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 agents with traditional software systems in a flexible and scalable way, allowing you to develop any architecture you need.
Agency's goal is to enable developers to create custom agent-based applications by providing a minimal 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.
- Straightforward class/method based agent and action definition
- Up to date documentation and examples for reference
- Supports multiprocessing and multithreading for concurrency
- AMQP support for networked agent systems
- Action and lifecycle callbacks
- Access policies and permission callbacks
- Detailed logging
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
- Docker configuration for reference and development
In Agency, all entities are represented as instances of the Agent class. This
includes all AI-driven agents, software interfaces, 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 + bThis 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 two included Space implementations to choose from:
LocalSpace- which connects agents within the same application.AMQPSpace- which connects agents across a network using an AMQP server like RabbitMQ.
Finally, here is a simple example of creating a LocalSpace and adding two
agents to it.
space = LocalSpace()
space.add(CalculatorAgent, "CalcAgent")
space.add(MyAgent, "MyAgent")
# The agents above can now communicateThese are just the basic features that Agency provides. For more information please see the help site.
pip install agencyor
poetry add agencyThe 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.
Please do!
If you're considering a contribution, please check out the contributing guide.
If you have any suggestions or otherwise, feel free to add an issue or open a discussion.