Agentic AI Framework

The Agentic AI Framework is a powerful tool for building autonomous agents that can perform tasks, manage memory, and interact with tools. It provides a high-level API for creating and managing agents, teams, and workflows, as well as a low-level API for interacting with individual agents and tools.

Features

- Agent Management: Create, configure, and manage autonomous agents.
- Team Management: Create and manage teams of agents.
- Workflow Management: Define and execute workflows for agents and teams.
- Memory Management: Manage agent memory and knowledge.
- Tool Interaction: Interact with tools and APIs.
- Logging and Tracing: Comprehensive logging and tracing for debugging and monitoring.

Documentation

For detailed documentation and examples, please refer to the documentation.

License

This project is licensed under the MIT License.

Installation

To install the framework, run the following command in your terminal:

``bash
npm i @virtron/agency
``

You may also need to install the tools package separately:

`bash
npm i @virtron/agency-tools
`

Agent vs. AgentFactory

In this framework, you can create agents in two primary ways: directly using the Agent class or by using the AgentFactory. The choice depends on your specific needs for control versus convenience.

$3

The AgentFactory is a high-level utility designed to simplify the creation and configuration of agents. It abstracts away the boilerplate code required for setting up LLM providers, managing API keys, and loading configurations.

When to use AgentFactory:

- Rapid Prototyping: Quickly get an agent running with a standard, supported LLM provider (e.g., Gemini, OpenAI, Anthropic).
- Standard Use Cases: When your needs are met by the built-in providers and components.
- Managing Multiple Agents: Easily create and manage a group of agents from a single configuration file.

$3

The following example shows how to use the AgentFactory to create an agent that uses a calculator tool.

`javascript
import 'dotenv/config';
import { AgentFactory } from '@virtron/agency';
// import { calculatorTool } from './calculator_tool.js';
import { calculatorTool } from '@virtron/agency-tools';

async function main() {
// 1. Create and configure the AgentFactory
const factory = new AgentFactory({
apiKeys: {
gemini: process.env.GEMINI_API_KEY,
}
});

// Register the calculator tool with the AgentFactory
factory.registerTool(calculatorTool);

// 2. Define the configuration for the agent
const agentConfig = {
id: 'gemini-agent',
name: 'Gemini Agent',
description: 'An agent that uses the Gemini API.',
provider: 'gemini',
llmConfig: {
model: 'gemini-2.5-flash-lite', // Or any other Gemini model
},
role: 'A helpful assistant that can perform calculations using the calculator tool.',
goals: ['Use the calculator tool when appropriate to perform mathematical calculations.'],
tools: {
calculator: 'calculator',
},
};

// 3. Create the agent
const agent = factory.createAgent(agentConfig);

// 4. Run the agent
const prompt = 'what is sqrt(64)?';
console.log(Running agent with prompt: "${prompt}");

try {
const response = await agent.run(prompt);
console.log('Agent Response:');
console.log(response);
} catch (error) {
console.error('An error occurred while running the agent:', error);
}
}

main();
`

$3

Directly instantiating the Agent class gives you maximum control and flexibility over the agent's components and configuration. This approach requires you to manually create and inject all dependencies.

When to use the Agent class:

- Custom Components: When you need to use a custom-built LLM provider, a specialized memory manager, or a unique tool handler that isn't supported by the factory.
- Fine-Grained Control: For precise control over the lifecycle and configuration of the agent and its dependencies, which is often necessary when integrating into a larger, existing application.
- Explicitness: If you prefer to have a clear, explicit dependency graph without the "magic" of a factory.

$3

The following example demonstrates the power of using the Agent class directly. Here, we manually instantiate the GeminiProvider and inject it, along with other components, into the Agent. This is a perfect illustration of a scenario where direct instantiation is necessary for custom control.

`javascript
import 'dotenv/config';
import { Agent, MemoryManager, ToolHandler } from './Agent.js';
import { GeminiProvider } from './GeminiProvider.js';

async function main() {
// 1. Manually create the LLM provider
const geminiProvider = new GeminiProvider(
process.env.GEMINI_API_KEY,
'gemini-1.5-flash-latest'
);

// 2. Define the agent configuration object
const agentConfig = {
id: 'gemini-agent',
name: 'Gemini Agent',
description: 'An agent that uses the Gemini API.',
provider: 'gemini', // This is still here but not used by the Agent class itself
llmConfig: {
model: 'gemini-1.5-flash-latest',
},
role: 'A helpful assistant.',

// 3. You must manually inject the dependencies
llmProvider: geminiProvider,
memoryManager: new MemoryManager(),
toolHandler: new ToolHandler(),
tools: {}, // No tools for this example
};

// 4. Create the agent instance directly
const agent = new Agent(agentConfig);

// 5. The rest of the code is the same
const prompt = 'Hello, tell me about yourself.';
console.log(Running agent with prompt: "${prompt}");

try {
const response = await agent.run(prompt);
console.log('Agent Response:');
console.log(response);
} catch (error) {
console.error('An error occurred while running the agent:', error);
}
}

main();
``