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lasagnaai 0.8.1

Lasagna AI

🥞 Layered agents!

Agents for your agents!
Tool-use and layering FTW 💪
Ever wanted a recursive agent? Now you can have one! 🤯
Parallel tool-calling by default.
Fully asyncio.
100% Python type hints.
Functional-style 😎
(optional) Easy & pluggable caching! 🏦

🚣 Streamable!

Event streams for everything.
Asyncio generators are awesome.

🗃️ Easy database integration!

Don't rage when trying to store raw messages and token counts. 😡 🤬
Yes, you can have both streaming and easy database storage.

↔️ Provider/model agnostic and interoperable!

Native support for OpenAI, Anthropic, NVIDIA NIM/NGC (+ more to come).
Message representations are canonized. 😇
Supports vision!
Easily build committees!
Swap providers or models mid-conversation.
Delegate tasks among model providers or model sizes.
Parallelize all the things.

Table of Contents

Installation
Used By
Quickstart
Debug Logging
Special Thanks
License

Installation
pip install -U lasagna-ai[openai,anthropic]

If you want to easily run all the ./examples, then you can
install the extra dependencies used by those examples:
pip install -U lasagna-ai[openai,anthropic,example-deps]

Used By
Lasagna is used in production by:

Quickstart
Here is the most simple agent (it doesn't add anything to the underlying model).
More complex agents would add tools and/or use layers of agents, but not this one!
Anyway, run it in your terminal and you can chat interactively with the model. 🤩
(taken from ./examples/quickstart.py)
from lasagna import (
bind_model,
build_most_simple_agent,
)

from lasagna.tui import (
tui_input_loop,
)

from typing import List, Callable

import asyncio

from dotenv import load_dotenv; load_dotenv()

MODEL_BINDER = bind_model('openai', 'gpt-3.5-turbo-0125')

async def main() -> None:
system_prompt = "You are grumpy."
tools: List[Callable] = []
my_agent = build_most_simple_agent(tools)
my_bound_agent = MODEL_BINDER(my_agent)
await tui_input_loop(my_bound_agent, system_prompt)

if __name__ == '__main__':
asyncio.run(main())

Want to add your first tool? LLMs can't natively do arithmetic
(beyond simple arithmetic with small numbers), so let's give our
model a tool for doing arithmetic! 😎
(full example at ./examples/quickstart_with_math_tool.py)
import sympy as sp

...

def evaluate_math_expression(expression: str) -> float:
"""
This tool evaluates a math expression and returns the result.
Pass math expression as a string, for example:
- "3 * 6 + 1"
- "cos(2 * pi / 3) + log(8)"
- "(4.5/2) + (6.3/1.2)"
- ... etc
:param: expression: str: the math expression to evaluate
"""
expr = sp.sympify(expression)
result = cast(float, expr.evalf())
return result

...

...
tools: List[Callable] = [
evaluate_math_expression,
]
my_agent = build_most_simple_agent(tools)
...

...

Simple RAG: Everyone's favorite tool: Retrieval Augmented Generation (RAG). Let's GO! 📚💨
See: ./examples/demo_rag.py
Debug Logging
This library logs using Python's builtin logging module. It logs mostly to INFO, so here's a snippet of code you can put in your app to see those traces:
import logging

logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
)

# ... now use Lasagna as you normally would, but you'll see extra log traces!

Special Thanks
Special thanks to those who inspired this library:

Numa Dhamani (buy her book: Introduction to Generative AI)
Dave DeCaprio's voice-stream library

License
lasagna-ai is distributed under the terms of the MIT license.
Joke Acronym
Layered Agents with toolS And aGeNts and Ai