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Getting startedOverview

Getting started

Naylence is a polyglot runtime for building agent fabrics – networks of agents, nodes, and sentinels that can run in browsers, backends, and mixed topologies.

These docs assume you already have a rough idea of what Naylence is from the main site.
If not, start with the Overview and then come back here.

Choose your lane

Naylence ships first-class SDKs for both TypeScript and Python.
The core fabric concepts are the same, but the ergonomics and typical use cases differ:

🐍Python

Start with a minimal command-line fabric in Python. This track is ideal for backend services, data/ML workloads, and integrating Naylence into existing Python stacks.

🧩TypeScript

Start with a minimal command-line fabric in Node.js. This track focuses on simple CLI examples first, then expands into browser and React integrations in later guides.

What you’ll do in each quickstart

The language-specific quickstarts intentionally keep things small and CLI-only:

  1. Install the Naylence SDK for your language.
  2. Run a tiny single-process fabric from the command line.
  3. Create one agent that logs any message it receives.
  4. Send a “Hello, Naylence” message and watch it flow through the fabric.

Once that works, later guides introduce multi-agent workflows, sentinels, and browser/React integrations.

Prerequisites

You don’t need any LLMs or external services to complete the quickstarts, but you’ll need:

  • General

    • Git and a terminal

  • For the TypeScript track

    • Node.js 20+ (or the version recommended in the example repo)
    • pnpm or npm

  • For the Python track

    • Python 3.12+ (or the version recommended in the example repo)
    • pip or your preferred virtual-env / dependency manager

If you’re comfortable in both languages and just want to see something run, start with the TypeScript quickstart – the CLI examples are fast to set up, then come back for Python and the Polyglot guides.

Next steps

Pick a language to continue:

Once you’re comfortable with the basics, you can dive deeper into:

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