pi-llm 0.1.2

Unified LLM provider abstraction for Python — streaming, tool calling, extended thinking, cost tracking

pi-llm

Unified LLM provider abstraction for Python. Provides streaming and non-streaming interfaces to LLM APIs with automatic model discovery, token and cost tracking, and tool call support.

Features

• Streaming & non-streaming — stream_simple() / complete_simple() with async iteration
• Model catalog — get_model() with built-in pricing and metadata for OpenAI models
• Tool calling — Define tools with JSON Schema, validate arguments automatically
• Extended thinking — Unified reasoning level interface across providers
• Cost tracking — Automatic token counting and dollar cost calculation
• Event-driven — Fine-grained streaming events (text, thinking, tool calls)
• Cross-provider types — Pydantic models with camelCase/snake_case interop

Supported Providers

• OpenAI (Responses API)

More providers coming soon.

Installation

pip install pi-llm

Quick Start

import asyncio
from pi_llm import get_model, stream_simple, complete_simple, Context, UserMessage
from pi_llm import register_builtin_providers, SimpleStreamOptions

# Register providers (call once at startup)
register_builtin_providers()

model = get_model("openai", "gpt-4o")

context = Context(
    system_prompt="You are a helpful assistant.",
    messages=[UserMessage(content="What is Python?", timestamp=0)],
)

# Option 1: Non-streaming
async def main():
    message = await complete_simple(
        model, context,
        SimpleStreamOptions(api_key="sk-...")
    )
    for block in message.content:
        if hasattr(block, "text"):
            print(block.text)

asyncio.run(main())

# Option 2: Streaming
async def main_stream():
    event_stream = stream_simple(
        model, context,
        SimpleStreamOptions(api_key="sk-...")
    )
    async for event in event_stream:
        if event.type == "text_delta":
            print(event.delta, end="", flush=True)

asyncio.run(main_stream())

Tools

Define tools with JSON Schema and handle tool calls in a loop:

from pi_llm import Tool, ToolCall, ToolResultMessage, TextContent
import time

weather_tool = Tool(
    name="get_weather",
    description="Get current weather for a city",
    parameters={
        "type": "object",
        "properties": {"city": {"type": "string"}},
        "required": ["city"],
    },
)

context = Context(
    system_prompt="You can check the weather.",
    messages=[UserMessage(content="What's the weather in Tokyo?", timestamp=0)],
    tools=[weather_tool],
)

async def tool_loop():
    while True:
        message = await complete_simple(
            model, context,
            SimpleStreamOptions(api_key="sk-...")
        )
        context.messages.append(message)

        # Check for tool calls
        tool_calls = [c for c in message.content if isinstance(c, ToolCall)]
        if not tool_calls:
            break  # No more tool calls, done

        # Execute tool calls and add results
        for tc in tool_calls:
            result = f"25°C and sunny in {tc.arguments['city']}"
            context.messages.append(ToolResultMessage(
                tool_call_id=tc.id,
                tool_name=tc.name,
                content=[TextContent(text=result)],
                timestamp=int(time.time() * 1000),
            ))

    # Print final response
    for block in message.content:
        if hasattr(block, "text"):
            print(block.text)

Streaming Events

The event stream emits these events in order:

Event	Description
StartEvent	Streaming begins
TextStartEvent	Text block begins
TextDeltaEvent	Incremental text chunk
TextEndEvent	Text block complete
ThinkingStartEvent	Reasoning block begins
ThinkingDeltaEvent	Reasoning chunk
ThinkingEndEvent	Reasoning block complete
ToolCallStartEvent	Tool call begins
ToolCallDeltaEvent	Tool call argument chunk
ToolCallEndEvent	Tool call complete
DoneEvent	Streaming finished successfully
ErrorEvent	Streaming ended with error

Extended Thinking

Enable reasoning for supported models:

message = await complete_simple(
    model, context,
    SimpleStreamOptions(
        api_key="sk-...",
        reasoning="medium",  # minimal, low, medium, high, xhigh
    )
)

License

MIT