AgentUse

Introduction to AgentUse

AgentUse is an AI framework built around the core concept of “Agent-as-Markdown.” It enables developers to define, compose, and run agents using plain text files (Markdown + YAML front matter), bringing AI development into a versionable and collaborative engineering workflow.

Philosophy: Agents Are Documents, Not Black Boxes

Traditional AI agent frameworks often require extensive code, configuration, and templates, resulting in scattered logic and challenging version control. AgentUse takes a lighter, more transparent approach—making the agent itself a Markdown file.

Here is a typical AgentUse agent definition example:

---
model: openai:gpt-4
tools:
    - search
memory: short_term
---

You are a technical blog editor. Please rewrite the following content to make it more natural:

In this file, the YAML front matter defines the model, tools, and context, while the body describes the task logic or instructions. This makes agent definitions clear, readable, and traceable—just like software configuration.

Core Features

The following table summarizes the main features and advantages of AgentUse.

Design Philosophy

AgentUse is built on three key principles:

• Declarative over Imperative
  Describe agents with declarative text instead of imperative code, making agent definition as simple as writing configuration.

• Text as Infrastructure
  Treat agents as part of your infrastructure—manage, deploy, and review them with Git, forming a “text-as-intelligence” development paradigm.

• Composable Intelligence
  Support modular agent composition, enabling multiple agents to collaborate on more complex problems.

Typical Use Cases

AgentUse’s flexibility makes it suitable for a wide range of scenarios:

• 🧾 Knowledge Agents
  Build Q&A bots, automatic summarization, or document analysis assistants.

• ⚙️ DevOps / CI Integration
  Embed intelligent review or auto-documentation in code commit or release workflows.

• 📚 Automated Content Generation
  Use Markdown agents to batch-generate reports, articles, or multilingual content.

• 🧠 Education & Experimentation
  Visually demonstrate AI workflows and collaboration logic in teaching scenarios.

• 🧩 Multi-Agent Systems
  Build collaborative, schedulable agent networks for research or production deployments.

Why Markdown

Markdown is more than a documentation format—it’s a natural language for knowledge structuring. AgentUse uses it as the “source code” for agents, offering these advantages:

• Highly readable: Anyone can directly understand the agent’s behavior and intent.
• Easy version management: Git diff clearly shows changes in agent logic.
• Collaboration-friendly: Team members can review and modify agents directly in PRs.
• Seamless automation: Perfect integration with CI/CD, GitHub Actions, static sites, and RAG systems.

Engineering Advantages

AgentUse offers the following benefits in engineering practice:

• Lightweight and dependency-free; quickly deployable on local, serverless, or container environments.
• Supports multilingual extensions and plugin mechanisms.
• Can interoperate with existing prompt engineering and agent frameworks.
• Compatible with mainstream LLM APIs and tool ecosystems.
• Easily integrates into existing code repositories or knowledge management systems.

Summary

AgentUse provides a new way to think about defining and collaborating on AI agents—no longer as “runtime entities,” but as readable, writable, and traceable knowledge objects.

In this framework:

• Prompts become Markdown;
• Agents are files;
• Collaboration is version control;
• Agent development becomes as natural as writing documentation.

✨ AgentUse’s vision: Make agent development open, transparent, and maintainable—so every Markdown file can be intelligent.