LangChain framework integration - Orq.ai Documentation

AI Gateway

Overview

LangChain is a framework for building LLM-powered applications through composable chains, agents, and integrations with external data sources. Connecting LangChain to Orq.ai’s AI Gateway provides access to 300+ models through a single base URL change.

Key Benefits

Orq.ai’s AI Gateway enhances LangChain applications with:

Complete Observability

Track every chain step, tool use, and LLM call with detailed traces

Built-in Reliability

Automatic fallbacks, retries, and load balancing for production resilience

Cost Optimization

Real-time cost tracking and spend management across all AI operations

Multi-Provider Access

Access 300+ LLMs and 20+ providers through a single, unified integration

Prerequisites

Before integrating LangChain with Orq.ai, ensure the following are in place:

An Orq.ai account and API Key
Python 3.9 or higher

To set up an API key, see API keys & Endpoints.

Installation

pip install langchain langchain-openai

Configuration

Configure LangChain to use Orq.ai’s AI Gateway via ChatOpenAI with a custom base_url:

Python

from langchain_openai import ChatOpenAI
import os

llm = ChatOpenAI(
    model="openai/gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

base_url: https://api.orq.ai/v3/router

Basic Example

Python

from langchain_openai import ChatOpenAI
import os

llm = ChatOpenAI(
    model="openai/gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

result = llm.invoke("Explain quantum computing in simple terms.")
print(result.content)

Chains

Build composable chains using LangChain’s pipe operator:

Python

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
import os

llm = ChatOpenAI(
    model="openai/gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("user", "{input}"),
])

chain = prompt | llm
result = chain.invoke({"input": "Tell me a joke about programming."})
print(result.content)

Streaming

Python

from langchain_openai import ChatOpenAI
import os

llm = ChatOpenAI(
    model="openai/gpt-4o",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

for chunk in llm.stream("Write a short poem about the ocean."):
    print(chunk.content, end="", flush=True)
print()

Model Selection

With Orq.ai, any supported model from 20+ providers can be used:

Python

from langchain_openai import ChatOpenAI
import os

# Use Claude
claude = ChatOpenAI(
    model="anthropic/claude-sonnet-4-6",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

# Use Gemini
gemini = ChatOpenAI(
    model="google-ai/gemini-2.5-flash",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

# Use Groq
groq = ChatOpenAI(
    model="groq/llama-3.3-70b-versatile",
    api_key=os.getenv("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

​AI Gateway

​Overview

​Key Benefits

Complete Observability

Built-in Reliability

Cost Optimization

Multi-Provider Access

​Prerequisites

​Installation

​Configuration

​Basic Example

​Chains

​Streaming

​Model Selection

AI Gateway

Overview

Key Benefits

Prerequisites

Installation

Configuration

Basic Example

Chains

Streaming

Model Selection