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https://github.com/agentic-ai/enact

A framework for generative software.
https://github.com/agentic-ai/enact

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A framework for generative software.

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# enact - A Framework for Generative Software.



Enact is an in-development python framework for building generative software,

specifically software that integrates with machine learning models or APIs that

generate distributions of outputs.



## Key features



Enact was created to help developers build generative software. Our focus is on

offering core framework functionality required for developing, inspecting and

improving software that samples from generative subsystems such as LLMs and

diffusion models. Another way to understand enact is as a framework for

working with *scaffolding programs*.



To this end, enact boosts your existing python programs by giving you the

ability to

* recursively track generative executions,

* persist execution data and system parameters in versioned storage,

* explore the space of possible system executions via rewind and replay,

* turn your existing code into asynchronous flows that can halt and

  sample input from external systems or users, and

* orchestrate higher-order generative flows in which AI systems write and run

  software.



Enact represents a first-principles approach to supporting development of

generative software close to the level of the programming language. It is

therefore different from and complementary to libraries that focus on unified

integrations (e.g., API wrappers) or on particular algorithm schemas for

orchestrating generative flows (e.g., chain/tree-of-thought based approaches).



See [why-enact](#why-enact) for a more in-depth discussion on why we chose this

approach. For an example of the kind of software that can be written with

enact, take a look at the [MCTS example](examples/generic_mcts.ipynb), which

builds a *generic* implementation of Monte Carlo Tree Search that directly

explores tree of possible executions of a scaffolding program.



## Installation and overview



Enact is available as a [pypi package](https://pypi.org/project/enact/) and can

be installed via:



```bash

pip install enact

```

### Invocations

Enact recursively tracks inputs and outputs of functions registered with the

framework. A record of an execution is called an `Invocation`, and can be

persisted to a store. This is useful for tracking the behavior of generative

software.



```python

import enact

import random



@enact.register

def roll_die(sides: int) -> int:

  """Roll a die."""

  return random.randint(1, sides)



@enact.register

def roll_sum(num_rolls: int) -> int:

  """Roll dice."""

  return sum(roll_die(6) for _ in range(num_rolls))



with enact.InMemoryStore() as store:

  invocation = enact.invoke(roll_sum, (2,))

  print(enact.invocation_summary(invocation))

```

_Output:_

```

->roll_sum(2) = 7

  ->roll_die(6) = 2

  ->roll_die(6) = 5

```



### Rewind/Replay

Invocations can be rewound and replayed:



```python

# Rewind the last roll and replay it.

with store:

  first_roll = invocation.rewind(1)    # Rewind by one roll.

  print('Partial invocation: ')

  print(enact.invocation_summary(first_roll))

  reroll_second = first_roll.replay()  # Replay the second die roll.

  print('\nReplayed invocation: ')

  print(enact.invocation_summary(reroll_second))

```

_Output:_

```python

Partial invocation:

->roll_sum(2) incomplete

  ->roll_die(6) = 2



Replayed invocation:

->roll_sum(2) = 8

  ->roll_die(6) = 2

  ->roll_die(6) = 6

```



### Human-in-the-loop



In enact, humans are treated as just another generative distribution. Their

input can be sampled using `enact.request_input`, which internally raises an

exception. Since the execution itself is persisted in a store, it can be 

replayed at a later time, replacing the exception with the input, thus 

continuing the execution.



```python

@enact.register

def human_rolls_die():

  """Query the user for a die roll."""

  return enact.request_input(requested_type=int, for_value='Please roll a die.')



@enact.register

def roll_dice_user_flow():

  """Request the number of die to roll and optionally sample die rolls."""

  num_rolls = enact.request_input(requested_type=int, for_value='Total number of rolls?')

  return sum(human_rolls_die() for _ in range(num_rolls))



request_responses = {

  'Total number of rolls?': 3,  # Roll 3 dice.

  'Please roll a die.': 6,      # Humans always roll 6.

}



with store:

  invocation_gen = enact.InvocationGenerator.from_callable(

    roll_dice_user_flow)

  # Process all user input requests in order.

  for input_request in invocation_gen:

    invocation_gen.set_input(request_responses[input_request.for_value()])

  print(enact.invocation_summary(invocation_gen.invocation))

```

_Output:_

```

->roll_dice_user_flow() = 18

  ->RequestInput(requested_type=, context=None)(Total number of rolls?) = 3

  ->human_rolls_die() = 6

    ->RequestInput(requested_type=, context=None)(Please roll a die.) = 6

  ->human_rolls_die() = 6

    ->RequestInput(requested_type=, context=None)(Please roll a die.) = 6

  ->human_rolls_die() = 6

    ->RequestInput(requested_type=, context=None)(Please roll a die.) = 6

```



Note that enact can also be applied to `async` python programs. The advantage of the

enactive-style of asynchronous input sampling is that the partial execution can be

persisted and resumed at a later time or on a different machine.



### Versioning



Enact components are structurally hashed and persisted in a store. This makes it

possible to access ephemeral configurations of software associated only with a

particular run:



```python

import dataclasses



@enact.register

@dataclasses.dataclass

class DiceRoll(enact.Invokable[None, int]):

  num_rolls: int

  sides_per_die: int



  def call(self) -> int:

    return sum(roll_die(self.sides_per_die) for _ in range(self.num_rolls))



with enact.InMemoryStore() as store:

  dice_roll = DiceRoll(2, 6)

  invocation = enact.invoke(DiceRoll(2, 6))

  # modify the diceroll object

  dice_roll.num_rolls = 3

  dice_roll.sides_per_die = 8

  # Fetch the version associated with the first roll:

  print(enact.invocation_summary(invocation))

  print(f'Dice roll object associated with the invocation: '

        f'{invocation.request().invokable()}')

  print(f'Dice roll object after modification: {dice_roll}')

```

_Output:_

```python

->DiceRoll(num_rolls=2, sides_per_die=6)(None) = 7

  ->(6) = 4

  ->(6) = 3

Dice roll object associated with the invocation: DiceRoll(num_rolls=2, sides_per_die=6)

Dice roll object after modification: DiceRoll(num_rolls=3, sides_per_die=8)

```



## Using enact



Full documentation is work in progress.

See [enact concepts](examples/enact_concepts.ipynb) for more information and

take a look at the [examples](examples/).



### Making your types enact compatible



Enact has built-in support for basic python datatypes such as primitives,

lists, sets, dictionaries and tuples.



If you need support for additional types, you can either define them as

`Resource` dataclasses in the framework or define a wrapper.



#### Defining a resource



```python

import dataclasses



@enact.register

@dataclasses.dataclass

class MyResource(enact.Resource):

  x: int

  y: list = dataclasses.field(default_factory=list)



with store:

  # Commit your resource to the store, obtain a reference.

  ref = enact.commit(MyResource(x=1, y=[2, 3]))

  # Check out your reference.

  print(ref.checkout())  # Equivalent to "print(ref())".

```

_Output:_

```python

MyResource(x=1, y=[2, 3])

```



#### Defining a wrapper

For existing types, it can be more convenient to wrap them rather than to

redefine them as enact `Resource` objects.



This can be accomplished by registering a `TypeWrapper` subclass.



```python

class Die:

  """Non-enact python type."""

  def __init__(self, sides):

    self.sides = sides



  @enact.register

  def roll(self):

    return random.randint(1, self.sides)



@enact.register

@dataclasses.dataclass

class DieWrapper(enact.TypeWrapper[Die]):

  """Wrapper for Die."""

  sides: int



  @classmethod

  def wrapped_type(cls):

    return Die



  @classmethod

  def wrap(cls, value: Die):

    """Translate to enact wrapper."""

    return DieWrapper(value.sides)



  def unwrap(self) -> Die:

    """Translate to basic python type."""

    return Die(self.sides)



with store:

  die = Die(sides=6)

  invocation = enact.invoke(die.roll)

  print(enact.invocation_summary(invocation))

```

_Output:_

```

-><__main__.Die object at 0x7f3464398340>.roll() = 4

```



## Why enact - A manifesto



Generative AI models are transforming the software development process.



Traditional software relies primarily on functional buildings blocks in which

inputs and system state directly determine outputs. In contrast, software

that samples one or more generative subsystems associates each input with

a range of possible outputs.



This small change in emphasis - from functions to conditional distributions -

implies a shift across multiple dimensions of the engineering process,

summarized in the table below.



|        |  Generative   |  Traditional |

| :----: | :-----------: | :----------: |

| Building blocks | Conditional distributions | Deterministic functions |

| Engineering | Improve output distributions | Add features, debug errors |

| Subsystems | Unique | Interchangeable |

| Code sharing | Components | Frameworks |

| Execution traces | Training data | Debugging |

| Interactivity | Within system components | At system boundaries |

| Code vs data | Overlapping | Distinct |



### Building generative software means fitting distributions



Generative AI can be used to quickly sketch out impressive end-to-end

experiences. Evolving such prototypes into production-ready systems can be

non-trivial though, as they may show undesirable behaviors a certain percentage

of the time, may fail to respond correctly to unusual user inputs or may simply

fail to meet a softly defined quality target.



Where traditional engineering focuses on implementing features and fixing bugs,

a generative software engineer must tune the distribution represented by the system

as a whole towards some implicitly or explicitly specified target.



In some cases, tuning generative systems can be achieved directly using machine

learning techniques and frameworks. For instance, systems that consist of a thin

software wrapper around a monolithic machine learning model may directly train

the underlying model to shape system behavior.



In other cases, particularly when running complex generative flows that involve

multiple interacting subcomponents that cannot be easily optimized end-to-end,

gradient-free methods are necessary to improve system performance. These range

from using programmers (either human or synthetic) to elaborate the algorithmic

guard-rails of the generative process, running experiments between API identical

subsystems or finding ideal parameters (e.g., prompt templates) for individual

components.



### Fitting generative software distributions involves program search



In generative software, individual components produce distributions that may be

more or less well-suited to the system's overall goal: An LLM that performs well

on tabular data may perform less well when used as a chat agent. One fine-tuned

diffusion model may produce images that are closer to the target style than

another. This is distinct from the the case of traditional software engineering,

where the choice between two correct, API-identical implementations of a module

is of little importance to the behavior of the system as a whole.



Fitting a piece of a generative software involves optimizing the output

distribution towards some quality target. In some cases this can be achieved

using machine learning techniques such as gradient descent, but in cases where

this is not possible, it involves searching for high performing solutions in the

space of software and software configurations.



This search can be manual or automatic, and it can involve changes to parameters

such as LLM prompts or to the scaffolding program itself, e.g., searching over

multiple candidate responses.



The profusion of base models and possible algorithmic elaborations of base

models suggests that gradient-free search methods will take a central place in

generative software development. This could be as simple as running an

experiment in which one component is compared against another or as complex as

searching the space of software, e.g., using LLM-boosted genetic algorithms.



As algorithms in the AlphaZero family have shown us, a particularly fruitful

approach is combining search with machine learning techniques in such a way

that search improves the reasoning power of the core ML model, whereas the ML

model can be improved with the experience gained through search.



By offering in-depth program telemetry and execution search over scaffolding

programs, enact aims to provide the basic infrastructure to enable these kinds

of approaches.



### Generative software is collaborative



There are many generative AI components that are mutually API-compatible (e.g.,

text-to-image generation, LLMs) but that cannot be directly ranked in terms of

quality since they represent a unique take on the problem domain. The

combination of API compatibility and variation lends itself to a more

evolutionary, collaborative style than traditional software, where shared effort

tends to centralize into fewer, lower-level frameworks.



This new collaborative approach is already evidenced by both widespread sharing

of prompt templates and the existence of public databases of fine-tuned models.



Enact aims to simplify collaboration using a content-addressable memory approach

to storage. A program and its executions can be uniquely identified by its hash,

which provides a shared ontology for collaborative program search.



### Generative software reflects on its executions.



Many popular programming languages offer capacity for reflection, wherein the

structure of code is programmatically interpretable by code. Generative software

additionally requires the ability for code to reflect on code execution.



Sampling a generative model is computationally intensive and provides valuable

information that may be used as system feedback or training data. Since

generative software may include complex, recursive algorithms over generative

components, this suggests a need for not simply tracking inputs and outputs

at system boundaries, but at every level of execution.



This is particularly relevant to higher order generative flows, in which the

output of one generative step structures the execution of the next: Such a

system can use call traces as feedback to improve its output, in the same way

that a human may use a debugger to step through an execution to debug an issue.



Enact provides the concept of invocations, and can support higher-order 

invocations: That is, provide in-depth telemetry for software that reflects on

executions of other software.



### Humans are in the loop



Unlike traditional systems, where user interactions happen at system boundaries,

AI and humans are often equal participants in generative computations. An

example is Reinforcement Learning from Human Feedback (RLHF), where humans

provide feedback on AI output, only to be replaced by an AI model that mimics

their feedback behavior during the training process. In other cases, critical

generative flows (e.g., the drafting of legal documents) may require a human

verification step, without which the system provides subpar results.



The choice between sampling human input and sampling a generative model

involves considerations such as cost and quality, and may be subject to change

during the development of a system. For example, an early deployment of a system

may heavily sample outputs or feedback from human participants, until there is

enough data to bootstrap the target distribution using automated generative

components.



Enact's execution search capablities allow humans to alter or inject data

into program executions: A partial execution that failed due to requiring

user attention can be persisted and replayed at a later time.



### Data is code, code is data



Traditional software systems tend to allow a clear distinction between code

(written by developers) and data (generated by the user and the system). In

generative systems this distinction breaks down: Approaches such as

[AutoGPT](https://github.com/Significant-Gravitas/Auto-GPT) or

[Voyager](https://github.com/MineDojo/Voyager) use generative AI to generate

programs (specified in code or plain text), which in turn may be interpreted by

generative AI systems; prompts for chat-based generative AI could equally be

considered code or data.



## Development and Contributing



The framework is open source and Apache licensed. We are actively looking for

contributors that are excited about the vision. If you're interested in the

project, feel free to reach out.



You can download the source code, report issues and create pull requests at

https://github.com/agentic-ai/enact.