autourgos-openaichat

A single, self-contained LLM wrapper for the OpenAI Chat Completions API, and by extension every provider that speaks the same protocol (Groq, Gemini, Azure, Ollama, and more). Part of the Autourgos agentic-AI framework, but has zero dependency on it: pip install openai and you're ready.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(model="gpt-4o")           # reads OPENAI_API_KEY
reply = llm.invoke("What is the capital of France?")
print(reply)
# Paris

Features

• One interface, any OpenAI-compatible provider: OpenAI, Azure, Groq, Gemini, Mistral, DeepSeek, Ollama, and more, switched with just base_url + model
• Sync and async generation, plus streaming for both
• Native tool / function calling, sync and async
• Structured output validated against a Pydantic model, or plain JSON mode
• Multi-modal vision input: file paths, URLs, or raw bytes
• Prompt templates with {placeholder} variables
• Multi-turn conversations via a plain message list
• Automatic retries with exponential back-off, plus a circuit breaker for cascading-failure protection
• Built-in cost and latency tracking
• Fully typed (py.typed), sync/async context managers, low-level raw-response access

Table of Contents

• Install
• Supported Providers
• Provider Examples
  • OpenAI
  • Azure OpenAI
  • Google Gemini
  • Groq
  • xAI (Grok)
  • OpenRouter
  • Together AI
  • Mistral AI
  • DeepSeek
  • Perplexity
  • Ollama
  • LM Studio
  • vLLM
  • Switching providers at runtime
• Core Usage
  • Text Generation
  • Async Generation
  • Streaming
  • Async Streaming
  • Batch Invocation
  • System Prompt
  • Prompt Templates
  • Vision Input
  • Structured Output
  • JSON Mode
  • Native Tool Calling
  • Multi-Turn Conversations
  • Cost Tracking
  • Context Manager
  • Circuit Breaker
  • Low-Level Access
  • Error Handling
• Constructor Reference
• API Reference
• License

Install

pip install autourgos-openaichat

Requires Python 3.10+ and openai>=1.0.0. Structured output (output_schema=) additionally needs pydantic>=2.0 if you use it.

Supported Providers

Almost every major LLM provider exposes an OpenAI-compatible API: same request format as OpenAI's Chat Completions endpoint. Point base_url at the provider and model at whatever they offer; nothing else changes.

Provider Examples

Every example below is the full, runnable snippet. Swap in your own key and go.

OpenAI

The default provider. No base_url needed.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="gpt-4o",
    api_key="sk-...",           # or set OPENAI_API_KEY env var
)
reply = llm.invoke("What is the capital of France?")
print(reply)
# Paris

Azure OpenAI

Azure hosts OpenAI models in your own subscription. model is your deployment name in Azure, not the base model name. Get your endpoint and key from the Azure Portal.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="gpt-4o",              # your deployment name in Azure
    api_key="...",               # Azure OpenAI key
    base_url="https://<your-resource>.openai.azure.com/openai/deployments/gpt-4o",
)
reply = llm.invoke("What is cloud computing?")
print(reply)
# Cloud computing is the delivery of computing services over the internet
# (servers, storage, databases, networking, software) on a pay-as-you-go basis.

Google Gemini

Gemini exposes an OpenAI-compatible endpoint, so no separate Google SDK is needed. Get your key at https://aistudio.google.com/apikey.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="gemini-2.0-flash",
    api_key="...",               # Gemini API key
    base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
)
reply = llm.invoke("Explain photosynthesis in one sentence.")
print(reply)
# Photosynthesis is the process by which plants convert sunlight, water, and
# carbon dioxide into glucose and oxygen.

Other Gemini models: gemini-2.0-flash-lite, gemini-1.5-pro, gemini-1.5-flash.

Groq (fastest inference, free tier available)

Groq runs open-source models (Llama 3, Mixtral, Gemma) at extremely high speed. Get your key at https://console.groq.com.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="llama3-70b-8192",
    api_key="gsk_...",           # Groq API key
    base_url="https://api.groq.com/openai/v1",
)
reply = llm.invoke("Explain quantum entanglement simply.")
print(reply)
# Quantum entanglement is when two particles become linked so that
# the state of one instantly affects the other, no matter how far apart they are.

Other Groq models: llama3-8b-8192, mixtral-8x7b-32768, gemma2-9b-it.

xAI (Grok)

Get your key at https://console.x.ai.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="grok-2-latest",
    api_key="xai-...",           # xAI API key
    base_url="https://api.x.ai/v1",
)
reply = llm.invoke("What makes Mars red?")
print(reply)
# Mars appears red because its surface is covered in iron oxide (rust),
# formed when iron in the soil reacted with trace oxygen long ago.

OpenRouter (one key, hundreds of models)

OpenRouter proxies dozens of providers (including Anthropic Claude and Google Gemini) behind a single OpenAI-compatible API and one API key. Get your key at https://openrouter.ai/keys.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="anthropic/claude-3.5-sonnet",   # or "google/gemini-2.0-flash-001", "openai/gpt-4o", ...
    api_key="sk-or-...",         # OpenRouter API key
    base_url="https://openrouter.ai/api/v1",
)
reply = llm.invoke("Write a Python one-liner to reverse a string.")
print(reply)
# s[::-1]

Together AI (wide model selection)

Together AI hosts hundreds of open-source models. Get your key at https://api.together.xyz.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="meta-llama/Llama-3-70b-chat-hf",
    api_key="...",                # Together AI key
    base_url="https://api.together.xyz/v1",
)
reply = llm.invoke("Write a Python function to reverse a string.")
print(reply)
# def reverse_string(s: str) -> str:
#     return s[::-1]

Other Together AI models: mistralai/Mixtral-8x7B-Instruct-v0.1, Qwen/Qwen2-72B-Instruct.

Mistral AI

Get your key at https://console.mistral.ai.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="mistral-large-latest",
    api_key="...",                # Mistral API key
    base_url="https://api.mistral.ai/v1",
)
reply = llm.invoke("What are the benefits of test-driven development?")
print(reply)
# TDD helps you write cleaner code, catch bugs early, and gives
# you confidence to refactor without breaking existing behaviour.

Other Mistral models: mistral-medium-latest, mistral-small-latest, open-mixtral-8x7b.

DeepSeek

Get your key at https://platform.deepseek.com.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="deepseek-chat",
    api_key="...",                # DeepSeek API key
    base_url="https://api.deepseek.com/v1",
)
reply = llm.invoke("Summarise the history of the Roman Empire in 2 sentences.")
print(reply)
# The Roman Empire rose from a small city-state to dominate the Mediterranean world
# for over 500 years. It split into Western and Eastern halves, with the West falling
# in 476 AD and the East (Byzantine Empire) surviving until 1453.

Other DeepSeek models: deepseek-reasoner.

Perplexity (web-connected models)

Perplexity's Sonar models can search the web in real time. Get your key at https://www.perplexity.ai/settings/api.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="llama-3.1-sonar-large-128k-online",
    api_key="pplx-...",           # Perplexity API key
    base_url="https://api.perplexity.ai",
)
reply = llm.invoke("What is the latest version of Python?")
print(reply)
# Python 3.13.x is the latest stable release as of 2025...

Ollama (run any model locally, no internet needed)

Ollama runs models entirely on your machine. Install from https://ollama.com, then pull a model:

ollama pull llama3

No API key needed for local use.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="llama3",
    api_key="ollama",             # can be any string, Ollama ignores it
    base_url="http://localhost:11434/v1",
)
reply = llm.invoke("What is machine learning?")
print(reply)
# Machine learning is a subset of AI where algorithms learn patterns
# from data to make predictions or decisions without explicit programming.

Other Ollama models: mistral, phi3, gemma2, codellama, qwen2, and anything you pull with ollama pull.

LM Studio (local models with a GUI)

LM Studio lets you download and run GGUF models locally. Start the local server in LM Studio, then:

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="local-model",          # use whatever model name LM Studio shows
    api_key="lm-studio",          # any string, ignored locally
    base_url="http://localhost:1234/v1",
)
reply = llm.invoke("Tell me a short joke.")
print(reply)
# Why do programmers prefer dark mode? Because light attracts bugs!

vLLM (self-hosted high-throughput serving)

vLLM lets you host your own models with high throughput. After starting your vLLM server:

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="meta-llama/Meta-Llama-3-8B-Instruct",
    api_key="EMPTY",              # vLLM's default when no auth is configured
    base_url="http://your-server:8000/v1",
)
reply = llm.invoke("What is the capital of Japan?")
print(reply)
# Tokyo

Switching providers at runtime

Because all these providers use the same interface, switching is trivial:

from autourgos_openaichat import OpenAIChatModel

PROVIDERS = {
    "openai": {
        "model": "gpt-4o-mini",
        "api_key": "sk-...",
        "base_url": None,
    },
    "groq": {
        "model": "llama3-8b-8192",
        "api_key": "gsk_...",
        "base_url": "https://api.groq.com/openai/v1",
    },
    "gemini": {
        "model": "gemini-2.0-flash",
        "api_key": "...",
        "base_url": "https://generativelanguage.googleapis.com/v1beta/openai/",
    },
}

for name, cfg in PROVIDERS.items():
    llm = OpenAIChatModel(**cfg)
    reply = llm.invoke("Say hello in one word.")
    print(f"{name}: {reply}")

# openai: Hello!
# groq:   Hello!
# gemini: Hello!

Core Usage

Text Generation

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="gpt-4o",
    api_key="sk-...",             # or set OPENAI_API_KEY env var
    temperature=0.7,
    max_tokens=256,
)

reply = llm.invoke("Explain machine learning in one sentence.")
print(reply)
# Machine learning is a branch of AI where systems learn from data
# to make predictions or decisions without being explicitly programmed.

Async Generation

import asyncio
from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(model="gpt-4o")

async def main():
    reply = await llm.ainvoke("What is the speed of light?")
    print(reply)
    # The speed of light in a vacuum is approximately 299,792,458 metres per second.

asyncio.run(main())

Streaming

Stream the response token by token, synchronously.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(model="gpt-4o")

for chunk in llm.stream("Write a haiku about rain."):
    print(chunk, end="", flush=True)

# Raindrops softly fall,
# Washing the grey streets below,
# Earth breathes once again.

You can also enable streaming at construction time so invoke() internally streams and returns the full joined text:

llm = OpenAIChatModel(model="gpt-4o", streaming=True)
reply = llm.invoke("Tell me a fun fact.")
print(reply)
# Honey never spoils. Archaeologists have found 3,000-year-old honey in Egyptian tombs.

Async Streaming

import asyncio
from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(model="gpt-4o")

async def main():
    async for chunk in llm.astream("Count from 1 to 5 slowly."):
        print(chunk, end="", flush=True)
    # 1... 2... 3... 4... 5...

asyncio.run(main())

Batch Invocation

Synchronous (sequential):

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(model="gpt-4o-mini")

prompts = [
    "Capital of Japan?",
    "Capital of Germany?",
    "Capital of Brazil?",
]

results = llm.batch_invoke(prompts)
for prompt, result in zip(prompts, results):
    print(f"{prompt} -> {result}")

# Capital of Japan?   -> Tokyo
# Capital of Germany? -> Berlin
# Capital of Brazil?  -> Brasilia

Async (concurrent):

import asyncio
from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(model="gpt-4o-mini")

async def main():
    results = await llm.abatch_invoke([
        "Capital of Japan?",
        "Capital of Germany?",
        "Capital of Brazil?",
    ])
    print(results)
    # ['Tokyo', 'Berlin', 'Brasilia']

asyncio.run(main())

System Prompt

Set a persistent system prompt for all requests.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="gpt-4o",
    system_prompt="You are a pirate. Always respond in pirate speak.",
)

reply = llm.invoke("What time is it?")
print(reply)
# Arrr, I know not the exact hour, but the sun be high in the sky, matey!

Prompt Templates

Define a reusable template with {placeholders} and fill them at call time.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="gpt-4o",
    prompt_template="Translate the following text to {language}:\n\n{text}",
)

reply = llm.invoke(prompt_variables={"language": "French", "text": "Good morning!"})
print(reply)
# Bonjour !

reply = llm.invoke(prompt_variables={"language": "Spanish", "text": "Thank you very much."})
print(reply)
# Muchas gracias.

Missing variables raise a clear error:

llm.invoke(prompt_variables={"language": "French"})
# ValueError: Missing prompt template variables: text

Vision Input

Pass image files, URLs, or raw bytes alongside text.

Note: vision support depends on the provider and model. GPT-4o, Gemini, LLaVA (on Ollama), and several others support it.

Warning: the file-path branch reads whatever local path it's given and base64-embeds its contents into the outgoing API request, with no path validation. Do not pass LLM- or tool-controlled paths through unchecked. An unchecked path could be used to exfiltrate arbitrary local files.

From a file path:

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(model="gpt-4o")
reply = llm.invoke("What objects are in this image?", files=["photo.jpg"])
print(reply)
# The image shows a wooden desk with a laptop, a coffee mug, and a notebook.

From a URL:

reply = llm.invoke(
    "Describe this chart.",
    files=["https://example.com/chart.png"],
)
print(reply)
# The chart is a bar graph showing monthly sales figures from January to December...

From raw bytes:

with open("diagram.png", "rb") as f:
    image_bytes = f.read()

reply = llm.invoke("What does this diagram show?", files=[image_bytes])
print(reply)
# The diagram illustrates the flow of data through a neural network...

Control the detail level:

reply = llm.invoke(
    "Read the text in this image carefully.",
    files=["screenshot.png"],
    image_detail="high",   # "low", "high", or "auto"
)
print(reply)
# The screenshot shows a terminal window with the command "pip install autourgos-openaichat" ...

Structured Output

Return a Pydantic model as JSON automatically.

from pydantic import BaseModel, Field
from autourgos_openaichat import OpenAIChatModel
import json

class CityInfo(BaseModel):
    city: str = Field(description="Name of the city")
    country: str = Field(description="Name of the country")
    population: int = Field(description="Approximate population")

llm = OpenAIChatModel(model="gpt-4o", output_schema=CityInfo)
result = llm.invoke("Tell me about Tokyo.")

# result is a metadata dict; the JSON string is in result["response"]
data = json.loads(result["response"])
print(data)
# {"city": "Tokyo", "country": "Japan", "population": 13960000}

JSON Mode

Force the model to return valid JSON without a schema.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="gpt-4o",
    response_mime_type="application/json",
    system_prompt="Always respond with valid JSON.",
)

reply = llm.invoke("Give me a person with name and age.")
print(reply)
# {"name": "Alice", "age": 30}

Native Tool Calling

Let the model decide when to call your functions.

Tool calling support varies by provider. OpenAI, Groq, Gemini, Together AI, Mistral, and DeepSeek all support it. Ollama supports it on compatible models.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(model="gpt-4o")

tools = [
    {
        "name": "get_weather",
        "description": "Get the current weather for a city.",
        "parameters": {
            "type": "object",
            "properties": {
                "city": {
                    "type": "string",
                    "description": "The city name, e.g. Paris",
                },
                "unit": {
                    "type": "string",
                    "enum": ["celsius", "fahrenheit"],
                    "description": "Temperature unit",
                },
            },
            "required": ["city"],
        },
    }
]

response = llm.invoke_with_tools("What is the weather in Tokyo right now?", tools)

if response.has_tool_calls:
    for call in response.tool_calls:
        print(f"Tool: {call.name}")
        print(f"Args: {call.arguments}")
        print(f"ID:   {call.call_id}")
    # Tool: get_weather
    # Args: {'city': 'Tokyo', 'unit': 'celsius'}
    # ID:   call_abc123

elif response.is_final_answer:
    print(response.text)

Async tool calling:

response = await llm.ainvoke_with_tools(
    "What is the weather in London?", tools
)

Full agentic loop example:

import json

def get_weather(city: str, unit: str = "celsius") -> str:
    # Replace with real API call
    return json.dumps({"city": city, "temp": 22, "unit": unit, "condition": "Sunny"})

tool_functions = {"get_weather": get_weather}

messages = [{"role": "user", "content": "What is the weather in Paris?"}]

while True:
    response = llm.invoke_with_tools(messages, tools)

    if response.is_final_answer:
        print("Final answer:", response.text)
        break

    # Execute each tool call
    messages.append({
        "role": "assistant",
        "tool_calls": [
            {
                "id": tc.call_id,
                "type": "function",
                "function": {"name": tc.name, "arguments": json.dumps(tc.arguments)},
            }
            for tc in response.tool_calls
        ],
    })

    for tc in response.tool_calls:
        result = tool_functions[tc.name](**tc.arguments)
        messages.append({
            "role": "tool",
            "tool_call_id": tc.call_id,
            "content": result,
        })

# Final answer: The current weather in Paris is 22°C and Sunny.

Multi-Turn Conversations

Pass a list of messages directly.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(model="gpt-4o")

messages = [
    {"role": "user",      "content": "My name is Jitin."},
    {"role": "assistant", "content": "Nice to meet you, Jitin!"},
    {"role": "user",      "content": "What is my name?"},
]

reply = llm.invoke(messages)
print(reply)
# Your name is Jitin.

Cost Tracking

Pass pricing (USD per 1 million tokens) to get cost breakdowns.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="gpt-4o",
    input_pricing=2.50,    # $2.50 per 1M input tokens
    output_pricing=10.00,  # $10.00 per 1M output tokens
    structured_output=True,
)

result = llm.invoke("Summarise the history of the internet in 3 sentences.")
print(result["model"])          # gpt-4o
print(result["response"])       # The internet began as ARPANET...
print(result["input_tokens"])   # 18
print(result["output_tokens"])  # 74
print(result["total_tokens"])   # 92
print(result["input_cost"])     # 0.000045
print(result["output_cost"])    # 0.00074
print(result["total_cost"])     # 0.000785
print(result["latency_ms"])     # 1243.5

Access the last metadata without structured_output=True:

llm = OpenAIChatModel(model="gpt-4o", input_pricing=2.50, output_pricing=10.00)
reply = llm.invoke("Hello!")
print(llm.last_metadata)
# {
#   "model": "gpt-4o",
#   "response": "Hello! How can I help you today?",
#   "input_tokens": 9,
#   "output_tokens": 10,
#   "total_tokens": 19,
#   "input_cost": 0.0000225,
#   "output_cost": 0.0001,
#   "total_cost": 0.0001225,
#   "latency_ms": 834.2
# }

Context Manager

Automatically closes the HTTP client when done.

from autourgos_openaichat import OpenAIChatModel

with OpenAIChatModel(model="gpt-4o") as llm:
    reply = llm.invoke("Ping!")
    print(reply)
    # Pong! How can I help you?
# Client is closed here automatically

Async context manager:

import asyncio
from autourgos_openaichat import OpenAIChatModel

async def main():
    async with OpenAIChatModel(model="gpt-4o") as llm:
        reply = await llm.ainvoke("Hello async!")
        print(reply)

asyncio.run(main())

Circuit Breaker

Protects against cascading failures. After circuit_failure_threshold consecutive API errors, all calls are blocked for circuit_cooldown_time seconds.

This is useful when you are using a local model (Ollama, LM Studio) or a rate-limited API. If the server goes down, the circuit breaker stops your code from hammering it with failed requests.

from autourgos_openaichat import OpenAIChatModel, CircuitBreakerOpenException

llm = OpenAIChatModel(
    model="gpt-4o",
    circuit_failure_threshold=3,   # open after 3 consecutive failures
    circuit_cooldown_time=60.0,    # block for 60 seconds
)

try:
    reply = llm.invoke("Hello!")
except CircuitBreakerOpenException as e:
    print(f"Circuit is open: {e}")
    # Circuit breaker OPEN for OpenAIChatModel: 3 consecutive failures.
    # Blocked until 1718500000.0.

The circuit automatically resets after the cooldown and allows one probe call through.

Low-Level Access

If you need direct access to the raw OpenAI response object:

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(model="gpt-4o")

messages = [{"role": "user", "content": "Hi"}]
raw_response = llm.create(messages)

print(raw_response.id)
print(raw_response.choices[0].message.content)
print(raw_response.usage.total_tokens)

Async:

raw_response = await llm.acreate(messages)

Error Handling

from autourgos_openaichat import (
    OpenAIChatModel,
    OpenAIChatModelAPIError,
    OpenAIChatModelResponseError,
    OpenAIChatModelConfigError,
    OpenAIChatModelImportError,
    CircuitBreakerOpenException,
)

llm = OpenAIChatModel(model="gpt-4o")

try:
    reply = llm.invoke("Hello!")
except OpenAIChatModelAPIError as e:
    # API request failed after all retries
    print(f"API error: {e}")
except OpenAIChatModelResponseError as e:
    # Response was received but text could not be extracted
    print(f"Response parse error: {e}")
except OpenAIChatModelConfigError as e:
    # Incompatible options (e.g. streaming + structured_output)
    print(f"Config error: {e}")
except OpenAIChatModelImportError as e:
    # openai SDK not installed
    print(f"Import error: {e}")
except CircuitBreakerOpenException as e:
    # Too many recent failures, circuit is open
    print(f"Circuit open: {e}")

Retry behaviour: by default the wrapper retries up to 3 times with exponential back-off.

Change with max_retries and backoff_factor:

llm = OpenAIChatModel(
    model="gpt-4o",
    max_retries=5,
    backoff_factor=1.0,   # waits: 1s, 2s, 4s, 8s then raises
)

Constructor Reference

API Reference

What Each Method Returns

ToolCallResponse fields

FunctionCall fields

Metadata dict (when structured_output=True, or via llm.last_metadata)

License

Apache License 2.0, Copyright (c) 2026 Jitin Kumar Sengar