agentia 0.1.1

Ergonomic LLM Agents

Agentia: Ergonomic LLM Agents

Ergonomic LLM Agents, with chat messages fully compatible with Vercel AI SDK.

Getting Started

Run agents with tools and MCP.

from agentia import Agent, MCPServer, MCPContext
from typing import Annotated

# Define a tool as a python function
def get_weather(location: Annotated[str, "The city name"]):
    """Get the current weather in a given location"""
    return { "temperature": 72 }

# Declare a MCP server:
calc = MCPServer(name="calculator", command="uvx", args=["mcp-server-calculator"])

# Create an agent
agent = Agent(model="openai/gpt-5-mini", tools=[get_weather, calc])

# Run the agent with the tool
async with MCPContext(): # This line can be omitted if not using MCP
    response = await agent.run("What is the weather like in boston?")

print(response.text)

# Output: The current temperature in Boston is 72°F.

The Magic Decorator

Create agent-powered magic functions.

Support both plain types and pydantic models as input and output.

from agentia import magic
from pydantic import BaseModel

class Forcast(BaseModel):
    location: str
    temperature_celsius: int

@magic
async def get_weather(weather_forcast: str) -> Forcast:
    """Create weather forcase object based on the input string"""
    ...

forcast = await get_weather("The current temperature in Boston is 72°F")

print(forcast.location) # Output: Boston
print(forcast.temperature_celsius) # Output: 22

Supported Parameter and Result Types

• Any types that can be passed to pydantic.TypeAdaptor:
  • Builtin types: int, float, str, bool, tuple[_], list[_], dict[_, _]
  • Enums: Literal['A', 'B', ...], StrEnum, IntEnum, and Enum
  • dataclasses
• pydantic.BaseModel subclasses

Run agent as a REPL app

1. Create a config file at ./robo.toml

[agent]
name = "Robo" # This is the only required field
icon = "🤖"
instructions = "You are a helpful assistant"
model = "openai/o3-mini"
plugins = ["clock"]

[mcp]
calc={ command = "uvx", args = ["mcp-server-calculator"] }

2. Load the agent

agent = Agent.from_config("./robo.toml")