Overview

An agent is an LLM-powered worker that can reason about a request, choose tools, use memory, read files, and keep going until it has an answer or has completed the task. Use an agent when the path must vary at runtime: answering a support question, researching an issue, drafting a reply, or deciding which tool to call next. If the work follows a known sequence, prefer a workflow; it can still use an agent or promptLlm() for the steps that need judgment.

Example requests

Ask your coding agent to build an agent, the tools it should use, and where people should interact with it.

“Build a support agent that answers product questions from our docs, checks customer details, and drafts replies in our support tone.”

“Create an agent that watches for tagged Slack messages, investigates the topic, and replies with a sourced summary.”

“Build a data agent that can query our warehouse and answer questions from a #data Slack channel.”

How agents are defined

In Keystroke, agents live in your project code under src/agents/. Each file default-exports a defineAgent() definition, and the runtime discovers it when you run or deploy the project.

import { defineAgent } from "@keystrokehq/keystroke/agent";
import { defineSandbox } from "@keystrokehq/keystroke/sandbox";

export default defineAgent({
  slug: "support",
  name: "Support",
  description: "Answers product questions using bundled docs and a support skill.",
  systemPrompt:
    "You are helpful support. Read /workspace/agent/support-instructions.md before answering.",
  model: "openai/gpt-5.6-luna",
  skills: ["support"],
  sandbox: defineSandbox({ files: true }),
});

What agents can use

Agents are useful when a task needs judgment, language understanding, tool use, or multi-turn context.

Capability	How it works
Instructions	`systemPrompt` tells the model how to behave
Models	`model` is an exact catalog id (`vendor/model-id`); optional `thinkingLevel`. See Models.
File system	Each session gets a `/workspace` with a built-in file system and bash (code execution) tools
Web search	Built-in `web_search` and `web_fetch` tools when a web provider is configured
Browser use	Built-in tools for agents to use a personal browser to interact with the web
Tools	Attach custom tools, MCP tools, subagents, and workflows as tools
Skills and files	Attach project skills from `src/skills/` and static files from `src/files/`
Memory	Sessions and persistent memory are enabled by default unless disabled
Sandbox	A built-in sandbox environment for using CLIs and more advanced code execution
Self-scheduling	Built-in tools let an agent schedule its own future or recurring runs (ephemeral triggers)

Agents are one of the main primitives in a project, alongside workflows, actions, and triggers. Actions are the usual way to give an agent reliable capabilities. Workflows are the usual way to orchestrate several deterministic steps around one or more agent calls.

How to run agents

You can use an agent from the CLI, API, a workflow, another agent, or an external gateway like Slack. You can also run an agent automatically by attaching a trigger (a schedule, webhook, or poll), and agents can even schedule their own runs with ephemeral triggers.

keystroke agents prompt support --message "What is Keystroke?"

Open Agents in the web app to see the agents available in your active project. Open History and filter to Agent when you want to inspect sessions after they run.

Running deployed agents

Deploy your project, then prompt the agent against the deployed project:

keystroke deploy --project <slug>   # or --filter agents/support
keystroke agents prompt support --message "Hi"

Next steps

Build agents

Define agents with tools, models, skills, files, and runtime options.

Run agents

Prompt agents from the CLI and inspect sessions.

Triggers

Run agents on a schedule, webhook, or poll.

Agent runs

Review conversation history, tool calls, traces, and errors.

Get started

Learn

Build with AI

Resources

Example requests

How agents are defined

What agents can use

How to run agents

Running deployed agents

Next steps

Build agents

Run agents

Triggers

Agent runs

​Example requests

​How agents are defined

​What agents can use

​How to run agents

​Running deployed agents

​Next steps

Build agents

Run agents

Triggers

Agent runs

Example requests

How agents are defined

What agents can use

How to run agents

Running deployed agents

Next steps