How to Use Langfuse for Chat workflows

Trace, evaluate, and improve AI agents with one open platform. Use production data to understand behavior, collaborate on fixes, and ship better quality at lower cost and latency. Confirm live details on langfuse.com before production use.

Practical Chat workflows examples

Example 1

Scenario:
Langfuse — Chat workflows (pass 1). Context: Trace, evaluate, and improve AI agents with one open platform. Use production data to understand behavior, collaborate on fixes, and ship be

Objective:
Deliver a reviewable chat workflows result using Langfuse.

Inputs:
- Verified facts from langfuse.com
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Chat, API, Open Source, Automation

Workflow:
Open Langfuse → Configure for chat workflows → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified Langfuse capabilities; do not invent features.
- Confirm live details on langfuse.com before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete chat workflows artifact plus a short verification checklist.

Example 2

Scenario:
Langfuse — Chat workflows (pass 2). Context: Trace, evaluate, and improve AI agents with one open platform. Use production data to understand behavior, collaborate on fixes, and ship be

Objective:
Deliver a reviewable chat workflows result using Langfuse.

Inputs:
- Verified facts from langfuse.com
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Chat, API, Open Source, Automation

Workflow:
Open Langfuse → Configure for chat workflows → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified Langfuse capabilities; do not invent features.
- Confirm live details on langfuse.com before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete chat workflows artifact plus a short verification checklist.

Example 3

Scenario:
Langfuse — Chat workflows (pass 3). Context: Trace, evaluate, and improve AI agents with one open platform. Use production data to understand behavior, collaborate on fixes, and ship be

Objective:
Deliver a reviewable chat workflows result using Langfuse.

Inputs:
- Verified facts from langfuse.com
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Chat, API, Open Source, Automation

Workflow:
Open Langfuse → Configure for chat workflows → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified Langfuse capabilities; do not invent features.
- Confirm live details on langfuse.com before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete chat workflows artifact plus a short verification checklist.

Example 4

Scenario:
Langfuse — Chat workflows (pass 4). Context: Trace, evaluate, and improve AI agents with one open platform. Use production data to understand behavior, collaborate on fixes, and ship be

Objective:
Deliver a reviewable chat workflows result using Langfuse.

Inputs:
- Verified facts from langfuse.com
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Chat, API, Open Source, Automation

Workflow:
Open Langfuse → Configure for chat workflows → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified Langfuse capabilities; do not invent features.
- Confirm live details on langfuse.com before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete chat workflows artifact plus a short verification checklist.

Example 5

Scenario:
Langfuse — Chat workflows (pass 5). Context: Trace, evaluate, and improve AI agents with one open platform. Use production data to understand behavior, collaborate on fixes, and ship be

Objective:
Deliver a reviewable chat workflows result using Langfuse.

Inputs:
- Verified facts from langfuse.com
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Chat, API, Open Source, Automation

Workflow:
Open Langfuse → Configure for chat workflows → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified Langfuse capabilities; do not invent features.
- Confirm live details on langfuse.com before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete chat workflows artifact plus a short verification checklist.

Checklist before you ship

• Confirm the workflow stays inside verified Langfuse capabilities
• Review outputs against langfuse.com when accuracy or pricing claims matter
• Keep a short verification list for any claim you would publish externally