Use case / chat agents

Ship chat agentsyour userskeep talking to

Chat-like agents are multi-turn, stateful, and stubbornly hard to debug. Langfuse traces every session end to end. See where a conversation went sideways, what each turn cost, and whether users are satisfied.

Start free Talk to sales Chat agent quickstart →

Where is my payout for last week?

User · 0.9s

Your payout for 12–18 May was $248.10.

Agent · retrieve_payouts · 0.4s

That's not what I asked. Last week.

Frustration true · flagged

You're right — 20–26 May: $412.60, settling tomorrow.

Agent · retry_payouts_v2 · 2.1s · helpfulness 0.92

Resolved

yes

Trace the full session, not one call

Chatbot trace with agent steps, tool calls, and a highlighted user disagreement score

Identify where conversations go sideways

Track detailed cost of interactions

Measure and improve quality of your chat agent

See deep insights into user inputs and agent responses. Dive deep into every step the agent takes in between. Inspect the overall user session as a whole and follow the conversation flow as your users did.

Session tracing →

Context grows with every turn and so does your bill. Break cost and token usage by turn, session, model, and release. Build dashboards and set alerts to stay on top of your spend.

Cost tracking →

Run LLM as a judge on production data to detect frustration, follow-ups, or other user signals that are worth investigating. Promote happy paths from production into datasets to measure and improve quality.

Evaluations →

Session tracing →

Teams observing and improving production chat agents on Langfuse

“Building on Langfuse we saved 30% of external BPO cost by deflecting 50% of support conversations to AI.”

“Langfuse hits the sweet spot between engineering requirements and empowerment of non-technical users to contribute their domain expertise.”

“Users were telling the coach they'd already shared something days earlier. These patterns, you only find them when you do structured error analysis.”