LangChain framework integration

AI Gateway

Route your LLM calls through the AI Gateway with a single base URL change. Zero vendor lock-in: always run on the best model at the lowest cost for your use case.

Observability

Use OpenInference and OpenTelemetry to capture LangChain and LangGraph traces.

AI Gateway

Overview

LangChain is a framework for building LLM-powered applications through composable chains, agents, and integrations with external data sources. By connecting LangChain to Toqan Router’s AI Gateway, you access 500+ models through a single base URL change.

Key Benefits

Toqan Router’s AI Gateway enhances your 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 your AI operations

Multi-Provider Access

Access 500+ LLMs and 30+ providers through a single, unified integration

Prerequisites

Before integrating LangChain with Toqan Router, ensure you have:

A Toqan Router account and API Key
Python 3.8 or higher

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

Installation

pip install langchain langchain-openai

Configuration

Configure LangChain to use Toqan Router’s AI Gateway via ChatOpenAI with a custom base_url:

Python

from langchain_openai import ChatOpenAI
import os

llm = ChatOpenAI(
    model="openai/gpt-5.6-sol",
    api_key=os.getenv("TOQAN_API_KEY"),
    base_url="https://router.toqan.ai/eu/v3/router",
)

base_url: https://router.toqan.ai/eu/v3/router

Basic Example

Python

from langchain_openai import ChatOpenAI
import os

llm = ChatOpenAI(
    model="openai/gpt-5.6-sol",
    api_key=os.getenv("TOQAN_API_KEY"),
    base_url="https://router.toqan.ai/eu/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-5.6-sol",
    api_key=os.getenv("TOQAN_API_KEY"),
    base_url="https://router.toqan.ai/eu/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-5.6-sol",
    api_key=os.getenv("TOQAN_API_KEY"),
    base_url="https://router.toqan.ai/eu/v3/router",
)

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

Model Selection

With Toqan Router, you can use any supported model from 30+ providers:

Python

from langchain_openai import ChatOpenAI
import os

# Use Claude
claude = ChatOpenAI(
    model="anthropic/claude-sonnet-5",
    api_key=os.getenv("TOQAN_API_KEY"),
    base_url="https://router.toqan.ai/eu/v3/router",
)

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

# Use Groq
groq = ChatOpenAI(
    model="groq/qwen/qwen3.8-27b",
    api_key=os.getenv("TOQAN_API_KEY"),
    base_url="https://router.toqan.ai/eu/v3/router",
)

Observability

Model calls through the AI Gateway appear in Traces. To capture the surrounding application workflow, use OpenTelemetry with a framework instrumentor and the regional OTLP endpoint. For Python, install openinference-instrumentation-langchain and follow the OpenInference LangChain setup. It supports LangChain and LangGraph. For Node.js, use OpenInference LangChain.js. Configure the exporter using the Observability quickstart:

export OTEL_EXPORTER_OTLP_ENDPOINT="https://router.toqan.ai/eu/v2/otel"
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer $TOQAN_API_KEY"

Use the provider API directly when exporting framework spans for the same model calls, or choose which layer to trace so one call is not recorded twice.

Overview

Instrument your app

Explore

Monitor

Export

Review

Data privacy & security

Framework integrations

AI Gateway

Observability

AI Gateway

Overview

Key Benefits

Complete Observability

Built-in Reliability

Cost Optimization

Multi-Provider Access

Prerequisites

Installation

Configuration

Basic Example

Chains

Streaming

Model Selection

Observability

AI Gateway

Observability

​AI Gateway

​Overview

​Key Benefits

Complete Observability

Built-in Reliability

Cost Optimization

Multi-Provider Access

​Prerequisites

​Installation

​Configuration

​Basic Example

​Chains

​Streaming

​Model Selection

​Observability

AI Gateway

Overview

Key Benefits

Prerequisites

Installation

Configuration

Basic Example

Chains

Streaming

Model Selection

Observability