Build an AI Chat Agent That Codes React Native Mobile Apps with Python

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Creating an intelligent chat-based coding assistant is no longer science fiction. In this tutorial, we’ll walk you through building an AI-powered agent that can generate complete React Native code for mobile apps based on user input.

We’ll be using Python, LangChain, OpenAI or Ollama, and a simple chat UI (like Streamlit or Flask).

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🛠️ Tools and Technologies

• Python 3.10+
• LangChain – for AI workflow
• LLM (OpenAI GPT-4 / Ollama local models)
• Streamlit – simple web-based chat interface
• React Native knowledge base (system prompt or context priming)

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🚀 Step-by-Step Tutorial

📦 1. Set Up Your Python Environment

Install required packages:

pip install langchain openai streamlit

If using Ollama (locally hosted models):

pip install langchain-community

Make sure Ollama is running:

ollama run codellama

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🧠 2. Configure the LLM Agent

Create a file called agent.py.

# agent.py
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, Tool, AgentType
from langchain.schema import SystemMessage

# Replace with your own API key or use a local LLM from Ollama
llm = ChatOpenAI(temperature=0, model="gpt-4")  # or from langchain_community.llms import Ollama

system_prompt = SystemMessage(content="""
You are an expert React Native developer.
When given instructions, generate full React Native mobile app code.
Your response must include:
- App.js
- Any additional components if necessary
Use functional components and hooks. Use React Navigation for navigation. Write clean code.
""")

def get_agent():
    return initialize_agent(
        tools=[],
        llm=llm,
        agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
        verbose=True
    )

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💬 3. Create the Chat Interface

Using Streamlit for simplicity:

# app.py
import streamlit as st
from agent import get_agent

st.set_page_config(page_title="React Native AI Coder", layout="wide")
st.title("🤖 React Native Code Generator")
st.write("Describe your mobile app and the AI will generate React Native code.")

agent = get_agent()

if "chat_history" not in st.session_state:
    st.session_state.chat_history = []

prompt = st.chat_input("Describe the app you want to build...")

if prompt:
    with st.spinner("Thinking..."):
        response = agent.run(prompt)
        st.session_state.chat_history.append(("user", prompt))
        st.session_state.chat_history.append(("ai", response))

for sender, msg in st.session_state.chat_history:
    if sender == "user":
        st.chat_message("user").markdown(msg)
    else:
        st.chat_message("ai").code(msg, language="javascript")

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🧪 4. Run the Application

streamlit run app.py

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🧠 5. Example Prompt and Output

Prompt:

I want a to-do list app with input, delete, and checkbox features.

Output:

// App.js
import React, { useState } from 'react';
import { SafeAreaView, TextInput, Button, FlatList, Text, View, TouchableOpacity } from 'react-native';

export default function App() {
  const [task, setTask] = useState('');
  const [tasks, setTasks] = useState([]);

  const addTask = () => {
    if (task.length === 0) return;
    setTasks([...tasks, { id: Date.now().toString(), title: task, completed: false }]);
    setTask('');
  };

  const toggleComplete = id => {
    setTasks(tasks.map(t => t.id === id ? { ...t, completed: !t.completed } : t));
  };

  const deleteTask = id => {
    setTasks(tasks.filter(t => t.id !== id));
  };

  return (
    <SafeAreaView style={{ padding: 20 }}>
      <TextInput
        placeholder="Enter task"
        value={task}
        onChangeText={setTask}
        style={{ borderWidth: 1, marginBottom: 10 }}
      />
      <Button title="Add Task" onPress={addTask} />
      <FlatList
        data={tasks}
        keyExtractor={item => item.id}
        renderItem={({ item }) => (
          <View style={{ flexDirection: 'row', marginTop: 10 }}>
            <TouchableOpacity onPress={() => toggleComplete(item.id)}>
              <Text style={{ textDecorationLine: item.completed ? 'line-through' : 'none' }}>{item.title}</Text>
            </TouchableOpacity>
            <Button title="X" onPress={() => deleteTask(item.id)} />
          </View>
        )}
      />
    </SafeAreaView>
  );
}

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📚 Advanced Tips

• Add file export functionality to save generated code.
• Use LangChain Tools for file generation, zip archiving, or Git integration.
• Integrate with Firebase or Supabase for backend options.

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✅ Summary

You’ve just created an AI agent in Python that can:

• Understand natural language descriptions
• Generate React Native code
• Interact via a real-time chat interface

This setup can be extended into a full SaaS product with versioning, deployment, and team collaboration.

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