Contrast Langfuse sessions with traces for a multi-turn chat product

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A trace is one end to end request through your backend (one user turn). A session is a grouping of related traces, identified by a sessionId you set on each trace. For a chat product you set sessionId = conversation_id so every turn of one conversation lands in the same session. You filter by session to inspect a full conversation; you filter by trace to inspect a single turn. Sessions are also how per-conversation cost is aggregated.

Your answer

A **trace** is one end to end request through your backend: one user turn, the retrieval and LLM calls it triggered, and the response. One user message plus the model's reply usually produces one trace. A **session** is a grouping of related traces. You create a session by giving multiple traces the same `sessionId`. Langfuse rolls them up so the UI shows the full conversation as a single grouped view. For a chat product, the natural pattern is: every time the user sends a message, your backend creates a new trace and sets `sessionId = conversation_id` (the same id you store in your database for that chat). Turn 1, turn 2, turn 3 all carry the same sessionId, so Langfuse groups them into one session. **Which one do you filter by?** Filter by **session** when you want to debug 'why did this conversation go sideways at turn 12?' You see all 12 turns in order. Filter by **trace** when you want to debug 'why was turn 7 specifically slow or wrong?' You drill into one turn's spans. Sessions are also where Langfuse aggregates per-conversation cost: total tokens and dollars across every turn of a session.

**Trace and session are the two grouping levels** Langfuse exposes for tying related work together. - A **trace** is one end to end backend request: a single user turn through your stack. It contains every span (retrieval, LLM calls, tool calls, post-processing) for that one turn. - A **session** is a grouping of traces that share a `sessionId`. You set the sessionId on each trace; Langfuse groups them in the UI. For a multi-turn chat product, the canonical wiring is `sessionId = your_conversation_id`. Every turn of conversation 'abc-123' carries `sessionId = 'abc-123'`. Langfuse then renders the whole conversation as a single session view: turns 1 through N in order, total cost, total duration, per-turn judge scores. Filter choice: - **Filter by trace** when debugging a single turn. 'Turn 7 returned a wrong answer; let me inspect its retrieval and prompt.' You see one trace's full span tree. - **Filter by session** when debugging a conversation arc. 'The conversation degraded around turn 12; show me the history.' You see every turn in sequence with summary stats. Sessions are also the aggregation unit for per-conversation cost, per-conversation judge averages, and 'top 10 most expensive conversations' dashboards. Without sessionId set, every turn is an isolated trace and you lose conversation-level analytics. The sessionId you choose should be a stable id from your own system (the conversation row id in your DB). Do not generate a fresh sessionId each turn; that breaks the grouping. Set it on every trace the conversation produces, including async tool calls.

TL;DR

Trace = one user turn (one request). Session = group of traces sharing a sessionId (full conversation). Filter by session for chat history, trace for one turn.

Memory aid

Easy to grasp

Imagine reading a play. Each line a character speaks is like a trace, one back and forth. The whole scene with all the lines together is the session. If you want to understand the plot, you read the scene. If you want to debug a specific awkward line, you focus on just that line. In a chat product, each user message and the assistant reply is one trace; the whole conversation of dozens of messages is one session. You tell Langfuse which session a turn belongs to by setting a sessionId, usually your own conversation_id from the database.

Key concepts

Observability & Tracing

Concept explanation~2 min read

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.

Langfuse exposes two grouping levels for organizing telemetry: traces and sessions. They look like the same primitive at first (both group spans, both have IDs, both appear in the UI) but they answer different questions. Confusing them, or forgetting to wire sessions correctly, breaks every conversation-level analytic in a multi-turn chat product.

This walkthrough explains what each level actually represents at the data-model level, how the sessionId wiring works in practice, what changes in your debugging workflow when you filter by one versus the other, and the operational patterns (propagation, granularity choice, agent-loop handling) that keep the abstraction useful as a product grows.

Mental model: trace is the request unit (one user turn through your backend). Session is the user-arc unit (a conversation, identified by a sessionId you control). Set sessionId to your own conversation_id and the two levels click into place.

The data model: trace and session as nesting levels

A trace contains spans

A Langfuse trace is one end to end backend request. Inside a trace are spans (and generations, which are LLM-specific spans). For a typical chat turn, the trace might contain:

A retrieval span (vector DB query + reranker).
An LLM generation span (prompt to Claude or GPT).
A tool-call span (if the model invoked a tool).
A post-processing span (formatting, redaction).

All of these nest under the one trace.

A session contains traces

A session is one level above. It is a grouping of traces that share a sessionId attribute. Sessions are not a separate data type with their own spans; they are a view over traces.

The sessionId is yours to choose

Langfuse does not generate sessionIds for you. You set them in the SDK call when you create a trace. The recommended pattern for chat products is sessionId = your_conversation_id. That ties Langfuse sessions directly to rows in your application database, so 'user reported a bad conversation, id abc-123' becomes 'open Langfuse session abc-123' in one step.

Relationship to userId

UserId is a separate attribute. One user has many conversations, so one userId maps to many sessionIds. Set both: userId for user-level analytics ('top users by cost'), sessionId for conversation-level analytics ('top conversations by length').

Wiring sessionId in practice

When to filter by trace versus session

Granularity decisions and agent-loop handling

Situations where this technique stops working.

2–4 min · Everything important, quickly.

Real products, models, and research that use this idea.

Langfuse 3.x exposes sessions as a first-class view; the docs recommend `sessionId = conversation_id` for chat products.
Chat-first products like Perplexity, Notion AI, and Claude.ai map conversation rows in their DB to Langfuse sessionIds 1-to-1.

What an interviewer would ask next. Try answering before peeking at the approach.

QHow would you compute per-conversation judge score in Langfuse?

Attach judge scores to each per-turn trace as usual; Langfuse aggregates them at the session level. The session view shows mean/median judge score across turns; you can also query the API for arbitrary aggregations.

2 more follow-ups an interviewer would ask next. Sign in to reveal them.

Red flags & common mistakes

The phrases that signal junior thinking. Click to expand.

Most common mistake

Forgetting to set sessionId on each turn. Without it, every turn is an island and the conversation never groups.

60 second bullets to scan on the way to the call.

What a trace is (one user turn / one request)
What a session is (group of traces sharing sessionId)

Primary sources. Browse if you want the original framing.