A fast and minimal framework for building agent-integrated systems
Project description
Summary
Agency is a python library that provides an Actor model framework for creating agent-integrated systems.
The library provides an easy to use API that enables you to connect intelligent agents with software systems of all kinds, making it simple to develop the architecture you need.
Agency's goal is to enable developers to create custom agent-based solutions by providing a minimal and scalable foundation to both experiment and build upon. So if you're looking to build a custom agent system of your own, Agency might be for you.
Features
Low-Level API Flexibility
- Straightforward class/method based agent and action definition
- Plug-and-play action discovery at runtime
Performance and Scalability
- Supports multiprocess and multithreading for concurrency
- AMQP support for networked systems
Observability and Control
- Action and lifecycle callbacks for observability or other needs
- Access policies and permission callbacks for access control
- Logging with support for
LOGLEVEL
environment variable
Multi-language Support
Multimodal/Multimedia support
Demo application available at examples/demo
- Multiple agent examples for experimentation
- Two OpenAI agent examples
- HuggingFace transformers agent example
- Operating system access
- Includes Gradio UI (An updated React UI is in progress. See above.)
- Docker configuration for reference and development
API Overview
In Agency, all entities are represented as instances of the Agent
class. This
includes all AI-driven agents, traditional software systems, or human users that
may communicate as part of your application.
All agents may expose "actions" that other agents can discover and invoke at run time. An example of a simple agent could be:
class CalculatorAgent(Agent):
@action
def add(a, b):
return a + b
This defines an agent with a single action: add
. Other agents will be able
to call this method by sending a message to an instance of CalculatorAgent
and
specifying the add
action. For example:
other_agent.send({
'to': 'CalcAgent',
'action': {
'name': 'add',
'args': {
'a': 1,
'b': 2,
}
},
})
Actions may specify an access policy, allowing you to control access for safety.
@action(access_policy=ACCESS_PERMITTED) # This allows the action at any time
def add(a, b):
...
@action(access_policy=ACCESS_REQUESTED) # This requires review before the action
def add(a, b):
...
Agents may also define callbacks for various purposes:
class CalculatorAgent(Agent):
...
def before_action(self, message: dict):
"""Called before an action is attempted"""
def after_action(self, message: dict, return_value: str, error: str):
"""Called after an action is attempted"""
def after_add(self):
"""Called after the agent is added to a space and may begin communicating"""
def before_remove(self):
"""Called before the agent is removed from the space"""
A Space
is how you connect your agents together. An agent cannot communicate
with others until it is added to a common Space
.
There are three included Space
implementations to choose from:
ThreadSpace
- which distributes and connects agents using multithreading, suitable for simple applications and testing.MultiprocessSpace
- which distributes agents using the multiprocessing module, for better parallelism.AMQPSpace
- which distributes agents across a network using an AMQP server like RabbitMQ.
Finally, here is a simple example of creating a MultiprocessSpace
and adding two
agents to it.
space = MultiprocessSpace()
space.add(CalculatorAgent, "CalcAgent")
space.add(MyAgent, "MyAgent")
# The agents above can now communicate
These are just a few of the features that Agency provides. For more detailed information please see the help site.
Install
pip install agency
or
poetry add agency
The Demo Application
The demo application is maintained as an experimental development environment and a showcase for library features. It includes multiple agent examples which may communicate with eachother and supports a "slash" syntax for invoking actions as an agent yourself.
To run the demo, please follow the directions at examples/demo.
The following is a screenshot of the Gradio UI that demonstrates the example
OpenAIFunctionAgent
following orders and interacting with the Host
agent.
FAQ
How does Agency compare to other agent libraries?
Though you could entirely create a simple agent using only the primitives in
Agency (see examples/demo/agents/
), it is not
intended to be an all-inclusive toolset like other libraries. For example, it
does not include support for constructing prompts or working with vector
databases, etc. Implementation of agent behavior is left up to you, and you are
free to use other libraries as needed for those purposes.
Agency focuses on the lower level concerns of communication, observation, scalability, and security. The library strives to provide the basic foundations of an agent system without imposing additional structure on you.
The goal is to allow you to experiment and discover the right approaches and technologies that work for you. And once you've found an implementation that works, you can scale it out to your needs.
What are some known limitations or issues?
-
Despite the 1.x.x version, Agency is still in early development. Like many projects in the AI agent space it is somewhat experimental at this time, with the goal of finding and providing a minimal yet useful foundation for building agent systems.
Expect changes to the API over time as features are added or changed. The library follows semver versioning. Minor version updates may contain breaking API changes. Patch versions should not.
-
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 generally. This is a design feature of Agency: that all entities are represented similarly and may be interacted with through common means.
The lack of roles may require extra work when integrating with role based APIs. See the implementation of
OpenAIFunctionAgent
for an example. -
There is currently 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 will likely be considered in the future.
Contributing
Please do!
If you're considering a contribution, please check out the contributing guide.
Planned Work
If you have any suggestions or otherwise, feel free to add an issue or open a discussion.
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