The rapid evolution of generative AI has unlocked endless possibilities—from chatbots and email generators to advanced research assistants. But creating full-stack GenAI apps can feel daunting without the right tools.
Therefore I tried to build some GenAI apps myself with the LangChain GenAI Apps repository: a hands-on template for building practical AI applications using LangChain, FastAPI, and Streamlit.
The project is organized into two main folders:
📦 fastapi-app/: The Backend
This is where the brain of your application lives. It uses FastAPI, a modern, high-performance Python web framework, to expose multiple endpoints for text generation tasks.
✨ Key Use Cases Included:
- General Text Generation: Using either OpenAI or a local Ollama model.
- Custom Prompt Generators:
- 🍽️ Recipe Generator
- 📧 Marketing Email Generator
- 🧠 Therapy Advice Generator
- ✍️ Blog Post Generator
- RAG (Retrieval-Augmented Generation): Generate responses grounded in external data.
- AI Agent with Web Search: A LangChain agent that uses web search to answer user queries.
🎨 streamlit-app/: The Frontend
Each backend use case is paired with a simple UI built in Streamlit—a Python library that turns scripts into interactive apps in minutes.
These apps let users input data, tweak prompts, and interact with the AI in real-time.
In the following section you will get an overview of the tech stack.
LangChain
LangChain is a powerful framework designed to simplify the development of applications using large language models (LLMs). It provides modular building blocks that make it easier to create both simple and advanced AI workflows. Some of the key components include:
- 🔗 Chains: Sequences of calls (to LLMs, tools, or other functions) that are composed together to perform complex tasks. Chains allow you to combine prompts, model outputs, and logic into coherent workflows.
- 🧠 Memory: Keeps track of previous interactions or context within a session. This is crucial for creating stateful agents or chatbots that “remember” past messages.
- 🛠️ Tools: External utilities that the LLM can call during execution, such as web search APIs, calculators, or custom functions.
- 🤖 Agents: Dynamic decision-making entities that use LLMs to choose which tools or actions to invoke based on user input. Agents are ideal for tasks where the flow can’t be predetermined.
- 📄 PromptTemplates: Templates used to generate consistent, structured prompts from user input and context variables, ensuring better LLM responses.
- 📚 Document Loaders & Text Splitters: Utilities to load and chunk documents for use in Retrieval-Augmented Generation (RAG) scenarios.
- 🔎 Retrievers & Vector Stores: Core components of RAG workflows. They retrieve relevant documents or data chunks based on semantic similarity to user queries.
In this project, LangChain is used to implement everything from basic text generation to more advanced workflows like RAG pipelines and web-search-enabled agents.
FastAPI
FastAPI is one of the fastest web frameworks for building APIs in Python. It’s intuitive, comes with built-in data validation, and is ideal for deploying LangChain workflows as production-ready endpoints. For an overview to get started with FastAPI and uv, check out this blog post.
Streamlit
Streamlit is a favorite among data scientists and AI engineers for building frontends fast. It’s Python-native, super quick to prototype with, and integrates seamlessly with FastAPI backends.
How to the repository
You can explore and clone the full project here:
👉 LangChain GenAI Apps GitHub Repository
- Clone the repo:
git clone https://github.com/your-username/langchain-genai-apps - Run the FastAPI backend from the
fastapi-appfolder. - Run the Streamlit frontend from the
streamlit-appfolder.
Each folder contains a README.md with full instructions.