# Introduction ### Background of Lunch Protocol “There is no such thing as a free lunch,” a famous quote from traditional economics — and that’s how Lunch Protocol was born. We believe that the potential of blockchain and AI can bring users tangible or financial benefits with minimal effort — close enough to free. Web3 is full of opportunities — and inherently, some risks as well. Yet as humans bound by physical limitations, our time and energy are limited. AI agents can and will do almost everything on behalf of users, even managing assets for you soon. ### Inside Lunch Protocol That’s where Lunch Protocol comes into play. At Lunch, we’re building AI agents for DeFi, or DeFAI agents called EGGi, to tailor each user’s needs and preferences. We’ve already built a platform filled with diverse yield and reward opportunities, featuring securely pre-whitelisted dApps and one-click access through full integrations — originally for users to easily make onchain interactions, and now the AI agents interact more efficiently than humans ever could. Also EGGi — our mascot as well as the conversational AI chatbot — matures as it aggregates, understands, and processes natural language prompts of DeFi users. Just enjoy unmatched, cutting-edge personalized assistants that farm — and serve Lunch — for you, 24/7. ### LUNCH Token The LUNCH token is the base currency for Lunch Protocol, forming the monetary backbone of the ecosystem. It has multiple utilities to not only better serve users but also incentivize builders and other AI agents across Human-Computer Interactions (HCI) as well as AI agent-to-AI agent interactions. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://lunch-protocol.gitbook.io/lunch-protocol-docs/readme.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.