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omp-docs/agentic-web-research/self-hosted-web-agents.md

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+---
+type: concept
+title: Self-Hosted Web Agents
+description: Tools and architectures that let an AI agent drive a real browser, self-hosted.
+tags:
+  - agentic
+  - browser
+  - scraping
+  - web-agent
+  - unverified
+verified: 2026-08-28
+---
+
+# Self-Hosted Web Agents
+
+A web agent is an AI that controls a browser. It sees the page, chooses an action, and repeats until it completes the task. You can run the whole loop on your own hardware.
+
+This page covers the leading open tools and the architecture the agent uses.
+
+## The Main Tools
+
+### Browser Use
+
+Browser Use is a Python library that uses Playwright under the hood. It is MIT-licensed. It supports many model providers, including local OpenAI-compatible endpoints such as llama.cpp.
+
+Key ideas:
+
+- It snapshots the DOM and converts it to Markdown for the model.
+- Ten watchdogs handle CAPTCHAs, crashes, popups, downloads, and security.
+- Real browser profiles carry saved logins.
+
+**On the "87.4 percent" number:** that result refers to Browser Use's managed Cloud agent (the Hybrid type that blends structured browser tools with screenshots). Peer-reviewed results for vision-only (CUA) models are much lower, about 44.5 percent on the same task type. Treat any single accuracy number as one benchmark, not a universal score.
+
+```python
+# Local model via llama.cpp (OpenAI-compatible endpoint)
+from browser_use import Agent
+from browser_use.llm.openai import ChatOpenAI
+
+agent = Agent(
+    task="Search for the latest AI news",
+    llm=ChatOpenAI(
+        base_url="http://192.168.0.235:7770/v1",
+        model="frank-default-small",
+        api_key="EMPTY",
+    ),
+)
+await agent.run()
+```
+
+### Skyvern
+
+Skyvern is an open-source browser agent (AGPL-3.0). It uses a vision model to see the page and Playwright to act. It extends Playwright with `page.act()`, `page.extract()`, and `page.validate()`. It ships a Docker Compose setup. Use it when you need a vision-first agent.
+
+### Crawl4AI
+
+Crawl4AI is an async crawler that returns LLM-friendly Markdown. It is built for retrieval pipelines, not for clicking through interactive pages. Use it to fetch and clean many pages quickly.
+
+Key ideas:
+
+- It produces clean Markdown and a noise-filtered "fit Markdown."
+- It supports deep crawling with breadth-first, depth-first, and best-first strategies.
+- It can run in Docker with a REST API and MCP endpoints.
+
+```bash
+pip install -U crawl4ai
+crawl4ai-setup
+crwl https://example.com --deep-crawl bfs --max-pages 10
+```
+
+## The Loop
+
+```mermaid
+graph TD
+    A[Task] --> B[Model chooses action]
+    B --> C[Browser or Crawler acts]
+    C --> D[Extract text / Markdown]
+    D --> B
+    D --> E[Task done?]
+    E -->|No| B
+    E -->|Yes| F[Return answer + citations]
+```
+
+## Which Tool to Use
+
+| Situation | Tool |
+|-----------|------|
+| Need to click, log in, or fill forms | Browser Use |
+| Need vision-based browsing | Skyvern |
+| Need to fetch and clean many static pages | Crawl4AI |
+| Research pipeline feeds an agent | Crawl4AI first, Browser Use for interactive steps |
+
+A hybrid works best. Use Crawl4AI for mass extraction and Browser Use for the interactive steps.
+
+## Citations
+
+- Browser Use repository: <https://github.com/browser-use/browser-use>
+- Browser Use documentation: <https://docs.browser-use.com>
+- Skyvern repository: <https://github.com/Skyvern-AI/skyvern>
+- Crawl4AI repository: <https://github.com/unclecode/crawl4ai>
+- Crawl4AI documentation: <https://docs.crawl4ai.com>
+
+## See Also
+
+- [Agentic Search Best Practices](agentic-search-best-practices.md) — The loop around the tools.
+- [Open Models for Web Use](open-models.md) — Models that drive these tools.
+- [Research Effectiveness](research-effectiveness.md) — Orchestration around the loop.