LangChain Integration

Overview

LangChain is a popular framework for developing applications powered by language models. The API works seamlessly with LangChain.

All requests through LangChain automatically flow through the TEE-protected gateway.

Installation

pip install langchain-openai

npm install langchain @langchain/openai

Python Integration

Basic Setup

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage

# Initialize ChatOpenAI with the platform
chat = ChatOpenAI(
    model="openai/gpt-5",
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1",
    temperature=0.7
)

# Send messages
messages = [
    SystemMessage(content="You are a helpful AI assistant."),
    HumanMessage(content="Explain quantum computing in simple terms")
]

response = chat.invoke(messages)
print(response.content)

Streaming Responses

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

chat = ChatOpenAI(
    model="openai/gpt-5",
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1",
    streaming=True
)

# Stream the response
for chunk in chat.stream([HumanMessage(content="Write a story about AI")]):
    print(chunk.content, end="", flush=True)

Using Multiple Models

The platform supports a broad range of models. Switch between them easily:

from langchain_openai import ChatOpenAI

# Use GPU TEE model for sensitive reasoning
tee = ChatOpenAI(
    model="z-ai/glm-5.1",
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

# Use confidential (is_tee) model for systems engineering
near = ChatOpenAI(
    model="z-ai/glm-5",
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

# Use Claude for reasoning tasks
claude = ChatOpenAI(
    model="anthropic/claude-sonnet-4.5",
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

# Use DeepSeek for coding tasks
deepseek = ChatOpenAI(
    model="deepseek/deepseek-chat-v3.1",
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

Conversation Chains

from langchain_openai import ChatOpenAI
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory

chat = ChatOpenAI(
    model="openai/gpt-5",
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

conversation = ConversationChain(
    llm=chat,
    memory=ConversationBufferMemory()
)

# Multi-turn conversation
response1 = conversation.predict(input="Hi, I'm learning about AI")
print(response1)

response2 = conversation.predict(input="What should I learn first?")
print(response2)

TypeScript/JavaScript Integration

Basic Setup

import { ChatOpenAI } from "@langchain/openai";
import { HumanMessage, SystemMessage } from "@langchain/core/messages";

const chat = new ChatOpenAI({
  model: "openai/gpt-5",
  apiKey: process.env.API_KEY,
  configuration: {
    baseURL: "https://api.redpill.ai/v1"
  },
  temperature: 0.7
});

const messages = [
  new SystemMessage("You are a helpful AI assistant."),
  new HumanMessage("Explain quantum computing in simple terms")
];

const response = await chat.invoke(messages);
console.log(response.content);

Streaming in TypeScript

import { ChatOpenAI } from "@langchain/openai";
import { HumanMessage } from "@langchain/core/messages";

const chat = new ChatOpenAI({
  model: "openai/gpt-5",
  apiKey: process.env.API_KEY,
  configuration: {
    baseURL: "https://api.redpill.ai/v1"
  },
  streaming: true
});

const stream = await chat.stream([
  new HumanMessage("Write a story about AI")
]);

for await (const chunk of stream) {
  process.stdout.write(chunk.content);
}

RAG (Retrieval Augmented Generation)

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
from langchain.chains import RetrievalQA

# Initialize embeddings with the platform
embeddings = OpenAIEmbeddings(
    model="openai/text-embedding-3-small",
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

# Create vector store
documents = [
    Document(page_content="The platform is a TEE-protected AI gateway"),
    Document(page_content="The platform supports a broad range of AI models"),
    Document(page_content="All requests are hardware-protected")
]
vectorstore = Chroma.from_documents(documents, embeddings)

# Initialize chat model
chat = ChatOpenAI(
    model="openai/gpt-5",
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

# Create RAG chain
qa_chain = RetrievalQA.from_chain_type(
    llm=chat,
    retriever=vectorstore.as_retriever()
)

# Query
result = qa_chain.invoke("How many models does the platform support?")
print(result["result"])

Function Calling with LangChain

from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate

# Define tools
@tool
def get_weather(location: str, unit: str = "celsius") -> str:
    """Get the current weather in a location"""
    return f"The weather in {location} is 22°{unit[0].upper()}"

@tool
def calculate(expression: str) -> float:
    """Calculate a mathematical expression"""
    return eval(expression)

# Initialize chat model
chat = ChatOpenAI(
    model="openai/gpt-5",
    api_key="YOUR_API_KEY",
    base_url="https://api.redpill.ai/v1"
)

# Create agent
tools = [get_weather, calculate]
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant"),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}")
])

agent = create_tool_calling_agent(chat, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)

# Execute
result = agent_executor.invoke({
    "input": "What's the weather in Paris and what's 25 + 17?"
})
print(result["output"])

Environment Variables

Store your API key securely:

.env

API_KEY=YOUR_API_KEY

Python

import os
from langchain_openai import ChatOpenAI

chat = ChatOpenAI(
    model="openai/gpt-5",
    api_key=os.environ["API_KEY"],
    base_url="https://api.redpill.ai/v1"
)

Supported Models

The available models work with LangChain:

Provider	Example Models
GPU TEE	`z-ai/glm-5.1`, `z-ai/glm-5`, `qwen/qwen3.5-27b`
OpenAI	`openai/gpt-5`, `openai/gpt-5-mini`, `openai/o4-mini`
Anthropic	`anthropic/claude-sonnet-4.5`, `anthropic/claude-opus-4.1`
Google	`google/gemini-2.5-flash`
DeepSeek	`deepseek/deepseek-chat-v3.1`

View All Models

Browse complete model list →

Best Practices

Use Environment Variables

Never hardcode API keys. Use environment variables or secret managers.

Choose the Right Model

GPU TEE models: Best for sensitive data (full TEE protection)
GLM 5.1: Best for confidential reasoning
GLM-5: Best for confidential systems engineering
GPT-5: Best for general tasks
Claude Sonnet 4.5: Best for reasoning and analysis
DeepSeek: Best for coding tasks

Enable Streaming for UX

Use streaming for better user experience in interactive applications.

Implement Error Handling

Wrap API calls in try-catch blocks to handle rate limits and errors gracefully.

Example Projects

Chatbot

Build a multi-turn conversational AI with memory

RAG System

Create a document Q&A system with embeddings

AI Agent

Build an autonomous agent with function calling

Content Generator

Generate articles, summaries, and creative content

Core

Privacy

Migration

Integrations

LangChain Integration

Overview

Installation

Python Integration

Basic Setup

Streaming Responses

Using Multiple Models

Conversation Chains

TypeScript/JavaScript Integration

Basic Setup

Streaming in TypeScript

RAG (Retrieval Augmented Generation)

Function Calling with LangChain

Environment Variables

Supported Models

View All Models

Best Practices

Example Projects

Chatbot

RAG System

AI Agent

Content Generator

Need Help?

​Overview

​Installation

​Python Integration

​Basic Setup

​Streaming Responses

​Using Multiple Models

​Conversation Chains

​TypeScript/JavaScript Integration

​Basic Setup

​Streaming in TypeScript

​RAG (Retrieval Augmented Generation)

​Function Calling with LangChain

​Environment Variables

​Supported Models

View All Models

​Best Practices

​Example Projects

Chatbot

RAG System

AI Agent

Content Generator

​Need Help?

Overview

Installation

Python Integration

Basic Setup

Streaming Responses

Using Multiple Models

Conversation Chains

TypeScript/JavaScript Integration

Basic Setup

Streaming in TypeScript

RAG (Retrieval Augmented Generation)

Function Calling with LangChain

Environment Variables

Supported Models

Best Practices

Example Projects

Need Help?