Unifying 3 LLM APIs in Python: OpenAI, Anthropic & Google with one SDK

A year ago, I released the first version of LLM API Adapter — a lightweight SDK that unified OpenAI, Anthropic, and Google APIs under one interface.

It got 7 ⭐ on GitHub and valuable feedback from early users.


That was enough motivation to take it to the next level.

What changed in the new version

The new version (v0.2.2) is now:

• 
  SDK-free — it talks directly to provider APIs, no external dependencies.
  


• 
  Unified — one chat() interface for all models (OpenAI, Anthropic, Google).
  


• 
  Transparent — automatic token and cost tracking.
  


• 
  Resilient — consistent error taxonomy across providers (auth, rate, timeout, token limits).
  


• 
  Tested — 98% unit test coverage.
  

Example: chat with any LLM

from llm_api_adapter.universal_adapter import UniversalLLMAPIAdapter



adapter = UniversalLLMAPIAdapter(provider="openai", model="gpt-5")



response = adapter.chat([

    {"role": "system", "content": "Be concise."},

    {"role": "user", "content": "Explain how LLM adapters work."},

])



print(response.content)

Switching models is as simple as changing two parameters:

adapter = UniversalLLMAPIAdapter(provider="anthropic",

                                 model="claude-sonnet-4-5")

# or

adapter = UniversalLLMAPIAdapter(provider="google",

                                 model="gemini-2.5-pro")

Token & cost tracking example

Every response now includes full token and cost accounting — no manual math needed.

from llm_api_adapter.universal_adapter import UniversalLLMAPIAdapter



google = UniversalLLMAPIAdapter(

    organization="google",

    model="gemini-2.5-pro",

    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})")

Output:

512 tokens (0.00025 USD)

137 tokens (0.00010 USD)

649 tokens (0.00035 USD)

Why I built this

Working with multiple LLMs used to mean rewriting the same code — again and again.


Each SDK had its own method names, parameter names, and error classes.

So I built a unified interface that abstracts those details.


One adapter — one consistent experience.

Join the project

You can try it now:

pip install llm-api-adapter

Docs & examples: github.com/Inozem/llm_api_adapter

If you like the idea — ⭐ star it or share feedback in Issues.