> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bumbleagi.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Agents overview

> One persistent entity, one harness — many ways to show up.

In Bumblebee, an **agent** is not a disposable chat session. It is a **digital entity**: configured in YAML, backed by durable memory and soma state, and run by the same harness whether you talk on Telegram, Discord, or the CLI.

## One brain, many surfaces

* **Identity and memory** are shared. The entity remembers people, episodes, and beliefs across every channel.
* **Cognition** (reflex, deliberate, tools) is the same stack for all inputs.
* **Presence** is **plugged in per platform**: each social adapter translates that channel’s messages into a common `perceive` pipeline and routes replies back.

So “building an agent” usually means: define **who** the entity is (traits, voice, drives), wire **where** it appears (platform blocks in YAML), and choose **where inference runs** (local Ollama, or a remote gateway to your GPU).

## Worker vs full `run`

| Mode             | Command                     | Typical use                                                                |
| ---------------- | --------------------------- | -------------------------------------------------------------------------- |
| **Full runtime** | `bumblebee run <entity>`    | Laptop or desktop: CLI + Telegram/Discord + daemon.                        |
| **Worker**       | `bumblebee worker <entity>` | Servers (e.g. Railway): messaging + daemon **without** an interactive CLI. |

The worker is the same codebase and the same entity — trimmed for headless operation. See [Worker runtime](/agents/worker).

## What to read next

* [Building on Bumblebee](/build/overview) — extension points and how to extend safely.
* [Presence & social surfaces](/build/presence-and-social) — Telegram, Discord, and the adapter pattern.
* [Portable inference](/build/portable-inference) — gateway, tunnels, and routing “local” inference from anywhere.
* [Hybrid Railway](/deployment/hybrid-railway) — end-to-end split: worker in cloud, brain at home.