How to Deploy LangChain Apps Fast: Langcorn, FastAPI & Vercel Made Easy

LangChain’s rapid development is revolutionizing how developers build and deploy language-powered applications. One significant challenge many teams face is how to deploy LangChain on Vercel quickly, securely, and cost-effectively. This guide will walk you through the process of using Langcorn, FastAPI, and Vercel to achieve painless and professional deployment in just a few minutes.

Why Deploy LangChain Apps with FastAPI and Vercel?

In fast-moving projects, having a robust deployment strategy is crucial. Langcorn is an open-source Python package that simplifies the deployment of applications, leveraging the performance of FastAPI and the scalability of Vercel’s serverless hosting.

Photo by Ilo Frey on Pexels

Key Benefits of Deployment

• Production-grade web server set up in minutes
• No backend code necessary – focus on core LangChain logic
• Automatic, well-documented REST API endpoints
• Asynchronous processing for rapid responses
• Completely free hosting with Vercel’s serverless tiers

“…you can easily deploy your langchain applications with a very cool Python package that uses FastAPI under the hood… In this video, I’m going to use Vercel where we can host our API for free.” – AssemblyAI

Setting Up: Tools & Prerequisites

What You’ll Need

• Python 3.8+
• Basic familiarity with building LangChain applications
• Vercel account (GitHub/GitLab login supported)
• OpenAI API Key (if using OpenAI models in LangChain)

Install Required Packages

pip install langcorn
# langcorn automatically installs langchain, fastapi, uvicorn dependencies

Step 1: Structure Your LangChain App

Example 1: Simple LLM Chain

Prepare your execution chain in a script (e.g., llm_chain.py):

from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

llm = OpenAI()
prompt = PromptTemplate("Write a tagline for a {product}.")
chain = LLMChain(llm=llm, prompt=prompt)

Example 2: Conversation Chain with Memory

from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain

memory = ConversationBufferMemory()
conversation = ConversationChain(llm=llm, memory=memory)

You can run and test these scripts locally before deployment.

Step 2: Add Langcorn Service Layer

Create a main.py to expose endpoints through Langcorn:

from langcorn import create_service

app = create_service([
  "llm_chain:chain",
  "conversation_chain:conversation"
])

How it works:

• create_service examines your chains, exposing their input/output fields.
• Automatically generates /[endpoint]/run POST endpoints per chain.

Step 3: Local Testing with Uvicorn & FastAPI

To verify your setup before deploying:

uvicorn main:app --host 0.0.0.0 --port 8000

Visit http://localhost:8000/docs for interactive API docs (thanks to FastAPI!). Here, you can send requests, examine responses, and debug locally.

Step 4: Deploying to Vercel

Vercel’s serverless functions make it easy to deploy LangChain on Vercel efficiently.

1. Restructure for Vercel

Move your code to the /api directory:

• /api/main.py (your entrypoint)
• Prefix imports to align with Vercel’s structure (e.g., api.llm_chain:chain)

Create a requirements.txt in your root with just one line:

langcorn

Add vercel.json with function routing config, which you can copy from the Langcorn repository if needed.

2. Deploy via CLI

1. Install the Vercel CLI:
2. npm install -g vercel

3. Run the deploy command and follow the prompts:
4. vercel

5. Log in (GitHub recommended), accept defaults, and receive your live API URL.

3. Secure Your API Key

In the Vercel dashboard, set the OPENAI_API_KEY as an environment variable under your project’s Settings. This ensures your backend can access models securely in production.

Example: Testing Your Live Endpoint

Visit <your-vercel-app-url>/docs to view your API documentation online. You can test the chain endpoints with sample payloads like:

{
  "product": "ice cream"
}

You’ll receive the generated response immediately, allowing you to integrate this endpoint into frontend apps, scripts, or use it as a public API.

Troubleshooting & Pro Tips

Common Pitfalls

• Naming mismatches: Ensure imports and variable names align in your main.py and Vercel routing config.
• Environment variables: Always set secrets for production use—never hard-code API keys.
• Python dependencies: Rely only on what’s in requirements.txt.

Actionable Tips

• Test locally before deploying; FastAPI docs UI is invaluable.
• Use multiple chains by adding more entries to your create_service list.
• Take advantage of async support for handling heavy loads.

“You don’t have to worry about writing the backend code yourself… you get well-documented RESTful API endpoints automatically.”

Key Takeaways & Next Steps

By mastering how to deploy LangChain on Vercel with Langcorn, FastAPI, and Vercel, you establish a seamless, repeatable workflow for turning language models into accessible APIs. Here’s what you gain:

• Speed: From concept to production in under 5 minutes.
• Scalability: Vercel handles load without additional configuration.
• Simplicity: No need to write server or REST code manually.

Now it’s your turn:

• Start with one chain, deploy it, and share your endpoint.
• Expand to more complex chains as needed.
• Integrate your deployment into frontend or automation workflows.

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