Dandy - Python AI Framework

Powerful Features

Everything you need to build, maintain and scale amazing AI software

Very Pythonic

Integrate AI logic into your codebase with zero context-switching, feels like native Python.

Self Correcting

Auto-recovers from 90% of common errors including API failures, rate limits, and malformed responses.

Flexible API

Pre-wired for common AI providers (OpenAI/Ollama) and connectors for custom API's.

Deployment Ready

Add AI features to live apps in hours, not weeks, with less downtime and project refactoring.

Incredibly Fast

Optimized for performance, in most cases adding less than 1ms to any runtime.

Safety Ready

Detailed interaction recording for building robust safety and governance.

Code Examples

See how simple it is to build AI applications with Dandy


# dandy_settings.py

import os
from pathlib import Path

ALLOW_DEBUG_RECORDING = True

BASE_PATH = Path.resolve(Path(__file__)).parent

LLM_CONFIGS = {
    'DEFAULT': {
        'TYPE': 'openai',
        'HOST': 'https://api.openai.com',
        'PORT': 443,
        'API_KEY': os.getenv('OPENAI_API_KEY'),
        'MODEL': 'gpt-4o-mini',
    }
}

# main.py

from dandy import Bot

response = Bot().process('What is the capital of Canada?')

print(response.content)

# Output: The capital of Canada is Ottawa


from dandy import Bot, Prompt

class AssistantBot(Bot):
    def process(self, user_prompt: Prompt | str):
        default_intel = self.llm.prompt_to_intel(
            prompt=user_prompt,
        )

        return default_intel

intel = AssistantBot().process('Can you give me an idea for a book?')

print(intel.content)

# Output: Here's an idea for a book: 'The Memory Thief's Daughter' - A mystery thriller ...


from dandy import BaseIntel, Bot

class ClownIntel(BaseIntel):
    clown_name: str
    can_juggle: bool
    real_name: str | None = None

bozo = ClownIntel(
    clown_name='Bozo',
    can_juggle=True
)

print(bozo.can_juggle)

# True

class FakeClownBot(Bot):
    def process(self, clown_description: str) -> ClownIntel:
        return self.llm.prompt_to_intel(
            prompt=clown_description,
            intel_class=ClownIntel,
            exclude_fields={'real_name'},
        )


another_clown = FakeClownBot().process(
    clown_description='I am a big fan of juggling, can you please create me a clown!',
)

print(another_clown)

# Output: clown_name='Bouncing Buddy' can_juggle=True real_name=None


from dandy import BaseIntel, Bot, Prompt


class CandyIntel(BaseIntel):
    short_name: str
    long_name: str
    description: str


class CandyDesignBot(Bot):
    llm_config = 'LLAMA_3_2_3B'
    instructions_prompt = (
        Prompt()
        .text('You are a candy design bot and will be given a request to make a new type of candy.')
        .line_break()
        .heading('Rules')
        .list([
            'Make sure you response is sugar based not chocolate based.',
            'Do not include any chocolate based words or phrases in the response.',
            'Incorporate the theme of the request into the response.',
        ])
    )
    intel_class = CandyIntel

    def process(self, user_prompt: Prompt | str, candy_theme: str) -> CandyIntel:
        prompt = (
            Prompt()
            .heading('Request')
            .prompt(user_prompt)
            .line_break()
            .heading('Theme')
            .prompt(candy_theme)
        )

        return self.llm.prompt_to_intel(
            prompt=prompt,
        )


candy_intel = CandyDesignBot().process(
    user_prompt='Strawberries and Cookie Dough',
    candy_theme='Medieval Times'
)

print(candy_intel.short_name)

# Output: Cinnamon Lances


from dandy import Map

class AnimalFamilyMap(Map):
    mapping_keys_description = 'Animal Sounds'
    mapping = {
        'barking': 'dog',
        'meowing': 'cat',
        'quacking': 'duck'
    }

animal_families = AnimalFamilyMap().process(
    'I was out on a walk and heard some barking', 
    max_return_values=1
)

print(animal_families[0])

# Output: dog


from dandy import Agent, Bot


class AssistantAgent(Agent):
    # This attribute is required and determines which other processors you want this agent to have access to using.
    processors = (
        Bot,
    )


intel = AssistantAgent().process('Can you give me an idea for a drawing?')

print(intel.content)

# Output: Here's a creative drawing idea for you: A steampunk robot cat ...

Build Intelligent Applications with Dandy