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langchain_kserve	langchain_kserve
tests	tests
README.md	README.md
pyproject.toml	pyproject.toml

langchain-kserve

A LangChain provider package for models hosted on KServe.

Connect any LangChain chain, agent, or multi-agent framework to self-hosted models on Kubernetes — including vLLM, TGI, Triton, and custom KServe runtimes.

Features

ChatKServe — BaseChatModel for chat/instruct models (Qwen, Llama, Mistral, …)
KServeLLM — BaseLLM for base completion models
Dual protocol — OpenAI-compatible (/v1/chat/completions) and V2 Inference Protocol (/v2/models/{model}/infer), auto-detected
Full streaming — sync and async, SSE for OpenAI-compat, chunked transfer for V2
Tool calling — full OpenAI function-calling format with tool_choice, parallel_tool_calls, and proper invalid_tool_calls handling
Vision / multimodal — send images alongside text via OpenAI content blocks
Token usage tracking — llm_output and generation_info populated from vLLM responses, including streaming
Logprobs — optional per-token log-probabilities in generation_info
Finish reason — always propagated in generation_info
Model introspection — get_model_info() returns unified KServeModelInfo for both protocols
Production-grade — TLS/CA bundle, bearer token + dynamic token provider (K8s SA tokens), exponential backoff retries, generous cold-start timeouts

Installation

pip install langchain-kserve

Quick Start

from langchain_kserve import ChatKServe

llm = ChatKServe(
    base_url="https://qwen-coder.default.svc.cluster.local",
    model_name="qwen2.5-coder-32b-instruct",
    temperature=0.2,
)

response = llm.invoke("Write a binary search in Python.")
print(response.content)

Configuration

Constructor parameters

Parameter	Type	Default	Description
`base_url`	`str`	—	Root URL of the KServe inference service
`model_name`	`str`	—	Model identifier
`protocol`	`"openai"` \| `"v2"` \| `"auto"`	`"auto"`	Inference protocol
`api_key`	`SecretStr`	`None`	Static bearer token
`token_provider`	`Callable[[], str]`	`None`	Dynamic token provider
`verify_ssl`	`bool`	`True`	Verify TLS certificates
`ca_bundle`	`str`	`None`	Path to custom CA cert bundle
`temperature`	`float`	`0.7`	Sampling temperature
`max_tokens`	`int`	`None`	Max tokens to generate
`top_p`	`float`	`1.0`	Nucleus sampling
`stop`	`List[str]`	`None`	Stop sequences
`streaming`	`bool`	`False`	Default streaming mode
`logprobs`	`bool`	`None`	Return per-token log-probabilities (OpenAI-compat only)
`top_logprobs`	`int`	`None`	Number of top logprobs per token (OpenAI-compat only)
`tool_choice`	`str \| dict`	`None`	Tool selection strategy: `"auto"`, `"required"`, `"none"`, or specific function
`parallel_tool_calls`	`bool`	`None`	Allow the model to call multiple tools in one turn
`response_format`	`dict`	`None`	Response format constraint (e.g. `{"type": "json_object"}`) (OpenAI-compat only)
`timeout`	`int`	`120`	Request timeout in seconds
`max_retries`	`int`	`3`	Retry attempts

Environment variables

export KSERVE_BASE_URL="https://qwen-coder.default.svc.cluster.local"
export KSERVE_MODEL_NAME="qwen2.5-coder-32b-instruct"
export KSERVE_API_KEY="my-token"          # optional
export KSERVE_PROTOCOL="openai"           # optional, default: auto
export KSERVE_CA_BUNDLE="/path/to/ca.crt" # optional

Usage Examples

Basic invocation

from langchain_kserve import ChatKServe

llm = ChatKServe(
    base_url="https://qwen-coder.default.svc.cluster.local",
    model_name="qwen2.5-coder-32b-instruct",
)

response = llm.invoke("Explain the GIL in Python.")
print(response.content)

Streaming

for chunk in llm.stream("Implement a red-black tree in Python."):
    print(chunk.content, end="", flush=True)

Async

import asyncio

async def main():
    response = await llm.ainvoke("What is KServe?")
    print(response.content)

    async for chunk in llm.astream("Explain transformers."):
        print(chunk.content, end="", flush=True)

asyncio.run(main())

In a LangChain chain

from langchain.prompts import ChatPromptTemplate
from langchain_kserve import ChatKServe

llm = ChatKServe(
    base_url="https://qwen-coder.default.svc.cluster.local",
    model_name="qwen2.5-coder-32b-instruct",
    temperature=0.2,
)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are an expert {language} developer."),
    ("human", "{input}"),
])

chain = prompt | llm
response = chain.invoke({"language": "Python", "input": "Implement a LRU cache."})
print(response.content)

Token usage tracking

Token usage is available in both llm_output (request-level) and generation_info (per-generation):

result = llm._generate([HumanMessage(content="Hello")])

# Request-level (on ChatResult)
print(result.llm_output)
# {"token_usage": {"prompt_tokens": 5, "completion_tokens": 12, "total_tokens": 17}, "model_name": "..."}

# Per-generation
gen_info = result.generations[0].generation_info
print(gen_info["prompt_tokens"])     # 5
print(gen_info["completion_tokens"]) # 12
print(gen_info["finish_reason"])     # "stop"

Token usage is also captured from the final streaming chunk (vLLM sends it when stream_options.include_usage=True):

chunks = list(llm.stream("Hello"))
last_chunk = chunks[-1]
print(last_chunk.generation_info)  # {"token_usage": {...}}

Logprobs

llm = ChatKServe(
    base_url="...",
    model_name="...",
    logprobs=True,
    top_logprobs=5,
    protocol="openai",
)

response = llm.invoke("Hello")
print(response.response_metadata["logprobs"])
# {"tokens": [...], "token_logprobs": [...], "top_logprobs": [...]}

Tool calling

Tool calling works when using the OpenAI-compatible endpoint (e.g., vLLM, TGI).

from langchain.tools import tool
from langchain_kserve import ChatKServe

@tool
def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    return f"It is sunny in {city}."

llm = ChatKServe(
    base_url="https://qwen-coder.default.svc.cluster.local",
    model_name="qwen2.5-coder-32b-instruct",
    protocol="openai",
    tool_choice="auto",
    parallel_tool_calls=True,
)

llm_with_tools = llm.bind_tools([get_weather])
response = llm_with_tools.invoke("What's the weather in Berlin and Paris?")

if response.tool_calls:
    for tc in response.tool_calls:
        print(f"Tool: {tc['name']}, Args: {tc['args']}")

# Malformed arguments land in invalid_tool_calls instead of crashing
if response.invalid_tool_calls:
    for itc in response.invalid_tool_calls:
        print(f"Invalid call: {itc['name']}, raw args: {itc['args']}")

Vision / multimodal

Pass images alongside text using OpenAI content blocks. Works with OpenAI-compatible runtimes that support vision (e.g., vLLM with a multimodal model).

from langchain.messages import HumanMessage
from langchain_kserve import ChatKServe
import base64, pathlib

llm = ChatKServe(
    base_url="https://llava.default.svc.cluster.local",
    model_name="llava-1.6",
    protocol="openai",
)

# Base64-encoded image (preferred for cluster-internal use)
image_data = base64.b64encode(pathlib.Path("chart.png").read_bytes()).decode()
message = HumanMessage(content=[
    {"type": "text", "text": "Describe what you see in this chart:"},
    {
        "type": "image_url",
        "image_url": {
            "url": f"data:image/png;base64,{image_data}",
            "detail": "high",
        },
    },
])

response = llm.invoke([message])
print(response.content)

URL-based images are also supported (the model pod fetches the image):

message = HumanMessage(content=[
    {"type": "text", "text": "What's in this image?"},
    {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}},
])

Model introspection

import asyncio
from langchain_kserve import ChatKServe

llm = ChatKServe(base_url="...", model_name="qwen2.5-coder-32b-instruct")
info = asyncio.run(llm.get_model_info())

print(info["model_name"])    # "qwen2.5-coder-32b-instruct"
print(info["platform"])      # "openai-compat" or V2 platform string
print(info["raw"])           # full response from the endpoint

Forcing V2 Inference Protocol

from langchain_kserve import ChatKServe

llm = ChatKServe(
    base_url="https://triton.default.svc.cluster.local",
    model_name="my-triton-model",
    protocol="v2",  # skip auto-detection
    temperature=0.5,
    max_tokens=512,
)

response = llm.invoke("Summarise this text in one sentence.")
print(response.content)

Kubernetes service account token (dynamic auth)

import pathlib
from langchain_kserve import ChatKServe

SA_TOKEN_PATH = "/var/run/secrets/kubernetes.io/serviceaccount/token"

def k8s_token() -> str:
    return pathlib.Path(SA_TOKEN_PATH).read_text().strip()

llm = ChatKServe(
    base_url="https://qwen-coder.my-namespace.svc.cluster.local",
    model_name="qwen2.5-coder-32b-instruct",
    token_provider=k8s_token,  # refreshed on every request
)

Custom CA certificate

from langchain_kserve import ChatKServe

llm = ChatKServe(
    base_url="https://kserve-internal.my-corp.com",
    model_name="llama-3-70b",
    ca_bundle="/etc/ssl/certs/my-corp-ca.crt",
)

DeepAgents integration

from langchain_kserve import ChatKServe
from langchain.tools import tool

@tool
def code_executor(code: str) -> str:
    """Execute Python code and return the output."""
    import subprocess
    result = subprocess.run(["python", "-c", code], capture_output=True, text=True)
    return result.stdout or result.stderr

llm = ChatKServe(
    base_url="https://qwen-coder.default.svc.cluster.local",
    model_name="qwen2.5-coder-32b-instruct",
    temperature=0.1,
    max_tokens=2048,
    protocol="openai",
)

from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful coding assistant with access to a Python executor."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, [code_executor], prompt)
executor = AgentExecutor(agent=agent, tools=[code_executor], verbose=True)
result = executor.invoke({"input": "Calculate the 10th Fibonacci number using code."})
print(result["output"])

Protocol Auto-Detection

When protocol="auto" (the default), langchain-kserve probes the endpoint:

GET /v1/models — if it responds 200, use OpenAI-compatible protocol
Otherwise fall back to V2 Inference Protocol

The detected protocol is cached per instance to avoid repeated probes.

JSON mode / Response format

Constrain the model output to valid JSON or a specific JSON schema (vLLM uses grammar-constrained decoding):

# Force valid JSON output
llm = ChatKServe(
    base_url="...",
    model_name="...",
    protocol="openai",
    response_format={"type": "json_object"},
)

# Force output matching a specific JSON schema
llm = ChatKServe(
    base_url="...",
    model_name="...",
    protocol="openai",
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "my_schema",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {"name": {"type": "string"}, "age": {"type": "integer"}},
                "required": ["name", "age"],
            },
        },
    },
)

Structured output

Use with_structured_output() for the high-level structured output API:

from langchain_kserve import ChatKServe
from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int

llm = ChatKServe(
    base_url="...",
    model_name="...",
    protocol="openai",
)

# function_calling (default) — most reliable, uses tool calling
structured_llm = llm.with_structured_output(Person)
result = structured_llm.invoke("Extract: John is 30 years old.")
print(result.name, result.age)  # John, 30

# json_schema — uses vLLM grammar-constrained decoding
structured_llm = llm.with_structured_output(Person, method="json_schema")

# json_mode — uses json_object response format with schema instruction
structured_llm = llm.with_structured_output(Person, method="json_mode")

# include_raw=True — get both raw AIMessage and parsed output
structured_llm = llm.with_structured_output(Person, include_raw=True)
result = structured_llm.invoke("Extract: John is 30.")
print(result["parsed"])   # Person(name='John', age=30)
print(result["raw"])      # AIMessage(...)
print(result["parsing_error"])  # None

Embeddings

from langchain_kserve import KServeEmbeddings

embeddings = KServeEmbeddings(
    base_url="https://my-embedding-model.cluster.example.com",
    model_name="Qwen/Qwen3-Embedding-0.6B",
)

# Embed documents (batched automatically)
vectors = embeddings.embed_documents(["Hello world", "How are you?"])
print(len(vectors), len(vectors[0]))  # 2, <embedding_dim>

# Embed a single query
query_vector = embeddings.embed_query("What is KServe?")

# Async
vectors = await embeddings.aembed_documents(["Hello world", "How are you?"])
query_vector = await embeddings.aembed_query("What is KServe?")

# With dimensions (Matryoshka models)
embeddings = KServeEmbeddings(
    base_url="...",
    model_name="...",
    dimensions=512,
)

Protocol Capability Matrix

Feature	OpenAI-compat	V2
Text generation	✅	✅
Streaming	✅	✅
Tool calling	✅	❌
Vision / multimodal	✅	❌
Logprobs	✅	❌
Token usage tracking	✅	❌
Finish reason	✅	partial
JSON mode / response_format	✅	❌
Structured output	✅	❌
Embeddings	✅	❌

Attempting tool calling, vision, or response_format with V2 raises KServeInferenceError immediately (before any HTTP call) with a clear message.

Error Handling

from langchain_kserve import ChatKServe
from langchain_kserve import (
    KServeConnectionError,
    KServeAuthenticationError,
    KServeModelNotFoundError,
    KServeInferenceError,
    KServeTimeoutError,
)

llm = ChatKServe(base_url="...", model_name="...")

try:
    response = llm.invoke("Hello")
except KServeConnectionError:
    print("Cannot reach the inference service (cold start, DNS, TLS issue)")
except KServeAuthenticationError:
    print("Invalid or missing bearer token")
except KServeModelNotFoundError:
    print("Model name does not match what is served")
except KServeTimeoutError:
    print("Inference timed out (model may be scaling up)")
except KServeInferenceError as e:
    print(f"Inference error: {e}")
    # Also raised for unsupported features on V2 (tools, vision)

Embeddings

from langchain_kserve import KServeEmbeddings

embeddings = KServeEmbeddings(
    base_url="https://my-embedding-model.cluster.example.com",
    model_name="Qwen/Qwen3-Embedding-0.6B",
    chunk_size=500,      # max texts per API call (default: 1000)
    dimensions=512,      # optional, for Matryoshka models
)

vectors = embeddings.embed_documents(["Hello world", "How are you?"])
query_vector = embeddings.embed_query("What is KServe?")

Parameter	Type	Default	Description
`base_url`	`str`	`KSERVE_EMBEDDINGS_BASE_URL` or `KSERVE_BASE_URL`	Embeddings service URL
`model_name`	`str`	`KSERVE_EMBEDDINGS_MODEL_NAME`	Embedding model name
`api_key`	`SecretStr`	`KSERVE_API_KEY`	Bearer token
`dimensions`	`int`	`None`	Output embedding dimensions
`encoding_format`	`"float" \| "base64"`	`"float"`	Wire format (base64 decoded automatically)
`chunk_size`	`int`	`1000`	Max texts per API call
`timeout`	`int`	`120`	Request timeout (seconds)
`max_retries`	`int`	`3`	Retry attempts

Base Completion Models (`KServeLLM`)

For non-chat, base completion models use KServeLLM:

from langchain_kserve import KServeLLM

llm = KServeLLM(
    base_url="https://llama-base.default.svc.cluster.local",
    model_name="llama-3-8b",
    max_tokens=128,
)

text = llm.invoke("The quick brown fox")
print(text)

# Streaming
for chunk in llm.stream("Once upon a time"):
    print(chunk, end="", flush=True)

# Token usage
result = llm.generate(["The quick brown fox"])
print(result.llm_output)  # {"token_usage": {...}}

Development

# Install dependencies
poetry install

# Run unit tests
pytest tests/unit_tests/

# Run integration tests (requires live KServe endpoint)
KSERVE_BASE_URL=https://... KSERVE_MODEL_NAME=... pytest -m integration

# Type checking
mypy --strict langchain_kserve/

# Linting
ruff check langchain_kserve/ tests/

License

MIT

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README.md

langchain-kserve

Features

Installation

Quick Start

Configuration

Constructor parameters

Environment variables

Usage Examples

Basic invocation

Streaming

Async

In a LangChain chain

Token usage tracking

Logprobs

Tool calling

Vision / multimodal

Model introspection

Forcing V2 Inference Protocol

Kubernetes service account token (dynamic auth)

Custom CA certificate

DeepAgents integration

Protocol Auto-Detection

JSON mode / Response format

Structured output

Embeddings

Protocol Capability Matrix

Error Handling

Embeddings

Base Completion Models (`KServeLLM`)

Development

License

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README.md

langchain-kserve

Features

Installation

Quick Start

Configuration

Constructor parameters

Environment variables

Usage Examples

Basic invocation

Streaming

Async

In a LangChain chain

Token usage tracking

Logprobs

Tool calling

Vision / multimodal

Model introspection

Forcing V2 Inference Protocol

Kubernetes service account token (dynamic auth)

Custom CA certificate

DeepAgents integration

Protocol Auto-Detection

JSON mode / Response format

Structured output

Embeddings

Protocol Capability Matrix

Error Handling

Embeddings

Base Completion Models (KServeLLM)

Development

License

Base Completion Models (`KServeLLM`)