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This guide walks you through every step of creating an AI agent from scratch. It highlights tools and techniques that can shrink your build time from two weeks to a single day.
- A working AI agent can be built in ~50 lines of Python: LLM wrapper + registered tool functions + an agent loop.
- Modular tools (discrete functions with clear inputs/outputs, like send_email()) beat hard-coding capabilities into prompts, and make testing easier.
- Adding a FIFO list or vector DB memory buffer (~12 lines) gives the agent multi-turn context without re-prompting.
- Docker plus a GitHub Actions CI/CD pipeline lets you go from prototype to deployed agent in a day, versus two weeks previously.
The x402 Starter Kit lets you configure and launch x402 APIs in minutes with three facilitator options (Coinbase, local, custom). It includes client-side testing, Docker deployment on EigenCompute, and supports one-prompt custom API creation. The project is open source and welcomes contributions.
- The x402 Starter Kit generates a fully configured x402 API from a single prompt, choosing between Coinbase, local, or custom facilitator setups.
- It includes built-in client-side testing so endpoints can be validated without writing separate test code.
- Everything runs in Docker, enabling one-step deployment to EigenCompute.
- It's open source on GitHub, allowing forking, prompt customization, and community contributions via pull requests.