Discover Meka Agent: The Open-Source Vision-Driven Computer Assistant

Tired of repetitive browser tasks? Meet the AI assistant that “sees” screens like humans do

What Is Meka Agent?

Meka Agent is an open-source autonomous computer operator that achieves browser automation through human-like visual interaction. Unlike traditional tools, it doesn’t rely on parsing webpage code but instead “observes” screen content to make operational decisions, just like humans do. This vision-based approach enables it to handle system-level elements like dropdown menus, browser alerts, and file uploads that conventional tools often struggle with.

Core Breakthroughs

  • Vision-first interaction: Understands interfaces through pixel data
  • Full-environment support: OS-level control capabilities
  • Modular architecture: Flexible combination of AI models and infrastructure

Meka's WebArena Benchmark Performance
Achieves 72.7% success rate on WebArena Benchmark (current state-of-the-art)

Technical Architecture Explained

Dual Core Components

graph LR
A[Vision Model] --> C[Meka Agent]
B[Infrastructure] --> C
C --> D[Automated Task]

1. Visual Processing Engine

Requires models with strong visual grounding capability. Recommended options:

  • OpenAI o3
  • Claude Sonnet 4
  • Claude Opus 4

2. Infrastructure Provider

Must support OS-level control (beyond basic browser access) because:

  • 30% of web elements are system-rendered
  • Must handle special controls like file pickers
  • Current recommended solution: Anchor Browser

Get Started in 5 Minutes

Environment Setup

npm install @trymeka/core @trymeka/ai-provider-vercel @ai-sdk/openai @trymeka/computer-provider-anchor-browser playwright-core

API Key Configuration (.env file)

OPENAI_API_KEY=your_openai_key
ANCHOR_BROWSER_API_KEY=your_anchorbrowser_key

Basic Example: News Summarizer

import { createAgent } from "@trymeka/core/ai/agent";
// Configure providers (OpenAI + Anchor combo)

const agent = createAgent({
  aiProvider: createVercelAIProvider({...}),
  computerProvider: createAnchorBrowserComputerProvider({...}),
});

const session = await agent.initializeSession();
const task = await session.runTask({
  instructions: "Summarize top news headlines",
  initialUrl: "https://news.ycombinator.com",
  outputSchema: z.object({...}) // Define output structure
});
console.log("Results:", task.result);

Design Philosophy

Three Principles of Human Behavior Simulation

  1. Visual-driven operation: Interfaces understood through screen pixels
  2. Tool-oriented thinking: Actions abstracted into reusable operations
  3. Contextual memory: Maintains task state across pages

“We insist on AI ‘seeing’ screens like humans do – this solves 90% of compatibility issues in traditional automation tools.” – Meka Technical Whitepaper

Four Core Advantages

1. Model Flexibility

| Model Type       | Recommended Option       | Best For            |
|------------------|--------------------------|---------------------|
| Vision Foundation| Claude Sonnet 4          | Complex UI parsing  |
| Fast Response    | Gemini 2.5 Flash         | Simple tasks        |
| Hybrid Evaluation| OpenAI o3 + Claude 4     | Precision operations|

2. Extensible Architecture

  • Custom tool hooks
  • Provider adapter interfaces
  • Task lifecycle monitoring

3. Type-Safe Implementation

// Strongly-typed output example
outputSchema: z.object({
  articles: z.array(
    z.object({
      title: z.string(),
      url: z.string().url(),
      summary: z.string().max(200)
    })
  )
})

4. Open-Source Ecosystem

  • MIT licensed
  • Tool development templates
  • Cross-platform testing suite

Real-World Applications

Case 1: E-commerce Price Tracking

1. Navigate to product page
2. Identify price display area
3. Extract numerical value
4. Trigger notification below threshold

Case 2: Research Data Collection

1. Access academic database
2. Input search keywords
3. Paginate through results
4. Generate BibTeX references

Case 3: Cross-System Data Migration

[Legacy System] -> [Visual Recognition] -> [Data Transformation] -> [New System]
                         ↑ Meka Agent ↑

Developer Guide

Multi-Model Strategy

// Configure model collaboration
aiProvider: {
  ground: o3AIProvider,       // Primary vision model
  alternateGround: claudeAI,  // Fallback vision model
  evaluator: geminiFlash      // Result validation model
}

Performance Optimization

  1. Screenshot compression: Maintain 1280×720 resolution
  2. Action timeout: Set 5-second limit per operation
  3. Cache reuse: Skip re-identification for identical screens

Frequently Asked Questions

Q1: Why is OS-level control necessary?

Due to browser security restrictions, traditional tools cannot handle:

  • File selection dialogs
  • Browser authentication popups
  • System-level notifications
    AnchorBrowser bypasses these via virtual machines

Q2: Which models work best?

Our testing shows:

  • English tasks: Claude Opus 4 (92% accuracy)
  • Chinese tasks: Not systematically tested (contributions welcome)
  • Cost-efficient: Gemini 2.5 Flash (optimal speed/cost)

Q3: How to handle dynamic content?

Triple-safeguard approach:

  1. Smart waiting (DOM stabilization)
  2. Scrolling screenshots (full-page capture)
  3. Multi-frame analysis (video stream processing)

Start Your Automation Journey

Zero-Code Experience

Visit the Meka App for $10 free credits (no installation).

Developer Path

git clone https://github.com/meka-agent/meka
cd meka/examples/e-commerce
npm run start

Committed to “vision as interface” philosophy, we continuously optimize human-like computer interaction. Join us in exploring autonomous agent technology boundaries.