@olivertj/agent-builder v0.1.5

Agent Builder

A TypeScript package for easily creating flexible and customizable AI agents to integrate into your own projects.

Core Concepts

Agent Builder structures AI agents around four key components:

1. State: Internal information held by the agent. This can include conversation history, goals, mood, memories, or any other data relevant to the agent's operation.
2. Providers: Supply information to the agent's context window. This includes external data sources (APIs, databases, time of day), descriptions of the agent's role or capabilities, and potentially filtered parts of the agent's State. Careful management of Providers is crucial for controlling the context sent to the underlying language model.
3. Evaluators: Represent the agent's reasoning, decision-making, and processing steps. Evaluators analyze the information from Providers and can modify the agent's State (e.g., updating goals, marking tasks complete) or decide to trigger Actions.
4. Actions: Define the concrete capabilities of the agent – the things it can do. This could involve calling external APIs, sending messages, interacting with a file system, etc. Knowledge of available actions is typically given via a Provider, and the decision to execute an action comes from an Evaluator.

These components are designed to be modular, allowing developers to define and combine custom implementations for each part.

Installation

This package is intended to be used as a dependency in your own projects.

1. Install:

   npm install <your-package-name> 
   # Or: yarn add <your-package-name>
   # (Replace <your-package-name> with the actual published name)

   Note: If you are developing locally, you might link the package or install it directly from its path.

2. Build (if developing locally): The package includes a build script:

   npm run build 

   This will compile the TypeScript code into the dist directory.

Basic Usage (Conceptual)

import { AgentBuilder } from 'agent-builder'; // Adjust import path as needed
import { myCustomStateProvider, myApiProvider, myGoalEvaluator, mySendMessageAction } from './myAgentComponents';

// 1. Initialize with a base prompt and model
const agent = new AgentBuilder(
    "You are a helpful assistant.", 
    "gemini-2.0-flash" // Or another supported model
);

// 2. Add providers (State, APIs, etc.)
agent.addProvider(myCustomStateProvider);
agent.addProvider(myApiProvider); 
// ... add more providers

// 3. Add evaluators (Decision making) - *Roadmap Feature*
// agent.addEvaluator(myGoalEvaluator); 

// 4. Add actions (Agent capabilities) - *Roadmap Feature*
// agent.addAction(mySendMessageAction);

// 5. execute runtime
async function runAgent() {
    try {
        const response = await agent.execute();
        console.log("Agent Response:", response);

        // Future: Trigger evaluations and actions based on response/state
        // const evaluationResult = await agent.evaluate();
        // if (evaluationResult.actionToExecute) {
        //     await agent.executeAction(evaluationResult.actionToExecute);
        // }

    } catch (error) {
        console.error("Error running agent:", error);
    }
}

runAgent();

Configuration

Environment Variables

Certain functionalities, particularly those involving specific AI model providers, require environment variables to be set in the project using this package.

• GEMINI_API_KEY: Required only when using Google Gemini models (e.g., gemini-2.0-flash, gemini-2.5-pro-exp-03-25). The agent will throw an error during response generation if a Google model is selected and this key is not found.

Future model providers (e.g., Claude, OpenAI) will require their own respective API keys.

You can manage environment variables using a .env file (with a library like dotenv) or by setting them directly in your deployment environment.

.env example:

GEMINI_API_KEY=your_google_api_key_here 

Current Status & Roadmap

• Implemented:
  • Core AgentBuilder class structure.
  • Provider system (addProvider, setProvider).
  • Basic prompt generation (prompt(), system()).
  • Response generation (generateResponse()) using Google Gemini models (requires GEMINI_API_KEY).
• Roadmap:
  • Implement Evaluator system.
  • Implement Action system.
  • Develop more sophisticated State management options.
  • Add support for additional LLM providers (e.g., Anthropic Claude, OpenAI GPT).
  • Implement more built-in Providers, Evaluators, and Actions.
  • Add comprehensive testing.
  • Publish to npm.

License

This project is licensed under the MIT License - see the LICENSE file for details (though a LICENSE file doesn't exist yet, this indicates the intent).

Contributing

Contributions are welcome! Please feel free to open issues or submit pull requests. (Add contribution guidelines later).