The Role of LLMs in Browser Automation
Browse Anything uses Large Language Models (LLMs) to bridge the gap between human intent and browser actions. Instead of requiring users to write code or configure complex rules, LLMs understand what you want to accomplish and figure out how to do it.
What LLMs Handle
- • Understanding natural language commands
- • Analyzing webpage structure and content
- • Identifying interactive elements
- • Planning multi-step actions
- • Extracting and summarizing data
- • Handling unexpected situations
Supported Models
Browse Anything supports multiple LLM providers. Each has different strengths, and you can choose based on your needs.
GPT-4o (OpenAI)
RecommendedOpenAI's flagship multimodal model. Excellent at understanding complex instructions and visual page analysis.
Best for:
Complex, multi-step tasks
Speed:
Medium
Cost:
Higher
GPT-4o Mini (OpenAI)
Fast & AffordableSmaller, faster version of GPT-4o. Good balance of capability and cost for simpler tasks.
Best for:
Simple tasks, high volume
Speed:
Fast
Cost:
Lower
Claude 3.5 Sonnet (Anthropic)
AlternativeAnthropic's model known for strong reasoning and following complex instructions.
Best for:
Nuanced reasoning tasks
Speed:
Medium
Cost:
Medium
Gemini 1.5 (Google)
AlternativeGoogle's multimodal model with strong visual understanding capabilities.
Best for:
Visual-heavy tasks
Speed:
Fast
Cost:
Variable
How the Process Works
Command Understanding
When you submit a task like "Find the cheapest flight from NYC to LA next Friday", the LLM parses this to understand: the goal (find flights), constraints (cheapest), origin (NYC), destination (LA), and timing (next Friday).
Page Analysis
As the browser navigates, the LLM receives information about the current page: visible text, interactive elements, form fields, and page structure. It uses this to understand what's on screen and what actions are possible.
Action Planning
The LLM decides the next action: click a button, type in a field, scroll, wait for content to load, or extract data. It outputs structured commands that our browser engine executes.
Iteration & Completion
This process repeats: execute action → observe result → plan next action. The LLM continues until the task is complete or it determines it can't proceed.
Writing Effective Prompts
The quality of your results depends significantly on how you describe your task. Here are guidelines for effective prompts.
Effective Prompt Examples
- Specific: "Search for hotels in downtown Chicago for Dec 20-22, filter by 4+ stars and under $200/night"
- Step-aware: "Go to amazon.com, search for 'wireless mouse', sort by customer reviews, and get the name and price of the top 3 results"
- Clear output: "Find the contact email on example.com and report it back to me"
Prompt Template
Go to [website],
[perform specific actions],
[apply any filters or criteria],
and [describe the expected output/result].
Bring Your Own Key (BYOK)
Enterprise users can use their own API keys from OpenAI, Anthropic, or Google. This gives you direct control over LLM costs and usage limits.
BYOK Benefits
- • Cost Control: Pay directly to LLM provider, use your existing agreements
- • Rate Limits: Use your own rate limits, not shared platform limits
- • Privacy: API calls go through your own API key
- • Security: Keys are encrypted end-to-end and never exposed
Experience LLM-Powered Automation
Try Browse Anything free and see how LLMs transform browser automation.