LangChain

export MUNITO_API_KEY="munitoai_..."

pip install langchain-openai

# pip install langchain-openai
import os

from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from pydantic import BaseModel

llm = ChatOpenAI(
    model="qwen/qwen3.8-27b",
    base_url="https://api.munito.ai/inference/v1",
    api_key=os.environ["MUNITO_API_KEY"],
    max_tokens=2048,
    reasoning_effort="low",
)

# 1. Invoke
print(llm.invoke("Name the capital of Portugal in one word.").content.strip())

# 2. Stream
for chunk in llm.stream("Count from 1 to 5, one number on each line."):
    print(chunk.content, end="", flush=True)
print()


# 3. Tools
@tool
def get_weather(city: str) -> str:
    """Return the current weather for a city."""
    return f"It is 18 degrees and sunny in {city}."


message = llm.bind_tools([get_weather]).invoke("What is the weather in Lyon?")
for call in message.tool_calls:
    print(call["name"], call["args"])


# 4. Structured output
class City(BaseModel):
    name: str
    country: str
    population: int


print(llm.with_structured_output(City).invoke("Give the facts for Munich."))

Lisbon
1
2
3
4
5
get_weather {'city': 'Lyon'}
name='Munich' country='Germany' population=1450000

pip install langchain-anthropic

# pip install langchain-anthropic
import os

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(
    model="qwen/qwen3.8-27b",
    base_url="https://api.munito.ai/inference",
    api_key=os.environ["MUNITO_API_KEY"],
    max_tokens=2048,
)
print(llm.invoke("Name the capital of Portugal in one word.").text.strip())
for chunk in llm.stream("Count from 1 to 3, one number on each line."):
    print(chunk.text, end="", flush=True)
print()