aiclient-llm 0.2.2

A minimal, generic client for AI models (OpenAI, Anthropic, Google, xAI) with middleware support.

aiclient-llm

A minimal, unified, and resilient Python client for modern LLMs.

Supports OpenAI, Anthropic (Claude 3), Google (Gemini), and xAI (Grok) with a single, consistent interface.

Documentation

• Getting Started: Installation, Configuration, Basic Usage.
• Features Guide: Agents, Multimodal, Local LLMs (Ollama), Structured Output.
• Middleware: Cost tracking, logging, resilience, and custom middleware.
• Memory: Conversation history management and persistence.
• Testing: Mock providers and testing utilities.
• Error Handling: Exception types and debugging.
• Examples: Runnable demo scripts for new features.

Key Features

• 🦄 Unified API: Works with OpenAI, Anthropic, Google Gemini, and Ollama.
• ⚡ Streaming Support: Real-time responses with a simple iterator interface.
• 👁️ Multimodal (Vision): Send images (paths, URLs, base64) to vision-capable models.
• 🚀 Prompt Caching: Native support for Anthropic Prompt Caching headers.
• 🏗️ Structured Outputs: Native strict JSON Schema support for OpenAI.
• 🛡️ Resilient: Circuit Breakers, Rate Limiters, and automatic retries.
• 🔭 Observability: Tracing and OpenTelemetry hooks.
• 🤖 Agent Primitives: Built-in ReAct loop for tool-using agents.
• 🔌 Model Context Protocol (MCP): Connect to 16K+ external tools (GitHub, Postgres, filesystem).
• 📊 Middleware: Inspect requests, track costs, or log data.
• 🧠 Memory Management: Built-in conversation history with token-aware truncation
• 🧪 Testing Utilities: Mock providers for deterministic unit tests
• 📦 Batch Processing: Efficiently process thousands of requests concurrently
• 🛡️ Type-Safe Errors: Specific exception types for better error handling

Installation

pip install aiclient-llm

Quick Start

Basic Chat

from aiclient import Client

client = Client(
    api_key_openai="sk-...", 
    api_key_anthropic="sk-ant-..."
)

# Call OpenAI
response = client.chat("gpt-4o").generate("Hello!")
print(response.text)

# Call Claude
response = client.chat("claude-3-opus-20240229").generate("Hello!")
print(response.text)

Multimodal (Vision)

from aiclient.data_types import UserMessage, Text, Image

msg = UserMessage(content=[
    Text(text="What's in this image?"),
    Image(path="./image.png") # Handles base64 automatically
])

response = client.chat("gpt-4o").generate([msg])
print(response.text)

Agents (Tool Use)

from aiclient.agent import Agent

def get_weather(location: str):
    return "Sunny in " + location

agent = Agent(
    model=client.chat("gpt-4o"),
    tools=[get_weather]
)

print(agent.run("Weather in SF?"))

MCP Integration 🔌

Connect to external tools using the Model Context Protocol.

agent = Agent(
    model=client.chat("gpt-4o"),
    mcp_servers={
        "filesystem": {
            "command": "npx",
            "args": ["-y", "@modelcontextprotocol/server-filesystem", "./workspace"]
        }
    }
)

# Agent can now use file system tools!
print(agent.run("List all Python files in the current directory"))

Local LLMs (Ollama) 🏠

Use the provider:model syntax to route requests to local models (e.g., via Ollama).

# Connects to http://localhost:11434/v1 by default
client.chat("ollama:llama3").generate("Why is the sky blue?")

# Connect to custom URL (e.g. LMStudio)
client = Client(ollama_base_url="http://localhost:1234/v1")
client.chat("ollama:mistral").generate("Hi")

Streaming

for chunk in client.chat("gpt-4o").stream("Write a poem"):
    print(chunk.text, end="", flush=True)

Configuration

Retries

# Retries up to 3 times with backoff
client = Client(max_retries=3, retry_delay=1.0)

License

Apache-2.0