A fast and minimal foundation for unifying human, AI, and other computing systems, in python

everything defines a common communication and action framework for integrating AI agents, humans, and traditional computing systems.

everything allows you to establish shared environments called "spaces" where any number of humans, artificial, or other computing systems may equally address each other as individual "operators" that you may perform "actions" on.

everything handles the details of the common messaging protocol and allows discovering and invoking actions across all parties, automatically handling things such as reporting exceptions, enforcing access restrictions, and more.

The API accommodates agent integration with systems as varied as:

• UI driven applications
• terminal environments
• software APIs
• other AI agents
• people
• ...
• anything

WARNING:
Running everything may result in exposing your computer to access by any connected Operator including AI agents. Please understand the risks before using this software and do not configure it for OS access otherwise.

If you want to enable OS access, to allow for file I/O for example, I HIGHLY RECOMMEND running your project within a Docker container to prevent direct access to your host, allowing you to limit the resources and directories that may be accessed.

Please note that everything is not yet at a stable release or published to pip but will be soon.

git clone [email protected]:operand/everything.git
pip install ./everything

everything is an implementation of the Actor model intended for integrating AI, human, and traditional computing systems.

In everything, all entities are represented as instances of the class Operator. This includes all humans, software, or AI agents.

The Operator class is a base class similar to "Object" in many object-oriented languages. All operators may expose "actions" which can be invoked by other operators, by simply defining instance methods on the class.

A Space is a subclass of Operator and is used to group multiple operators together.

A space can be thought of as both a collection of operators and a "router" for their communication. An operator cannot communicate with others until it is first added to a space.

Spaces may be nested, allowing for namespacing and hierarchical organization of the operators in your application.

All operators may define public "actions" that other operators can discover and invoke at run time. Actions also specify an access policy, allowing you to control access and ensure safety.

To summarize, the two classes of Operator and Space together create a simple API for defining applications that may mix AI, human, and traditional computing systems, in a way that is intended for all to equally understand and use.

Let's walk through a thorough example to see how this works in practice.

Please note that the example classes used in this walkthrough are implemented for you to explore and try out, but should be considered "proof of concept" quality at this time.

Let's start by instantiating a demo space.

space = Space("DemoSpace")

Spaces, like all operators, must be given an id. So the line above instantiates a single space called "DemoSpace" that we can now add operators to.

Now, let's add our first operator to the space, a simple transformers library backed chatbot class named ChattyAI. You can browse the source code for ChattyAI here.

space.add(ChattyAI("Chatty", model="EleutherAI/gpt-neo-125m"))

The line above adds a new ChattyAI instance to the space, with the id of "Chatty". It also passes the model argument to the constructor, which is used to initialize the HuggingFace transformers language model.

At this point "Chatty" has a fully qualified id of "Chatty.DemoSpace". This is because "Chatty" is a member of the "DemoSpace" space.

This way, spaces establish a namespace for their member operators which can later be used to address them.

Looking at ChattyAI's source code, you'll see that it is a subclass of Operator, and that it exposes a single action called say.

The say action is defined as a method on the ChattyAI class, using the following signature:

def _action__say(self, content: str):
    """Use this action to say something to Chatty"""
    ...

The prefix _action__ is used to indicate that this is an action that can be invoked by other Operator's. The suffix say is the name of the action.

This say action takes a single string argument content. This action is intended to allow other operators to chat with Chatty, as expressed in its docstring.

When ChattyAI receives a say action, it will generate a response using its prompt format with the language model, and return the result to the sender.

At the end of the ChattyAI._action__say() method, we see an example of using everything's messaging protocol. ChattyAI returns a response to the sender by calling:

...
self._send({
    "to": self._current_message['from'],
    "thoughts": "",
    "action": "say",
    "args": {
      "content": response_content,
    }
})

This is a simple implementation that demonstrates the basic idea of how to invoke an action on another operator.

When an operator receives a message, it invokes the action method specified in by the "action" field of the message, passing the "args" to the action method as keyword arguments.

So here we see that Chatty is invoking the say action on the sender of the original message, passing the response as the "content" argument.

In the example above, we see the format that is used when sending actions.

In describing the messaging format, there are two terms that are used similarly: "action" and "message".

An "action" is the format you use when sending, as seen in the _send() call above. You do not specify the "from" field, as it will be automatically added when routing.

A "message" then, is a "received action" which includes the additional "from" field containing the sender's fully qualified id.

Continuing the example above, the original sender would receive a response message from Chatty that would look something like:

{
    "from": "Chatty.DemoSpace",
    "to": "Sender.DemoSpace",
    "thoughts": "",
    "action": "say",
    "args": {
      "content": "Whatever Chatty said",
    }
}

This is an example of the full message schema that is used for all messages sent between operators in everything.

This format is intended to be simple and extensible enough to support any use case while remaining human readable.

So when the sending operator receives the above response, it in-turn invokes their own say action, for them to process as they choose.

Note that the "thoughts" field is defined as a distinct argument for providing a natural language explanation to accompany any action, but as of this writing ChattyAI does not make use of it. DemoAgent discussed below, does.

For more details on the common message schema see schema.py.

All actions must declare an access policy like the following example seen above the ChattyAI._action__say() method:

@access_policy(ACCESS_PERMITTED)
def _action__say(self, content: str):
    """Use this action to say something to Chatty"""
    ...

Access control policies are used to control what actions can be invoked by other operators.

The access policy can currently be one of three values:

• ACCESS_PERMITTED - which permits any operator to use that action at any time
• ACCESS_DENIED - which prevents use
• ACCESS_REQUESTED - which will prompt the receiving operator for permission when access is attempted. Access will await approval or denial. If denied, the sender is notified of the denial.

If ACCESS_REQUESTED is used, the receiving operator will be prompted at run time to approve the action.

If any actions require permission, you must implement the _request_permission() method with the following signature:

def _request_permission(self, proposed_message: MessageSchema) -> bool:
    ...

This method is called when an operator attempts to invoke an action that has been marked as ACCESS_REQUESTED. Your method should inspect the proposed_message and return a boolean indicating whether or not to permit the action.

You can use this approach to protect against dangerous actions being taken. For example if you allow terminal access, you may want to review commands before they are invoked.

This implementation of access control is just a start, and further development of the mechanics is a priority for this project.

A single chatting AI wouldn't be useful without someone to chat with, so now let's add humans into the space so that they can chat with "Chatty". To do this, we'll use the WebApp class, which is a subclass of Space.

Why is WebApp a subclass of Space and not Operator? This is an arbitrary choice up to the developer, but the guideline is:

If you want to include multiple operators as a group, you should create a Space subclass and implement any additional logic necessary to forward messages to individual operators in the group.

We could implement WebApp as a subclass of Operator, and create a web application in a way where the web app would appear as a single operator, perhaps running it locally, if that fits the use case.

But since a typical web application serves multiple users, it may make sense to implement it as a Space subclass, so that individual users of the web application can be addressed by other operators using a namespace associated with the web application, as we'll see below.

So this is not the only way this could be accomplished but is intended as a complex example to showcase why one might want to define a Space subclass to group operators when it makes sense.

The implementation located here defines a simple Flask based web application that hosts a single page React based chat UI.

The implementation takes some shortcuts, but in it you'll see that we actually define two classes, one for the web application which extends Space, called WebApp, and a second class to represent users of the web app, called WebAppUser.

The WebAppUser class is where we define the actions that an individual web app user may expose to others.

Using the asyncio library you'll see that we simply forward messages as-is to the React frontend, and allow the client code to handle rendering and parsing of input as actions back to the Flask application, which in-turn sends them to their intended receiver in the space.

Now that we've defined our new WebApp class, we can add it to DemoSpace with:

space.add(WebApp("WebApp", port=os.getenv('WEB_APP_PORT')))

Whenever any operator is added to a space, its fully qualified id becomes namespaced by the space's id.

For example, after running the line above the WebApp being an operator as well, receives an id of "WebApp.DemoSpace".

At this point, we have integrated the following operators listed using their fully qualified id's

• "DemoSpace" - The root space
• "ChattyAI.DemoSpace" - ChattyAI's fully qualified id
• "WebApp.DemoSpace" - the root of the "WebApp" space

Users of the web application, as they log in or out, may be added dynamically under the "WebApp" namespace allowing them to be addressed with a fully qualified id of, for example "Dan.WebApp.DemoSpace".

(Note that login/out functionality is not implemented as of this writing.)

At this point, we have a system where human users of the web application can chat with ChattyAI, using just a single action called "say" that both Operator classes implement.

Now we'll add an operator that exposes many different actions, the Host class.

space.add(Host("Host"))

The Host class allows access to the host operating system where the python application is running. It exposes actions such as read_file and shell_command which allow other operators to take actions on the host.

This class is a good example of one with potentially dangerous actions that need to be accessed with care. You'll notice that all the methods in the Host class have been given the access policy:

@access_policy(ACCESS_REQUESTED)

By declaring this access policy, all actions on the host will require a confirmation from the terminal where the application is being run. This is thanks to the implementation of _request_permission() in the Host class.

Note that this implementation of _request_permission() is just one possibility. We could have implemented, for example, a phone notification for a human to review from anywhere.

At this point, we can demonstrate how discovery works from the perspective of a human user of the web application.

Once added to a space, each operator may send a help message to discover other operators and actions that are available in the space.

The WebApp which hosts a simple chat UI supports a "slash" syntax summarized here:

/actionname arg1:val1 arg2:val2 ...

(Note that quotes may be used for values that contain spaces)

So a person using the chat UI can discover available actions with:

/help

This will broadcast a help action to all other operators, who will individually respond with a list of their available actions. The returned list of actions from the Host operator, would look something like:

[
    {
        "to": "Host.DemoSpace",
        "action": "delete_file",
        "thoughts": "Delete a file",
        "args": {
          "filepath": "str"
        }
    },
    {
        "to": "Host.DemoSpace",
        "action": "list_files",
        "thoughts": "List files in a directory",
        "args": {
          "directory_path": "str"
        }
    },
    ...
]

Notice that each action lists the fully qualified id of the operator in the "to" field, the docstring of the action's method in the "thoughts" field, and each argument along with its type in the "args" field.

So a person using the web app UI can invoke the list_files action on "Host.DemoSpace" with the following syntax:

/list_files to:Host.DemoSpace directory_path:/app

This will send the list_files action to the Host operator who will (after being granted permission) return the results back to "Dan.WebApp.DemoSpace" rendering it to the web user interface.

Note the use of the fully qualified id of Host.DemoSpace used with the to: field

If we omit the to:Host.DemoSpace portion of the command above, the message will be broadcast, and any operators who implement a list_files action will respond.

This is also how the /help command works. If you want to request help from just a single operator you can use something like:

/help to:Host.DemoSpace

Note that point-to-point messages (messages that define the "to" field) will result in an error if the action is not defined on the target operator.

Broadcast messages will not return an error, but will silently be ignored by operators who do not implement the given action.

Finally we get to the good part!

We'll now add an intelligent agent into this environment and see that it is easily able to understand and interact with any of the systems or humans we've connected thus far.

To add the DemoAgent class to the environment:

space.add(
    DemoAgent("Demo",
        model="text-davinci-003",
        openai_api_key=os.getenv("OPENAI_API_KEY")))

Note that the DemoAgent class is implemented to use the OpenAI API as a language model backend.

I recommend using language models on par with GPT-3.5 or better for the best results with agents.

What makes the DemoAgent able to intelligently discover and interact with others is largely embodied in the DemoAgent._prompt_head() method. In it you'll notice a few key points:

1. The prompt is written from the first person perspective as though it is the agent's own thoughts. This differs slightly from common practice, which usually uses the second-person perspective. I do not think this makes a large difference but was worth mentioning. This is more of a personal preference.

2. I frame the situation clearly and accurately for the agent, telling it enough about who it is, its goals, and the JSON format that it uses to communicate.

3. I "pretend" that the bottom portion is a terminal application. By signaling a change in context with the %%%%% Terminal %%%%% header, we help make clear to the language model that this is a distinct section of content with its own text patterns to continue. I do not believe that this is a crucial technique either, but it is worth noting.

4. I use the _message_log_to_list() method to dynamically insert the previous conversation up to the current point. See the mixin class PromptMethods for the implementation. There is no summarization used, so the current implementation will eventually hit the context window after a short time.

5. I insert a fake event at the beginning of the terminal portion of the prompt, pretending that the agent themself executed the help action proactively, and display the resulting list. This is just a slick way to insert the available actions while keeping the supposed context of the terminal, and providing a one-shot example to begin from.

Note that ChattyAI uses a more typical prompt, showing that prompt techniques need not be shared by all agents connected to a space, but can be entirely unique to each agent.

The following is the full implementation (minus imports) of the above walkthrough that you can try out on your own. Note that Space.run() starts a thread, so we simply keep the application alive with a while loop.

# demo.py

if __name__ == '__main__':

    space = Space("DemoSpace") 

    space.add(
        WebApp("WebApp", port=os.getenv('WEB_APP_PORT')))

    space.add(
        ChattyAI("Chatty", model="EleutherAI/gpt-neo-125m"))

    space.add(
        Host("Host"))

    space.add(
        DemoAgent("Demo",
            model="text-davinci-003",
            openai_api_key=os.getenv("OPENAI_API_KEY")))

    space.run()

    # keep alive
    while True:
        time.sleep(1)

If you run the above python script, after a short boot time you can visit the web app on the port you specify (WEB_APP_PORT) and you should see a simple chat interface.

The following is a screenshot of a conversation that showcases DemoAgent's ability to intelligently interact with the other operators in the environment, including running commands on the host, or chatting with "Chatty".

Note that my messages are broadcasted in the below conversation, which explains why Chatty responds to each message also. There is an obvious difference in quality, of course.

I also demonstrate the results of rejecting an action and asking him to use a different approach.

Behind the scenes, Demo did message Chatty directly, and after I explained my rejection of the read_file action, Demo used the shell_command action with wc -l Dockerfile which was more appropriate. And the file indeed has 75 lines.

The following examples are not implemented, but are presented as additional ideas for integrations that everything could support.

Space([

    # Integrate access to a remote server
    Server("Ubuntu",
        ip="192.168.1.100"),

    # Add a voice assistant interface
    VoiceAssistant("VoiceyAI")

    # Use email to send/receive messages from others
    Email("Dan", address="[email protected]"),

    # Integrate other ML services, like for images
    DiffusionModel("ImageAI"),

    # Horizontal scaling could be achieved by simply duplicating operators
    # (notice we repeat the last one)
    DiffusionModel("ImageAI"),

    # Existing AI agents may integrate as well
    LangChainAgent("MyLangChainAgent"))

    # Development related tasks like model training may also be accomplished.
    # You would only need to add one new `Operator` that reads a data set and
    # sends it as messages to the `Operator` class used for inference, provided
    # the underlying model is first switched to a training mode. For example:
    DatasetTrainer("DatasetTrainer",
      trainee: "ChattyAIInTraining"
    )
    ChattyAI("ChattyAIInTraining",
      training_mode: True,
      ...
    )

    # Network and share your LMs and Agents with others
    RemoteAgent("AgentHelperDude",
      url: "https://agent.helper.dude:2023",
      ...
    )

    # You get the idea...
    AnySystemOrPersonOrFunctionAtAllThatYouWantToShareChannel(
      "Guest",
      ...
    )

]).create()

Though you could entirely create a simple agent using only the primitives in everything (see DemoAgent), it is not intended to be a full-fledged agent toolset. It can be thought of as more of an "agent integration framework".

Projects like LangChain, AutoGPT and others are exploring how to create purpose-built agents that solve diverse problems using tools.

everything is concerned with creating a safe and dynamic environment for these types of agents to work, where they can freely discover and communicate with the tools, each other, and any humans available in their environment.

everything provides a simple means for defining actions, callbacks, and access policies that you can use to monitor and ensure safety for the systems you expose to your agents.

A central part of the design is that humans and other systems can easily integrate as well, using a simple common format for messages. You can even use everything to set up a basic chat room to use with friends or other systems and not use agents at all!

An additional benefit of its general design is that everything may also simplify some agent development workflows. See the hypothetical examples above.

So, everything is a more general framework intended to support agent development and to ultimately enable agents to safely integrate with anything, in any way imaginable.

Please do!

If you have any questions, suggestions, or problems, please open an issue.

git clone [email protected]:operand/everything.git
cd everything
poetry install

You can run the test suite with:

poetry run pytest

The test suite is currently set up to run on pull requests to the main branch.

The goal is to maintain a minimal, natural, and practical API for bringing human, artificial, and other computing systems together, with the following priorities.

• Speed: Performance is always a concern. If it's not performant, it's not practical. Currently the limitations of pythong multi-threading are a bottleneck
• Access Control and Safety: Designing an effective access control solution for AI integrated systems is a fundamental problem to solve in order to ensure safety. I believe I've included a sane first attempt at such a pattern, but further exploration will be a focus of this project.
• Compatibility and Usability: In general, I believe this is a fair start in defining a set of patterns for creating AI integrated systems. I intend to continually improve the API, protocol, and other aspects of its design as needed based on feedback from real world use. So please let me know!
• Documentation: I hope to ensure documentation is kept small, accurate and up to date. This readme serves as a start.

• Add web app i/o examples
  • image
  • audio
  • video
• Add multimodal model example
• Add message broker/networking support (RabbitMQ)
• Add integration example for mlc-llm
• Add integration example for gorilla
• Add integration example for LangChain
• Add model training example
• Consider alternative multiprocessing approaches
• Consider adding a storage API
• Consider prior work on distributed access control
• Add docker assets to encourage using it
• feel free to make suggestions