alea-llm-client 0.3.1

ALEA LLM client abstraction library for Python

ALEA LLM Client

This is a simple, two-dependency (httpx, pydantic) LLM client for ~OpenAI APIs like:

• OpenAI (GPT-5.4, GPT-5.2, GPT-5.1, o-series)
• Anthropic (Claude Opus 4.6, Claude Sonnet 4.6, Claude Haiku 4.5)
• Google (Gemini 3.1 Pro, Gemini 3 Flash, Gemini 2.5)
• xAI (Grok 4, Grok 4.20, Grok Code Fast)
• VLLM

Supported Patterns

It provides the following patterns for all endpoints:

• complete and complete_async -> str via ModelResponse
• chat and chat_async -> str via ModelResponse
• json and json_async -> dict via JSONModelResponse
• pydantic and pydantic_async -> pydantic models
• responses and responses_async -> structured output with tool use, grammar constraints, and reasoning modes

Default Models

Provider	Default Model	Context Window	Max Output
OpenAI	gpt-5.4	1.05M	128K
Anthropic	claude-sonnet-4-6	1M	64K
Google	gemini-3.1-pro-preview	2M	8K
xAI	grok-4-fast-non-reasoning	2M	8K

Model Registry & Capabilities

Version 0.3.0 includes a comprehensive model registry with 130+ models across all providers:

• OpenAI: 88 models (GPT-5.4, GPT-5.2, GPT-5.1, o-series, codex, pro variants)
• Anthropic: 17 models (Claude 4.6, 4.5, 4.0, 3.7, 3.5 legacy)
• Google: 12 models (Gemini 3.1, 3.0, 2.5, 2.0)
• xAI: 17 models (Grok 4.20, 4.1, 4, 3, code)

from alea_llm_client.llms import (
    get_models_with_context_window_gte,
    filter_models,
    compare_models,
    get_model_details
)

# Find models with large context windows
large_context = get_models_with_context_window_gte(1000000)

# Filter by multiple criteria
efficient = filter_models(
    min_context=100000,
    capabilities=["tools", "vision"],
    tiers=["mini", "flash"],
    exclude_deprecated=True
)

# Compare specific models
comparison = compare_models(["gpt-5.4", "claude-sonnet-4-6", "gemini-3.1-pro-preview"])

Provider-Agnostic Helper

Use get_llm_kwargs to write provider-independent code:

from alea_llm_client import OpenAIModel, AnthropicModel, get_llm_kwargs

# Works with any provider — translates effort/tier to provider-specific params
model = OpenAIModel()
kwargs = get_llm_kwargs(model, effort="low", tier="flex")
response = model.chat(messages=[{"role": "user", "content": "Hello"}], **kwargs)
# Sends: reasoning_effort="none", service_tier="flex"

model = AnthropicModel()
kwargs = get_llm_kwargs(model, effort="low")
response = model.chat(messages=[{"role": "user", "content": "Hello"}], **kwargs)
# Sends: output_config={"effort": "low"}

effort	OpenAI	Anthropic	Google
"low"	reasoning_effort="none"	output_config={"effort": "low"}	thinking_level="minimal"
"medium"	reasoning_effort="medium"	output_config={"effort": "medium"}	thinking_level="medium"
"high"	reasoning_effort="high"	output_config={"effort": "high"}	thinking_level="high"

Advanced Features

Service Tier & Reasoning Control (OpenAI)

from alea_llm_client import OpenAIModel

model = OpenAIModel()  # defaults to gpt-5.4

# Control reasoning effort and service tier
response = model.chat(
    messages=[{"role": "user", "content": "Solve this complex problem..."}],
    reasoning_effort="xhigh",  # none, minimal, low, medium, high, xhigh
    service_tier="flex",       # auto, default, flex, scale, priority
)

# max_tokens auto-converts to max_completion_tokens for GPT-5.x and o-series
response = model.chat(
    messages=[{"role": "user", "content": "Write a story"}],
    max_tokens=4096,  # automatically sent as max_completion_tokens
)

Tool Helpers (OpenAI Responses API)

from alea_llm_client import OpenAIModel
from alea_llm_client.llms.constants import (
    create_web_search_tool,
    create_function_tool,
    create_code_interpreter_tool,
)

model = OpenAIModel()
response = model.responses(
    input="What is the current weather in Tokyo?",
    tools=[create_web_search_tool(search_context_size="medium")],
)

Thinking Mode & Output Config (Anthropic)

from alea_llm_client import AnthropicModel

model = AnthropicModel()  # defaults to claude-sonnet-4-6

# Extended thinking
response = model.chat(
    messages=[{"role": "user", "content": "Solve this complex problem..."}],
    thinking={"enabled": True, "budget_tokens": 4000},
    max_tokens=8000,
)
print(response.thinking)  # Access thinking content

# Output effort control and service tier
response = model.chat(
    messages=[{"role": "user", "content": "Quick question"}],
    output_config={"effort": "low"},  # low, medium, high, max
    service_tier="auto",              # auto, standard_only
)

Tool Helpers (Anthropic)

from alea_llm_client.llms.constants import (
    create_anthropic_web_search_tool,
    create_anthropic_code_execution_tool,
    create_anthropic_bash_tool,
    create_anthropic_text_editor_tool,
)

# Web search with domain filtering
ws = create_anthropic_web_search_tool(allowed_domains=["wikipedia.org"])

# Code execution (latest REPL-persistent version)
ce = create_anthropic_code_execution_tool()  # code_execution_20260120

Thinking Level (Google Gemini)

from alea_llm_client import GoogleModel

model = GoogleModel()  # defaults to gemini-3.1-pro-preview

response = model.chat(
    messages=[{"role": "user", "content": "Explain quantum computing"}],
    thinking_level="high",  # minimal, low, medium, high
)
print(f"Thinking tokens used: {response.reasoning_tokens}")

Grammar Constraints (OpenAI GPT-5)

from alea_llm_client import OpenAIModel

model = OpenAIModel(model="gpt-5.4")
response = model.responses(
    input="Answer yes or no: Is 2+2=4?",
    grammar='start: "yes" | "no"',
    grammar_syntax="lark"
)

Deprecated Model Handling (Anthropic)

from alea_llm_client import AnthropicModel

# Emits DeprecationWarning at construction
model = AnthropicModel(model="claude-3-5-sonnet-20241022")
# DeprecationWarning: Model 'claude-3-5-sonnet-20241022' is deprecated.
# Use 'claude-sonnet-4-6' instead.

# 404 errors include replacement suggestion
# ALEAModelError: Model 'claude-3-5-sonnet-20241022' returned 404.
# This model has been deprecated. Use 'claude-sonnet-4-6' instead.

Response Caching

Result caching is disabled by default for predictable API client behavior.

To enable caching for better performance, you can either:

• set ignore_cache=False for each method call (complete, chat, json, pydantic)
• set ignore_cache=False as a kwarg at model construction

# Enable caching at model level
model = OpenAIModel(ignore_cache=False)

# Enable caching for specific calls
response = model.chat("Hello", ignore_cache=False)

Cached objects are stored in ~/.alea/cache/{provider}/{endpoint_model_hash}/{call_hash}.json in compressed .json.gz format. You can delete these files to clear the cache.

Authentication

Authentication is handled in the following priority order:

• an api_key provided at model construction
• a standard environment variable (e.g., ANTHROPIC_API_KEY or OPENAI_API_KEY)
• a key stored in ~/.alea/keys/{provider} (e.g., openai, anthropic, gemini, grok)

Streaming

Given the research focus of this library, streaming generation is not supported. However, you can directly access the httpx objects on .client and .async_client to stream responses directly if you prefer.

Installation

pip install alea-llm-client

Examples

Basic JSON Example

from alea_llm_client import VLLMModel

if __name__ == "__main__":
    model = VLLMModel(
        endpoint="http://my.vllm.server:8000",
        model="Qwen/Qwen2.5-0.5B-Instruct"
    )

    messages = [
        {
            "role": "user",
            "content": "Give me a JSON object with keys 'name' and 'age' for a person named Alice who is 30 years old.",
        },
    ]

    print(model.json(messages=messages, system="Respond in JSON.").data)

# Output: {'name': 'Alice', 'age': 30}

Pydantic Example

from pydantic import BaseModel
from alea_llm_client import AnthropicModel, format_prompt, format_instructions

class Person(BaseModel):
    name: str
    age: int

if __name__ == "__main__":
    model = AnthropicModel()

    instructions = [
        "Provide one random record based on the SCHEMA below.",
    ]
    prompt = format_prompt(
        {
            "instructions": format_instructions(instructions),
            "schema": Person,
        }
    )

    person = model.pydantic(prompt, system="Respond in JSON.", pydantic_model=Person)
    print(person)

# Output: name='Olivia Chen' age=29

Design

Class Inheritance

classDiagram
    BaseAIModel <|-- OpenAICompatibleModel
    OpenAICompatibleModel <|-- AnthropicModel
    OpenAICompatibleModel <|-- OpenAIModel
    OpenAICompatibleModel <|-- VLLMModel
    OpenAICompatibleModel <|-- GrokModel
    BaseAIModel <|-- GoogleModel

    class BaseAIModel {
        <<abstract>>
    }
    class OpenAICompatibleModel
    class AnthropicModel
    class OpenAIModel
    class VLLMModel
    class GrokModel
    class GoogleModel

Testing

196 integration tests across all providers with 71% code coverage:

# Run all tests
uv run pytest tests/

# Run specific provider tests
uv run pytest tests/test_openai.py
uv run pytest tests/test_anthropic.py
uv run pytest tests/test_google.py
uv run pytest tests/test_grok.py

# Custom VLLM server testing
export VLLM_ENDPOINT="http://192.168.4.200:8000/"
export VLLM_MODEL="Qwen/Qwen2.5-0.5B-Instruct"
uv run pytest tests/test_vllm.py

Rate Limiting Configuration

export GOOGLE_API_DELAY=2.0        # Seconds between calls (default: 2.0)
export ANTHROPIC_API_DELAY=0.5     # Seconds between calls (default: 0.5)
export OPENAI_API_DELAY=0.2        # Seconds between calls (default: 0.2)
export XAI_API_DELAY=1.0           # Seconds between calls (default: 1.0)

Migration Guide

Upgrading from v0.2.x to v0.3.0

Default models have changed to the latest available:

Provider	v0.2.x Default	v0.3.0 Default
OpenAI	gpt-5-chat-latest	gpt-5.4
Anthropic	claude-sonnet-4-20250514	claude-sonnet-4-6
Google	gemini-2.0-flash-exp	gemini-3.1-pro-preview
xAI	grok-2-1212	grok-4-fast-non-reasoning

Deprecated models now emit warnings:

• Claude 3.5, 3.7, and 3-Opus models emit DeprecationWarning at construction
• 404 errors from retired models include replacement suggestions

New parameters:

• OpenAI: service_tier, reasoning_effort expanded to include "none" and "xhigh"
• Anthropic: service_tier, output_config, metadata
• Google: thinking_level
• max_tokens auto-converts to max_completion_tokens for GPT-5.x and o-series

No breaking API changes. All existing code continues to work.

License

The ALEA LLM client is released under the MIT License. See the LICENSE file for details.

Support

If you encounter any issues or have questions about using the ALEA LLM client library, please open an issue on GitHub.

Learn More

To learn more about ALEA and its software and research projects like KL3M and leeky, visit the ALEA website.