llm-blanket 0.1.3

Unified Python library for LLM APIs (OpenAI, Anthropic, Gemini, xAI, Groq, custom)

llm-blanket

Unified Python library for LLM APIs: OpenAI, Anthropic, Gemini, xAI (Grok), Groq, and custom OpenAI-compatible endpoints.

• Single interface: specify a model, get an LLM instance, call invoke(messages).
• Per-model provider: specify which provider backs each model via model_provider; if not set, provider is inferred from model name (e.g. gpt-4o → OpenAI, claude-* → Anthropic).
• Per-provider overrides: override base URL and API key per provider in config; if not set, default URLs and API keys from .env are used.

Install

pip install llm-blanket

Optional provider dependencies (install only what you use):

pip install "llm-blanket[openai]"       # OpenAI + Groq + xAI + custom (OpenAI-compatible)
pip install "llm-blanket[anthropic]"    # Anthropic Claude
pip install "llm-blanket[gemini]"       # Google Gemini
pip install "llm-blanket[all]"          # All providers

Examples

Runnable scripts are in the examples/ directory:

• examples/quickstart.py – create an LLM and call invoke() with a user message.
• examples/streaming.py – stream tokens with invoke_stream().
• examples/config_and_url_override.py – LLMConfig, base_urls, base_url, and explicit provider.

Run from the repo root (set the appropriate API key first):

OPENAI_API_KEY=sk-... python examples/quickstart.py

Quick start

from llm_blanket import get_llm, Message

# Provider inferred from model name
llm = get_llm("gpt-4o")

# Option 1: system and user as named arguments
resp = llm.invoke(system="You are helpful.", user="Hello!")
print(resp.content)

# Option 2: messages list (Message objects or OpenAI-style dicts)
resp = llm.invoke([Message("user", "Hi")])
resp = llm([{"role": "user", "content": "Hi"}])

# Option 3: common parameters (temperature, max_tokens, etc.) are passed through to the provider
resp = llm.invoke(user="Hello!", temperature=0.7, max_tokens=256)

# Streaming: same signature as invoke(), yields StreamChunk (content delta, optional finish_reason)
for chunk in llm.invoke_stream(user="Hello!", temperature=0.7):
    print(chunk.content, end="", flush=True)
print()

Streaming

Use invoke_stream() with the same arguments as invoke(). It yields StreamChunk objects (.content is the text delta; .finish_reason is set on the final chunk when the provider supplies it):

from llm_blanket import get_llm

llm = get_llm("gpt-4o-mini")
for chunk in llm.invoke_stream(system="You are concise.", user="Count to 5."):
    print(chunk.content, end="", flush=True)
    if chunk.finish_reason:
        print(f"\n[Done: {chunk.finish_reason}]")

Streaming is supported for OpenAI (and OpenAI-compatible), Anthropic, and Gemini.

Configuration

1. Per-model provider

Specify which provider backs each model with model_provider (model name → provider). If a model is not in the map, the provider is inferred from the model name (e.g. gpt-* → openai, claude-* → anthropic). Groq models like llama-3-70b-8192 have no unique prefix, so put them in the map or pass provider="groq" for that call.

from llm_blanket import get_llm, LLMConfig

config = LLMConfig(
    model_provider={
        "llama-3-70b-8192": "groq",
        "mixtral-8x7b-32768": "groq",
        "my-custom-model": "custom",
    }
)
llm = get_llm("llama-3-70b-8192", config=config)  # uses groq
llm = get_llm("gpt-4o", config=config)            # inferred openai

2. Per-provider URL and API key

For each provider you can override the base URL and API key. If you don't, the library uses its default base URL for that provider and the API key from the environment.

Environment variables (default API keys):

Provider	Environment variable
OpenAI	OPENAI_API_KEY
Anthropic	ANTHROPIC_API_KEY
Gemini	GOOGLE_API_KEY
xAI	XAI_API_KEY
Groq	GROQ_API_KEY
Custom	OPENAI_API_KEY (or set in config)

Override in config:

config = LLMConfig(
    model_provider={"llama-3-70b-8192": "groq"},
    base_urls={
        "openai": "https://my-openai-proxy.com/v1",
        "groq": "https://api.groq.com/openai/v1",
    },
    api_keys={
        "openai": "sk-openai-...",
        "anthropic": "sk-ant-...",
    },
)
openai_llm = get_llm("gpt-4o", config=config)
groq_llm = get_llm("llama-3-70b-8192", config=config)
anthropic_llm = get_llm("claude-3-5-sonnet-20241022", config=config)

Single-call overrides: provider, api_key, and base_url still override for that call only.

Supported models / providers

Provider	Inferred from	Notes
OpenAI	gpt-*, o1-*, o3-*	Default base: https://api.openai.com/v1
Anthropic	claude-*	Uses Anthropic Messages API
Gemini	gemini-*	Uses Google GenAI SDK
xAI	grok*, grok-*	OpenAI-compatible
Groq	Set in model_provider or provider="groq"	Models like llama-3-70b-8192; OpenAI-compatible
Custom	Set in model_provider or provider="custom" and base_url	Any OpenAI-compatible endpoint

Extensibility

• Unified response: invoke() returns an LLMResponse with content, model, usage, finish_reason, and optional raw (provider-specific object) and tool_calls.
• Provider-specific options: Pass extra kwargs to invoke() (e.g. temperature, max_tokens); they are forwarded to the underlying API. Use LLMConfig(extra={...}) for client-level options.
• Custom backends: Implement BaseLLM (see llm_blanket.base) and register or construct your backend explicitly; the factory is focused on the built-in providers.

Example: multiple providers with per-model and per-provider config

from llm_blanket import get_llm, LLMConfig, Message

# Per-model provider + per-provider URL (and optionally api_keys; else from .env)
config = LLMConfig(
    model_provider={"llama-3-70b-8192": "groq", "mixtral-8x7b-32768": "groq"},
    base_urls={
        "openai": "https://my-proxy.com/openai/v1",
        "groq": "https://api.groq.com/openai/v1",
    },
)

openai_llm = get_llm("gpt-4o-mini", config=config)
groq_llm = get_llm("llama-3-70b-8192", config=config)

for llm in [openai_llm, groq_llm]:
    r = llm.invoke([Message("user", "Say hi in one word.")])
    print(f"{llm.provider}: {r.content}")

License

MIT