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AI Gateway

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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.

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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

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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.

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Installation

pip install langchain langchain-openai

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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

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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)

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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)

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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()

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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",
)