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Agent Squad

Anthropic Agent

Overview

The AnthropicAgent is a powerful and flexible agent class in the Agent Squad System. It leverages the Anthropic API to interact with various Large Language Models (LLMs) provided by Anthropic, such as Claude. This agent can handle a wide range of processing tasks, making it suitable for diverse applications such as conversational AI, question-answering systems, and more.

Key Features

  • Integration with Anthropic’s API
  • Support for multiple LLM models available on Anthropic’s platform
  • Streaming and non-streaming response options
  • Customizable inference configuration
  • Ability to set and update custom system prompts
  • Optional integration with retrieval systems for enhanced context
  • Support for Tool use within the conversation flow

Creating an AnthropicAgent

Here are various examples showing different ways to create and configure an AnthropicAgent:

Python Package

If you haven’t already installed the Anthropic-related dependencies, make sure to install them:

Terminal window
pip install "agent-squad[anthropic]"

Basic Examples

1. Minimal Configuration

const agent = new AnthropicAgent({
  name: 'Anthropic Assistant',
  description: 'A versatile AI assistant',
  apiKey: 'your-anthropic-api-key'
});

2. Using Custom Client

import { Anthropic } from '@anthropic-ai/sdk';
const customClient = new Anthropic({ apiKey: 'your-anthropic-api-key' });
const agent = new AnthropicAgent({
  name: 'Anthropic Assistant',
  description: 'A versatile AI assistant',
  client: customClient
});

3. Custom Model and Streaming

const agent = new AnthropicAgent({
  name: 'Anthropic Assistant',
  description: 'A streaming-enabled assistant',
  apiKey: 'your-anthropic-api-key',
  modelId: 'claude-3-opus-20240229',
  streaming: true
});

4. With Inference Configuration

const agent = new AnthropicAgent({
  name: 'Anthropic Assistant',
  description: 'An assistant with custom inference settings',
  apiKey: 'your-anthropic-api-key',
  inferenceConfig: {
    maxTokens: 500,
    temperature: 0.7,
    topP: 0.9,
    stopSequences: ['Human:', 'AI:']
  }
});

5. With Simple System Prompt

const agent = new AnthropicAgent({
  name: 'Anthropic Assistant',
  description: 'An assistant with custom prompt',
  apiKey: 'your-anthropic-api-key',
  customSystemPrompt: {
    template: 'You are a helpful AI assistant focused on technical support.'
  }
});

6. With System Prompt Variables

const agent = new AnthropicAgent({
  name: 'Anthropic Assistant',
  description: 'An assistant with variable prompt',
  apiKey: 'your-anthropic-api-key',
  customSystemPrompt: {
    template: 'You are an AI assistant specialized in {{DOMAIN}}. Always use a {{TONE}} tone.',
    variables: {
      DOMAIN: 'customer support',
      TONE: 'friendly and helpful'
    }
  }
});

7. With Custom Retriever

const retriever = new CustomRetriever({
  // Retriever configuration
});
const agent = new AnthropicAgent({
  name: 'Anthropic Assistant',
  description: 'An assistant with retriever',
  apiKey: 'your-anthropic-api-key',
  retriever: retriever
});

8. With Tool Configuration

const agent = new AnthropicAgent({
  name: 'Anthropic Assistant',
  description: 'An assistant with tool support',
  apiKey: 'your-anthropic-api-key',
  toolConfig: {
    tool: [
      {
        name: "Weather_Tool",
        description: "Get current weather data",
        input_schema: {
          type: "object",
          properties: {
            location: {
              type: "string",
              description: "City name",
            }
          },
          required: ["location"]
        }
      }
    ],
    useToolHandler: (response, conversation) => {
      return {
        role: ParticipantRole.USER,
        content: {
          "type": "tool_result",
          "tool_use_id": "weather_tool",
          "content": "Current weather data for the location"
        }
      }
    }
  }
});

9. Complete Example with All Options

import { AnthropicAgent } from 'agent-squad';


const agent = new AnthropicAgent({
  // Required fields
  name: 'Advanced Anthropic Assistant',
  description: 'A fully configured AI assistant powered by Anthropic models',
  apiKey: 'your-anthropic-api-key',


  // Optional fields
  modelId: 'claude-3-opus-20240229',  // Choose Anthropic model
  streaming: true, // Enable streaming responses
  retriever: customRetriever, // Custom retriever for additional context


  // Inference configuration
  inferenceConfig: {
    maxTokens: 500,      // Maximum tokens to generate
    temperature: 0.7,    // Control randomness (0-1)
    topP: 0.9,          // Control diversity via nucleus sampling
    stopSequences: ['Human:', 'AI:']  // Sequences that stop generation
  },


  // Tool configuration
  toolConfig: {
    tool: [{
      name: "Weather_Tool",
      description: "Get the current weather for a given location",
      input_schema: {
        type: "object",
        properties: {
          latitude: {
            type: "string",
            description: "Geographical WGS84 latitude"
          },
          longitude: {
            type: "string",
            description: "Geographical WGS84 longitude"
          }
        },
        required: ["latitude", "longitude"]
      }
    }],
    useToolHandler: (response, conversation) => ({
      role: ParticipantRole.USER,
      content: {
        type: "tool_result",
        tool_use_id: "tool_user_id",
        content: "Response from the tool"
      }
    })
  },


  // Custom system prompt with variables
  customSystemPrompt: {
    template: `You are an AI assistant specialized in {{DOMAIN}}.
              Your core competencies:
              {{SKILLS}}


              Communication style:
              - Maintain a {{TONE}} tone
              - Focus on {{FOCUS}}
              - Prioritize {{PRIORITY}}`,
    variables: {
      DOMAIN: 'scientific research',
      SKILLS: [
        '- Advanced data analysis',
        '- Statistical methodology',
        '- Research design',
        '- Technical writing'
      ],
      TONE: 'professional and academic',
      FOCUS: 'accuracy and clarity',
      PRIORITY: 'evidence-based insights'
    }
  }
});

Option Explanations

  • name and description: Identify and describe the agent’s purpose.
  • apiKey: Your Anthropic API key for authentication.
  • modelId: Specifies the LLM model to use (e.g., Claude 3 Sonnet).
  • streaming: Enables streaming responses for real-time output.
  • inferenceConfig: Fine-tunes the model’s output characteristics.
  • retriever: Integrates a retrieval system for enhanced context.
  • toolConfig: Defines tools the agent can use and how to handle their responses (See AgentTools for Agents for seamless tool definition)

Setting a New Prompt

You can dynamically set or update the system prompt for the agent:

agent.setSystemPrompt(
  `You are an AI assistant specialized in {{DOMAIN}}.
   Your main goal is to {{GOAL}}.
   Always maintain a {{TONE}} tone in your responses.`,
  {
    DOMAIN: "cybersecurity",
    GOAL: "help users understand and mitigate potential security threats",
    TONE: "professional and reassuring"
  }
);

This method allows you to dynamically change the agent’s behavior and focus without creating a new instance.

Adding the Agent to the Orchestrator

To integrate the Anthropic Agent into your orchestrator, follow these steps:

  1. First, ensure you have created an instance of the orchestrator:
import { AgentSquad } from "agent-squad";


const orchestrator = new AgentSquad();
  1. Then, add the agent to the orchestrator:
orchestrator.addAgent(agent);
  1. Now you can use the orchestrator to route requests to the appropriate agent, including your Anthropic agent:
const response = await orchestrator.routeRequest(
  "What is the base rate interest for 30 years?",
  "user123",
  "session456"
);

By leveraging the AnthropicAgent, you can create sophisticated, context-aware AI agents capable of handling a wide range of tasks and interactions, all powered by the latest LLM models available through Anthropic’s platform.