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Hyperagent is a platform where you give a fleet of AI agents a project, and they research, build, host, and keep it updated. It targets outputs like apps, sites, decks, and market briefs, and plugs into Slack, Telegram, webhooks, or schedules.
- Deliverables are built and hosted live as HTML, not pasted into a document, and they update as your data changes.
- Each agent gets its own computing environment, and you can watch every search and decision as it happens.
- The system learns over time, generating new memories and skills from prior work.
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.