diff --git a/environments/community/capital_city_env/README.md b/environments/community/capital_city_env/README.md
new file mode 100644
index 000000000..28ad31f8c
--- /dev/null
+++ b/environments/community/capital_city_env/README.md
@@ -0,0 +1,17 @@
+# Capital City Quiz Environment
+
+A simple reinforcement learning environment that trains LLMs to correctly
+identify capital cities of countries around the world.
+
+## Overview
+- **Task**: Given a country name, output the correct capital city
+- **Reward**: Binary — 1.0 for correct, 0.0 for incorrect
+- **Dataset**: 30 countries built-in (no external dataset needed)
+
+## Usage
+```bash
+python environments/community/capital_city_env/capital_city_env.py serve
+```
+
+## Author
+Enzamk
diff --git a/environments/community/capital_city_env/__init__.py b/environments/community/capital_city_env/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/environments/community/capital_city_env/capital_city_env.py b/environments/community/capital_city_env/capital_city_env.py
new file mode 100644
index 000000000..f658d0008
--- /dev/null
+++ b/environments/community/capital_city_env/capital_city_env.py
@@ -0,0 +1,129 @@
+"""
+Capital City Quiz Environment for Atropos.
+Trains LLMs to correctly identify capital cities of countries.
+"""
+
+import random
+from typing import Optional
+
+from atroposlib.envs.base import BaseEnv, BaseEnvConfig, ScoredDataGroup
+from atroposlib.type_definitions import Item
+
+CAPITAL_CITIES = {
+    "France": "Paris",
+    "Germany": "Berlin",
+    "Japan": "Tokyo",
+    "Brazil": "Brasília",
+    "Australia": "Canberra",
+    "Canada": "Ottawa",
+    "India": "New Delhi",
+    "China": "Beijing",
+    "Egypt": "Cairo",
+    "South Africa": "Pretoria",
+    "Argentina": "Buenos Aires",
+    "Mexico": "Mexico City",
+    "Italy": "Rome",
+    "Spain": "Madrid",
+    "Russia": "Moscow",
+    "Nigeria": "Abuja",
+    "Kenya": "Nairobi",
+    "Indonesia": "Jakarta",
+    "Pakistan": "Islamabad",
+    "Bangladesh": "Dhaka",
+    "Turkey": "Ankara",
+    "Saudi Arabia": "Riyadh",
+    "Sweden": "Stockholm",
+    "Norway": "Oslo",
+    "Netherlands": "Amsterdam",
+    "Belgium": "Brussels",
+    "Switzerland": "Bern",
+    "Portugal": "Lisbon",
+    "Greece": "Athens",
+    "Poland": "Warsaw",
+}
+
+SYSTEM_PROMPT = (
+    "You are a geography expert. When given a country name, "
+    "respond with ONLY the capital city name. Nothing else."
+)
+
+
+class CapitalCityEnvConfig(BaseEnvConfig):
+    """Configuration for the Capital City Quiz Environment."""
+
+    env_name: str = "CapitalCityEnv"
+
+
+class CapitalCityEnv(BaseEnv):
+    """
+    An environment that trains LLMs to identify capital cities.
+    The model receives a country name and must respond with the correct capital.
+    Rewards are binary: 1.0 for correct, 0.0 for incorrect.
+    """
+
+    name = "CapitalCityEnv"
+
+    def __init__(self, config: CapitalCityEnvConfig, *args, **kwargs):
+        super().__init__(config, *args, **kwargs)
+        self.countries = list(CAPITAL_CITIES.keys())
+
+    @classmethod
+    def config_init(cls) -> CapitalCityEnvConfig:
+        return CapitalCityEnvConfig()
+
+    async def collect_trajectories(
+        self, item: Item
+    ) -> tuple[Optional[ScoredDataGroup], list[Item]]:
+        country = item[0]
+        correct_capital = CAPITAL_CITIES[country]
+
+        messages = [
+            {"role": "system", "content": SYSTEM_PROMPT},
+            {"role": "user", "content": f"What is the capital city of {country}?"},
+        ]
+
+        completions = await self.server.completion(
+            messages=messages,
+            n=self.config.group_size,
+        )
+
+        scored_data = ScoredDataGroup()
+        scored_data["tokens"] = []
+        scored_data["masks"] = []
+        scored_data["scores"] = []
+
+        for completion in completions.choices:
+            answer = completion.message.content.strip()
+            score = 1.0 if correct_capital.lower() in answer.lower() else 0.0
+
+            tokens, masks = self.tokenize_for_trainer(messages, answer)
+            scored_data["tokens"].append(tokens)
+            scored_data["masks"].append(masks)
+            scored_data["scores"].append(score)
+
+        return scored_data, []
+
+    async def get_next_item(self) -> Item:
+        country = random.choice(self.countries)
+        return (country,)
+
+    async def evaluate(self):
+        correct = 0
+        total = len(self.countries)
+        for country in self.countries:
+            correct_capital = CAPITAL_CITIES[country]
+            messages = [
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {"role": "user", "content": f"What is the capital city of {country}?"},
+            ]
+            completion = await self.server.completion(messages=messages, n=1)
+            answer = completion.choices[0].message.content.strip()
+            if correct_capital.lower() in answer.lower():
+                correct += 1
+
+        accuracy = correct / total
+        return {"eval/capital_city_accuracy": accuracy}
+
+
+if __name__ == "__main__":
+    CapitalCityEnv.cli()