langchain-moonshot 0.1.0

An integration package connecting Moonshot AI and LangChain

LangChain Moonshot

langchain-moonshot is a standalone LangChain integration package for Moonshot AI chat models.

Upgrade Notes

0.1.0 is a breaking rewrite of the package.

• If you are upgrading from 0.0.6 or 0.0.7, note that the old PyPI releases exposed OpenAI, ChatOpenAI, and OpenAIEmbeddings.
• Starting with 0.1.0, this project is rebuilt around ChatMoonshot as the primary public integration.
• The project homepage/repository has moved from https://github.com/RyanFeiluX/langchain_moonshot to https://github.com/ArcadiaLin/langchain-moonshot.

It provides a ChatMoonshot implementation built on top of langchain-openai, with Moonshot-specific support for:

• reasoning_content in non-streaming and streaming responses
• thinking, prompt_cache_key, safety_identifier, and max_completion_tokens
• Moonshot model capability profiles
• tool calling, structured output, and multimodal inputs supported by Moonshot models

Overview

Integration details

Class	Package	Status	Notes
ChatMoonshot	langchain-moonshot	alpha	Standalone provider package for Moonshot AI

Model features

Tool calling	Structured output	Image input	Video input	Token-level streaming	Native async	Token usage	Reasoning traces
✅	✅	✅	✅	✅	✅	✅	✅

Installation

Install from PyPI:

pip install langchain-moonshot

For local development:

uv sync --group dev --group test_integration

Standard Tests

This repository keeps only LangChain standard chat-model tests under tests/:

• tests/unit_tests/test_standard.py
• tests/integration_tests/test_standard.py

Run the full standard suite in the current uv project and write the output to pytest_standard.txt:

RUN_MOONSHOT_INTEGRATION=1 uv run pytest tests/unit_tests/test_standard.py tests/integration_tests/test_standard.py -q > pytest_standard.txt 2>&1

Run only the unit standard tests:

uv run pytest tests/unit_tests/test_standard.py -q > pytest_standard.txt 2>&1

Run only the live integration standard tests:

RUN_MOONSHOT_INTEGRATION=1 uv run pytest tests/integration_tests/test_standard.py -q > pytest_standard.txt 2>&1

Live integration tests require RUN_MOONSHOT_INTEGRATION=1 and MOONSHOT_API_KEY.

Current standard-test coverage and behavior:

• Default live standard model: kimi-k2.5 with thinking=False and temperature=0.6
• All standard tests in this repository are constrained to kimi-k2.5; the suite does not switch to any other Moonshot model
• Covered by standard tests: serialization, env-based init, tool calling, structured output, json_mode, image inputs, streaming, async, and VCR-backed test_stream_time
• Standard tests intentionally keep these capabilities unsupported: forced tool_choice, runtime model override, PDF inputs, audio inputs, image/PDF tool messages, and Anthropic-style inputs
• Known expected limitation: output_version="v1" streaming tool calls can emit a natural-language preamble before the tool chunk stream, so the standard test_tool_calling path for that variant is marked xfail
• Practical boundary: for day-to-day use, prefer invoke() or default streaming output for tool calling; do not rely on output_version="v1" streamed content_blocks as the sole source of tool-call arguments

Credentials

Create a Moonshot API key in the Moonshot console, then set MOONSHOT_API_KEY.

import getpass
import os

if not os.getenv("MOONSHOT_API_KEY"):
    os.environ["MOONSHOT_API_KEY"] = getpass.getpass("Enter your Moonshot API key: ")

If you are using Moonshot's China endpoint explicitly, you can also set:

os.environ.setdefault("MOONSHOT_API_BASE", "https://api.moonshot.cn/v1")

To enable LangSmith tracing:

os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

Instantiation

from langchain_moonshot import ChatMoonshot

llm = ChatMoonshot(
    model="kimi-k2.5",
    thinking=True,
    temperature=1.0,
    max_retries=2,
)

Invocation

messages = [
    ("system", "You are a concise bilingual assistant."),
    ("human", "Summarize why Moonshot reasoning models are useful in two bullet points."),
]

ai_msg = llm.invoke(messages)

print(ai_msg.text)
print(ai_msg.additional_kwargs.get("reasoning_content"))
print(ai_msg.usage_metadata)

Streaming

streaming_llm = ChatMoonshot(
    model="kimi-k2.5",
    thinking=True,
    temperature=1.0,
    stream_usage=True,
)

full = None
for chunk in streaming_llm.stream("Explain streaming output in two short bullet points."):
    if full is None:
        full = chunk
    else:
        full += chunk

    if chunk.text:
        print(chunk.text, end="")

    reasoning = chunk.additional_kwargs.get("reasoning_content")
    if reasoning:
        print(f"\n[reasoning] {reasoning}", end="")

print()
print(full.usage_metadata if full is not None else None)

Tool Calling

from langchain_core.messages import ToolMessage
from langchain_core.tools import tool


@tool
def add(a: int, b: int) -> int:
    """Add two integers."""
    return a + b


@tool
def multiply(a: int, b: int) -> int:
    """Multiply two integers."""
    return a * b


llm_with_tools = ChatMoonshot(
    model="kimi-k2.5",
    thinking=False,
    temperature=0.6,
).bind_tools([add, multiply])

messages = [
    ("system", "Use the provided math tools before answering."),
    ("human", "Add 17 and 25, multiply 12 by 13, then summarize the results."),
]

response = llm_with_tools.invoke(messages)
print(response.tool_calls)

if response.tool_calls:
    tool_results = []
    for tool_call in response.tool_calls:
        if tool_call["name"] == "add":
            result = add.invoke(tool_call["args"])
        else:
            result = multiply.invoke(tool_call["args"])
        tool_results.append(
            ToolMessage(content=str(result), tool_call_id=tool_call["id"])
        )

    final_response = llm_with_tools.invoke([*messages, response, *tool_results])
    print(final_response.text)

Structured Output

Moonshot does not expose a distinct json_schema steering path in this package. ChatMoonshot.with_structured_output(..., method="json_schema") is intentionally downgraded to function_calling.

from pydantic import BaseModel, Field


class WeatherAnswer(BaseModel):
    city: str = Field(description="City name")
    summary: str = Field(description="One-sentence weather summary")


structured_llm = ChatMoonshot(
    model="kimi-k2.5",
    thinking=False,
    temperature=0.6,
).with_structured_output(WeatherAnswer)

result = structured_llm.invoke("Summarize today's weather in Shanghai.")
print(result)

Multimodal Input

Vision-capable Moonshot models accept OpenAI-style image_url content blocks.

from langchain_core.messages import HumanMessage

vision_llm = ChatMoonshot(
    model="moonshot-v1-32k-vision-preview",
)

message = HumanMessage(
    content=[
        {"type": "text", "text": "Describe the image and mention one concrete detail."},
        {
            "type": "image_url",
            "image_url": {"url": "data:image/png;base64,<your-base64-image>"},
        },
    ]
)

response = vision_llm.invoke([message])
print(response.text)

LangGraph Compatibility

ChatMoonshot works as a normal LangChain chat model inside LangGraph agent loops.

from typing import Annotated, Sequence, TypedDict

from langchain_core.messages import BaseMessage
from langgraph.graph import END, START, StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode


class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], add_messages]


tools = [add, multiply]
model = ChatMoonshot(
    model="kimi-k2.5",
    thinking=False,
    temperature=0.6,
).bind_tools(tools)


def model_call(state: AgentState) -> AgentState:
    response = model.invoke([("system", "Use tools when needed."), *state["messages"]])
    return {"messages": [response]}


def should_continue(state: AgentState) -> str:
    last = state["messages"][-1]
    return "continue" if getattr(last, "tool_calls", None) else "end"


graph = StateGraph(AgentState)
graph.add_node("agent", model_call)
graph.add_node("tools", ToolNode(tools=tools))
graph.add_edge(START, "agent")
graph.add_conditional_edges("agent", should_continue, {"continue": "tools", "end": END})
graph.add_edge("tools", "agent")

result = graph.compile().invoke(
    {"messages": [("user", "Add 3 and 4, multiply 12 by 13, then summarize.")]}
)
print(result["messages"][-1].content)

Moonshot-Specific Notes

• kimi-k2.5 is validated more strictly than generic OpenAI-compatible models.
• When thinking=True, kimi-k2.5 expects temperature=1.0.
• When thinking=False, kimi-k2.5 expects temperature=0.6.
• For kimi-k2.5, top_p must remain 0.95, n must remain 1, and both penalties must remain 0.0.
• The integration accepts standard tool_choice values for API compatibility.
• Moonshot does not reliably force tool calls in live tests, so forced tool_choice is treated as unsupported in standard tests.
• For kimi-k2.5 with thinking=True, tool forcing still must remain tool_choice="auto" or "none".
• Moonshot builtin $web_search is rejected when thinking=True.

Local Verification

uv run ruff check .
uv run mypy langchain_moonshot tests
uv run pytest tests/unit_tests/test_standard.py
RUN_MOONSHOT_INTEGRATION=1 uv run pytest tests/integration_tests/test_standard.py
RUN_MOONSHOT_INTEGRATION=1 uv run pytest tests/unit_tests/test_standard.py tests/integration_tests/test_standard.py -q > pytest_standard.txt 2>&1