autourgos-openaichat 1.0.1

Autourgos LLM wrapper for the OpenAI Chat Completions API

autourgos-openaichat

LLM wrapper for the OpenAI Chat Completions API, part of the Autourgos framework.

Fully self-contained — no autourgos-core dependency required. Just pip install openai and you are ready.

---

Why use this?

Almost every major LLM provider today — Groq, Together AI, Mistral, Perplexity, DeepSeek, Ollama, LM Studio, vLLM, Azure OpenAI — exposes an OpenAI-compatible API. This means they all accept the same request format as OpenAI's Chat Completions endpoint.

autourgos-openaichat takes advantage of this. You set base_url to any provider's endpoint and model to whatever model they offer. One package, any LLM. You never have to learn a new SDK or rewrite your code when you switch providers.

OpenAI ─────────────────────────────────────┐
Groq (Llama, Mixtral, Gemma) ───────────────┤
Together AI (70B, 8x7B, ...) ───────────────┤  autourgos-openaichat
Mistral AI (mistral-large, ...) ────────────┤  (one interface)
DeepSeek (deepseek-chat, ...) ──────────────┤
Perplexity (sonar models) ──────────────────┤
Ollama — any local model ───────────────────┤
LM Studio — any local model ────────────────┤
vLLM — self-hosted ─────────────────────────┤
Azure OpenAI ───────────────────────────────┘

---

Table of Contents

• Install
• Works With Any LLM
• Quick Start
• Basic Text Generation
• Async Generation
• Streaming
• Async Streaming
• Batch Invocation
• System Instruction
• Prompt Templates
• Multi-Modal Vision Input
• Structured Output
• JSON Mode
• Native Tool Calling
• Multi-Turn Conversations
• Cost Tracking
• Context Manager
• Circuit Breaker
• Error Handling
• Constructor Reference
• What Each Method Returns

---

Install

pip install autourgos-openaichat

Requires Python 3.10+ and openai>=1.0.0.

---

Works With Any LLM

All you need to switch providers is base_url and the right model name. Your API key comes from the provider you choose.

OpenAI (default)

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

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

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 — 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 default when no auth is set
    base_url="http://your-server:8000/v1",
)
reply = llm.invoke("What is the capital of Japan?")
print(reply)
# Tokyo

Azure OpenAI

Azure hosts OpenAI models in your own Azure subscription. 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.

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",
    },
    "ollama": {
        "model": "llama3",
        "api_key": "ollama",
        "base_url": "http://localhost:11434/v1",
    },
}

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!
# ollama: Hello!

---

Quick Start

from autourgos_openaichat import OpenAIChatModel

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

---

Basic 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 Instruction

Set a persistent system prompt for all requests.

from autourgos_openaichat import OpenAIChatModel

llm = OpenAIChatModel(
    model="gpt-4o",
    system_instruction="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

---

Multi-Modal Vision Input

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

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

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 detail level

reply = llm.invoke(
    "Read the text in this image carefully.",
    files=["screenshot.png"],
    image_detail="high",   # "low", "high", or "auto"
)

---

Structured Output

Return a Pydantic model as JSON automatically.

from pydantic import BaseModel, Field
from autourgos_openaichat import OpenAIChatModel

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", response_schema=CityInfo)
result = llm.invoke("Tell me about Tokyo.")

# result is a metadata dict; the JSON string is in result["response"]
import json
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_instruction='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, 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
)

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:

Attempt	Wait before retry
1st failure	0.5 s
2nd failure	1.0 s
3rd failure	2.0 s
4th failure	raises OpenAIChatModelAPIError

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

Parameter	Type	Default	Description
model	str	required	Model name. e.g. "gpt-4o", "llama3-70b-8192", "mistral-large-latest"
api_key	str	OPENAI_API_KEY env	API key for the provider you are using
base_url	str	OPENAI_BASE_URL env	Provider endpoint. e.g. "https://api.groq.com/openai/v1" or "http://localhost:11434/v1"
organization	str	None	OpenAI organization ID (OpenAI only)
project	str	None	OpenAI project ID (OpenAI only)
system_instruction	str	None	System prompt prepended to every request
prompt_template	str	None	Template with {variable} placeholders
temperature	float	None	Sampling temperature 0–2. Higher = more random
top_p	float	None	Nucleus sampling 0–1
max_tokens	int	None	Maximum tokens to generate
response_schema	BaseModel / dict	None	Pydantic model or JSON schema for structured output
response_mime_type	str	None	"application/json" enables JSON object mode
structured_output	bool	False	If True, invoke() returns a metadata dict
streaming	bool	False	If True, invoke() streams internally and joins
max_retries	int	3	Retry attempts on transient API errors
timeout	float	60.0	Request timeout in seconds
backoff_factor	float	0.5	Exponential back-off base (wait = factor × 2^attempt)
input_pricing	float	None	USD per 1 million input tokens
output_pricing	float	None	USD per 1 million output tokens
circuit_failure_threshold	int	5	Consecutive failures before the circuit opens
circuit_cooldown_time	float	30.0	Seconds the circuit stays open before probing

---

What Each Method Returns

Method	Returns
invoke(prompt)	str — generated text (or dict if structured_output=True)
ainvoke(prompt)	same as invoke, async
stream(prompt)	Iterator[str] — text chunks
astream(prompt)	AsyncIterator[str] — text chunks
batch_invoke(prompts)	list[str] — one result per prompt
abatch_invoke(prompts)	list[str] — concurrent results
invoke_with_tools(prompt, tools)	ToolCallResponse — .tool_calls list or .text
ainvoke_with_tools(prompt, tools)	same as invoke_with_tools, async
create(messages)	Raw OpenAI ChatCompletion response object
acreate(messages)	same as create, async

ToolCallResponse fields

Field	Type	Description
.tool_calls	list[FunctionCall]	Tool calls the model wants to make (empty if final answer)
.text	str | None	Final text answer (None if tool calls present)
.raw	Any	Raw OpenAI response object
.has_tool_calls	bool	True when tool_calls is non-empty
.is_final_answer	bool	True when text is present and tool_calls is empty

FunctionCall fields

Field	Type	Description
.name	str	Tool function name
.arguments	dict	Parsed JSON arguments
.call_id	str | None	Call ID for multi-turn tracking

Metadata dict (when structured_output=True)

Key	Type	Description
"model"	str	Model name used
"response"	str	Generated text
"input_tokens"	int | None	Input token count
"output_tokens"	int | None	Output token count
"total_tokens"	int | None	Total token count
"input_cost"	float	Input cost in USD (only if input_pricing set)
"output_cost"	float	Output cost in USD (only if output_pricing set)
"total_cost"	float	Total cost in USD (only if both pricing set)
"latency_ms"	float	Request round-trip time in milliseconds

---

Supported Providers (quick reference)

Provider	base_url	Notes
OpenAI	(default)	GPT-4o, GPT-4o-mini, GPT-3.5-turbo
Groq	https://api.groq.com/openai/v1	Llama 3, Mixtral, Gemma — very fast
Together AI	https://api.together.xyz/v1	100+ open-source models
Mistral AI	https://api.mistral.ai/v1	mistral-large, mixtral, codestral
DeepSeek	https://api.deepseek.com/v1	deepseek-chat, deepseek-reasoner
Perplexity	https://api.perplexity.ai	Web-connected sonar models
Ollama	http://localhost:11434/v1	Runs locally, no API key needed
LM Studio	http://localhost:1234/v1	Runs locally, GUI-based
vLLM	http://your-server:8000/v1	Self-hosted, high throughput
Azure OpenAI	https://<resource>.openai.azure.com/...	Enterprise OpenAI

---

License

MIT — Copyright (c) 2026 Jitin Kumar Sengar