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langcorn

msoedov/langcorn

Serving LangChain LLM apps and agents automagically with FastApi

GitHub Website

GraphCanon updated 1mo · GitHub synced 1mo

938 stars69 forksLast push 2y Python MIT

Decision brief

LangCorn is a tool that serves LangChain LLM apps and agents with FastApi.

Good fit when

When you are deploying applications built with Large Language Models (LLMs) like OpenAI.
If you want to integrate your LLM-based chains into FastAPI services quickly without manual setup.

Avoid when

When you require a framework other than FastAPI for your deployment needs.
If you are looking for broader support beyond LangChain-compatible projects.

Observed Jul 15, 2026 · Source: enrich:decision_facts

langcorn alternatives Trust report

langcorn vs sglang langcorn vs vllm

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Maintenance and security

Full trust report

Maintenance: Dormant (735d since push); As of 1mo
Provenance: Not a fork · Personal account; As of 1mo
Security (OSV): 131 low (131 low); As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install langcorn

PyPI

How it fits your stack(4)

Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.

Relationship graph

Optional deeper exploration of typed edges and category neighbours.

Langchain-Chatchat39k★

langfuse32k★

langchainjs18k★

AlternativeIntegratesDepends onRelated

Langchain-Chatchat39k★

langfuse32k★

langchainjs18k★

AlternativeIntegratesDepends onRelated

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Same-category neighbours not already linked as typed edges.

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Langchain-Chatchat

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

A tool that facilitates the deployment of applications and agents utilizing Large Language Models (LLM) by integrating them seamlessly into FastAPI services, enabling rapid server setup and management.

Capability facts

CLI: CLI entrypoint
Source: pyproject.toml:[project.scripts] · Jul 21, 2026
Languages: python
Source: github.language+pyproject.toml · Jul 21, 2026

Graph entities

LangChainframework

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

LangChain integrationLangChain

Source: README excerpt (regex_v1, Jul 21, 2026)

from langchain import LLMMathChain, OpenAI

Source link

Python runtimePython

Source: README excerpt (regex_v1, Jul 21, 2026)

```python import os

Source link

README

📦 Installation

To get started with LangCorn, simply install the package using pip:


pip install langcorn

⛓️ Quick Start

Example LLM chain ex1.py


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:

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

python -m langcorn server examples.ex1:chain

Run multiple chains

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:


from fastapi import FastAPI
from langcorn import create_service

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

Multiple chains


from fastapi import FastAPI
from langcorn import create_service

app:FastAPI = create_service("examples.ex2:chain", "examples.ex1:chain")

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:


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

Now, your LangChain models and pipelines are accessible via the LangCorn API server.

License

LangCorn is released under the MIT License.

For agents

This page has a .md twin and JSON over the API.

/tools/msoedov-langcorn.md GET /api/graphcanon/tools/msoedov-langcorn GET /api/graphcanon/graph?tool=msoedov-langcorn