🚀 Inference Gateway Python SDK

A modern and easy-to-use Python SDK for the Inference Gateway

Connect to multiple LLM providers through a unified interface • Stream responses • Function calling • Vision support • MCP tools support • Pydantic validation

Installation • Quick Start • Examples • License

🚀 Inference Gateway Python SDK

Installation

To install the SDK, use pip:

pip install inference-gateway

Requires Python 3.12+.

Usage

Creating a Client

To create a client, instantiate InferenceGatewayClient:

from inference_gateway import InferenceGatewayClient, Message

client = InferenceGatewayClient("http://localhost:8080/v1")

The client also supports authentication, custom timeouts, and an optional httpx backend:

# With authentication
client = InferenceGatewayClient(
    "http://localhost:8080/v1",
    token="your-api-token",
    timeout=60.0,
)

# Using httpx instead of the default requests backend
client = InferenceGatewayClient(
    "http://localhost:8080/v1",
    use_httpx=True,
)

# Use as a context manager to ensure the underlying HTTP client is closed
with InferenceGatewayClient("http://localhost:8080/v1") as client:
    models = client.list_models()

Listing Models

To list available models, use the list_models method:

# List all models from all providers
models = client.list_models()
print("All available models:", models)

# List models for a specific provider
openai_models = client.list_models(provider="openai")
print("OpenAI models:", openai_models)

Listing MCP Tools

MCP (Model Context Protocol) tools are listed over the MCP JSON-RPC endpoint with mcp_jsonrpc("tools/list") - see MCP JSON-RPC Endpoint below. This is only available when MCP_ENABLE and MCP_EXPOSE are set on the Inference Gateway server.

Server-Side Tool Management

Listing the connected tools is particularly useful for UI applications that need to display them to users. The key advantage is that tools are managed server-side:

Automatic Tool Injection: Tools are automatically inferred and injected into requests by the Inference Gateway server
Simplified Client Code: No need to manually manage or configure tools in your client application
Transparent Tool Calls: During streaming chat completions with configured MCP servers, tool calls appear in the response stream - no special handling required except optionally displaying them to users

MCP JSON-RPC Endpoint

The gateway exposes itself as an MCP server at POST /mcp, aggregating every server in MCP_SERVERS behind one JSON-RPC 2.0 endpoint. Use mcp_jsonrpc to call it; the required params._meta block and the MCP-Protocol-Version / Mcp-Method / Mcp-Name headers are filled in for you:

# Discover the supported protocol versions and capabilities
discover = client.mcp_jsonrpc("server/discover")
print(discover.result)

# List the aggregated, namespaced tools of every healthy MCP server
tools = client.mcp_jsonrpc("tools/list")

# Call a tool - names are namespaced mcp_<server alias>_<tool name>
response = client.mcp_jsonrpc(
    "tools/call",
    {
        "name": "mcp_deepwiki_ask_question",
        "arguments": {
            "repoName": "inference-gateway/inference-gateway",
            "question": "How is MCP wired up?",
        },
    },
)

if response.error:
    print(f"JSON-RPC error {response.error.code}: {response.error.message}")
else:
    print(response.result)

Protocol-level failures come back as JSON-RPC error envelopes on the response object rather than as exceptions. The endpoint lives at the root, not under /v1, so a /v1 suffix on the client's base URL is stripped automatically. It requires MCP_ENABLED=true and MCP_EXPOSE=true on the server.

When the gateway runs with authentication enabled, MCP clients can discover its authorization server through the RFC 9728 metadata document:

metadata = client.get_mcp_protected_resource_metadata()
print(metadata.resource, metadata.authorization_servers)

Generating Content

To generate content using a model, use the create_chat_completion method:

Note: Some models support reasoning capabilities. You can use the reasoning_format parameter to control how reasoning is provided in the response. The model's reasoning will be available in the reasoning or reasoning_content fields of the response message.

from inference_gateway import InferenceGatewayClient, Message

client = InferenceGatewayClient("http://localhost:8080/v1")

response = client.create_chat_completion(
    model="ollama/llama2",
    messages=[
        Message(role="system", content="You are a helpful assistant."),
        Message(role="user", content="What is Python?"),
    ],
)

print(response.choices[0].message.content.root)

# If reasoning was requested and the model supports it
if response.choices[0].message.reasoning:
    print("Reasoning:", response.choices[0].message.reasoning)

Vision Support

The SDK supports multimodal messages with images for vision-capable models like GPT-4o. You can include images via URLs or base64-encoded data URLs.

Simple Text Message

from inference_gateway import InferenceGatewayClient, Message

client = InferenceGatewayClient("http://localhost:8080/v1")

response = client.create_chat_completion(
    model="openai/gpt-4o",
    messages=[Message(role="user", content="What is the Python programming language?")],
)

Vision Message with Image URL

from inference_gateway import (
    InferenceGatewayClient,
    Message,
    TextContentPart,
    ImageContentPart,
    ImageURL,
)

client = InferenceGatewayClient("http://localhost:8080/v1")

response = client.create_chat_completion(
    model="openai/gpt-4o",
    messages=[
        Message(
            role="user",
            content=[
                TextContentPart(type="text", text="What is in this image?"),
                ImageContentPart(
                    type="image_url",
                    image_url=ImageURL(
                        url="https://example.com/image.jpg",
                        detail="auto",
                    ),
                ),
            ],
        )
    ],
)

Vision Message with Base64 Encoded Image

from inference_gateway import ImageContentPart, ImageURL

ImageContentPart(
    type="image_url",
    image_url=ImageURL(
        url="data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD...",
        detail="high",  # better quality, more expensive
    ),
)

Multiple Images in One Message

Message(
    role="user",
    content=[
        TextContentPart(type="text", text="Compare these images:"),
        ImageContentPart(type="image_url", image_url=ImageURL(url="https://example.com/image1.jpg")),
        ImageContentPart(type="image_url", image_url=ImageURL(url="https://example.com/image2.jpg")),
    ],
)

Image Detail Levels:

"auto": Automatic detail level (default)
"low": Lower resolution, faster and cheaper
"high": Higher resolution, better quality but more expensive

For a complete example, see the chat example.

Using ReasoningFormat

You can enable reasoning capabilities by setting the reasoning_format parameter in your request:

from inference_gateway import InferenceGatewayClient, Message

client = InferenceGatewayClient("http://localhost:8080/v1")

response = client.create_chat_completion(
    model="anthropic/claude-3-opus-20240229",
    messages=[
        Message(role="system", content="You are a helpful assistant. Please include your reasoning for complex questions."),
        Message(role="user", content="What is the square root of 144 and why?"),
    ],
    reasoning_format="parsed",  # "raw" or "parsed" - defaults to "parsed"
)

print("Content:", response.choices[0].message.content.root)
if response.choices[0].message.reasoning:
    print("Reasoning:", response.choices[0].message.reasoning)

Sampling and Output Controls

create_chat_completion and create_chat_completion_stream accept the full set of OpenAI-compatible request parameters as keyword arguments. The SDK only sends the parameters you set explicitly - spec defaults you do not specify are left off the request so each provider applies its own.

from inference_gateway import InferenceGatewayClient, Message

client = InferenceGatewayClient("http://localhost:8080/v1")

response = client.create_chat_completion(
    model="openai/gpt-4o",
    messages=[Message(role="user", content="Describe Python as a JSON object.")],
    temperature=0.2,            # 0-2, sampling temperature
    top_p=0.9,                  # 0-1, nucleus sampling
    n=1,                        # 1-128, number of choices to generate
    stop=["\n\n"],              # a string, or up to 4 strings
    frequency_penalty=0.0,      # -2..2
    presence_penalty=0.0,       # -2..2
    seed=42,                    # best-effort determinism
    max_completion_tokens=512,  # preferred over the deprecated max_tokens
    response_format={"type": "json_object"},
    reasoning_effort="medium",  # minimal | low | medium | high
)

response_format and tool_choice are oneOf unions. You can pass plain dicts (they are validated for you) or use the exported typed models:

from inference_gateway import (
    ChatCompletionNamedToolChoice,
    ResponseFormatJsonObject,
)

response = client.create_chat_completion(
    model="openai/gpt-4o",
    messages=[Message(role="user", content="What's the weather in Paris?")],
    response_format=ResponseFormatJsonObject(type="json_object"),
    tool_choice=ChatCompletionNamedToolChoice(
        type="function",
        function={"name": "get_weather"},
    ),
)

Note: max_tokens is deprecated in favor of max_completion_tokens.

Streaming Content

To generate content using streaming mode, use the create_chat_completion_stream method. It yields SSEvent objects:

import json
from pydantic import ValidationError
from inference_gateway import InferenceGatewayClient, Message
from inference_gateway.models import CreateChatCompletionStreamResponse

client = InferenceGatewayClient("http://localhost:8080/v1")

for chunk in client.create_chat_completion_stream(
    model="ollama/llama2",
    messages=[
        Message(role="system", content="You are a helpful assistant."),
        Message(role="user", content="Tell me a story."),
    ],
):
    if not chunk.data:
        continue

    try:
        data = json.loads(chunk.data)
        stream_response = CreateChatCompletionStreamResponse.model_validate(data)
    except (json.JSONDecodeError, ValidationError):
        continue

    for choice in stream_response.choices:
        # Reasoning content (both reasoning and reasoning_content fields)
        if choice.delta.reasoning:
            print(f"💭 Reasoning: {choice.delta.reasoning}")
        if choice.delta.reasoning_content:
            print(f"💭 Reasoning: {choice.delta.reasoning_content}")

        if choice.delta.content:
            print(choice.delta.content, end="", flush=True)

Tool-Use

To use tools with the SDK, define a tool with the type-safe Pydantic models and pass it to the request:

from inference_gateway import InferenceGatewayClient, Message
from inference_gateway.models import ChatCompletionTool, FunctionObject, FunctionParameters

client = InferenceGatewayClient("http://localhost:8080/v1")

tools = [
    ChatCompletionTool(
        type="function",
        function=FunctionObject(
            name="get_current_weather",
            description="Get the current weather in a given location",
            parameters=FunctionParameters(
                type="object",
                properties={
                    "location": {
                        "type": "string",
                        "enum": ["san francisco", "new york", "london", "tokyo", "sydney"],
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "The temperature unit to use",
                    },
                },
                required=["location"],
            ),
        ),
    ),
    ChatCompletionTool(
        type="function",
        function=FunctionObject(
            name="get_current_time",
            description="Get the current time in a given location",
            parameters=FunctionParameters(
                type="object",
                properties={
                    "location": {
                        "type": "string",
                        "enum": ["san francisco", "new york", "london", "tokyo", "sydney"],
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                },
                required=["location"],
            ),
        ),
    ),
]

response = client.create_chat_completion(
    model="openai/gpt-4o",
    messages=[
        Message(role="system", content="You are a helpful assistant with access to weather and time information."),
        Message(role="user", content="What is the weather like in New York?"),
    ],
    tools=tools,
)

# Inspect any tool calls made by the model
if response.choices[0].message.tool_calls:
    for tool_call in response.choices[0].message.tool_calls:
        print(f"Tool called: {tool_call.function.name}")
        print(f"Arguments: {tool_call.function.arguments}")

Provider-Specific Tool-Call Metadata

Some providers attach opaque, per-call metadata that must be echoed back on follow-up requests. The most notable case is Google Gemini's reasoning models, which return a thought_signature on each tool call - the next request must round-trip it verbatim or the provider will reject it.

The SDK preserves this automatically as long as you append the assistant message back to the conversation as a model object (rather than reconstructing it from a dict):

response = client.create_chat_completion(
    model="google/gemini-3-pro",
    messages=messages,
    tools=tools,
)

assistant_message = response.choices[0].message
messages.append(assistant_message)  # preserves extra_content.google.thought_signature

# ... append your tool results, then send the follow-up request ...

If you need to construct one explicitly:

from inference_gateway import Google, ToolCallExtraContent

extra = ToolCallExtraContent(google=Google(thought_signature="..."))

The field is fully optional - providers that don't use it ignore it entirely, and model_dump(exclude_none=True) strips it from the wire when unset.

Messages API (Anthropic-compatible)

The gateway also exposes an Anthropic-compatible Messages API (POST /messages). Use create_message for standard requests - it returns a validated MessagesResponse:

from inference_gateway import InferenceGatewayClient, MessagesMessage, MessagesTextBlock

client = InferenceGatewayClient("http://localhost:8080/v1")

response = client.create_message(
    model="anthropic/claude-sonnet-5",
    messages=[
        MessagesMessage(role="user", content="Hello! Please introduce yourself briefly."),
    ],
    max_tokens=100,
    system="You are a helpful assistant",
)

for block in response.content:
    if isinstance(block.root, MessagesTextBlock):
        print(block.root.text)

print(f"Stop reason: {response.stop_reason}")
print(f"Usage: {response.usage.input_tokens} in / {response.usage.output_tokens} out")

create_message_stream yields typed MessagesStreamEvent objects (message_start, content_block_delta, message_stop, ...) - no manual SSE parsing needed:

for event in client.create_message_stream(
    model="anthropic/claude-sonnet-5",
    messages=[
        MessagesMessage(role="user", content="Tell me a short story."),
    ],
    max_tokens=200,
):
    if event.type == "content_block_delta" and event.delta and event.delta.text:
        print(event.delta.text, end="", flush=True)

Tools use the MessagesTool shape, and tool calls come back as MessagesToolUseBlock content blocks:

from inference_gateway import MessagesTool, MessagesToolUseBlock
from inference_gateway.models import FunctionParameters

tools = [
    MessagesTool(
        name="get_current_weather",
        description="Get the current weather in a given location",
        input_schema=FunctionParameters(
            type="object",
            properties={
                "location": {"type": "string", "description": "The city, e.g. San Francisco"},
            },
            required=["location"],
        ),
    ),
]

response = client.create_message(
    model="anthropic/claude-sonnet-5",
    messages=[MessagesMessage(role="user", content="What is the weather in New York?")],
    max_tokens=200,
    tools=tools,
)

for block in response.content:
    if isinstance(block.root, MessagesToolUseBlock):
        print(f"Tool called: {block.root.name}")
        print(f"Input: {block.root.input}")

Note: Not every provider implements the Messages API. Requests routed to a provider without support return a 400 error - use create_chat_completion for those providers.

Proxy Requests

To proxy a raw request directly to a provider's API through the gateway, use proxy_request:

response = client.proxy_request(
    provider="openai",
    path="/v1/models",
    method="GET",
)

print("OpenAI models:", response)

Health Check

To check if the API is healthy:

if client.health_check():
    print("API is healthy")
else:
    print("API is unavailable")

Error Handling

The SDK provides several exception types:

from inference_gateway import (
    InferenceGatewayError,
    InferenceGatewayAPIError,
    InferenceGatewayValidationError,
)

try:
    response = client.create_chat_completion(...)
except InferenceGatewayAPIError as e:
    print(f"API Error: {e} (Status: {e.status_code})")
    print("Response:", e.response_data)
except InferenceGatewayValidationError as e:
    print(f"Validation Error: {e}")
except InferenceGatewayError as e:
    print(f"General Error: {e}")

Examples

For more detailed examples and use cases, check out the examples directory. The examples include:

List Example - How to list available models
Chat Example - Basic and advanced chat completion examples
Tools Example - Function calling and tool usage
Messages Example - Anthropic-compatible Messages API usage
MCP Example - Model Context Protocol integration examples

Each example includes its own README with specific instructions and explanations.

Supported Providers

The SDK supports the following LLM providers:

Ollama ("ollama")
Ollama Cloud ("ollama_cloud")
Groq ("groq")
OpenAI ("openai")
DeepSeek ("deepseek")
Cloudflare ("cloudflare")
Cohere ("cohere")
Anthropic ("anthropic")
Google ("google")
Mistral AI ("mistral")
MiniMax ("minimax")
Moonshot ("moonshot")
NVIDIA ("nvidia")

License

This SDK is distributed under the Apache 2.0 License, see LICENSE for more information.

Name		Name	Last commit message	Last commit date
.flox		.flox
.githooks		.githooks
.github		.github
examples		examples
inference_gateway		inference_gateway
templates		templates
tests		tests
.editorconfig		.editorconfig
.gitattributes		.gitattributes
.gitignore		.gitignore
.releaserc.yaml		.releaserc.yaml
AGENTS.md		AGENTS.md
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
README.md		README.md
Taskfile.yml		Taskfile.yml
openapi.yaml		openapi.yaml
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt

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🚀 Inference Gateway Python SDK

A modern and easy-to-use Python SDK for the Inference Gateway

Installation

Usage

Creating a Client

Listing Models

Listing MCP Tools

MCP JSON-RPC Endpoint

Generating Content

Vision Support

Simple Text Message

Vision Message with Image URL

Vision Message with Base64 Encoded Image

Multiple Images in One Message

Using ReasoningFormat

Sampling and Output Controls

Streaming Content

Tool-Use

Provider-Specific Tool-Call Metadata

Messages API (Anthropic-compatible)

Proxy Requests

Health Check

Error Handling

Examples

Supported Providers

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🚀 Inference Gateway Python SDK

A modern and easy-to-use Python SDK for the Inference Gateway

Installation

Usage

Creating a Client

Listing Models

Listing MCP Tools

MCP JSON-RPC Endpoint

Generating Content

Vision Support

Simple Text Message

Vision Message with Image URL

Vision Message with Base64 Encoded Image

Multiple Images in One Message

Using ReasoningFormat

Sampling and Output Controls

Streaming Content

Tool-Use

Provider-Specific Tool-Call Metadata

Messages API (Anthropic-compatible)

Proxy Requests

Health Check

Error Handling

Examples

Supported Providers

License

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