llmstitch 0.1.0

A provider-agnostic LLM toolkit with tool calling, skills, and parallel execution.

llmstitch

A provider-agnostic LLM toolkit with tool calling, skills, and parallel execution.

Stitch together Anthropic, OpenAI, and Gemini behind one Agent loop. Define tools with a decorator, compose behaviors as skills, and execute tool calls concurrently — all with a tiny, typed core.

Install

pip install llmstitch[anthropic]       # just the Anthropic SDK
pip install llmstitch[openai]          # just the OpenAI SDK
pip install llmstitch[gemini]          # just the Gemini SDK
pip install llmstitch[all]             # all three

The bare pip install llmstitch has zero runtime dependencies — provider SDKs are opt-in extras.

30-second example

import asyncio
from llmstitch import Agent, tool
from llmstitch.providers.anthropic import AnthropicAdapter

@tool
def get_weather(city: str) -> str:
    """Return a canned weather report for the given city."""
    return f"{city}: 72°F and sunny"

agent = Agent(
    provider=AnthropicAdapter(),
    model="claude-opus-4-7",
    system="You are a helpful weather assistant.",
)
agent.tools.register(get_weather)

messages = asyncio.run(agent.run("What's the weather in Tokyo?"))
print(messages[-1].content)

Features

• Provider-agnostic — swap AnthropicAdapter for OpenAIAdapter or GeminiAdapter without touching your agent code.
• Typed @tool decorator — JSON Schema generated from type hints (Optional, Literal, defaults, async).
• Parallel tool execution — when a model returns multiple tool calls in one turn, they run concurrently.
• Skills — bundle a system prompt with a set of tools; compose with .extend().
• PEP 561 typed — ships with py.typed, fully checked under mypy --strict.

Status

v0.1.0 alpha. Streaming, retries, and MCP support are on the roadmap. See CHANGELOG.md and the project plan.

License

MIT