Pagerunner vs Browser Use
Browser Use is the popular generalist. Pagerunner is the specialist for authenticated, privacy-sensitive, learning-enabled workflows. Browser Use executes. Pagerunner learns and protects.
Feature comparison
| Feature | Pagerunner | Browser Use |
|---|---|---|
| Site intelligence (adapters, selector tracking) | Learns from usage | Stateless |
| PII anonymization | Local ONNX NER | Raw content to LLM |
| Persistent profiles | Named, encrypted | Reuses Chrome profile |
| Session checkpoints | Full state + scroll | None |
| MCP-native | Built for MCP | Added after launch |
| Multi-LLM support | Any MCP client | Claude, GPT, Gemini, Ollama |
| Cloud offering | Local only | Browser Use Cloud |
| Language | Rust | Python |
| Price | Free (Apache-2.0) | Free OSS / Cloud paid |
Where Pagerunner wins
- Site intelligence compounds. Adapters and selector tracking mean repeated tasks get faster and more reliable over time.
- PII anonymization runs locally. Local ONNX NER strips sensitive data before it reaches your LLM. Not 100% guaranteed, but a significant protection layer.
- Encrypted profiles. Named profiles with AES-256-GCM encryption keep credentials separate and secure.
- Session checkpoints with full state. Restore scroll position, DOM state, and page context after failures.
- LLM-aware responses. Metadata hints help your agent make better decisions with less token burn.
Where Browser Use wins
- Multi-LLM native. Direct support for Claude, GPT, Gemini, and Ollama without MCP intermediation.
- Cloud offering. Can run browser sessions at scale without local infrastructure.
- Python ecosystem. Native to ML and data science workflows.
When to use which
Use Pagerunner when
You need authenticated workflows that learn from repeated use, privacy protection for sensitive data, or MCP integration with coding agents like Claude Code or Cursor.
Use Browser Use when
You need Python-native integration in ML pipelines, cloud execution at scale, or multi-LLM support outside the MCP ecosystem.