Build agents that act on the web — and stay cheap at scale
Practical, copy-pasteable guides for giving an LLM agent a real browser, compiling reusable skills, and bending the cost curve down with a semantic dispatch cache.
Everything from first run to flat-cost scale
Each guide is answer-shaped: question headings, runnable code, and the economics spelled out.
How to give an AI agent a browser
A practical guide to giving an LLM agent a real, authenticated browser — from a single goal to deterministic, replayable action — without re-running the model on every step.
Read the guideCutting LLM cost in browser automation
Why most browser automation gets more expensive as you scale, and how a compile-once + semantic-cache + deterministic-replay pipeline bends the cost curve down instead of up.
Read the guideHow to compile a reusable browser skill
Turn a one-off agent run into a parameterized, reusable skill you can replay deterministically — and share across your team through the skill library and cross-tenant corpus.
Read the guideConnect Twin to LangChain or AutoGen
Register Twin Browser as a single tool in LangChain or AutoGen so your agent can act on the web — with compile-once economics instead of paying the model on every browser step.
Read the guideRun a browser agent over MCP
Expose Twin Browser to Cursor, Claude Desktop, Claude Code, and Cline through the Model Context Protocol — three tools (run, compile_skill, run_skill) and your editor can drive a real browser.
Read the guideGive your agent a browser today
Start free, run your first goal in minutes, and watch the marginal cost per run fall as your skills compile.