getting-started.md

There are two ways to use Agora: as a sender agent (i.e. a client) or as a receiver agent (i.e. a server). An agent can also act as both a sender and a receiver.

In this quick tutorial, we'll establish a channel between two agents:

• A LangChain agent that wants to retrieve temperature data
• A Camel agent that has access to weather data

Installation

pip install agora-protocol

For this tutorial, you'll also want to install two agent frameworks:

pip install langchain_openai
pip install Pillow requests_oauthlib # Dependencies for camel-ai
pip install camel-ai

We'll use an OpenAI model as base. You can set the API key via the OPENAI_API_KEY= environmental variable.

Sender

The Sender is an agent designed to execute tasks. Defining a task is as simple as taking a documented Python function and adding the @sender.task decorator.

importagorafromlangchain_openaiimportChatOpenAImodel=ChatOpenAI(model="gpt-4o-mini")
toolformer=agora.toolformers.LangChainToolformer(model)
sender=agora.Sender.make_default(toolformer)
@sender.task()defget_temperature(city : str) ->int:
""" Get the temperature for a given city. Parameters: city: The name of the city for which to retrieve the weather Returns: The temperature in °C for the given city. """pass

Note: any properly annotated function with Google-style docstrings can be automatically converted to a task. Refer to this page for other ways to describe tasks.

The function is automatically converted to a task function. A task function takes exactly the same arguments, in addition to a keyword-only argument target which represents the address of the remote agent.

response=get_temperature('New York', target='http://localhost:5000')
print(response) # Output: 25

When running this code, the Sender agent will begin a conversation with the Receiver agent on localhost:5000. The two will exchange information first using natural language and then, depending on the need, with structured data and automatic routines (see [specification]). All of this is abstracted away and happens under the hood.

Receiver

Let's now setup a Receiver instance on port 5000. This time, we'll use a Camel agent with one tool, weather_db:

importagoraimportcamel.types# Needs to be installed separatelytoolformer=agora.toolformers.CamelToolformer(
camel.types.ModelPlatformType.OPENAI,
camel.types.ModelType.GPT_4O
)
defweather_db(city: str) ->dict:
"""Gets the temperature and precipitation in a city. Args: city: The name of the city for which to retrieve the weather Returns: A dictionary containing the temperature and precipitation in the city (both ints) """# Put your tool logic herereturn {
'temperature': 25,
'precipitation': 12
}
receiver=agora.Receiver.make_default(toolformer, tools=[weather_db])

A receiver needs to be wrapped in a server capable of handling HTTP queries. For convinience's sake, the Agora client provides a Flask server that does that out of the box:

server = agora.ReceiverServer(receiver)
server.run(port=5000)

We're done! The get_temperature task can now be used seamlessly in your custom workflow, or even by another agent.