# langcorn: Serve LangChain LLM Apps and Agents with FastAPI

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Langcorn is an innovative API server designed to effortlessly deploy LangChain models and pipelines. It leverages the high-performance FastAPI framework, offering a robust and scalable solution for serving large language model applications. With features like easy installation, built-in authentication, and support for custom API keys, Langcorn streamlines the process of bringing your LLM projects to production.

GitHub: https://github.com/msoedov/langcorn
OSRepos URL: https://osrepos.com/repo/msoedov-langcorn

## Summary

Langcorn is an innovative API server designed to effortlessly deploy LangChain models and pipelines. It leverages the high-performance FastAPI framework, offering a robust and scalable solution for serving large language model applications. With features like easy installation, built-in authentication, and support for custom API keys, Langcorn streamlines the process of bringing your LLM projects to production.

## Topics

- api
- fastapi
- langchain
- llm
- llmops
- python
- rest-api
- large-language-models

## Repository Information

Last analyzed by OSRepos: Mon Mar 02 2026 16:11:10 GMT+0000 (Western European Standard Time)
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## Content

## Introduction

Langcorn is an API server that simplifies the deployment of LangChain models and pipelines. It integrates seamlessly with FastAPI, providing a high-performance, scalable, and robust solution for serving your language processing applications and agents. Designed for LLMops, Langcorn automates the serving process, allowing developers to focus on building powerful LLM-powered experiences.

Key features include easy deployment, ready-to-use authentication, well-documented RESTful API endpoints, asynchronous processing, and support for custom pipelines and processing.

## Installation

Getting started with Langcorn is straightforward. You can install the package using pip:

shell
pip install langcorn


## Examples

### Quick Start

To run a single LangChain chain, define it in a Python file (e.g., `examples.ex1:chain`) and then start the Langcorn server:

shell
langcorn server examples.ex1:chain


Alternatively, you can run it as a Python module:

shell
python -m langcorn server examples.ex1:chain


### Serving Multiple Chains

Langcorn supports serving multiple chains simultaneously. Simply list them when starting the server:

shell
python -m langcorn server examples.ex1:chain examples.ex2:chain


### Integrating with FastAPI

For more control, you can integrate Langcorn directly into an existing FastAPI application:

python
from fastapi import FastAPI
from langcorn import create_service

app: FastAPI = create_service("examples.ex1:chain")

# To serve multiple chains:
# app: FastAPI = create_service("examples.ex2:chain", "examples.ex1:chain")

# Then run your FastAPI app with Uvicorn:
# uvicorn main:app --host 0.0.0.0 --port 8000


## Why Use It

Langcorn addresses the challenges of deploying LangChain applications by offering a streamlined, high-performance solution. Its integration with FastAPI ensures robust API serving, while features like built-in authentication and custom API key handling provide necessary security and flexibility. Developers benefit from automatic documentation via FastAPI's `/docs` endpoint, asynchronous processing for faster responses, and the ability to manage LLM kwargs per request. It also provides structured handling for LLM memory, making it ideal for conversational AI applications. Langcorn simplifies the operational aspects of LLM deployment, allowing you to focus on the core logic of your AI agents and applications.

## Links

*   **GitHub Repository**: [https://github.com/msoedov/langcorn](https://github.com/msoedov/langcorn){target="_blank"}
*   **Live Example**: [https://langcorn-ift9ub8zg-msoedov.vercel.app/docs#/](https://langcorn-ift9ub8zg-msoedov.vercel.app/docs#/){target="_blank"}