Anirudh Kamath is a founder and hands-on engineering leader with 11 years of experience building AI-first developer tools and production ML systems from 0 to scale. As Tech/Product Lead at Browserbase he drove Stagehand to 1M+ monthly downloads and 15k+ GitHub stars, making it the most-installed AI framework for browser automation used by companies like Stripe and Ramp. He co-founded Smithery, which operates a large open marketplace for MCP servers (5,000+ servers, 100k tool calls/day), and has repeatedly shipped full-stack systems that blend prompt engineering, inference integration, and product-driven design. Earlier roles include founding engineer work on HIPAA/GDPR-compliant clinical trial tooling and full-stack ML at Whatnot, giving him deep experience across compliance, ranking systems, and high-traffic production models. He’s an active open-source contributor—authoring core Stagehand functionality and sophisticated prompt templates that expand LLM tool support—and values building simple, extensible developer experiences. Based in San Francisco, he pairs product instincts with implementation rigor and a knack for turning researchy ideas into widely adopted tools.
11 years of coding experience
4 years of employment as a software developer
Bachelor of Science (B.S.) Mathematics and Data Science Minor in Business Administration, Bachelor of Science (B.S.) Mathematics and Data Science Minor in Business Administration at Northeastern University
Study Abroad in Greece Computer Science, Study Abroad in Greece Computer Science at ACT - American College of Thessaloniki
High School Diploma, High School Diploma at Providence High School
An AI web browsing framework focused on simplicity and extensibility.
Role in this project:
Full-stack Developer
Contributions:4 releases, 233 reviews, 267 PRs in 5 months
Contributions summary:Anirudh implemented core functionality for the AI web browsing framework, focusing on prompt engineering and integration. They updated and extended various prompt templates within the `lib/prompt.ts` file to enhance the framework's interaction with LLMs. The changes include the introduction of new tools, more sophisticated prompt structures, and improvements to the inference and evaluation components. The user also worked on the project's evals and added support for different models.
Contributions:4 reviews, 73 PRs, 66 pushes in 2 months
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