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Introduction to BotServer

Build conversational AI bots in minutes, not months. BotServer lets you create intelligent chatbots by writing simple BASIC scripts and dropping in your documents. No complex frameworks, no cloud dependencies, no AI expertise required.

Quick Example

Want a student enrollment bot? Here's all you need:

  1. Drop your documents in a folder:
edu.gbkb/
  enrollment-policy.pdf
  course-catalog.pdf
  1. Write a simple tool (optional):
' enrollment.bas
PARAM name, email, course
SAVE "enrollments.csv", name, email, course
TALK "Welcome to " + course + "!"
  1. Chat naturally:
User: I want to enroll in computer science
Bot: I'll help you enroll! What's your name?
User: Sarah Chen
Bot: Welcome to Computer Science, Sarah!

The Flow

Conversation Flow

The AI handles everything else - understanding intent, collecting information, executing tools, answering from documents. Zero configuration.

What Makes BotServer Different

Just Run It

./botserver

That's it. No Kubernetes, no cloud accounts. The bootstrap process installs everything locally in 2-5 minutes. PostgreSQL, vector database, object storage, cache - all configured automatically with secure credentials.

Real BASIC, Real Simple

Remember BASIC from the 80s? We brought it back for conversational AI:

' save-note.bas - A simple tool
PARAM topic, content
SAVE "notes.csv", topic, content, NOW()
TALK "Note saved!"

Four lines. That's a working tool the AI can call automatically.

Documents = Knowledge

Drop PDFs, Word docs, or text files into .gbkb/ folders. They're instantly searchable. No preprocessing, no configuration, no pipelines. The bot answers questions from your documents automatically.

Tools = Functions

Create .bas files that the AI discovers and calls automatically. Need to save data? Send emails? Call APIs? Just write a tool. The AI figures out when and how to use it.

Architecture at a Glance

BotServer is a single binary that includes everything:

BotServer Architecture

One process, one port, one command to run. Deploy anywhere - laptop, server, container.

Real-World Use Cases

Customer Support Bot

documents: FAQs, policies, procedures
tools: ticket creation, status lookup
result: 24/7 support that actually helps

Employee Assistant

documents: HR policies, IT guides, company info
tools: leave requests, equipment orders
result: Instant answers, automated workflows

Sales Catalog Bot

documents: product specs, pricing sheets
tools: quote generation, order placement
result: Interactive product discovery

Meeting Assistant

documents: agenda, previous minutes
tools: action item tracking, scheduling
result: AI-powered meeting facilitator

The Package System

Bots are organized as packages - just folders with a naming convention:

my-bot.gbai/                    # Package root
├── my-bot.gbdialog/            # BASIC scripts
│   └── start.bas               # Entry point
├── my-bot.gbkb/                # Knowledge base
│   ├── policies/               # Document collection
│   └── procedures/             # Another collection
└── my-bot.gbot/                # Configuration
    └── config.csv              # Bot settings

How It Works

Package System Flow

That's it. No XML, no JSON schemas, no build process. Copy the folder to deploy.

Getting Started in 3 Steps

1. Install (2 minutes)

wget https://github.com/GeneralBots/BotServer/releases/latest/botserver
chmod +x botserver
./botserver

2. Open Browser

http://localhost:8080

3. Start Chatting

The default bot is ready. Ask it anything. Modify templates/default.gbai/ to customize.

Core Philosophy

  1. Simplicity First - If it needs documentation, it's too complex
  2. Everything Included - No external dependencies to manage
  3. Production Ready - Secure, scalable, enterprise-grade from day one
  4. Developer Friendly - Clear errors, hot reload, great debugging
  5. AI Does the Work - Don't write logic the LLM can handle

Technical Highlights

  • Language: Written in Rust for performance and safety
  • Database: PostgreSQL with Diesel ORM
  • Cache: Valkey (Redis-compatible) for sessions
  • Storage: S3-compatible object store
  • Vectors: Qdrant for semantic search
  • Security: Argon2 passwords, AES encryption
  • LLM: OpenAI API or local models
  • Scripting: Rhai-powered BASIC interpreter

What's Next?

Community

BotServer is open source (AGPL-3.0) developed by Pragmatismo.com.br and contributors worldwide.

Ready to build your bot? Turn to Chapter 01 and let's go!