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This guide provides instructions for integrating LangChain with the Truefoundry AI Gateway.

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What is LangChain?

LangChain is a framework for developing applications powered by large language models (LLMs). It provides a comprehensive suite of tools and integrations that streamline the entire lifecycle of LLM applications, from development to deployment and monitoring.

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Key Features of LangChain

  • Modular Components: Offers a range of building blocks including chains, agents, prompt templates, and memory modules that can be composed together to create complex LLM applications
  • Extensive Integrations: Supports integrations with various LLM providers, embedding models, vector stores, and external tools, facilitating seamless connectivity within the AI ecosystem
  • Production-Ready Tools: Includes LangGraph for building stateful agents and LangSmith for monitoring and evaluating applications, ensuring robust deployment and maintenance of LLM-powered solutions

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Quickstart Guide

TrueFoundry is compatible with the OpenAI signature, so you can connect to TrueFoundry’s unified LLM gateway through the ChatOpenAI interface.

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Installation & Setup

  1. Sign up for a TrueFoundry account
  2. Follow the steps here in Quick start and generate a Personal Access Token (PAT)
TrueFoundry playground showing unified code snippet with base URL and model name for LangChain integration

Get Base URL and Model Name from Unified Code Snippet

  • Set the base_url to your TrueFoundry endpoint
  • Set the api_key to your TRUEFOUNDRY_PAT
  • Use TrueFoundry model names in the format provider-main/model-name

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Installation

pip install langchain-openai

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Basic Setup

Connect to TrueFoundry by updating the ChatOpenAI model in LangChain: 
from langchain_openai import ChatOpenAI

TRUEFOUNDRY_PAT = "..."  # Your TrueFoundry Personal Access Token
TRUEFOUNDRY_BASE_URL = "..."  # Your TrueFoundry unified endpoint

llm = ChatOpenAI(
    api_key=TRUEFOUNDRY_PAT,
    base_url=TRUEFOUNDRY_BASE_URL,
    model="openai-main/gpt-4o" #similarly you can call any model from any model provider like anthropic, gemini
)

llm.invoke("What is the meaning of life, universe and everything?")
The request is routed through your TrueFoundry gateway to the specified model provider. TrueFoundry automatically handles authentication, load balancing, and logging.

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Advanced Example with LangGraph

from langgraph.graph import StateGraph, MessagesState
from langchain_core.messages import HumanMessage

# Build workflow
workflow = StateGraph(MessagesState)
workflow.add_node("agent", call_model)
workflow.set_entry_point("agent")
workflow.set_finish_point("agent")

app = workflow.compile()

# Run agent through TrueFoundry
result = app.invoke({"messages": [HumanMessage(content="Hello!")]})

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Observability and Governance

Monitor your LangChain applications through TrueFoundry’s metrics tab: TrueFoundry metrics With TrueFoundry’s AI gateway, you can monitor and analyze:
  • Performance Metrics: Track key latency metrics like Request Latency, Time to First Token (TTFS), and Inter-Token Latency (ITL) with P99, P90, and P50 percentiles
  • Cost and Token Usage: Gain visibility into your application’s costs with detailed breakdowns of input/output tokens and the associated expenses for each model
  • Usage Patterns: Understand how your application is being used with detailed analytics on user activity, model distribution, and team-based usage
  • Rate limit and Load balancing: You can set up rate limiting, load balancing and fallback for your models
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