llm-api-adapter 0.2.4

Lightweight, pluggable adapter for multiple LLM APIs (OpenAI, Anthropic, Google)

LLM API Adapter SDK for Python

Overview

This lightweight SDK for Python allows you to use LLM APIs from various providers and models through a unified interface. It is designed to be minimal, dependency-free, and easy to integrate into any Python project. Currently, the project supports API integration for OpenAI, Anthropic, and Google, focusing on chat functionality with consistent cost tracking and unified error handling.

Version

Current version: 0.2.4

Features

• Unified Interface: Work seamlessly with different LLM providers using a single, consistent API.
• Multiple Provider Support: Currently supports OpenAI, Anthropic, and Google APIs, allowing easy switching between them.
• Chat Functionality: Provides an easy way to interact with chat-based LLMs.
• Extensible Design: Built to easily extend support for additional providers and new functionalities in the future.
• Error Handling: Standardized error messages across all supported LLMs, simplifying integration and debugging.
• Flexible Configuration: Manage request parameters like temperature, max tokens, and other settings for fine-tuned control.
• Token and Cost Accounting: Automatic calculation of token usage and cost per request.
• Pricing Registry: Model prices are stored in a unified JSON registry with per-model input/output pricing and currency support.
• Unified Reasoning Support: A single reasoning_level parameter that works identically across all providers.

Installation

To install the SDK, you can use pip:

pip install llm-api-adapter

Note: You will need to obtain API keys from each LLM provider you wish to use (OpenAI, Anthropic, Google). Refer to their respective documentation for instructions on obtaining API keys.

Getting Started

Importing and Setting Up the Adapter

To start using the adapter, you need to import the necessary components:

from llm_api_adapter.models.messages.chat_message import (
    AIMessage, Prompt, UserMessage
)
from llm_api_adapter.universal_adapter import UniversalLLMAPIAdapter

Sending a Simple Request

The SDK supports three types of messages for interacting with the LLM:

• Prompt: Use Prompt to set the context or initial prompt for the model.
• UserMessage: Use UserMessage to send messages from the user during a conversation.
• AIMessage: Use AIMessage to simulate responses from the assistant during a conversation.

Here is an example of how to send a simple request to the adapter:

messages = [
    UserMessage("Hi! Can you explain how artificial intelligence works?")
]

adapter = UniversalLLMAPIAdapter(
    organization="openai",
    model="gpt-5",
    api_key=openai_api_key
)

response = adapter.chat(
    messages=messages,
    max_tokens=max_tokens,
    temperature=temperature,
    top_p=top_p
)
print(response.content)

Parameters

• max_tokens: The maximum number of tokens to generate in the response. This limits the length of the output.

• temperature: Controls the randomness of the response. Higher values (e.g., 0.8) make the output more random, while lower values (e.g., 0.2) make it more focused and deterministic. Default value: 1.0 (range: 0 to 2).

• top_p: Limits the response to a certain cumulative probability. This is used to create more focused and coherent responses by considering only the highest probability options. Default value: 1.0 (range: 0 to 1).

Alternative Message Format

In addition to the built-in message classes, the SDK also supports the standard OpenAI-style message format for quick adoption and compatibility:

messages = [
    {"role": "system", "content": "You are a friendly assistant who answers only yes or no."},
    {"role": "user", "content": "Do you know how AI learns?"},
    {"role": "assistant", "content": "Yes."},
    {"role": "user", "content": "Can you explain it in one sentence?"}
]

response = adapter.chat(messages=messages, max_tokens=50)
print(response.content)

Note
The adapter automatically normalizes message input — you can mix custom message classes and OpenAI-style dicts in one list.

Handling Errors

Common Errors

The SDK provides a set of standardized errors for easier debugging and integration:

API Errors

• LLMAPIError: Base class for all API-related errors. This error is also used for any unexpected LLM API errors.

• LLMAPIAuthorizationError: Raised when authentication or authorization fails.

• LLMAPIRateLimitError: Raised when rate limits are exceeded.

• LLMAPITokenLimitError: Raised when token limits are exceeded.

• LLMAPIClientError: Raised when the client makes an invalid request.

• LLMAPIServerError: Raised when the server encounters an error.

• LLMAPITimeoutError: Raised when a request times out.

• LLMAPIUsageLimitError: Raised when usage limits are exceeded.

Config Errors

• LLMConfigError: Raised when the request configuration is invalid or incompatible.

• LLMReasoningLevelError: Raised only for Anthropic models when max_tokens is less than reasoning_level.

Configuration and Management

Using Different Providers and Models

The SDK allows you to easily switch between LLM providers and specify the model you want to use. Currently supported providers are OpenAI, Anthropic, and Google.

• OpenAI: You can use models like gpt-5.2-pro, gpt-5.2, gpt-5.1, gpt-5, gpt-5-mini, gpt-5-nano, gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, gpt-4o, gpt-4o-mini.
• Anthropic: Available models include claude-opus-4-5, claude-sonnet-4-5, claude-haiku-4-5, claude-opus-4-1, claude-opus-4-0, claude-sonnet-4-0, claude-3-7-sonnet-latest, claude-3-5-haiku-latest, claude-3-haiku-20240307.
• Google: Models such as gemini-3-pro-preview, gemini-3-flash-preview, gemini-2.5-pro, gemini-2.5-flash, gemini-2.5-flash-lite, gemini-2.0-flash, gemini-2.0-flash-lite can be used.

Example:

adapter = UniversalLLMAPIAdapter(
    organization="openai",
    model="gpt-5",
    api_key=openai_api_key
)

To switch to another provider, simply change the organization and model parameters.

Switching Providers

Here is an example of how to switch between different LLM providers using the SDK:

Note: Each instance of UniversalLLMAPIAdapter is tied to a specific provider and model. You cannot change the organization parameter for an existing adapter object. To use a different provider, you must create a new instance.

gpt = UniversalLLMAPIAdapter(
    organization="openai",
    model="gpt-5",
    api_key=openai_api_key
)
gpt_response = gpt.chat(messages=messages)
print(gpt_response.content)

claude = UniversalLLMAPIAdapter(
    organization="anthropic",
    model="claude-sonnet-4-5",
    api_key=anthropic_api_key
)
claude_response = claude.chat(messages=messages)
print(claude_response.content)

google = UniversalLLMAPIAdapter(
    organization="google",
    model="gemini-2.5-flash",
    api_key=google_api_key
)
google_response = google.chat(messages=messages)
print(google_response.content)

Example Use Case

Here is a comprehensive example that showcases all possible message types and interactions:

from llm_api_adapter.models.messages.chat_message import (
    AIMessage, Prompt, UserMessage
)                                               
from llm_api_adapter.universal_adapter import UniversalLLMAPIAdapter

messages = [
    Prompt(
        "You are a friendly assistant who explains complex concepts "
        "in simple terms."
    ),
    UserMessage("Hi! Can you explain how artificial intelligence works?"),
    AIMessage(
        "Sure! Artificial intelligence (AI) is a system that can perform "
        "tasks requiring human-like intelligence, such as recognizing images "
        "or understanding language. It learns by analyzing large amounts of "
        "data, finding patterns, and making predictions."
    ),
    UserMessage("How does AI learn?"),
]

adapter = UniversalLLMAPIAdapter(
    organization="openai",
    model="gpt-5",
    api_key=openai_api_key
)

response = adapter.chat(
    messages=messages,
    max_tokens=256,
    temperature=1.0,
    top_p=1.0
)
print(response.content)

The ChatResponse object returned by chat includes:

1. model: The model that generated the response.
2. response_id: Unique identifier for the response.
3. timestamp: Response generation time.
4. usage: Object containing input_tokens, output_tokens, and total_tokens.
5. currency: The currency used for cost calculation.
6. cost_input: Cost of input tokens.
7. cost_output: Cost of output tokens.
8. cost_total: Total combined cost.
9. content: The generated text response.
10. finish_reason: Reason why generation stopped (e.g., "stop", "length").

Reasoning Support

This section describes the unified reasoning_level parameter that works the same way for all supported providers and their models.

response = adapter.chat(
    messages=[UserMessage("Solve this step-by-step")],
    reasoning_level=2048,
)

Default behavior

If reasoning_level is not passed, reasoning is:

• fully disabled where the provider allows it, or
• reduced to the minimal supported level if it cannot be turned off.

This keeps behavior consistent when switching providers or models.

reasoning_level parameter

reasoning_level is optional and provider‑agnostic.

Supported forms:

• int — explicit numeric level
• str — one of: "none", "low", "medium", "high"

Internal mapping:

{
  "none": 0,
  "low": 100,
  "medium": 1000,
  "high": 10000
}

String values are automatically converted to numbers, and numeric values can be normalized back to named levels.

Usage examples

# Named level
response = adapter.chat(
    messages=[UserMessage("Explain this")],
    reasoning_level="medium",
)

# Explicit numeric level
response = adapter.chat(
    messages=[UserMessage("Solve this step-by-step")],
    reasoning_level=2048,
)

# Reasoning disabled (default)
response = adapter.chat(
    messages=[UserMessage("Simple answer, no reasoning")],
)

Provider independence

reasoning_level has the same semantics for all providers:

• same parameter name
• same string levels
• same numeric mapping

This allows switching between OpenAI, Anthropic, and Google without changing reasoning configuration in your code.

Token Usage and Pricing

Token Usage and Pricing Example

google = UniversalLLMAPIAdapter(
    organization="google",
    model="gemini-2.5-flash",
    api_key=google_api_key
)

response = google.chat(**chat_params)

print(response.usage.input_tokens, "tokens", f"({response.cost_input} {response.currency})")
print(response.usage.output_tokens, "tokens", f"({response.cost_output} {response.currency})")
print(response.usage.total_tokens, "tokens", f"({response.cost_total} {response.currency})")

Overriding Pricing or Currency

google = UniversalLLMAPIAdapter(
    organization="google",
    model="gemini-2.5-flash",
    api_key=google_api_key
)

google.pricing.set_in_per_1m(1.5)
google.pricing.set_out_per_1m(3)
google.pricing.set_currency("EUR")

response = google.chat(**chat_params)
print(response.content)
print(response.usage.input_tokens, "tokens", f"({response.cost_input} {response.currency})")
print(response.usage.output_tokens, "tokens", f"({response.cost_output} {response.currency})")
print(response.usage.total_tokens, "tokens", f"({response.cost_total} {response.currency})")

Logging

The library uses Python’s standard logging module and does not configure handlers. Loggers are module-based under llm_api_adapter.* (e.g., llm_api_adapter.universal_adapter).

• Default behavior: No handlers installed, effective level = WARNING.
• No secrets are logged — API keys and request bodies are excluded. Only event metadata and errors are logged.

Enable logs (console)

import logging

logging.basicConfig(level=logging.INFO)  # or DEBUG
# Optionally limit logging to this library
logging.getLogger("llm_api_adapter").setLevel(logging.DEBUG)

Write logs to a file

import logging

handler = logging.FileHandler("llm_api_adapter.log")
handler.setFormatter(logging.Formatter(
    "%(asctime)s %(levelname)s %(name)s %(message)s"
))
root = logging.getLogger()
root.setLevel(logging.INFO)
root.addHandler(handler)

Per-request correlation (optional)

import logging
logger = logging.getLogger("llm_api_adapter")

req_id = "req-123"
logger = logging.LoggerAdapter(logger, {"request_id": req_id})
logger.info("starting call")

To include request_id in log output, add %(request_id)s to your log formatter.

Reduce noise / silence logs

import logging
logging.getLogger("llm_api_adapter").setLevel(logging.WARNING)   # silence info logs
logging.getLogger("urllib3").setLevel(logging.WARNING)           # if using requests

Env-based log level toggle

# app.py
import logging, os
level = os.getenv("LLM_ADAPTER_LOGLEVEL", "WARNING").upper()
logging.getLogger("llm_api_adapter").setLevel(level)

Tip: For HTTP-level debugging with requests, also set:

import http.client as http_client, logging
http_client.HTTPConnection.debuglevel = 1
logging.getLogger("urllib3").setLevel(logging.DEBUG)

Use this only in development.

Development & Testing

Note
This section is intended for developers working with the source code from GitHub.
It is not relevant for users installing the package from PyPI.

This project uses pytest for testing. Tests are located in the tests/ directory.

To run all tests, use the following command:

pytest

Alternatively, you can run the tests using the tests_runner.py script:

python tests/tests_runner.py

Dependencies

Ensure you have the required dependencies installed. You can install them using:

pip install -r requirements-test.txt

Test Structure

• unit/: Contains unit tests for individual components.
• integration/: Contains integration tests to verify the interaction between different parts of the system.

License

This project is licensed under the terms of the MIT License.
See the LICENSE file for details.