llmstitch 0.1.2

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, Gemini, Groq, and OpenRouter 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[groq]            # just the Groq SDK
pip install llmstitch[openrouter]      # OpenRouter (reuses the openai SDK)
pip install llmstitch[all]             # all five

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, GeminiAdapter, GroqAdapter, or OpenRouterAdapter 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.
• Streaming — Agent.run_stream() yields provider-neutral events (TextDelta, ToolUseStart / Delta / Stop, MessageStop, terminal StreamDone) and handles tool execution between turns.
• 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.

Streaming example

import asyncio
from llmstitch import Agent, TextDelta, StreamDone
from llmstitch.providers.anthropic import AnthropicAdapter

async def main() -> None:
    agent = Agent(provider=AnthropicAdapter(), model="claude-opus-4-7")
    async for event in agent.run_stream("Tell me a haiku about streams."):
        if isinstance(event, TextDelta):
            print(event.text, end="", flush=True)
        elif isinstance(event, StreamDone):
            print(f"\n[stop_reason={event.response.stop_reason}]")

asyncio.run(main())

More examples

The examples/ directory has runnable scripts for:

• basic.py — minimal agent with one tool.
• skills_demo.py — composing two Skills with .extend().
• streaming.py — Agent.run_stream with rich event handling.
• providers_gallery.py — the same agent against every provider.
• parallel_tools.py — parallel tool execution with order-preserving results.
• async_and_timeout.py — async tools, per-call timeout, captured-exception semantics.

Status

Alpha. Retries and MCP support are on the roadmap. See CHANGELOG.md for release history and ARCHITECTURE.md for a walkthrough of how the library is put together.

License

MIT