CircuitAI

A powerful TypeScript library that helps developers efficiently and scalably orchestrate multiple artificial intelligence agents.

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Concepts

It was inspired by how humans work in teams, where each person has a specific role and responsibility. In CircuitAI, each agent has a goal, context, and rules to follow. The agents can communicate with each other and the supervisor to achieve the project's goal.

Project

Much like a real-life project, it has a name, description, agents, and a supervisor. The project is responsible for managing the agents and their interactions to achieve a business goal.

Agent

An agent is a participant in the project. It has a name, goal, context, and rules. The agent can perform actions based on the input it receives and can also communicate with other agents and the supervisor.

Supervisor

The supervisor is an agent with special privileges. It can oversee other agents, provide guidance, and make decisions based on the agents' actions.

Task

A task is a specific action that one or more agents can perform. It can be a simple task like "check credit score" or a complex task like "detect fraud in transactions." Tasks are optional and can be used to chain specific agents and actions together.

Example

import { Project, Agent } from "circuitai";

const managerAgent = new Agent({
  name: "Manager",
  goal: "Supervise other agents",
  context: "You are the manager of the project...",
  action: async ({ content, llmAdapter }) => {
    // Call the LLM
    const response = await llmAdapter?.chatCompletion({...});
    // Do something with the response
    return { output: response };
  },
});

const fraudAgent = new Agent({
  name: "FraudAnalysis",
  goal: "Analyze data to detect fraud",
  context: "You have a dataset with transactions...",
  action: async ({ content, llmAdapter }) => {
    // Or instead calling the LLM, do whatever you want here
    // ... and return the output
  },
});

const creditAgent = new Agent({
  name: "CreditAnalysis",
  goal: "Check credit score and limit",
  context: "Your customer is buying a product...",
  rules: "You dont have to check credit score if the transaction ...",
  action: async ({ content, llmAdapter }) => {
    const response = await llmAdapter?.chatCompletion({
      messages: [{ role: "user_request", content: content.input }],
    });

    return { output: response };
  },
});

const project = new Project({
  name: "Super Bank",
  description: "Analyzes transactions and checks credit",
  agents: [fraudAgent, creditAgent],
  supervisor: managerAgent,
});
// ...

const response = await project.sendMessage({
  input: "Check credit score of the customer {customer_id}",
});

console.log(response);
// Output: { output: "The credit score of the customer is 750", parsed: 750 }

Features

• 🚀 Scalable: Easily add new agents to your project
• 🧠 Contextual: Agents can have different contexts
• 📚 Rules: Define rules for each agent
• 🤖 LLM Integration: Use LLMs to generate responses
• 📦 Extensible: Create custom adapters for different LLMs
• 📝 Typescript: Written in TypeScript