> ## Documentation Index > Fetch the complete documentation index at: https://docs.agentuse.io/llms.txt > Use this file to discover all available pages before exploring further. # Introduction > Own and operate Markdown-defined AI agents on infrastructure you control AgentUse Logo AgentUse Logo Dark ## Own the agents doing your company's work **AgentUse** is an open-source agent runtime. Define agents in Markdown, run them with Claude, OpenAI, or open models on infrastructure you control, and keep every definition as a plain file in your repository. Let agents work autonomously. Put consequential actions behind human approval. Run your first agent without installing AgentUse or configuring an API key Define instructions, models, tools, and runtime behavior in Markdown Pause consequential actions for human review, then resume the same session Run AgentUse locally, on a server, in CI, or inside Docker ## Why AgentUse? Managed agent platforms provide a runtime but make their hosted environment the operational center. Agent frameworks provide flexible libraries but leave you to assemble the application and operations layer. AgentUse starts with a plain agent file and supplies the runtime around it: the agent loop, tools, durable sessions, schedules, approvals, HTTP endpoints, and an operations dashboard. Instructions are natural-language Markdown and configuration is readable YAML. Diff, review, version, and adapt agents using the tools you already use for source code. AgentUse manages tool calls, sessions, schedules, failures, approval waits, artifacts, and results without requiring you to build an SDK application. Use the same agent file from the command line, a schedule, an HTTP trigger, CI/CD, or Docker. No hosted AgentUse control plane is required. Add `approval: true` when work should pause for review. A reviewer can approve, reject, or comment, and AgentUse resumes the durable session with that decision. Your agent definitions, runtime, and operational state stay under your control. ## Key Features Put model configuration and capabilities in YAML frontmatter, then describe the work in plain language. Use Anthropic, OpenAI, OpenRouter, OpenCode Go, Amazon Bedrock, or a compatible local endpoint. Override the model at runtime without rewriting the agent. Use allowlisted filesystem and shell access, built-in runtime tools, reusable skills, subagents, stores, and Model Context Protocol servers. Trigger agents from the CLI, schedules, HTTP, CI/CD, or Docker. Inspect durable sessions and review consequential actions from the operations dashboard. ## Try It Now No API key is needed for this hosted example: ```bash theme={"system"} npx -y agentuse@latest run https://agentuse.io/hello.agentuse ``` ## How It Works An AgentUse agent is a Markdown file with YAML configuration and plain-English instructions: ```markdown theme={"system"} --- model: anthropic:claude-haiku-4-5 --- You are a helpful assistant. ## Task Say hello to the user and ask how you can help them today. ``` Save the file as `hello.agentuse` and run: ```bash theme={"system"} npx -y agentuse@latest run hello.agentuse ``` The filename becomes the default agent id. The same file can later gain tools, a schedule, approval requirements, skills, subagents, or persistent stores without becoming an application project. ## What Can You Build? * **Operational agents** that monitor systems, prepare reports, and surface work that needs attention * **Approval-gated agents** that draft, validate, and pause before sending, publishing, deploying, or deleting * **Repository and CI agents** that inspect changes, run checks, and produce reviewable artifacts * **MCP-powered agents** that work with databases, APIs, browsers, and business systems * **Multi-agent systems** that delegate bounded work to specialized child agents * **Stateful agents** that preserve structured knowledge and metrics across runs This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.