the operation that should become a trace. For a multi-turn interaction, decide how your application will supply the same stable session identifier across the related turns.
- List the steps that can change the outcome. Include model generations, agent decisions, tool or API calls, retrieval, and custom application work where those steps affect behavior. If a step is absent from the captured data, a trace cannot show what happened inside it.
- Choose observation types that describe those steps. Use specific types such as
generation,agent,tool, andretrieverwhere they fit; use a genericspanfor other duration-bearing work and aneventfor a discrete occurrence. - Select a capture route for your current stack. Langfuse documents Python and JavaScript SDKs, framework and library integrations, OpenTelemetry, and gateway-based capture such as LiteLLM. Its getting-started material also covers OpenAI clients, Vercel AI SDK, LangChain, and custom tracing. If your application already emits OpenTelemetry spans, the OpenTelemetry route is an option to investigate.
- Configure the destination and deployment model. The setup material covers Langfuse Cloud and self-hosting, and says the instrumentation code is the same for both. For Cloud, select an appropriate base URL for the region and service arrangement you use; verify current endpoint availability and your organization’s data-residency requirements before configuring it.
- Run a representative workflow and inspect its trace. Check whether the recorded steps form the structure you intended and whether important model, tool, retrieval, and custom work appears. If a step is missing or disconnected, revisit the instrumentation for that component and its relationship to the surrounding work.
- Account for how your process exits. Langfuse describes SDKs that queue events locally and send them asynchronously in batches. For a short-lived process, verify that its telemetry has time to flush before shutdown, using the current SDK guidance for your implementation.
SDK packages, initialization details, framework support, and regional endpoints can change. Use Langfuse’s current documentation for the exact installation and initialization instructions for your language and integration; do not assume that a code sample written for one SDK or version applies unchanged to another.
Which instrumentation route should I choose?
There is no single route that is best for every application. Choose based on the components you already run and the amount of custom instrumentation needed to capture the workflow you actually want to inspect.
| Route | When it may fit | What to check |
|---|---|---|
| Python or JavaScript SDK | Your application can add Langfuse instrumentation directly. | Current package and initialization instructions, plus how each relevant step is recorded. |
| Framework or library integration | Your application uses a framework or library covered by a Langfuse integration. | Whether the integration captures the steps you need and whether additional custom instrumentation is required. |
| OpenTelemetry | Your application already emits OpenTelemetry spans, or you want to use that instrumentation path. | How existing spans map to the trace structure and whether agent, tool, and retrieval work is represented adequately. |
| Gateway-based capture | Your model requests pass through a gateway such as LiteLLM. | Whether gateway logging covers enough of the workflow; tool calls, retrieval, and custom application steps may need separate instrumentation. |
| Custom tracing | You need to record application-specific work that an existing integration does not represent. | Which operations should be observations, how they should be related, and how telemetry is flushed in your process. |
These are implementation choices, not performance rankings. Langfuse’s documentation describes the available capture routes but does not provide a neutral benchmark comparing them. A model gateway can be a convenient place to capture model traffic, for example, but it does not by itself guarantee a complete account of the application’s tools, retrieval, or custom logic.
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Does tracing slow down the application?
Langfuse says its SDKs send trace data asynchronously in the background, queue events locally, and flush them in batches. Its overview answers its latency FAQ by saying tracing does not affect application response time. Treat that as a vendor statement about product behavior, not as an independently measured guarantee for every integration or workload.
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The operational detail to check is whether the process remains alive long enough for queued telemetry to flush, especially for short-lived jobs or functions. Follow the current SDK instructions for your environment and validate that expected observations arrive; the general asynchronous design does not remove the need to handle process shutdown appropriately.
What deployment and data-residency choices should I make?
Langfuse documents both Cloud and self-hosting, with the same instrumentation code for either deployment model. The choice is therefore not necessarily a rewrite of your tracing calls, but it does affect where you operate the service and which endpoint or hosting arrangement your organization can use.
The getting-started material lists regional Cloud base URLs, including EU, US, Japan, and HIPAA-specific endpoints. Availability and suitability can change, so verify the current endpoint details and the requirements that apply to your data before selecting a region. The existence of an endpoint does not by itself determine whether a particular workload meets an organization’s legal, contractual, or compliance obligations.
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What does Langfuse Core cost, and what limits apply?
As listed on Langfuse’s pricing page on October 26, 2026, the Core plan includes 100,000 usage units, charges $8 per additional 100,000 units, provides 90 days of historical data access, and lists ingestion throughput of 4,000 requests per minute. These are vendor-published Core terms, not limits that should be generalized to other plans or assumed to remain unchanged.
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| Core item | Vendor-listed term on October 26, 2026 |
|---|---|
| Included usage | 100,000 usage units |
| Additional usage | $8 per additional 100,000 units |
| Historical data access | 90 days |
| Ingestion throughput | 4,000 requests per minute |
The same pricing page distinguishes tiers by history retention, feature access, and throughput. Review the current plan details before estimating costs or selecting a tier: the Core figures above describe that plan’s listed terms on the stated date, not a universal allowance or a future guarantee. Langfuse lists features including agent traces and graphs, sessions, user tracking, token and cost tracking, native integrations, SDKs, OpenTelemetry, LiteLLM proxy logging, and custom API capture; check the current plan comparison to confirm which features and limits apply to the tier you would use.
What should I check when a trace does not explain a run?
A trace is only as informative as the work your application records and connects. If the final result remains difficult to explain, use the missing context as a debugging signal rather than assuming the trace is a complete execution log.
- A model result appears, but no tool work does: Check whether tool execution is instrumented separately from generation. A model call and the action taken by your application are different steps.
- Retrieval is absent: Confirm that the retrieval component is captured as an observation. Without it, a trace may show the model’s response without showing the retrieved material that informed it.
- Steps appear disconnected: Review how observations are nested or associated with the trace. A set of separate records is less useful for understanding causality than a trace that expresses how the steps relate.
- Conversation turns do not group together: Check that the application propagates the same valid
sessionIdacross observations intended to belong to the interaction. A changed or overlong identifier will not provide the intended grouping. - Recent work is missing after a process exits: Check the current SDK guidance for flushing queued data, particularly for short-lived processes. Asynchronous, batched delivery means shutdown behavior deserves attention.
- Usage or history differs from expectations: Compare your assumptions with the current pricing page for the selected tier. Limits and retention differ across plans and may change over time.
For a useful debugging record, capture enough context to distinguish the stages that can alter a result. That does not mean instrumenting every line of code: prioritize the model, agent, tool, retrieval, and custom operations whose behavior you may need to understand, then expand only where a trace leaves a meaningful question unanswered.
Frequently Asked Questions
Can Langfuse trace a multi-turn chat as one record?
It groups related traces under a session rather than requiring every turn to be treated as a single trace. Your application needs to propagate the identifier consistently so the turns can be associated.
Can I use Langfuse if my app already has OpenTelemetry?
Yes. Langfuse documents an OpenTelemetry capture path, which is worth considering when your application already emits spans. Check the current guide to understand how those spans map to the trace structure you need.
Can I self-host Langfuse instead of using Cloud?
Langfuse documents both self-hosting and Cloud, and says the instrumentation code is the same for both. The deployment decision still requires checking current setup details and your organization’s operational and data-residency needs.
Does Langfuse provide a benchmark showing which integration is fastest?
The available product documentation describes capture options, but it does not establish a neutral comparative performance benchmark across them. Treat any route selection as an architectural fit decision rather than a documented speed ranking.
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