> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ag-ui.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Agents

> Learn about agents in the Agent User Interaction Protocol

# Agents

Agents are the core components in the AG-UI protocol that process requests and
generate responses. They establish a standardized way for front-end applications
to communicate with AI services through a consistent interface, regardless of
the underlying implementation.

## What is an Agent?

In AG-UI, an agent is a class that:

1. Manages conversation state and message history
2. Processes incoming messages and context
3. Generates responses through an event-driven streaming interface
4. Follows a standardized protocol for communication

Agents can be implemented to connect with any AI service, including:

* Large language models (LLMs) like GPT-4 or Claude
* Custom AI systems
* Retrieval augmented generation (RAG) systems
* Multi-agent systems

## Agent Architecture

All agents in AG-UI extend the `AbstractAgent` class, which provides the
foundation for:

* State management
* Message history tracking
* Event stream processing
* Tool usage

```typescript theme={null}
import { AbstractAgent } from "@ag-ui/client"

class MyAgent extends AbstractAgent {
  run(input: RunAgentInput): RunAgent {
    // Implementation details
  }
}
```

### Core Components

AG-UI agents have several key components:

1. **Configuration**: Agent ID, thread ID, and initial state
2. **Messages**: Conversation history with user and assistant messages
3. **State**: Structured data that persists across interactions
4. **Events**: Standardized messages for communication with clients
5. **Tools**: Functions that agents can use to interact with external systems

## Agent Types

AG-UI provides different agent implementations to suit various needs:

### AbstractAgent

The base class that all agents extend. It handles core event processing, state
management, and message history.

### HttpAgent

A concrete implementation that connects to remote AI services via HTTP:

```typescript theme={null}
import { HttpAgent } from "@ag-ui/client"

const agent = new HttpAgent({
  url: "https://your-agent-endpoint.com/agent",
  headers: {
    Authorization: "Bearer your-api-key",
  },
})
```

### Custom Agents

You can create custom agents to integrate with any AI service by extending
`AbstractAgent`:

```typescript theme={null}
class CustomAgent extends AbstractAgent {
  // Custom properties and methods

  run(input: RunAgentInput): RunAgent {
    // Implement the agent's logic
  }
}
```

## Implementing Agents

### Basic Implementation

To create a custom agent, extend the `AbstractAgent` class and implement the
required `run` method:

```typescript theme={null}
import {
  AbstractAgent,
  RunAgent,
  RunAgentInput,
  EventType,
  BaseEvent,
} from "@ag-ui/client"
import { Observable } from "rxjs"

class SimpleAgent extends AbstractAgent {
  run(input: RunAgentInput): RunAgent {
    const { threadId, runId } = input

    return () =>
      new Observable<BaseEvent>((observer) => {
        // Emit RUN_STARTED event
        observer.next({
          type: EventType.RUN_STARTED,
          threadId,
          runId,
        })

        // Send a message
        const messageId = Date.now().toString()

        // Message start
        observer.next({
          type: EventType.TEXT_MESSAGE_START,
          messageId,
          role: "assistant",
        })

        // Message content
        observer.next({
          type: EventType.TEXT_MESSAGE_CONTENT,
          messageId,
          delta: "Hello, world!",
        })

        // Message end
        observer.next({
          type: EventType.TEXT_MESSAGE_END,
          messageId,
        })

        // Emit RUN_FINISHED event
        observer.next({
          type: EventType.RUN_FINISHED,
          threadId,
          runId,
        })

        // Complete the observable
        observer.complete()
      })
  }
}
```

## Agent Capabilities

Agents in the AG-UI protocol provide a rich set of capabilities that enable
sophisticated AI interactions:

### Interactive Communication

Agents establish bi-directional communication channels with front-end
applications through event streams. This enables:

* Real-time streaming responses character-by-character
* Immediate feedback loops between user and AI
* Progress indicators for long-running operations
* Structured data exchange in both directions

### Tool Usage

Agents can use tools to perform actions and access external resources.
Importantly, tools are defined and passed in from the front-end application to
the agent, allowing for a flexible and extensible system:

```typescript theme={null}
// Tool definition
const confirmAction = {
  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",
      },
      details: {
        type: "string",
        description: "Additional details about the action",
      },
    },
    required: ["action"],
  },
}

// Running an agent with tools from the frontend
agent.runAgent({
  tools: [confirmAction], // Frontend-defined tools passed to the agent
  // other parameters
})
```

Tools are invoked through a sequence of events:

1. `TOOL_CALL_START`: Indicates the beginning of a tool call
2. `TOOL_CALL_ARGS`: Streams the arguments for the tool call
3. `TOOL_CALL_END`: Marks the completion of the tool call

Front-end applications can then execute the tool and provide results back to the
agent. This bidirectional flow enables sophisticated human-in-the-loop workflows
where:

* The agent can request specific actions be performed
* Humans can execute those actions with appropriate judgment
* Results are fed back to the agent for continued reasoning
* The agent maintains awareness of all decisions made in the process

This mechanism is particularly powerful for implementing interfaces where AI and
humans collaborate. For example, [CopilotKit](https://docs.copilotkit.ai/)
leverages this exact pattern with their
[`useCopilotAction`](https://docs.copilotkit.ai/guides/frontend-actions) hook,
which provides a simplified way to define and handle tools in React
applications.

By keeping the AI informed about human decisions through the tool mechanism,
applications can maintain context and create more natural collaborative
experiences between users and AI assistants.

### State Management

Agents maintain a structured state that persists across interactions. This state
can be:

* Updated incrementally through `STATE_DELTA` events
* Completely refreshed with `STATE_SNAPSHOT` events
* Accessed by both the agent and front-end
* Used to store user preferences, conversation context, or application state

```typescript theme={null}
// Accessing agent state
console.log(agent.state.preferences)

// State is automatically updated during agent runs
agent.runAgent().subscribe((event) => {
  if (event.type === EventType.STATE_DELTA) {
    // State has been updated
    console.log("New state:", agent.state)
  }
})
```

### Multi-Agent Collaboration

AG-UI supports agent-to-agent handoff and collaboration:

* Agents can delegate tasks to other specialized agents
* Multiple agents can work together in a coordinated workflow
* State and context can be transferred between agents
* The front-end maintains a consistent experience across agent transitions

For example, a general assistant agent might hand off to a specialized coding
agent when programming help is needed, passing along the conversation context
and specific requirements.

### Human-in-the-Loop Workflows

Agents support human intervention and assistance:

* Agents can request human input on specific decisions
* Front-ends can pause agent execution and resume it after human feedback
* Human experts can review and modify agent outputs before they're finalized
* Hybrid workflows combine AI efficiency with human judgment

This enables applications where the agent acts as a collaborative partner rather
than an autonomous system.

### Conversational Memory

Agents maintain a complete history of conversation messages:

* Past interactions inform future responses
* Message history is synchronized between client and server
* Messages can include rich content (text, structured data, references)
* The context window can be managed to focus on relevant information

```typescript theme={null}
// Accessing message history
console.log(agent.messages)

// Adding a new user message
agent.messages.push({
  id: "msg_123",
  role: "user",
  content: "Can you explain that in more detail?",
})
```

### Metadata and Instrumentation

Agents can emit metadata about their internal processes:

* Reasoning steps through custom events
* Performance metrics and timing information
* Source citations and reference tracking
* Confidence scores for different response options

This allows front-ends to provide transparency into the agent's decision-making
process and help users understand how conclusions were reached.

## Using Agents

Once you've implemented or instantiated an agent, you can use it like this:

```typescript theme={null}
// Create an agent instance
const agent = new HttpAgent({
  url: "https://your-agent-endpoint.com/agent",
})

// Add initial messages if needed
agent.messages = [
  {
    id: "1",
    role: "user",
    content: "Hello, how can you help me today?",
  },
]

// Run the agent
agent
  .runAgent({
    runId: "run_123",
    tools: [], // Optional tools
    context: [], // Optional context
  })
  .subscribe({
    next: (event) => {
      // Handle different event types
      switch (event.type) {
        case EventType.TEXT_MESSAGE_CONTENT:
          console.log("Content:", event.delta)
          break
        // Handle other events
      }
    },
    error: (error) => console.error("Error:", error),
    complete: () => console.log("Run complete"),
  })
```

## Agent Configuration

Agents accept configuration through the constructor:

```typescript theme={null}
interface AgentConfig {
  agentId?: string // Unique identifier for the agent
  description?: string // Human-readable description
  threadId?: string // Conversation thread identifier
  initialMessages?: Message[] // Initial messages
  initialState?: State // Initial state object
}

// Using the configuration
const agent = new HttpAgent({
  agentId: "my-agent-123",
  description: "A helpful assistant",
  threadId: "thread-456",
  initialMessages: [
    { id: "1", role: "system", content: "You are a helpful assistant." },
  ],
  initialState: { preferredLanguage: "English" },
})
```

## Agent State Management

AG-UI agents maintain state across interactions:

```typescript theme={null}
// Access current state
console.log(agent.state)

// Access messages
console.log(agent.messages)

// Clone an agent with its state
const clonedAgent = agent.clone()
```

## Conclusion

Agents are the foundation of the AG-UI protocol, providing a standardized way to
connect front-end applications with AI services. By implementing the
`AbstractAgent` class, you can create custom integrations with any AI service
while maintaining a consistent interface for your applications.

The event-driven architecture enables real-time, streaming interactions that are
essential for modern AI applications, and the standardized protocol ensures
compatibility across different implementations.