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

#python #openai #anthropic #gemini

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.