--- title: "langcorn" type: "tool" slug: "msoedov-langcorn" canonical_url: "https://www.graphcanon.com/tools/msoedov-langcorn" github_url: "https://github.com/msoedov/langcorn" homepage_url: "https://langcorn.vercel.app/docs" stars: 938 forks: 69 primary_language: "Python" license: "MIT" categories: ["inference-serving", "developer-tools"] tags: ["llmops", "fastapi", "api", "langchain"] updated_at: "2026-07-07T18:48:37.603533+00:00" --- # langcorn > Serving LangChain LLM apps and agents with FastApi LangCorn is an API server that allows for the easy deployment of LangChain models and pipelines using FastAPI. ## Facts - Repository: https://github.com/msoedov/langcorn - Homepage: https://langcorn.vercel.app/docs - Stars: 938 · Forks: 69 · Open issues: 21 · Watchers: 7 - Primary language: Python - License: MIT - Last pushed: 2024-07-15T18:05:52+00:00 ## Categories - [Inference & Serving](/categories/inference-serving.md) - [Developer Tools](/categories/developer-tools.md) ## Tags llmops, fastapi, api, langchain ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,962) - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,417) - [prompts.chat](/tools/f-prompts-chat.md) - The world's largest open-source prompt library for AI (★ 165,019) - [JavaGuide](/tools/snailclimb-javaguide.md) - Snailclimb/JavaGuide: 面试 & 后端通用面试指南，覆盖计算机基础、数据库、分布式、高并发、系统设计与 AI 应用开发 (★ 156,863) - [open-webui](/tools/open-webui-open-webui.md) - User-friendly AI Interface (Supports Ollama, OpenAI API, ...) (★ 144,575) - [browser-use](/tools/browser-use-browser-use.md) - 🌐 Make websites accessible for AI agents. Automate tasks online with ease. (★ 103,315) - [caveman](/tools/juliusbrussee-caveman.md) - Cuts 65% of tokens in AI coding agent responses. (★ 86,150) - [vllm](/tools/vllm-project-vllm.md) - A high-throughput and memory-efficient inference and serving engine for LLMs (★ 85,612) ## README (excerpt) ```text # Langcorn LangCorn is an API server that enables you to serve LangChain models and pipelines with ease, leveraging the power of FastAPI for a robust and efficient experience.

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## Features - Easy deployment of LangChain models and pipelines - Ready to use auth functionality - High-performance FastAPI framework for serving requests - Scalable and robust solution for language processing applications - Supports custom pipelines and processing - Well-documented RESTful API endpoints - Asynchronous processing for faster response times ## 📦 Installation To get started with LangCorn, simply install the package using pip: ```shell pip install langcorn ``` ## ⛓️ Quick Start Example LLM chain ex1.py ```python import os from langchain import LLMMathChain, OpenAI os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "sk-********") llm = OpenAI(temperature=0) chain = LLMMathChain(llm=llm, verbose=True) ``` Run your LangCorn FastAPI server: ```shell langcorn server examples.ex1:chain [INFO] 2023-04-18 14:34:56.32 | api:create_service:75 | Creating service [INFO] 2023-04-18 14:34:57.51 | api:create_service:85 | lang_app='examples.ex1:chain':LLMChain(['product']) [INFO] 2023-04-18 14:34:57.51 | api:create_service:104 | Serving [INFO] 2023-04-18 14:34:57.51 | api:create_service:106 | Endpoint: /docs [INFO] 2023-04-18 14:34:57.51 | api:create_service:106 | Endpoint: /examples.ex1/run INFO: Started server process [27843] INFO: Waiting for application startup. INFO: Application startup complete. INFO: Uvicorn running on http://127.0.0.1:8718 (Press CTRL+C to quit) ``` or as an alternative ```shell python -m langcorn server examples.ex1:chain ``` Run multiple chains ```shell python -m langcorn server examples.ex1:chain examples.ex2:chain [INFO] 2023-04-18 14:35:21.11 | api:create_service:75 | Creating service [INFO] 2023-04-18 14:35:21.82 | api:create_service:85 | lang_app='examples.ex1:chain':LLMChain(['product']) [INFO] 2023-04-18 14:35:21.82 | api:create_service:85 | lang_app='examples.ex2:chain':SimpleSequentialChain(['input']) [INFO] 2023-04-18 14:35:21.82 | api:create_service:104 | Serving [INFO] 2023-04-18 14:35:21.82 | api:create_service:106 | Endpoint: /docs [INFO] 2023-04-18 14:35:21.82 | api:create_service:106 | Endpoint: /examples.ex1/run [INFO] 2023-04-18 14:35:21.82 | api:create_service:106 | Endpoint: /examples.ex2/run INFO: Started server process [27863] INFO: Waiting for application startup. INFO: Application startup complete. INFO: Uvicorn running on http://127.0.0.1:8718 (Press CTRL+C to quit) ``` Import the necessary packages and create your FastAPI app: ```python from fastapi import FastAPI from langcorn import create_service app:FastAPI = create_service("examples.ex1:chain") ``` Multiple chains ```python from fastapi import FastAPI from langcorn import create_service app:FastAPI = create_service("examples.ex2:chain", "examples.ex1:chain") ``` or ```python from fastapi import FastAPI from langcorn import create_service app: FastAPI = create_service( "examples.ex1:chain", "examples.ex2:chain", "examples.ex3:chain", "examples.ex4:sequential_chain", "examples.ex5:conversation", "examples.ex6:conversation_with_summary", "examples.ex7_agent:agent", ) ``` Run your LangCorn FastAPI server: ```shell uvicorn ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/msoedov-langcorn`](/api/graphcanon/tools/msoedov-langcorn) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_