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Introduction

Build AI agent teams that handle your tasks and beyond.

What is The Beacon Labs?

A task-oriented AI agent framework for digital workers and vertical AI agents.

The Beacon Labs offers a cutting-edge, enterprise-ready framework where you can orchestrate LLM calls, agents, and computer use to complete tasks cost-effectively.

It provides more reliable agents, scalability, and a task-oriented structure that you need while completing real-world cases.

How The Beacon Labs Works

Component
Description
Key Features

Tasks

The job we want to complete

  • Have clear objectives

  • Use specific tools

  • Feed into larger processes

  • Produce actionable results

Agents

LLMs that use tools to complete tasks

  • Actions over tools

  • Self-reflection

  • Memory

  • Context Compression

Secure Runtime

Isolated environment to run agents

  • On-prem

  • Cloud

  • Customization

Model Context Protocols

A tool standard for LLMs, supported by companies and communities

  • Wide range of tool support

Key Features

Tasks

Easily complete the tasks you need and run them in various ways to get results. Focus on the tasks, not the process.

Automatic Characterization

Share your company’s URL and objective, then input a job title for the agent. The Beacon Labs framework will generate a persona and assign tasks accordingly.

MCP Support

Directly integrate with a comprehensive tool pool developed by the community and companies. Achieve stability with official tools.

Scalable

The most critical components are internally positioned on the server-side, allowing you to deploy the server via Docker and perform a lightweight integration with your application on the client side in a stateless manner.

Direct LLM Call

If the task you need to complete is simple and doesn’t require sub-tasks, you don’t need to spend time with agents. You can directly make an LLM call and get the results instantly.

Object as Response

When working with LLMs, getting more refined results requires programmatic responses. In this regard, you can define how you want the response by specifying it as a class and receiving it as an object.

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