How to Use DeepEval for Agents workflows

DeepEval is the open-source LLM evaluation framework for testing and benchmarking LLM applications. Confirm live details on deepeval.com before production use.

Practical Agents workflows examples

Example 1

Scenario:
DeepEval — Agents workflows (pass 1). Context: DeepEval is the open-source LLM evaluation framework for testing and benchmarking LLM applications.

Objective:
Deliver a reviewable agents workflows result using DeepEval.

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

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

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

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

Example 2

Scenario:
DeepEval — Agents workflows (pass 2). Context: DeepEval is the open-source LLM evaluation framework for testing and benchmarking LLM applications.

Objective:
Deliver a reviewable agents workflows result using DeepEval.

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

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

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

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

Example 3

Scenario:
DeepEval — Agents workflows (pass 3). Context: DeepEval is the open-source LLM evaluation framework for testing and benchmarking LLM applications.

Objective:
Deliver a reviewable agents workflows result using DeepEval.

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

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

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

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

Example 4

Scenario:
DeepEval — Agents workflows (pass 4). Context: DeepEval is the open-source LLM evaluation framework for testing and benchmarking LLM applications.

Objective:
Deliver a reviewable agents workflows result using DeepEval.

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

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

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

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

Example 5

Scenario:
DeepEval — Agents workflows (pass 5). Context: DeepEval is the open-source LLM evaluation framework for testing and benchmarking LLM applications.

Objective:
Deliver a reviewable agents workflows result using DeepEval.

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

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

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

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

Checklist before you ship

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