LangChain Setup Guide

LangChain is one of the most common LLM application frameworks for chat flows, prompt chains, tool use, RAG, agent-style workflows, and application-level orchestration. For Crazyrouter, the most reliable route is LangChain’s official OpenAI-compatible component stack.

Overview

With LangChain’s OpenAI components, you can send requests to Crazyrouter with:

recommended protocol: OpenAI-compatible API
base URL: https://crazyrouter.com/v1
auth variable: OPENAI_API_KEY
main Python package: langchain-openai
main JavaScript / TypeScript package: @langchain/openai

If you are integrating Crazyrouter into real application code rather than just chatting in a desktop client, LangChain is often the most natural engineering path.

Best For

developers integrating Crazyrouter into Python or Node.js applications
teams building prompt chains, RAG, tool calling, or workflow orchestration
users who want an abstraction layer instead of manually writing raw HTTP requests
projects that want to keep future model switching flexible

Protocol Used

Recommended protocol: OpenAI-compatible API Core Crazyrouter settings:

OPENAI_API_KEY=sk-xxx
BASE_URL=https://crazyrouter.com/v1

In LangChain, that usually maps to:

Python: api_key + base_url
JavaScript / TypeScript: apiKey + configuration.baseURL

Prerequisites

Item	Notes
Crazyrouter account	Create one at crazyrouter.com
Crazyrouter token	Create a dedicated token for your LangChain project
Python	Prefer `Python 3.10+`
Node.js	Prefer `Node.js 18+`
LangChain packages	`langchain-openai` for Python and `@langchain/openai` for JS / TS
Allowed models	Allow at least one chat model; if you use vector search, allow an embedding model too

Suggested starter allowlist:

gpt-5.4
claude-sonnet-4-6
gemini-3-pro-preview
text-embedding-3-large

Full Python Path

Recommended Windows Path

py -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -U pip
pip install -U langchain-openai langchain-community
python --version
pip --version

If you want the FAISS example too:

pip install faiss-cpu

Recommended macOS / Linux Path

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -U pip
pip install -U langchain-openai langchain-community
python --version
pip --version

If you want the FAISS example too:

pip install faiss-cpu

Full JavaScript / TypeScript Path

Windows PowerShell

npm init -y
npm install @langchain/openai @langchain/core
node -v
npm -v

macOS / Linux

npm init -y
npm install @langchain/openai @langchain/core
node -v
npm -v

Detailed Setup

Step 1: Create a LangChain-specific Crazyrouter token

For the first pass, allow only:

gpt-5.4
claude-sonnet-4-6
text-embedding-3-large

Add more models only after the basic route is already stable.

Step 2: Set the environment variable first

macOS / Linux
Windows PowerShell

export OPENAI_API_KEY=sk-xxx
echo $OPENAI_API_KEY

$env:OPENAI_API_KEY = "sk-xxx"
echo $env:OPENAI_API_KEY

If you want a persistent setup:

Linux Bash
macOS / Zsh
Windows PowerShell

echo 'export OPENAI_API_KEY=sk-xxx' >> ~/.bashrc
source ~/.bashrc

echo 'export OPENAI_API_KEY=sk-xxx' >> ~/.zshrc
source ~/.zshrc

[System.Environment]::SetEnvironmentVariable("OPENAI_API_KEY", "sk-xxx", "User")
$env:OPENAI_API_KEY = "sk-xxx"

Step 3: Run the smallest Python chat validation

Create test_langchain_chat.py:

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-5.4",
    api_key="sk-xxx",
    base_url="https://crazyrouter.com/v1",
    temperature=0,
)

response = llm.invoke("Reply only OK")
print(response.content)

Run:

python test_langchain_chat.py

Step 4: Switch to the env-var version

Once the route works, avoid hard-coding the key:

import os
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-5.4",
    api_key=os.environ["OPENAI_API_KEY"],
    base_url="https://crazyrouter.com/v1",
    temperature=0,
)

Step 5: Run the smallest JavaScript / TypeScript chat validation

Create test-langchain-chat.mjs:

import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  model: "gpt-5.4",
  apiKey: process.env.OPENAI_API_KEY,
  configuration: {
    baseURL: "https://crazyrouter.com/v1",
  },
  temperature: 0,
});

const response = await llm.invoke("Reply only OK");
console.log(response.content);

Run:

node test-langchain-chat.mjs

Step 6: Add embeddings, prompts, and RAG gradually

Recommended order:

single-turn chat first
prompt + parser second
embeddings third
RAG or agent flows last

Python Examples

Minimal Chat Example

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-5.4",
    api_key="sk-xxx",
    base_url="https://crazyrouter.com/v1",
    temperature=0.7,
)

response = llm.invoke("What is LangChain?")
print(response.content)

Embeddings Example

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    model="text-embedding-3-large",
    api_key="sk-xxx",
    base_url="https://crazyrouter.com/v1",
)

vectors = embeddings.embed_documents(["Text one", "Text two"])
print(len(vectors), len(vectors[0]))

Prompt Chain Example

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

llm = ChatOpenAI(
    model="gpt-5.4",
    api_key="sk-xxx",
    base_url="https://crazyrouter.com/v1",
)

prompt = ChatPromptTemplate.from_template("Explain {topic} in simple terms")
chain = prompt | llm | StrOutputParser()

result = chain.invoke({"topic": "quantum computing"})
print(result)

Minimal RAG Example

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough

llm = ChatOpenAI(
    model="gpt-5.4",
    api_key="sk-xxx",
    base_url="https://crazyrouter.com/v1",
)

embeddings = OpenAIEmbeddings(
    model="text-embedding-3-large",
    api_key="sk-xxx",
    base_url="https://crazyrouter.com/v1",
)

texts = [
    "Crazyrouter supports multiple AI model protocols",
    "Crazyrouter supports OpenAI-compatible routing",
]

vectorstore = FAISS.from_texts(texts, embeddings)
retriever = vectorstore.as_retriever()

prompt = ChatPromptTemplate.from_template(
    "Answer the question based on the following context:\n{context}\n\nQuestion: {question}"
)

chain = {"context": retriever, "question": RunnablePassthrough()} | prompt | llm

result = chain.invoke("Which protocol is the best fit for LangChain on Crazyrouter?")
print(result.content)

JavaScript / TypeScript Example

Minimal Chat Example

import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  model: "gpt-5.4",
  apiKey: process.env.OPENAI_API_KEY,
  configuration: {
    baseURL: "https://crazyrouter.com/v1",
  },
  temperature: 0,
});

const response = await llm.invoke("Hello");
console.log(response.content);

Recommended Model Setup

Use case	Recommended model	Why
first-pass validation	`gpt-5.4`	verified successfully in production on March 23, 2026, and best for proving that LangChain and Crazyrouter are connected
higher-quality long-form and complex chains	`claude-sonnet-4-6`	better for complex explanation, summaries, and heavier reasoning
Gemini fallback path	`gemini-3-pro-preview`	useful as a second compatibility-validation path
vector retrieval	`text-embedding-3-large`	strong first embedding baseline

Token Setup Best Practices

Setting	Recommendation	Notes
dedicated token	Required	do not share LangChain project tokens with desktop clients
model allowlist	Strongly recommended	start with only the chat model plus embedding model you need
quota cap	Strongly recommended	chains, RAG, and agents can multiply spend quickly
environment split	Recommended	separate dev, staging, and production
leak response	Rotate immediately	never commit the key to Git

Verification Checklist

Python or Node.js runtime is ready
OPENAI_API_KEY is set correctly
langchain-openai or @langchain/openai is installed
the chat model is set to https://crazyrouter.com/v1 through base_url or baseURL
the first Reply only OK request succeeds
Crazyrouter logs show the matching request
if embeddings are used, the embedding model is also allowed
if RAG is used, it was first validated on a tiny dataset

Common Errors And Fixes

Symptom	Likely cause	Fix
`401 unauthorized`	wrong, expired, or badly pasted `OPENAI_API_KEY`	generate a new token and set it again
`404`	wrong `base_url` or missing `/v1`	use `https://crazyrouter.com/v1`
`model not found`	wrong model name or the token does not allow it	switch back to `gpt-5.4` and check the allowlist
embeddings fail	the embedding model was not allowed	add `text-embedding-3-large` to the token allowlist
RAG fails in many places	too many components were added at once	go back to single-turn chat, then rebuild step by step
spend rises too quickly	chains, retrieval, and multi-turn agent loops are stacking cost	reduce scope and separate budgets by environment

FAQ

Which protocol should LangChain use with Crazyrouter?

Use the OpenAI-compatible route.

What base URL should I set?

Use https://crazyrouter.com/v1.

Which Python package should I use?

Prefer langchain-openai.

Which JavaScript / TypeScript package should I use?

Prefer @langchain/openai.

Why not jump straight into agents or large RAG pipelines?

Because once a LangChain flow becomes complex, debugging becomes much harder. Minimal chat first, then layer features gradually.

If you are integrating Crazyrouter into a real application codebase, LangChain is still one of the most important framework guides to keep detailed and accurate.

Getting Started

Features

Chat - OpenAI

Chat - Claude

Chat - Gemini

Chat - Responses API

Image Generation

Video Generation

Audio

Embeddings & Rerank

Other APIs

API Key & Console Management

SDK & Code Examples

Integrations

Reference

​Overview

​Best For

​Protocol Used

​Prerequisites

​Full Python Path

​Recommended Windows Path

​Recommended macOS / Linux Path

​Full JavaScript / TypeScript Path

​Windows PowerShell

​macOS / Linux

​Detailed Setup

​Python Examples

​Minimal Chat Example

​Embeddings Example

​Prompt Chain Example

​Minimal RAG Example

​JavaScript / TypeScript Example

​Minimal Chat Example

​Recommended Model Setup

​Token Setup Best Practices

​Verification Checklist

​Common Errors And Fixes

​FAQ

​Which protocol should LangChain use with Crazyrouter?

​What base URL should I set?

​Which Python package should I use?

​Which JavaScript / TypeScript package should I use?

​Why not jump straight into agents or large RAG pipelines?

Overview

Best For

Protocol Used

Prerequisites

Full Python Path

Recommended Windows Path

Recommended macOS / Linux Path

Full JavaScript / TypeScript Path

Windows PowerShell

macOS / Linux

Detailed Setup

Python Examples

Minimal Chat Example

Embeddings Example

Prompt Chain Example

Minimal RAG Example

JavaScript / TypeScript Example

Minimal Chat Example

Recommended Model Setup

Token Setup Best Practices

Verification Checklist

Common Errors And Fixes

FAQ

Which protocol should LangChain use with Crazyrouter?

What base URL should I set?

Which Python package should I use?

Which JavaScript / TypeScript package should I use?

Why not jump straight into agents or large RAG pipelines?