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Convert POML to AI-Ready Prompts & Chat Messages with Zero Dependencies

RealSimple SolutionsRealSimple Solutions
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2/3/2026
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POML → Prompt/Messages (No-Deps)

Poml Logo

What this does

Turns POML markup into either a single Markdown prompt or chat-style messages[] — using a zero-dependency n8n Code node. It supports variable substitution (via context), basic components (headings, lists, code, images, tables, line breaks), and optional schema-driven validation using componentSpec + attributeSpec.

Credits

Created by Real Simple Solutions as an n8n template friendly POML compiler (no dependencies) for full POML feature parity.

View more of our templates here

Who’s it for

Teams who author prompts in POML and want a template-safe way to turn them into either a single Markdown prompt or chat-style messages—without installing external modules. Works on n8n Cloud and self-hosted.

What it does

This workflow converts POML into:

  • prompt (Markdown) for single-shot models, or
  • messages[] (system|user|assistant) for chat APIs when speakerMode is true.

It supports variable substitution via a context object ({{dot.path}}), lists, headings, code blocks, images (incl. base64 → data: URL), tables from JSON (records/columns), and basic message components.

How it works

  • Set (Specs & Context): Provide componentSpec (allowed attrs per tag), attributeSpec (typing/coercion), and optional context.
  • Code (POML → Prompt/Messages): A zero-dependency compiler parses the POML and emits prompt or messages[].

> Add a yellow Sticky Note that includes this description and any setup links. Use additional neutral sticky notes to explain each step.

How to set up

  1. Import the template.

  2. Open the first Set node and paste your componentSpec, attributeSpec, and context (examples included).

  3. In the Code node, choose:

    • speakerMode: true to get messages[], or false for a single prompt.
    • listStyle: dash | star | plus | decimal | latin.

  4. Run → inspect prompt/messages in the output.

Requirements

  • No credentials or community nodes.
  • Works without external libraries (template-compliant).

How to customize

  • Add message tags (<system-msg>, <user-msg>, <ai-msg>) in your POML when using speakerMode: true.
  • Extend componentSpec/attributeSpec to validate or coerce additional tags/attributes.
  • Preformat arrays in context (e.g., bulleted, csv) for display, or add a small Set node to build them on the fly.
  • Rename nodes and keep all user-editable fields grouped in the first Set node.

Security & best practices

  • Never hardcode API keys in nodes.
  • Remove any personal IDs before publishing.
  • Keep your Sticky Note(s) up to date and instructional.

n8n Workflow: Convert POML to AI-Ready Prompts/Chat Messages

This n8n workflow provides a foundational structure for processing input data and preparing it for use with AI agents and chat models. While the specific transformation logic is left to be defined within the "Edit Fields" and "Code" nodes, this template sets up the necessary components for an AI-driven workflow.

What it does

This workflow is designed to be a starting point for transforming raw input into a format suitable for AI processing.

  1. Manual Trigger: Initiates the workflow upon a manual click, making it easy for testing and one-off executions.
  2. Edit Fields (Set): This node is intended for initial data manipulation. You can use it to rename, add, or remove fields from the incoming data, preparing it for subsequent steps.
  3. Code: This node allows for custom JavaScript logic. It's the primary place to implement complex transformations, parsing, or formatting of your data (e.g., converting POML to a specific prompt structure).
  4. AI Agent: This node represents an AI agent (likely from LangChain) that can execute a series of steps, make decisions, and use tools based on the processed input.
  5. OpenAI Chat Model: This node integrates with an OpenAI chat model, allowing the AI agent to generate responses or perform conversational tasks based on the input it receives.
  6. Sticky Note: Provides a space for comments or notes within the workflow, enhancing readability and documentation.

Prerequisites/Requirements

  • n8n Instance: A running n8n instance (self-hosted or cloud).
  • OpenAI Account & API Key: Required for the "OpenAI Chat Model" node. You will need to configure an OpenAI credential in n8n.
  • LangChain Integration (if not built-in): Ensure your n8n instance has the LangChain nodes installed and enabled.

Setup/Usage

  1. Import the Workflow:
    • In your n8n instance, go to "Workflows".
    • Click "New" -> "Import from JSON".
    • Paste the provided JSON content into the import dialog.
  2. Configure Credentials:
    • Locate the "OpenAI Chat Model" node.
    • Click on the "Credential" field and select your existing OpenAI API Key credential, or create a new one if you haven't already.
  3. Define Input Data:
    • The "Edit Fields (Set)" node is currently empty. You will need to configure it to accept and manipulate your initial input data. For example, if you're converting POML, you might set a field named poml_content with your raw POML text.
  4. Implement Transformation Logic:
    • Open the "Code" node. This is where you will write JavaScript to transform your input (e.g., POML) into the desired AI-ready prompt or chat message format.
    • The output of this node should be structured in a way that the "AI Agent" and "OpenAI Chat Model" can understand.
  5. Configure AI Agent (Optional but Recommended):
    • Open the "AI Agent" node. Configure its parameters, such as the agent type, tools it should use, and how it should interact with the chat model.
  6. Test the Workflow:
    • Click the "Execute Workflow" button (the play icon) in the "When clicking ‘Execute workflow’" node to run a test.
    • Review the output of each node to ensure the data is being transformed as expected.
  7. Activate the Workflow:
    • Once configured and tested, activate the workflow by toggling the "Active" switch in the top right corner of the workflow editor.

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