# Lasagna AI
[](https://pypi.org/project/lasagna-ai)
[](https://pypi.org/project/lasagna-ai)
[](https://pepy.tech/project/lasagna-ai)
- 🥞 **Layered agents!**
- Agents for your agents!
- Tool-use, structured output ("extraction"), 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!**
- Core support for [OpenAI](https://platform.openai.com/docs/models) and [Anthropic](https://docs.anthropic.com/en/docs/welcome).
- Experimental support for [Ollama](https://ollama.com/search) and [NVIDIA NIM/NGC](https://build.nvidia.com/explore/reasoning).
- 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](#installation)
- [Used By](#used-by)
- [Quickstart](#quickstart)
- [Debug Logging](#debug-logging)
- [Special Thanks](#special-thanks)
- [License](#license)
## Installation
```console
pip install -U lasagna-ai[openai,anthropic]
```
If you want to easily run all the [./examples](./examples), then you can
install the extra dependencies used by those examples:
```console
pip install -U lasagna-ai[openai,anthropic,example-deps]
```
## Used By
Lasagna is used in production by:
[](https://www.autoauto.ai/)
## 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](./examples/quickstart.py))
```python
from lasagna import ( # <-- pip install -U lasagna-ai[openai,anthropic]
known_models,
build_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 = known_models.BIND_OPENAI_gpt_4o_mini()
async def main() -> None:
system_prompt = "You are grumpy."
tools: List[Callable] = []
my_agent = build_simple_agent(name = 'agent', tools = 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](./examples/quickstart_with_math_tool.py))
```python
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 = float(expr.evalf())
return result
...
...
tools: List[Callable] = [
evaluate_math_expression,
]
my_agent = build_simple_agent(name = 'agent', tools = tools)
...
...
```
**Simple RAG:** Everyone's favorite tool: _Retrieval Augmented Generation_ (RAG). Let's GO! 📚💨
See: [./examples/demo_rag.py](./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:
```python
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](https://a.co/d/03dHnRmX))
- Dave DeCaprio's [voice-stream library](https://github.com/DaveDeCaprio/voice-stream)
## License
`lasagna-ai` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
## Joke Acronym
Layered Agents with toolS And aGeNts and Ai
Raw data
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"description": "\n\n# Lasagna AI\n\n[](https://pypi.org/project/lasagna-ai)\n[](https://pypi.org/project/lasagna-ai)\n\n[](https://pepy.tech/project/lasagna-ai)\n\n- \ud83e\udd5e **Layered agents!**\n - Agents for your agents!\n - Tool-use, structured output (\"extraction\"), and layering FTW \ud83d\udcaa\n - Ever wanted a _recursive_ agent? Now you can have one! \ud83e\udd2f\n - _Parallel_ tool-calling by default.\n - Fully asyncio.\n - 100% Python type hints.\n - Functional-style \ud83d\ude0e\n - (optional) Easy & pluggable caching! \ud83c\udfe6\n\n- \ud83d\udea3 **Streamable!**\n - Event streams for _everything_.\n - Asyncio generators are awesome.\n\n- \ud83d\uddc3\ufe0f **Easy database integration!**\n - Don't rage when trying to store raw messages and token counts. \ud83d\ude21 \ud83e\udd2c\n - Yes, you _can_ have _both_ streaming and easy database storage.\n\n- \u2194\ufe0f **Provider/model agnostic and interoperable!**\n - Core support for [OpenAI](https://platform.openai.com/docs/models) and [Anthropic](https://docs.anthropic.com/en/docs/welcome).\n - Experimental support for [Ollama](https://ollama.com/search) and [NVIDIA NIM/NGC](https://build.nvidia.com/explore/reasoning).\n - Message representations are canonized. \ud83d\ude07\n - Supports vision!\n - Easily build committees!\n - Swap providers or models mid-conversation.\n - Delegate tasks among model providers or model sizes.\n - Parallelize all the things.\n\n-----\n\n## Table of Contents\n\n- [Installation](#installation)\n- [Used By](#used-by)\n- [Quickstart](#quickstart)\n- [Debug Logging](#debug-logging)\n- [Special Thanks](#special-thanks)\n- [License](#license)\n\n## Installation\n\n```console\npip install -U lasagna-ai[openai,anthropic]\n```\n\nIf you want to easily run all the [./examples](./examples), then you can\ninstall the extra dependencies used by those examples:\n\n```console\npip install -U lasagna-ai[openai,anthropic,example-deps]\n```\n\n## Used By\n\nLasagna is used in production by:\n\n[](https://www.autoauto.ai/)\n\n## Quickstart\n\nHere is the _most simple_ agent (it doesn't add *anything* to the underlying model).\nMore complex agents would add tools and/or use layers of agents, but not this one!\nAnyway, run it in your terminal and you can chat interactively with the model. \ud83e\udd29\n\n(taken from [./examples/quickstart.py](./examples/quickstart.py))\n\n```python\nfrom lasagna import ( # <-- pip install -U lasagna-ai[openai,anthropic]\n known_models,\n build_simple_agent,\n)\n\nfrom lasagna.tui import (\n tui_input_loop,\n)\n\nfrom typing import List, Callable\n\nimport asyncio\n\nfrom dotenv import load_dotenv; load_dotenv()\n\n\nMODEL_BINDER = known_models.BIND_OPENAI_gpt_4o_mini()\n\n\nasync def main() -> None:\n system_prompt = \"You are grumpy.\"\n tools: List[Callable] = []\n my_agent = build_simple_agent(name = 'agent', tools = tools)\n my_bound_agent = MODEL_BINDER(my_agent)\n await tui_input_loop(my_bound_agent, system_prompt)\n\n\nif __name__ == '__main__':\n asyncio.run(main())\n```\n\n**Want to add your first tool?** LLMs can't natively do arithmetic\n(beyond simple arithmetic with small numbers), so let's give our\nmodel a tool for doing arithmetic! \ud83d\ude0e\n\n(full example at [./examples/quickstart_with_math_tool.py](./examples/quickstart_with_math_tool.py))\n\n```python\nimport sympy as sp\n\n...\n\ndef evaluate_math_expression(expression: str) -> float:\n \"\"\"\n This tool evaluates a math expression and returns the result.\n Pass math expression as a string, for example:\n - \"3 * 6 + 1\"\n - \"cos(2 * pi / 3) + log(8)\"\n - \"(4.5/2) + (6.3/1.2)\"\n - ... etc\n :param: expression: str: the math expression to evaluate\n \"\"\"\n expr = sp.sympify(expression)\n result = float(expr.evalf())\n return result\n\n...\n\n ...\n tools: List[Callable] = [\n evaluate_math_expression,\n ]\n my_agent = build_simple_agent(name = 'agent', tools = tools)\n ...\n\n...\n```\n\n**Simple RAG:** Everyone's favorite tool: _Retrieval Augmented Generation_ (RAG). Let's GO! \ud83d\udcda\ud83d\udca8 \nSee: [./examples/demo_rag.py](./examples/demo_rag.py)\n\n## Debug Logging\n\nThis 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:\n\n```python\nimport logging\n\nlogging.basicConfig(\n level=logging.INFO,\n format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n)\n\n# ... now use Lasagna as you normally would, but you'll see extra log traces!\n```\n\n## Special Thanks\n\nSpecial thanks to those who inspired this library:\n- Numa Dhamani (buy her book: [Introduction to Generative AI](https://a.co/d/03dHnRmX))\n- Dave DeCaprio's [voice-stream library](https://github.com/DaveDeCaprio/voice-stream)\n\n## License\n\n`lasagna-ai` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.\n\n## Joke Acronym\n\nLayered Agents with toolS And aGeNts and Ai\n",
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