I Built a Pokémon Trainer AI With LangChain

As I’ve gotten older, I’ve noticed something a little uncomfortable about one of my favorite lifelong hobbies: Pokémon just isn’t that hard anymore. After a few decades of playing, the type matchups, speed tiers, and common strategies start to feel second nature. I’ve dabbled in competitive formats before, but most of the time I just want to test a new team idea or strategy without coordinating with another human or laddering for hours. So I started wondering: What if I could build my own AI Pokémon trainer? Could I build one that actually adapts, reasons, and maybe even teaches me something?

Using LangChain, Pokémon Showdown, and Express, I built a Pokémon battle simulator that let me battle against an LLM-driven opponent. This opponent reasons through each turn and logs why it made the decisions it did.

I had two goals going into this project:

• Create a challenging opponent I could use to test team compositions and strategies.
• Explore LangChain in a real, interactive system, beyond toy prompts and demos.

The System

At a high level, the system looks like this:

Pokémon Showdown handles the actual battle simulation and rules engine.
Express acts as the glue between the battle engine and the AI agent.
LangChain powers the decision-making logic for the AI opponent.

Each turn, Pokémon Showdown provides the current battle state (active Pokémon, moves, stats, legal commands, etc.). That state is fed into a LangChain-powered agent, which evaluates its options and emits a legal Showdown command (i.e. selecting a damaging move, a defensive move, a stat boosting move, or switching into a more optimal matchup).

Additionally, the system also logs the AI’s reasoning. That means after a loss, I can go back and inspect why the AI made its decisions and see things like:

• Why it left the current Pokémon in battle instead of swapping to another member of its team.
• Why it chose to use a setup move, increasing its Pokémon stats over immediate damage.
• What risks it thought were acceptable. For instance, can it survive a strong attack from the current opponent? What strategies are common for the current opposing Pokémon, and is it leaving it open to do as it pleases?

This was one of my favorite parts of the project. I initially thought that this functionality wouldn’t serve much purpose outside of debugging; giving insight into the current reasoning capabilities behind the AI. As the AI got more refined, however, it slowly started replicating thoughts I had or even options I didn’t consider when making its moves. This showcased to me that maybe this project could be more than just a battle simulator.

What Is LangChain?

LangChain is a framework for building applications around large language models that go beyond simple prompt → response interactions.

Instead of treating an LLM as a black box, LangChain lets you:

• Define tools the model can call
• Structure multi-step reasoning flows
• Maintain state across interactions
• Build agents that decide how to act, not just what to say

In other words, LangChain is designed for systems where an LLM needs to think, decide, and act– which makes it a great fit for turn-based games.

How the Pokémon Agent Works

I defined a LangChain agent using LangGraph, LangChain’s state-machine-style orchestration layer.

Instead of asking the model “what move should you use?”, I structured the agent as a multi-step decision process.

Structured Tools, Not Free-Form Prompts

The agent has access to several tools:

rank_attack
rank_switch
rank_setup
emit_command

Each tool extracts structured information from the battle state and frames it in Pokémon-specific terms: speed comparisons, setup opportunities, bench health, and general matchup considerations.

For example, rank_attack doesn’t ask the model to pick a move directly. Instead, it provides a list of available moves, speed comparisons, and high-level competitive heuristics (STAB, risk vs. reward, status effects).

This forces the model to reason about the situation before acting, rather than jumping straight to a command. This led to a more skillful opponent as opposed to my first approach of providing a general overview on how battling works and letting the model make decisions on its own heuristics.

State Machines Over Single Prompts

Using LangGraph, the agent follows a predictable flow:

1. Analyze the current context.
2. Call ranking tools to evaluate options.
3. Compare choices internally.
4. Emit a legal Pokémon Showdown command.

This is implemented as a graph with explicit transitions between LLM calls and tool executions. If the model forgets to emit a command, the system nudges it back on track.

The result is an agent that behaves less like a chatbot and more like a turn-based decision engine.

The Results

Of course, things didn’t always go according to plan.

One of the more memorable failures happened when the AI was controlling a Psychic-type Pokémon Mew with a Ground-type move. The model correctly identified that Ground-type moves are strong in certain matchups– but then assumed that because the move was Ground-type, the Pokémon itself must also be Ground-type. As a result, it confidently stayed in battle against matchups it had no business staying in, and was promptly KO’d.

Without additional structured data– explicit type charts, Pokémon metadata, or a dedicated knowledge base– the base model filled in the gaps on its own. Sometimes correctly. Sometimes… not so much.

Final Thoughts

With more time, there are a few clear paths to improving this system. First, I would inject a Pokémon-specific knowledge base instead of relying on general model knowledge. I might even consider running a local LLM fine-tuned on Pokémon data to remove the need for an internet connection, while still producing fine-tuned results. I’d also tighten the context so assumptions like “move type = Pokémon type” are impossible.

I still think there’s real potential here for a genuinely useful sparring partner—or even a coaching tool that helps explain why a particular line of play is strong. But for now, this turned out to be a great way to learn about both my favorite childhood game and modern AI tooling at the same time.