https://github.com/charlesyuan02/legends-za-teambuilder
An actor-critic agent pipeline made w/ Google ADK to suggest a final Pokemon for my team in Legends Z-A.
https://github.com/charlesyuan02/legends-za-teambuilder
adk-python agents
Last synced: about 2 months ago
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An actor-critic agent pipeline made w/ Google ADK to suggest a final Pokemon for my team in Legends Z-A.
- Host: GitHub
- URL: https://github.com/charlesyuan02/legends-za-teambuilder
- Owner: CharlesYuan02
- Created: 2026-03-21T04:44:06.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2026-03-21T04:54:41.000Z (2 months ago)
- Last Synced: 2026-03-21T20:41:42.666Z (2 months ago)
- Topics: adk-python, agents
- Language: Python
- Homepage:
- Size: 2.11 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Legends Z-A Teambuilder
LLMs still suck at Pokemon — so I fixed that with a generator-critic pipeline.
When I needed a 6th team member for Pokemon Legends Z-A, ChatGPT suggested Pokemon that weren't in the game and Gemini invented Mega Evolutions that don't exist. After correcting them, I realized the recommendations were just my own bias parroted back to me — a failure mode that hits real AI systems too.
So I built this: a generator-critic agentic pipeline using [Google ADK](https://google.github.io/adk-docs/) and Gemini that actually self-corrects.
**How it works:**
1. A **generator** proposes 3 team candidates.
2. A **critic** (with Google Search access) fact-checks every Pokemon, move, item, and mechanic against real game data.
3. A **refiner** fixes what the critic flags.
Loops until the critic finds zero issues, or 5 iterations — whichever comes first.
## Setup
**Prerequisites:** Python 3.13+, [uv](https://docs.astral.sh/uv/)
1. Clone the repo and install dependencies:
```bash
uv sync
```
2. Copy `.env.example` to `.env` and add your Google API key:
```bash
cp .env.example .env
```
```
GOOGLE_API_KEY=your_key_here
```
3. Run:
```bash
uv run main.py
```