LangChain / LangGraph Integration

from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema.output_parser import StrOutputParser

llm = ChatOpenAI(
    model="ant:auto",
    openai_api_base="https://antbase.ai/v1",
    openai_api_key="your-ant-api-key",
)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant that explains concepts simply."),
    ("user", "{topic}"),
])

chain = prompt | llm | StrOutputParser()

result = chain.invoke({"topic": "machine learning"})
print(result)

from langchain_openai import ChatOpenAI
from langchain.agents import create_openai_functions_agent, AgentExecutor
from langchain.tools import tool
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder

@tool
def search_web(query: str) -> str:
    """Search the web for information."""
    return f"Search results for: {query}"

# Use ant:best for agent reasoning
llm = ChatOpenAI(
    model="ant:best",
    openai_api_base="https://antbase.ai/v1",
    openai_api_key="your-ant-api-key",
)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful research assistant."),
    ("user", "{input}"),
    MessagesPlaceholder("agent_scratchpad"),
])

agent = create_openai_functions_agent(llm, [search_web], prompt)
executor = AgentExecutor(agent=agent, tools=[search_web])

result = executor.invoke({"input": "Find information about the latest AI developments"})
print(result["output"])

from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, START, END
from typing import TypedDict, Annotated
import operator

class State(TypedDict):
    messages: Annotated[list, operator.add]

llm = ChatOpenAI(
    model="ant:auto",
    openai_api_base="https://antbase.ai/v1",
    openai_api_key="your-ant-api-key",
)

def chat_node(state: State):
    response = llm.invoke(state["messages"])
    return {"messages": [response]}

builder = StateGraph(State)
builder.add_node("chat", chat_node)
builder.add_edge(START, "chat")
builder.add_edge("chat", END)

graph = builder.compile()

result = graph.invoke({"messages": [("user", "Hello!")]})
print(result["messages"][-1].content)

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="ant:auto",
    openai_api_base="https://antbase.ai/v1",
    openai_api_key="your-ant-api-key",
    streaming=True,
)

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