> ## Documentation Index
> Fetch the complete documentation index at: https://apidocs.mor.org/llms.txt
> Use this file to discover all available pages before exploring further.
# LangChain Integration
> How to integrate Morpheus Inference API with LangChain
# How To Integrate with LangChain
### What is LangChain?
LangChain is the most popular framework for building applications powered by large language models. It provides a standard interface for chains, agents, and memory, making it easy to build complex AI applications.
Since the Morpheus Inference API is **fully OpenAI-compatible**, you can use it with LangChain by simply configuring the OpenAI provider with a custom base URL.
## Prerequisites
* Morpheus API Key from [app.mor.org](https://app.mor.org)
* Python 3.8+ or Node.js 18+
* LangChain installed
## Python Integration
### Installation
```bash theme={null}
pip install langchain langchain-openai
```
### Basic Setup
```python theme={null}
import os
from langchain_openai import ChatOpenAI
# Configure the Morpheus-powered LLM
llm = ChatOpenAI(
model="llama-3.3-70b",
api_key=os.getenv("MORPHEUS_API_KEY"),
base_url="https://api.mor.org/api/v1"
)
# Simple invocation
response = llm.invoke("What is the capital of France?")
print(response.content)
```
### Streaming
```python theme={null}
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
model="llama-3.3-70b",
api_key=os.getenv("MORPHEUS_API_KEY"),
base_url="https://api.mor.org/api/v1",
streaming=True
)
for chunk in llm.stream("Write a short poem about AI"):
print(chunk.content, end="", flush=True)
```
### Using with Chains
```python theme={null}
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
llm = ChatOpenAI(
model="llama-3.3-70b",
api_key=os.getenv("MORPHEUS_API_KEY"),
base_url="https://api.mor.org/api/v1"
)
prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful assistant that explains concepts simply."),
("user", "{input}")
])
chain = prompt | llm | StrOutputParser()
response = chain.invoke({"input": "Explain quantum computing"})
print(response)
```
### Using with Agents
```python theme={null}
from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.tools import tool
llm = ChatOpenAI(
model="llama-3.3-70b",
api_key=os.getenv("MORPHEUS_API_KEY"),
base_url="https://api.mor.org/api/v1"
)
@tool
def get_weather(city: str) -> str:
"""Get the current weather for a city."""
# Your weather API implementation
return f"The weather in {city} is sunny, 72°F"
@tool
def calculate(expression: str) -> str:
"""Evaluate a mathematical expression."""
return str(eval(expression))
tools = [get_weather, calculate]
prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful assistant with access to tools."),
("user", "{input}"),
("placeholder", "{agent_scratchpad}")
])
agent = create_tool_calling_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
response = agent_executor.invoke({
"input": "What's the weather in Tokyo and what is 15 * 23?"
})
print(response["output"])
```
### Embeddings
```python theme={null}
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(
model="text-embedding-bge-m3",
api_key=os.getenv("MORPHEUS_API_KEY"),
base_url="https://api.mor.org/api/v1"
)
# Create embeddings
vectors = embeddings.embed_documents([
"Hello world",
"How are you?"
])
# Single query embedding
query_vector = embeddings.embed_query("What is AI?")
```
## JavaScript/TypeScript Integration
### Installation
```bash theme={null}
npm install langchain @langchain/openai
```
### Basic Setup
```typescript theme={null}
import { ChatOpenAI } from "@langchain/openai";
const llm = new ChatOpenAI({
model: "llama-3.3-70b",
apiKey: process.env.MORPHEUS_API_KEY,
configuration: {
baseURL: "https://api.mor.org/api/v1",
},
});
const response = await llm.invoke("What is the capital of France?");
console.log(response.content);
```
### Streaming
```typescript theme={null}
import { ChatOpenAI } from "@langchain/openai";
const llm = new ChatOpenAI({
model: "llama-3.3-70b",
apiKey: process.env.MORPHEUS_API_KEY,
configuration: {
baseURL: "https://api.mor.org/api/v1",
},
streaming: true,
});
const stream = await llm.stream("Write a short poem about AI");
for await (const chunk of stream) {
process.stdout.write(chunk.content);
}
```
### Using with Chains
```typescript theme={null}
import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";
const llm = new ChatOpenAI({
model: "llama-3.3-70b",
apiKey: process.env.MORPHEUS_API_KEY,
configuration: {
baseURL: "https://api.mor.org/api/v1",
},
});
const prompt = ChatPromptTemplate.fromMessages([
["system", "You are a helpful assistant that explains concepts simply."],
["user", "{input}"],
]);
const chain = prompt.pipe(llm).pipe(new StringOutputParser());
const response = await chain.invoke({ input: "Explain quantum computing" });
console.log(response);
```
## Environment Variables
Store your API key securely:
```bash theme={null}
# .env
MORPHEUS_API_KEY=sk-your-api-key-here
```
Never commit API keys to version control. Add `.env` to your `.gitignore` file.
## Next Steps
See all models available through Morpheus.
Direct OpenAI SDK integration guide.
Full API reference for chat completions.
Create embeddings for RAG applications.