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Documentation Index

Fetch the complete documentation index at: https://docs.guild.ai/llms.txt

Use this file to discover all available pages before exploring further.

An llmAgent pairs a system prompt with a tool set. You define what the agent knows and what it can do — the LLM figures out how to do it. This is the simplest way to build a Guild agent. No TypeScript logic required — just a prompt and tools.

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Example

import { guildTools, llmAgent } from "@guildai/agents-sdk"
import { gitHubTools } from "@guildai-services/guildai~github"

const systemPrompt = `
You are a code review assistant.

When given a pull request, retrieve the latest changes
using the GitHub tools and provide helpful feedback.
`

export default llmAgent({
  description: "Reviews pull requests and provides feedback.",
  tools: { ...gitHubTools, ...guildTools },
  systemPrompt,
})

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Input and output

Every llmAgent uses the same fixed schemas: 
// Input: the initial prompt with task details
type Input = { type: "text"; text: string }

// Output: the agent's final response
type Output = { type: "text"; text: string }

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Execution modes

llmAgent supports two modes:
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mode
'one-shot' | 'multi-turn'
default:"'one-shot'"
  • "one-shot" — The agent processes the input, returns a single response, then terminates.
  • "multi-turn" — The agent continues interacting with the user until it calls the __submit__ tool to signal task completion.
export default llmAgent({
  description: "An agent that can have back-and-forth conversations.",
  tools: {},
  systemPrompt:
    "Help users interactively. Continue asking questions until you have all the information you need.",
  mode: "multi-turn",
})

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Tool recommendations

  • userInterfaceTools is included automatically — no need to add it.
  • Include guildTools if the agent uses tools that require authorization (e.g., GitHub access), so it can request credentials when needed.

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Selecting specific tools

Use pick to include only the tools you need. See Selecting specific tools in the SDK reference.

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When to use LLM agents

SituationUse llmAgent?
Task is expressible as a prompt + toolsYes
You need deterministic, repeatable behaviorNo — use coded agents
You want to minimize LLM token costsNo — use coded agents