Pit Bot

An AI assistant for your FRC competition pit. Pit Bot answers questions from judges, scouts, sponsors, and curious visitors — so your drive team can stay focused on the match.

Built by FRC Team 3966 — Gryphon Command at L&N STEM Academy, Knoxville, TN.

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What It Does

Pit Bot is a conversational AI agent that knows everything about your team. Deploy it on a laptop in your pit, point it at a local language model, and visitors can ask it questions like:

• "What does your robot do this season?"
• "How many students are on the team?"
• "What awards have you won?"
• "How do I join FRC?"

It stays on-topic (FRC and your team only), represents your team with the right tone, and never makes up facts it doesn't know.

How It Works

Visitor question
      |
      v
  Pit Bot agent  <--  system_prompt.md  (persona, rules, tone)
      |          <--  fact_file.md      (everything about your team)
      |
      v
  Local LLM  (runs on your laptop via LM Studio — no internet required)
      |
      v
  Answer

The entire application lives in a single Python file (main.py). It uses pydantic-ai to build the agent and can run two ways:

• CLI mode — chat with the bot directly in your terminal
• API server mode — exposes an OpenAI-compatible HTTP API so any chat UI can connect to it

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Prerequisites

You need three things on your laptop before you start:

1. Python 3.13+

Check your version:

python --version

Download from python.org if needed.

2. uv (Python package manager)

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

3. LM Studio (local LLM runner)

Download from lmstudio.ai. This runs an AI model locally on your laptop — no internet or API keys needed at competition.

Once installed:

1. Download a model (the gpt-oss-20b or any instruction-tuned model works well)
2. Start the local server in LM Studio (usually at http://localhost:1234)

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Quick Start

# 1. Clone this repo
git clone https://github.com/gryphoncommand/pit-bot.git
cd pit-bot

# 2. Install dependencies
uv sync

# 3. Start LM Studio and load a model, then run Pit Bot in CLI mode
uv run main.py cli

# 4. Or run the API server (connect any chat UI to http://localhost:8000)
uv run main.py serve

That's it. The bot is running.

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Configuration

Pit Bot reads configuration from environment variables. The defaults work out of the box with LM Studio:

Variable	Default	What It Does
OPENAI_BASE_URL	http://127.0.0.1:1234/v1	URL of your LLM backend
OPENAI_API_KEY	lm-studio	API key (LM Studio doesn't require a real one)
MODEL_NAME	openai/gpt-oss-20b	Model identifier
HOST	0.0.0.0	Address to bind in serve mode
PORT	8000	Port to listen on in serve mode

To override any of these, set them before running:

MODEL_NAME="my-model" uv run main.py serve

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Running with Docker

If you'd rather not install Python, you can use Docker instead.

# Build the image
docker build -t pit-bot:local .

# Run it (connects to LM Studio running on your host machine)
bash scripts/docker_run.sh

The Docker container runs in serve mode by default and exposes the API on port 8000.

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Adapting Pit Bot for Your Team

You only need to edit two files:

fact_file.md — Your Team's Knowledge Base

This is what the bot knows about your team. Replace the Gryphon Command content with facts about your own team:

• Team number, name, school, location
• Current season robot name, game strategy, and mechanisms
• Awards history
• Team roster, coaches, and mentors
• Sponsors
• Outreach programs and community partnerships
• Anything you'd want a judge or scout to know

The more specific and accurate this file is, the better the bot answers.

system_prompt.md — Bot Persona and Rules

This defines how the bot talks and what it's allowed to discuss. You can customize:

• The bot's name and identity
• Tone (enthusiastic, formal, etc.)
• Topics it will and won't answer
• Any specific instructions for your team's situation

Keep the gracious professionalism rules — never talk negatively about other teams.

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Testing the API

With the server running (uv run main.py serve), send a test message:

bash scripts/openai_api_curl.sh

Or manually with curl:

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "Who are you?"}]}' \
  | jq

The API is OpenAI-compatible, so you can point any chat frontend that supports custom OpenAI endpoints at http://localhost:8000.

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Project Structure

pit-bot/
├── main.py            # Entire application — agent, CLI, and API server
├── system_prompt.md   # Bot persona, tone, and rules
├── fact_file.md       # Team knowledge base (update this every season)
├── Dockerfile         # Container build
├── pyproject.toml     # Python dependencies
└── scripts/
    ├── docker_run.sh        # Run the Docker container
    └── openai_api_curl.sh   # Test the API endpoint

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Questions or Ideas?

• Check out our Chief Delphi Open Alliance thread — we document our build process publicly
• Find us on The Blue Alliance: Team 3966
• Instagram: @gryphoncommand3966

Feel free to fork this repo and adapt Pit Bot for your own team. Gracious Professionalism extends to open source too.