agency 1.0.1

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

agency

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

What is agency?

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

agency 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 "agents" that you may perform "actions" on.

agency 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

Install

WARNING:
Running agency may result in exposing your computer to access by any connected Agent class. 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.

pip install agency

API Overview

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

In agency, all entities are called "agents" and represented as instances of the Agent class. This includes all humans, software, and AI-driven agents.

The Agent class is a base class similar to "Object" in many object-oriented languages. All agents may expose "actions" that other agents can discover and invoke at run time. Actions also specify an access policy, allowing you to monitor and control actions to ensure safety.

A Space is also an Agent and is used to group multiple agents together.

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

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

To summarize, the two classes of Agent and Space together create a simple API for creating 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.

Example Walkthrough

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.

Creating a Space

Let's start by instantiating a demo space.

space = Space("DemoSpace")

Space's, like all Agent's, must be given an id. So the line above instantiates a single space called "DemoSpace" that we can now add agents to.

Adding an Agent to a Space

Now, let's add our first agent 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 agents which can later be used to address them.

Defining Actions

Looking at ChattyAI's source code, you'll see that it is a subclass of Agent, 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 agents. The suffix say is the name of the action.

The say action takes a single string argument content. This action is intended to allow other agents 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.

Invoking Actions

At the end of the ChattyAI._action__say() method, we see an example of using agency'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 agent.

When an agent 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.

The Common Message Schema

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

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. OpenAICompletionAgent discussed below, does.

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

Access Control

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

An access policy can currently be one of three values:

• ACCESS_PERMITTED - which permits any agent to use that action at any time
• ACCESS_DENIED - which prevents use
• ACCESS_REQUESTED - which will prompt the receiving agent 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 agent 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 agent attempts to invoke an action that has been marked as ACCESS_REQUESTED. Your method should inspect 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 functionality is a priority for this project.

Adding Human Users With the WebApp Class

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 choose to subclass Space and not Agent? This is an arbitrary choice up to the developer, and may depend on what they want to accomplish.

We could implement WebApp as a subclass of Agent. This would represent the web application as a single agent within the system. Users of the web application would not be able to be addressed individually by other agents.

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 agents 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 agents when it makes sense.

Examining the WebApp Class

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 which extends Agent and is 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 forwards them to their intended receiver in the space.

This way, we allow individual web users to appear as individual agents to others in the space.

Namespacing and Adding the Web Application

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

space.add(
    WebApp("WebApp", port='8080'))

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

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

At this point, we have integrated the following agents 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.)

Adding OS Access with the Host class

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 Agent classes implement.

Now we'll add an agent 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 agents to interact with 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 elsewhere.

Discovering Actions

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 agent may send a help message to discover other agents and actions that are available in the space.

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

/actionname arg1:val1 arg2:val2 ...

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

/help

This will broadcast a help action to all other agents, who will individually respond with a list of their available actions. The returned list of actions 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"
        }
    },
    {
        "to": "ChattyAI.DemoSpace",
        "action": "say",
        "thoughts": "Use this action to say something to Chatty",
        "args": {
          "content": "str"
        }
    },
    ...
]

Notice that each action lists the fully qualified id of the agent 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 agent who will (after being granted permission) return the results back to "Dan.WebApp.DemoSpace" rendering it to the web user interface as a message.

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

Broadcast vs Point-to-Point Messaging

If we omit the to:Host.DemoSpace portion of the command above, the message will be broadcast, and any agents 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 agent 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 agent.

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

Adding an Environment-Aware Agent

Finally we get to the good part!

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

Note that the following OpenAIFunctionAgent class uses the newly released openai function calling API.

To add the OpenAIFunctionAgent class to the environment:

space.add(
    OpenAIFunctionAgent("FunctionAI",
        model="gpt-3.5-turbo-16k",
        openai_api_key=os.getenv("OPENAI_API_KEY"),
        # user_id determines the "user" role in the chat API
        user_id="Dan.WebApp.DemoSpace"))

The user_id argument determines which agent is represented as the "user" role to the chat API. Since the chat API is limited to a predefined set of roles, we need to indicate which is the main "user".

For an implementation that uses a plain text completion API, see OpenAICompletionAgent.

Complete Demo Implementation

The following is the full implementation (minus imports) of the above walkthrough that you can try out on your own, including both OpenAI agent examples and the HuggingFace based "Chatty". 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",
            demo_user_id="Dan", # hardcoded for simplicity
            port='8080'))

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

    space.add(
        Host("Host"))

    space.add(
        OpenAIFunctionAgent("FunctionAI",
            model="gpt-3.5-turbo-16k",
            openai_api_key=os.getenv("OPENAI_API_KEY"),
            # user_id determines the "user" role in the OpenAI chat
            user_id="Dan.WebApp.DemoSpace"))

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

    space.run()

    print("pop!")

    # 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 at http://localhost:8080 and you should see a simple chat interface.

The following is a screenshot of a conversation that showcases all the agents intelligently interacting and following orders.

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

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

After I explained my rejection of the read_file action (which happened behind the scenes on the terminal), "FunctionAI" appropriately used the shell_command action with wc -l Dockerfile. The Dockerfile indeed has 73 lines.

CompletionAI used that command on the first try. Anecdotally as of this writing, CompletionAI seems to be more accurate, even though it is using the text completion API vs the function calling feature of the chat API. This may be due to the implementation or issues arising from the translation into roles discussed elsewhere.

Hypothetical Examples

The following examples are not implemented, but are presented as additional ideas for integrations that agency 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="[e-mail]"),

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

    # Horizontal scaling could be achieved by simply duplicating agents
    # (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 `Agent` that reads a data set and sends
    # it as messages to the `Agent` 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()

FAQ

How does agency compare to agent libraries like LangChain?

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

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

agency 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.

agency 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 agency to set up a basic chat room to use with friends or other systems and not use AI-driven agents at all!

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

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

What are some known limitations or issues?

• It's a new project, so keep that in mind in terms of completeness, but see the plans below for where this is heading. Core functionality is pretty well tested at the moment.

• This library makes use of threads for each individual agent. Multithreading is limited by python's GIL, meaning if you run a CPU bound model other agents will have to wait for their "turn". This goes for anything else you might define as an "agent", if it is CPU heavy it will block other agents. Note that I/O does not block, so networked backends or services will execute in parallel. Other forms of multiprocessing to avoid the GIL may eventually be considered.

• This API does NOT assume or enforce predefined roles like "user", "system", "assistant", etc. This is an intentional decision and is not likely to change.

  agency is intended to allow potentially large numbers of agents, systems, and people to come together. A small predefined set of roles gets in the way of representing many things uniquely and independently.

  This is a core feature of agency: that all things are treated the same and may be interacted with through common means.

  The lack of roles introduces some challenges in integrating with role based APIs. See the implementation of OpenAIFunctionAgent for an example.

• There is not much by way of storage support. That is mostly left up to you and I'd suggest looking at the many technologies that focus on that. The Agent class implements a simple _message_log array which you can make use of or overwrite to back it with longer term storage. More direct support for storage APIs may be considered in the future.

Contributing

Please do!

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

Development Installation

git clone [e-mail]:operand/agency.git
cd agency
poetry install

Test Suite

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.

Roadmap

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

Priorities

• Speed: Performance is always a concern. If it's not performant, it's not practical. Currently the limitations of python multi-threading are a bottleneck.
• Access Control and Safety: An effective access control solution for agent-integrated systems is fundamental to ensure safety. I believe I've included a sane first step at such a pattern, but further development 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 agent systems. I hope to ensure ensure the API is kept small, and compatible with a wide variety of use cases.
• Documentation: I hope to ensure documentation is kept organized, clear, and accurate. This readme serves as a start.

Planned Work

• Add schema support for OpenAI Function calling
• Add web app multimodal i/o examples
  • image
  • audio
  • video
• Add multimodal model integration 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