ccs-llmconnector 1.1.4
Lightweight wrapper around different LLM provider Python SDK Responses APIs.
License: MIT
Requires Python: >=3.8

# ccs-llmconnector

`ccs-llmconnector` is a thin Python wrapper around leading large-language-model SDKs,
including the OpenAI Responses API, Google's Gemini SDK, Anthropic's Claude
Messages API, and xAI's Grok chat API. It exposes a minimal interface that
forwards the most common options such as API key, prompt, optional reasoning
effort hints, token limits, and image inputs, and includes helpers to enumerate
the models available to your account with each provider.

## Installation

```bash
# from PyPI (normalized project name)
pip install ccs-llmconnector

# install additional providers
pip install "ccs-llmconnector[gemini]"
pip install "ccs-llmconnector[anthropic]"
pip install "ccs-llmconnector[xai]"
pip install "ccs-llmconnector[all]"

# or from source (this repository)
pip install .
```

### Requirements

- `openai` (installed automatically with the base package)
- Optional extras:
  - `ccs-llmconnector[gemini]` -> `google-genai`
  - `ccs-llmconnector[anthropic]` -> `anthropic`
  - `ccs-llmconnector[xai]` -> `xai-sdk` (Python 3.10+)
  - `ccs-llmconnector[all]` -> all providers

## Components

- `OpenAIResponsesClient` - direct wrapper around the OpenAI Responses API, ideal when your project only targets OpenAI models. Includes a model discovery helper.
- `GeminiClient` - thin wrapper around the Google Gemini SDK, usable when `google-genai` is installed. Includes a model discovery helper.
- `AnthropicClient` - lightweight wrapper around the Anthropic Claude Messages API, usable when `anthropic` is installed. Includes a model discovery helper.
- `GrokClient` - wrapper around the xAI Grok chat API, usable when `xai-sdk` is installed. Includes a model discovery helper.
- `LLMClient` - provider router that delegates to registered clients (OpenAI included by default) so additional vendors can be added without changing call sites.

## Common Options

All clients expose the same optional controls:

- `messages`: list of `{role, content}` entries (e.g., `system`, `user`, `assistant`). If both `prompt` and `messages` are provided, `prompt` is appended as the last user message.
- `request_id`: free-form request identifier for tracing/logging.
- `timeout_s`: optional timeout in seconds (best-effort depending on provider).
- `max_retries` and `retry_backoff_s`: retry count and exponential backoff base delay.

Async counterparts are available as `async_generate_response`, `async_generate_image`, and `async_list_models`.

## GeminiClient

### Usage

Use the `GeminiClient` when you want direct access to the Google Gemini SDK without
going through the provider router.

> Requires the `google-genai` Python package (installed automatically with `llmconnector`).

```python
from llmconnector import GeminiClient

client = GeminiClient()

text_response = client.generate_response(
    api_key="your-gemini-api-key",
    prompt="Summarize the key benefits of unit testing.",
    model="gemini-2.5-flash",
    max_tokens=2000,
)

vision_response = client.generate_response(
    api_key="your-gemini-api-key",
    prompt="Describe the main action in this image.",
    model="gemini-2.5-flash",
    images=[
        "/absolute/path/to/local-image.png",
        "https://example.com/sample.jpg",
    ],
)
```

### Parameters

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `api_key` | `str` | Yes | GEMINI or GOOGLE API key used for authentication. |
| `prompt` | `Optional[str]` | Conditional | Plain-text prompt. Required unless `images` is supplied. |
| `model` | `str` | Yes | Target model identifier, e.g. `gemini-2.5-flash`. |
| `max_tokens` | `int` | No | Defaults to `32000`. Passed to the SDK as `max_output_tokens`. |
| `reasoning_effort` | `Optional[str]` | No | Present for parity with the OpenAI client; currently ignored by the Gemini SDK. |
| `images` | `Optional[Sequence[str \| Path]]` | No | Image references (local paths, URLs, or data URLs) read and forwarded to the Gemini SDK. |

references are automatically converted into the appropriate `types.Part` instances,
allowing you to mix text and visuals in a single request.

### Image Generation

Use `generate_image` to create images using Gemini's image generation models (e.g., `gemini-3-pro-image-preview`).

```python
image_bytes = client.generate_image(
    api_key="your-gemini-api-key",
    prompt="Generate an infographic of the current weather in Tokyo.",
    model="gemini-3-pro-image-preview",
    image_size="2K",  # Optional, defaults to "2K"
    aspect_ratio="16:9",  # Optional, e.g. "16:9", "4:3"
)

with open("weather_tokyo.png", "wb") as f:
    f.write(image_bytes)
```

You can also provide an input image for editing tasks:

```python
image_bytes = client.generate_image(
    api_key="your-gemini-api-key",
    prompt="Make the background a sunset.",
    model="gemini-3-pro-image-preview",
    image="/path/to/original.png",
)
```

### Listing models

Use `list_models` to enumerate the Gemini models available to your account:

```python
from llmconnector import GeminiClient

client = GeminiClient()
for model in client.list_models(api_key="your-gemini-api-key"):
    print(model["id"], model["display_name"])
```

## AnthropicClient

### Usage

Use the `AnthropicClient` when you want direct access to Anthropic's Claude Messages API.

> Requires the `anthropic` Python package (installed automatically with `llmconnector`).

```python
from llmconnector import AnthropicClient

client = AnthropicClient()

text_response = client.generate_response(
    api_key="sk-ant-api-key",
    prompt="Summarize the key benefits of unit testing.",
    model="claude-sonnet-4-5-20250929",
    max_tokens=2000,
)

vision_response = client.generate_response(
    api_key="sk-ant-api-key",
    prompt="Describe the main action in this image.",
    model="claude-3-5-sonnet-20241022",
    images=[
        "/absolute/path/to/local-image.png",
        "https://example.com/sample.jpg",
    ],
)
```

### Parameters

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `api_key` | `str` | Yes | Anthropic API key used for authentication. |
| `prompt` | `Optional[str]` | Conditional | Plain-text prompt. Required unless `images` is supplied. |
| `model` | `str` | Yes | Target model identifier, e.g. `claude-3-5-sonnet-20241022`. |
| `max_tokens` | `int` | No | Defaults to `32000`. Passed to the SDK as `max_tokens`. |
| `reasoning_effort` | `Optional[str]` | No | Present for parity with other clients; currently ignored by the Anthropic SDK. |
| `images` | `Optional[Sequence[str \| Path]]` | No | Image references (local paths, URLs, or data URLs) read and converted to base64 blocks. |

The method returns the generated model output as a plain string. Optional image
references are automatically transformed into Anthropic `image` blocks so you can
mix text and visual inputs in a single request.

### Listing models

Use `list_models` to enumerate the Anthropic models available to your account:

```python
from llmconnector import AnthropicClient

client = AnthropicClient()
for model in client.list_models(api_key="sk-ant-api-key"):
    print(model["id"], model["display_name"])
```

## GrokClient

### Usage

Use the `GrokClient` when you want direct access to xAI's Grok chat API.

> Requires the `xai-sdk` Python package (installed automatically with `llmconnector`).
> Note: `xai-sdk` targets Python 3.10 and newer.

```python
from llmconnector import GrokClient

client = GrokClient()

text_response = client.generate_response(
    api_key="xai-api-key",
    prompt="Summarize the key benefits of unit testing.",
    model="grok-3",
    max_tokens=2000,
)

vision_response = client.generate_response(
    api_key="xai-api-key",
    prompt="Describe the main action in this image.",
    model="grok-2-vision",
    images=[
        "/absolute/path/to/local-image.png",
        "https://example.com/sample.jpg",
    ],
)
```

### Parameters

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `api_key` | `str` | Yes | xAI API key used for authentication. |
| `prompt` | `Optional[str]` | Conditional | Plain-text prompt. Required unless `images` is supplied. |
| `model` | `str` | Yes | Target model identifier, e.g. `grok-3`. |
| `max_tokens` | `int` | No | Defaults to `32000`. Passed to the Grok API as `max_tokens`. |
| `reasoning_effort` | `Optional[str]` | No | Hint for reasoning-focused models (`"low"` or `"high"`). |
| `images` | `Optional[Sequence[str \| Path]]` | No | Image references (local paths converted to data URLs, or remote URLs passed through). |

### Listing models

Use `list_models` to enumerate the Grok language models available to your account:

```python
from llmconnector import GrokClient

client = GrokClient()
for model in client.list_models(api_key="xai-api-key"):
    print(model["id"], model["display_name"])
```

## OpenAIResponsesClient

### Usage

Use the `OpenAIResponsesClient` when you want direct access to the OpenAI Responses API.

> Requires the `openai` Python package. It is declared as a dependency of `llmconnector`, but you can also install it manually with `pip install openai`.

```python
from llmconnector import OpenAIResponsesClient

client = OpenAIResponsesClient()

text_response = client.generate_response(
    api_key="sk-your-api-key",                 # required OpenAI API key
    prompt="Summarize the key benefits of unit testing.",  # plain-text prompt
    model="gpt-4o",                            # any Responses API compatible model
    reasoning_effort="medium",                 # optional: "low" | "medium" | "high"
    max_tokens=2000,                           # optional: caps the full response length
)

vision_response = client.generate_response(
    api_key="sk-your-api-key",
    prompt="Describe the main action in this image.",
    model="gpt-4o-mini",
    images=[
        "/absolute/path/to/local-image.png",   # local file paths are converted to data URLs
        "https://example.com/sample.jpg",      # remote URLs are passed through directly
    ],
)
```

### Parameters

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `api_key` | `str` | Yes | OpenAI API key used for authentication. |
| `prompt` | `Optional[str]` | Conditional | Plain-text prompt. Required unless `images` is supplied. |
| `model` | `str` | Yes | Target model identifier, e.g. `gpt-4o`. |
| `max_tokens` | `int` | No | Defaults to `32000`. Passed to the Responses API as `max_output_tokens`. |
| `reasoning_effort` | `Optional[str]` | No | For models that support reasoning hints (`"low"`, `"medium"`, `"high"`). |
| `images` | `Optional[Sequence[str \| Path]]` | No | List of image URLs or local paths converted to data URLs. |

The method returns the generated model output as a plain string.

> The wrapper accepts `prompt` as plain text and translates it into the structured
> input format expected by the Responses API.

### Listing models

Use `list_models` to enumerate the OpenAI models available to your account:

```python
from llmconnector import OpenAIResponsesClient

client = OpenAIResponsesClient()
for model in client.list_models(api_key="sk-your-api-key"):
    print(model["id"], model["display_name"])
```

## LLMClient

### Usage

`LLMClient` routes requests to whichever provider has been registered; OpenAI, Gemini, Anthropic, and Grok (alias: `xai`) are configured by default when their dependencies are available. The client also exposes `list_models` to surface the identifiers available for the selected provider.

```python
from llmconnector import LLMClient

llm_client = LLMClient()

response_via_router = llm_client.generate_response(
    provider="openai",                         # selects the OpenAI wrapper
    api_key="sk-your-api-key",
    prompt="List three advantages of integration testing.",
    model="gpt-4o",
    max_tokens=1500,
)

# async usage
# response_via_router = await llm_client.async_generate_response(
#     provider="openai",
#     api_key="sk-your-api-key",
#     messages=[{"role": "system", "content": "You are concise."}],
#     prompt="Summarize the plan.",
#     model="gpt-4o-mini",
# )

gemini_response = llm_client.generate_response(
    provider="gemini",                         # google-genai is installed with llmconnector
    api_key="your-gemini-api-key",
    prompt="Outline best practices for prompt engineering.",
    model="gemini-2.5-flash",
    max_tokens=1500,
)

anthropic_response = llm_client.generate_response(
    provider="anthropic",
    api_key="sk-ant-api-key",
    prompt="Summarize when to rely on retrieval-augmented generation.",
    model="claude-sonnet-4-5-20250929",
    max_tokens=1500,
)

# Additional providers can be registered at runtime:
# llm_client.register_provider("custom", CustomProviderClient())
# llm_client.generate_response(provider="custom", ...)

# Image generation (currently only supported by Gemini)
image_bytes = llm_client.generate_image(
    provider="gemini",
    api_key="your-gemini-api-key",
    prompt="A futuristic city",
    model="gemini-3-pro-image-preview",
    aspect_ratio="16:9",
)
```

### Listing models

```python
from llmconnector import LLMClient

llm_client = LLMClient()
for model in llm_client.list_models(provider="openai", api_key="sk-your-api-key"):
    print(model["id"], model["display_name"])
```

### Parameters

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `provider` | `str` | Yes | Registered provider key (default registry includes `'openai'`, `'gemini'`, `'anthropic'`, `'grok'`/`'xai'`). |
| `api_key` | `str` | Yes | Provider-specific API key. |
| `prompt` | `Optional[str]` | Conditional | Plain-text prompt. Required unless `images` is supplied. |
| `messages` | `Optional[Sequence[dict]]` | No | Chat-style messages (`role`, `content`). |
| `model` | `str` | Yes | Provider-specific model identifier. |
| `max_tokens` | `int` | No | Defaults to `32000`. |
| `reasoning_effort` | `Optional[str]` | No | Reasoning hint forwarded when supported. |
| `images` | `Optional[Sequence[str \| Path]]` | No | Image references forwarded to the provider implementation. |
| `request_id` | `Optional[str]` | No | Request identifier for tracing/logging. |
| `timeout_s` | `Optional[float]` | No | Timeout in seconds (best-effort). |
| `max_retries` | `Optional[int]` | No | Retry count for transient failures. |
| `retry_backoff_s` | `Optional[float]` | No | Base delay (seconds) for exponential backoff. |

Use `LLMClient.register_provider(name, client)` to add additional providers that implement
`generate_response` with the same signature.

## CLI

The package provides a simple CLI entry point named `client_cli` (see `pyproject.toml`).
It reads API keys from environment variables and supports generating responses and
listing models.

- API key environment variables:
  - OpenAI: `OPENAI_API_KEY`
  - Gemini: `GEMINI_API_KEY` (fallback: `GOOGLE_API_KEY`)
  - Anthropic: `ANTHROPIC_API_KEY`
  - Grok/xAI: `GROK_API_KEY` or `XAI_API_KEY` (either works)

Examples:

```bash
# Generate a response
client_cli respond --provider openai --model gpt-4o --prompt "Hello!"

# Generate with retry/timeout controls
client_cli respond --provider openai --model gpt-4o --prompt "Hello!" --timeout-s 30 --max-retries 2

# List models for one provider (human-readable)
client_cli models --provider gemini

# List models for one provider (JSON)
client_cli models --provider anthropic --json

# List models for all registered providers
client_cli all-models
```

## Development

The project uses a standard `pyproject.toml` based packaging layout with sources
stored under `src/`. Install the project in editable mode and run your preferred tooling:

```bash
pip install -e .
```

Requires Python 3.8+.