Invoke Providers and Proxies via SDKs¶

Once you have deployed an LLM Provider or LLM Proxy in the AI Workspace, you can invoke it using any supported AI SDK by pointing it at the gateway's Invoke URL and authenticating with your generated API key.

The examples below apply to both providers and proxies — the only difference between the two is the Invoke URL you supply.

Prerequisites¶

An LLM Provider or Proxy deployed to a gateway
The Invoke URL for the deployed endpoint
A generated API key

Authentication¶

All requests to the gateway must include your API key in the location configured in the Security tab of your provider or proxy. By default this is the X-API-Key request header, and the code examples below use that default.

Note

Depending on the SDK or provider you use, you can choose the key name and location that works best and configure it in the Security tab. See Configure Inbound Authentication. The examples below use the default X-API-Key header.

OpenAI¶

Invoke URL format

Append /v1 to the Invoke URL shown in the console:

https://{gateway-host}/{context}/v1

OpenAI SDKLangChain

Install: pip install openai

Basic chat completion:

from openai import OpenAI

INVOKE_URL = "https://<gateway-host>/<context>/v1"
API_KEY = "<your-gateway-api-key>"

client = OpenAI(
    api_key=API_KEY,
    base_url=INVOKE_URL,
    default_headers={"X-API-Key": API_KEY},
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What is WSO2?"}],
)

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

Streaming:

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Count from 1 to 5."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content if chunk.choices else None
    if delta:
        print(delta, end="", flush=True)

Install: pip install langchain-openai

Basic invoke:

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

INVOKE_URL = "https://<gateway-host>/<context>/v1"
API_KEY = "<your-gateway-api-key>"

llm = ChatOpenAI(
    model="gpt-4o",
    api_key=API_KEY,
    base_url=INVOKE_URL,
    default_headers={"X-API-Key": API_KEY},
)

response = llm.invoke([HumanMessage(content="What is WSO2?")])
print(response.content)

Streaming:

for chunk in llm.stream([HumanMessage(content="Count from 1 to 5.")]):
    if chunk.content:
        print(chunk.content, end="", flush=True)

Anthropic¶

Anthropic SDKLangChain

Install: pip install anthropic

Note

The Anthropic SDK sends the api_key parameter as the x-api-key header automatically. No additional header configuration is needed.

Basic message:

import anthropic

INVOKE_URL = "https://<gateway-host>/<context>"
API_KEY = "<your-gateway-api-key>"

client = anthropic.Anthropic(
    api_key=API_KEY,
    base_url=INVOKE_URL,
)

response = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    messages=[{"role": "user", "content": "What is WSO2?"}],
)

print(response.content[0].text)

Streaming:

with client.messages.stream(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Count from 1 to 5."}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Install: pip install langchain-anthropic

Basic invoke:

from langchain_anthropic import ChatAnthropic
from langchain_core.messages import HumanMessage

INVOKE_URL = "https://<gateway-host>/<context>"
API_KEY = "<your-gateway-api-key>"

llm = ChatAnthropic(
    model="claude-sonnet-4-5",
    api_key=API_KEY,
    anthropic_api_url=INVOKE_URL,
    default_headers={"X-API-Key": API_KEY},
    max_tokens=1024,
)

response = llm.invoke([HumanMessage(content="What is WSO2?")])
print(response.content)

Streaming:

for chunk in llm.stream([HumanMessage(content="Count from 1 to 5.")]):
    if chunk.content:
        print(chunk.content, end="", flush=True)

Gemini¶

Google GenAI SDKLangChain

Install: pip install google-genai

Note

The Gemini SDK normally sends its key as x-goog-api-key, which the gateway does not use for authentication. Pass api_key="placeholder" to satisfy the SDK and supply the real gateway key via X-API-Key in HttpOptions.

Basic content generation:

from google import genai
from google.genai import types as genai_types

INVOKE_URL = "https://<gateway-host>/<context>"
API_KEY = "<your-gateway-api-key>"

http_options = genai_types.HttpOptions(
    base_url=INVOKE_URL,
    headers={"X-API-Key": API_KEY},
)

client = genai.Client(api_key="placeholder", http_options=http_options)

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="What is WSO2?",
)

print(response.text)

Streaming:

for chunk in client.models.generate_content_stream(
    model="gemini-2.5-flash",
    contents="Count from 1 to 5.",
):
    if chunk.text:
        print(chunk.text, end="", flush=True)

Install: pip install langchain-google-genai

Basic invoke:

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.messages import HumanMessage

INVOKE_URL = "https://<gateway-host>/<context>"
API_KEY = "<your-gateway-api-key>"

llm = ChatGoogleGenerativeAI(
    model="gemini-2.5-flash",
    google_api_key=API_KEY,
    client_options={"api_endpoint": INVOKE_URL},
    additional_headers={"X-API-Key": API_KEY},
)

response = llm.invoke([HumanMessage(content="What is WSO2?")])
print(response.content)

Streaming:

for chunk in llm.stream([HumanMessage(content="Count from 1 to 5.")]):
    if chunk.content:
        print(chunk.content, end="", flush=True)

Mistral AI¶

Mistral exposes both a native SDK and an OpenAI-compatible API at /v1.

Mistral SDKOpenAI SDKLangChain

Install: pip install mistralai httpx

Note

The Mistral SDK sends its API key as a Bearer token. Since the gateway requires X-API-Key, an httpx event hook injects this header on every outgoing request.

Basic chat completion:

import httpx
from mistralai import Mistral

INVOKE_URL = "https://<gateway-host>/<context>"
API_KEY = "<your-gateway-api-key>"

def _inject_api_key(request):
    request.headers["X-API-Key"] = API_KEY

http_client = httpx.Client(
    event_hooks={"request": [_inject_api_key]},
)

client = Mistral(
    api_key=API_KEY,
    server_url=INVOKE_URL,
    client=http_client,
)

response = client.chat.complete(
    model="mistral-small-latest",
    messages=[{"role": "user", "content": "What is WSO2?"}],
)

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

Streaming:

with client.chat.stream(
    model="mistral-small-latest",
    messages=[{"role": "user", "content": "Count from 1 to 5."}],
) as stream:
    for event in stream:
        if event.data.choices and event.data.choices[0].delta.content:
            print(event.data.choices[0].delta.content, end="", flush=True)

Install: pip install openai

Mistral's API is OpenAI-compatible. Append /v1 to the Invoke URL.

Basic chat completion:

from openai import OpenAI

INVOKE_URL = "https://<gateway-host>/<context>/v1"
API_KEY = "<your-gateway-api-key>"

client = OpenAI(
    api_key=API_KEY,
    base_url=INVOKE_URL,
    default_headers={"X-API-Key": API_KEY},
)

response = client.chat.completions.create(
    model="mistral-small-latest",
    messages=[{"role": "user", "content": "What is WSO2?"}],
)

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

Streaming:

stream = client.chat.completions.create(
    model="mistral-small-latest",
    messages=[{"role": "user", "content": "Count from 1 to 5."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content if chunk.choices else None
    if delta:
        print(delta, end="", flush=True)

Install: pip install langchain-openai

LangChain's ChatOpenAI works with Mistral's OpenAI-compatible endpoint. Append /v1 to the Invoke URL.

Basic invoke:

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage

INVOKE_URL = "https://<gateway-host>/<context>/v1"
API_KEY = "<your-gateway-api-key>"

llm = ChatOpenAI(
    model="mistral-small-latest",
    api_key=API_KEY,
    base_url=INVOKE_URL,
    default_headers={"X-API-Key": API_KEY},
)

response = llm.invoke([HumanMessage(content="What is WSO2?")])
print(response.content)

Streaming:

for chunk in llm.stream([HumanMessage(content="Count from 1 to 5.")]):
    if chunk.content:
        print(chunk.content, end="", flush=True)

Azure OpenAI¶

Note

The model / azure_deployment parameter must be your Azure deployment name, not the underlying model name.

Azure OpenAI SDKLangChain

Install: pip install openai

Basic chat completion:

from openai import AzureOpenAI

INVOKE_URL = "https://<gateway-host>/<context>"
API_KEY = "<your-gateway-api-key>"

client = AzureOpenAI(
    api_key=API_KEY,
    azure_endpoint=INVOKE_URL,
    api_version="2024-10-21",
    default_headers={"X-API-Key": API_KEY},
)

response = client.chat.completions.create(
    model="<your-deployment-name>",
    messages=[{"role": "user", "content": "What is WSO2?"}],
)

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

Streaming:

stream = client.chat.completions.create(
    model="<your-deployment-name>",
    messages=[{"role": "user", "content": "Count from 1 to 5."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content if chunk.choices else None
    if delta:
        print(delta, end="", flush=True)

Install: pip install langchain-openai

Basic invoke:

from langchain_openai import AzureChatOpenAI
from langchain_core.messages import HumanMessage

INVOKE_URL = "https://<gateway-host>/<context>"
API_KEY = "<your-gateway-api-key>"

llm = AzureChatOpenAI(
    azure_deployment="<your-deployment-name>",
    api_version="2024-10-21",
    azure_endpoint=INVOKE_URL,
    api_key=API_KEY,
    default_headers={"X-API-Key": API_KEY},
)

response = llm.invoke([HumanMessage(content="What is WSO2?")])
print(response.content)

Streaming:

for chunk in llm.stream([HumanMessage(content="Count from 1 to 5.")]):
    if chunk.content:
        print(chunk.content, end="", flush=True)

Azure AI Foundry¶

Note

The model / azure_deployment parameter must be your Azure deployment name.

Azure OpenAI SDKLangChain

Install: pip install openai

Basic chat completion:

from openai import AzureOpenAI

INVOKE_URL = "https://<gateway-host>/<context>"
API_KEY = "<your-gateway-api-key>"

client = AzureOpenAI(
    api_key=API_KEY,
    azure_endpoint=INVOKE_URL,
    api_version="2024-05-01-preview",
    default_headers={"X-API-Key": API_KEY},
)

response = client.chat.completions.create(
    model="<your-deployment-name>",
    messages=[{"role": "user", "content": "What is WSO2?"}],
)

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

Streaming:

stream = client.chat.completions.create(
    model="<your-deployment-name>",
    messages=[{"role": "user", "content": "Count from 1 to 5."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content if chunk.choices else None
    if delta:
        print(delta, end="", flush=True)

Install: pip install langchain-openai

Basic invoke:

from langchain_openai import AzureChatOpenAI
from langchain_core.messages import HumanMessage

INVOKE_URL = "https://<gateway-host>/<context>"
API_KEY = "<your-gateway-api-key>"

llm = AzureChatOpenAI(
    azure_deployment="<your-deployment-name>",
    api_version="2024-05-01-preview",
    azure_endpoint=INVOKE_URL,
    api_key=API_KEY,
    default_headers={"X-API-Key": API_KEY},
)

response = llm.invoke([HumanMessage(content="What is WSO2?")])
print(response.content)

Streaming:

for chunk in llm.stream([HumanMessage(content="Count from 1 to 5.")]):
    if chunk.content:
        print(chunk.content, end="", flush=True)