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Tools

Tools are a fundamental concept in the AG-UI protocol that enable AI agents to interact with external systems and incorporate human judgment into their workflows. By defining tools in the frontend and passing them to agents, developers can create sophisticated human-in-the-loop experiences that combine AI capabilities with human expertise.

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What Are Tools?

In AG-UI, tools are functions that agents can call to:
  1. Request specific information
  2. Perform actions in external systems
  3. Ask for human input or confirmation
  4. Access specialized capabilities
Tools bridge the gap between AI reasoning and real-world actions, allowing agents to accomplish tasks that would be impossible through conversation alone.

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Tool Structure

Tools follow a consistent structure that defines their name, purpose, and expected parameters: 
interface Tool {
  name: string // Unique identifier for the tool
  description: string // Human-readable explanation of what the tool does
  parameters: {
    // JSON Schema defining the tool's parameters
    type: "object"
    properties: {
      // Tool-specific parameters
    }
    required: string[] // Array of required parameter names
  }
}
The parameters field uses JSON Schema to define the structure of arguments that the tool accepts. This schema is used by both the agent (to generate valid tool calls) and the frontend (to validate and parse tool arguments).

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Frontend-Defined Tools

A key aspect of AG-UI’s tool system is that tools are defined in the frontend and passed to the agent during execution: 
// Define tools in the frontend
const userConfirmationTool = {
  name: "confirmAction",
  description: "Ask the user to confirm a specific action before proceeding",
  parameters: {
    type: "object",
    properties: {
      action: {
        type: "string",
        description: "The action that needs user confirmation",
      },
      importance: {
        type: "string",
        enum: ["low", "medium", "high", "critical"],
        description: "The importance level of the action",
      },
    },
    required: ["action"],
  },
}

// Pass tools to the agent during execution
agent.runAgent({
  tools: [userConfirmationTool],
  // Other parameters...
})
This approach has several advantages:
  1. Frontend control: The frontend determines what capabilities are available to the agent
  2. Dynamic capabilities: Tools can be added or removed based on user permissions, context, or application state
  3. Separation of concerns: Agents focus on reasoning while frontends handle tool implementation
  4. Security: Sensitive operations are controlled by the application, not the agent

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Tool Call Lifecycle

When an agent needs to use a tool, it follows a standardized sequence of events:
  1. ToolCallStart: Indicates the beginning of a tool call with a unique ID and tool name 
    {
  type: EventType.TOOL_CALL_START,
  toolCallId: "tool-123",
  toolCallName: "confirmAction",
  parentMessageId: "msg-456" // Optional reference to a message
}
  2. ToolCallArgs: Streams the tool arguments as they’re generated 
    {
  type: EventType.TOOL_CALL_ARGS,
  toolCallId: "tool-123",
  delta: '{"act' // Partial JSON being streamed
}

    {
  type: EventType.TOOL_CALL_ARGS,
  toolCallId: "tool-123",
  delta: 'ion":"Depl' // More JSON being streamed
}

    {
  type: EventType.TOOL_CALL_ARGS,
  toolCallId: "tool-123",
  delta: 'oy the application to production"}' // Final JSON fragment
}
  3. ToolCallEnd: Marks the completion of the tool call 
    {
  type: EventType.TOOL_CALL_END,
  toolCallId: "tool-123"
}
The frontend accumulates these deltas to construct the complete tool call arguments. Once the tool call is complete, the frontend can execute the tool and provide results back to the agent.

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Tool Results

After a tool has been executed, the result is sent back to the agent as a “tool message”: 
{
  id: "result-789",
  role: "tool",
  content: "true", // Tool result as a string
  toolCallId: "tool-123" // References the original tool call
}
This message becomes part of the conversation history, allowing the agent to reference and incorporate the tool’s result in subsequent responses.

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Human-in-the-Loop Workflows

The AG-UI tool system is especially powerful for implementing human-in-the-loop workflows. By defining tools that request human input or confirmation, developers can create AI experiences that seamlessly blend autonomous operation with human judgment. For example:
  1. Agent needs to make an important decision
  2. Agent calls the confirmAction tool with details about the decision
  3. Frontend displays a confirmation dialog to the user
  4. User provides their input
  5. Frontend sends the user’s decision back to the agent
  6. Agent continues processing with awareness of the user’s choice
This pattern enables use cases like:
  • Approval workflows: AI suggests actions that require human approval
  • Data verification: Humans verify or correct AI-generated data
  • Collaborative decision-making: AI and humans jointly solve complex problems
  • Supervised learning: Human feedback improves future AI decisions

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CopilotKit Integration

CopilotKit provides a simplified way to work with AG-UI tools in React applications through its useCopilotAction hook: 
import { useCopilotAction } from "@copilotkit/react-core"

// Define a tool for user confirmation
useCopilotAction({
  name: "confirmAction",
  description: "Ask the user to confirm an action",
  parameters: {
    type: "object",
    properties: {
      action: {
        type: "string",
        description: "The action to confirm",
      },
    },
    required: ["action"],
  },
  handler: async ({ action }) => {
    // Show a confirmation dialog
    const confirmed = await showConfirmDialog(action)
    return confirmed ? "approved" : "rejected"
  },
})
This approach makes it easy to define tools that integrate with your React components and handle the tool execution logic in a clean, declarative way.

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Tool Examples

Here are some common types of tools used in AG-UI applications:

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User Confirmation

{
  name: "confirmAction",
  description: "Ask the user to confirm an action",
  parameters: {
    type: "object",
    properties: {
      action: {
        type: "string",
        description: "The action to confirm"
      },
      importance: {
        type: "string",
        enum: ["low", "medium", "high", "critical"],
        description: "The importance level"
      }
    },
    required: ["action"]
  }
}

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Data Retrieval

{
  name: "fetchUserData",
  description: "Retrieve data about a specific user",
  parameters: {
    type: "object",
    properties: {
      userId: {
        type: "string",
        description: "ID of the user"
      },
      fields: {
        type: "array",
        items: {
          type: "string"
        },
        description: "Fields to retrieve"
      }
    },
    required: ["userId"]
  }
}

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User Interface Control

{
  name: "navigateTo",
  description: "Navigate to a different page or view",
  parameters: {
    type: "object",
    properties: {
      destination: {
        type: "string",
        description: "Destination page or view"
      },
      params: {
        type: "object",
        description: "Optional parameters for the navigation"
      }
    },
    required: ["destination"]
  }
}

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Content Generation

{
  name: "generateImage",
  description: "Generate an image based on a description",
  parameters: {
    type: "object",
    properties: {
      prompt: {
        type: "string",
        description: "Description of the image to generate"
      },
      style: {
        type: "string",
        description: "Visual style for the image"
      },
      dimensions: {
        type: "object",
        properties: {
          width: { type: "number" },
          height: { type: "number" }
        },
        description: "Dimensions of the image"
      }
    },
    required: ["prompt"]
  }
}

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Best Practices

When designing tools for AG-UI:
  1. Clear naming: Use descriptive, action-oriented names
  2. Detailed descriptions: Include thorough descriptions to help the agent understand when and how to use the tool
  3. Structured parameters: Define precise parameter schemas with descriptive field names and constraints
  4. Required fields: Only mark parameters as required if they’re truly necessary
  5. Error handling: Implement robust error handling in tool execution code
  6. User experience: Design tool UIs that provide appropriate context for human decision-making

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Conclusion

Tools in AG-UI bridge the gap between AI reasoning and real-world actions, enabling sophisticated workflows that combine the strengths of AI and human intelligence. By defining tools in the frontend and passing them to agents, developers can create interactive experiences where AI and humans collaborate efficiently. The tool system is particularly powerful for implementing human-in-the-loop workflows, where AI can suggest actions but defer critical decisions to humans. This balances automation with human judgment, creating AI experiences that are both powerful and trustworthy.