poml
POML is a markup language (like HTML for prompts) that helps developers structure and manage prompts for large language models. It includes a VS Code extension, SDKs for Node.js and Python, and built-in templating to handle complex prompt engineering at scale.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | microsoft/poml |
| Owner | microsoft |
| Primary language | TypeScript |
| License | MIT — OSI-approved |
| Stars | 4.9k |
| Forks | 246 |
| Open issues | 57 |
| Latest release | Unknown |
| Last updated | 2026-01-14 |
| Source | https://github.com/microsoft/poml |
What poml is
POML provides semantic XML-like markup components (<role>, <task>, <document>, <img>) with CSS-like styling, an integrated templating engine (variables, loops, conditionals), and SDKs (JavaScript/TypeScript, Python) for programmatic prompt composition and LLM integration.
Get the poml source
Clone the repository and explore it locally.
git clone https://github.com/microsoft/poml.gitcd poml# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires team familiarization with POML syntax and semantic components; plan onboarding time, especially for developers unfamiliar with markup languages.
- SDK integration (Node.js/Python) needs to be wired into existing LLM/prompt pipelines; ensure compatibility with your framework (LangChain, etc.).
- VS Code extension requires manual API key/endpoint configuration for each team member; establish shared secrets management strategy (e.g., environment variables, vaults).
- Templating logic complexity can grow quickly; establish naming conventions and reusable component patterns to avoid maintenance debt.
- Output rendering must be tested against target LLM models; CSS-like styling effects may vary in effectiveness depending on model and prompt engineering approach.
When to avoid it — and what to weigh
- Simple, single-turn prompts — POML introduces markup overhead. For basic LLM queries, plain text or lightweight templating (Jinja2, Handlebars) may be more pragmatic.
- Minimal tooling requirements — Requires adoption of VS Code extension, npm/pip SDKs, and POML syntax learning. Teams with no IDE/SDK ecosystem investment may find adoption friction high.
- No templating or external data needs — If prompts are static and do not integrate external documents, tables, or images, the structured markup approach offers limited advantage over plain strings.
- Strict vendor lock-in avoidance — POML is Microsoft-owned. Organizations requiring vendor-neutral prompt formats may prefer open community standards or home-grown solutions.
License & commercial use
Licensed under MIT (MIT License), an OSI-approved permissive license. Allows commercial use, modification, and distribution with minimal restrictions (requires license notice and copyright attribution).
MIT license permits commercial use without licensing fees or restrictions. However, confirm Microsoft's trademark and brand guidelines compliance (referenced in README) if using POML branding in commercial products. No warranty is provided; evaluate support SLA and community responsiveness for production readiness.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | High |
Prompt injection and LLM-specific safety (e.g., instruction following, data leakage) are inherent to LLM use, not POML-specific. API key/endpoint configuration in VS Code extension and SDK calls must use secure secret management; credentials stored in settings.json or environment variables should follow least-privilege practices. No explicit security audit or threat model disclosed; requires review for sensitive production use.
Alternatives to consider
LangChain Prompts / PromptTemplates
Mature Python/JS ecosystem for prompt composition; tighter integration with LLM chains and retrieval pipelines; lower learning curve if already using LangChain.
Jinja2 / Handlebars templating
Industry-standard templating engines; lightweight, no new syntax to learn; works with any language/framework; simpler for data-driven prompt generation without markup.
Custom YAML/JSON prompt schemas
Organization-specific structure; avoids external dependency; integrates trivially with existing config/IaC tooling; loses semantic richness and tooling (VSCode extension, styling).
Build on poml with DEV.co software developers
Install the POML VS Code extension or npm/pip SDKs today. Review the documentation and join the Discord community to get started with declarative, maintainable prompt orchestration.
Talk to DEV.coRelated on DEV.co
Explore the category and the services that help you build with it.
poml FAQ
Does POML improve LLM output quality?
Can I use POML with non-OpenAI/Azure/Google LLMs?
Is POML production-ready?
Can I migrate existing prompts to POML?
Work with a software development agency
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If poml is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.
Ready to structure your prompt engineering?
Install the POML VS Code extension or npm/pip SDKs today. Review the documentation and join the Discord community to get started with declarative, maintainable prompt orchestration.