Build

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What are AgentMark Prompts?

• Readable — Human-friendly syntax that’s easy to review and understand
• Reusable — Share components across prompts and use variables for dynamic content
• Type-Safe — Full TypeScript support for props and outputs
• Version-Controlled — Store prompts in git alongside your code
• Testable — Run experiments with datasets and automated evaluations

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Basic Structure

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1. Frontmatter (YAML)

---
name: example
text_config:
  model_name: gpt-4o-mini
  temperature: 0.7
---

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2. Template Content

<System>
You are a helpful assistant.
</System>

<User>
Summarize the following text: {props.text}
</User>

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Creating Prompts

• Cloud
• Local

Use the Dashboard’s visual editor to create and edit prompts — no coding or git knowledge required.

• Write and edit prompts with syntax highlighting
• Test prompts directly in the editor
• Configure model settings through visual controls
• Preview outputs in real-time

Step-by-step guide →

Create .prompt.mdx files in your project’s agentmark/ directory:

agentmark/greeting.prompt.mdx

---
name: greeting
text_config:
  model_name: gpt-4o-mini
  temperature: 0.7
---

<System>
You are a friendly assistant.
</System>

<User>
Say hello to {props.name}.
</User>

Run it from the CLI:

agentmark run-prompt agentmark/greeting.prompt.mdx

Or execute it from your application — see Running Prompts.

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Key Features

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Message Tags

• <System> — System-level instructions
• <User> — User messages
• <Assistant> — Assistant responses (for few-shot examples)

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Dynamic Variables

<User>
Hello {props.userName}, you have {props.messageCount} new messages.
</User>

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Conditional Logic and Loops

<User>
  {#if props.isPremium}
    You have access to premium features.
  {/if}

  Products:
  {#each props.products as product}
    - {product.name}: ${product.price}
  {/each}
</User>

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Generation Types

• Text — Natural language responses
• Object — Structured JSON with schema validation
• Image — Image generation
• Speech — Audio generation

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Advanced Features

• Tools & Agents — Extend prompts with function calling and multi-step agent workflows
• Components — Create shared, reusable components across prompts
• Schema References — Reuse JSON schema definitions across prompts
• File Attachments — Include images and documents in prompts
• MCP Integration — Connect to Model Context Protocol servers
• Playground — Compare prompts across multiple models side-by-side
• Version Control — Track and manage prompt versions

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Next Steps