Model Parameter

One model parameter, one canonical name — model=

model= is the canonical name for the model on every agent class; llm= is a deprecated alias for it.

Quick Start

1. Use the canonical name

   from praisonaiagents import Agent
   
   agent = Agent(
       instructions="Summarise the given article in three bullets.",
       model="gpt-4o",
   )
   
   agent.start("<article text>")

2. The deprecated alias still works

   from praisonaiagents import Agent
   
   agent = Agent(
       instructions="Summarise the given article.",
       llm="gpt-4o",
   )

3. Passing both is refused

   from praisonaiagents import Agent
   
   # ❌ Passing both is refused
   Agent(instructions="…", llm="gpt-4o", model="gpt-3.5-turbo")
   # TypeError: Agent() received both llm= and model=. They are the same
   #   parameter, so passing both is ambiguous. Pass only one; model= is
   #   the canonical name (llm= is a deprecated alias).

How It Works

Every agent class routes llm= and model= through one resolver so the rule is identical everywhere.

Where It Applies

The llm= / model= pair resolves the same way on every class below.

from praisonaiagents import Agent, AgentTeam, Task

# Default model for members that did not name one themselves
team = AgentTeam(
    agents=[Agent(instructions="Researcher"), Agent(instructions="Writer")],
    tasks=[Task(description="Write a two-paragraph brief on quantum sensing.")],
    model="gpt-4o-mini",
)

team.start()

Custom LLM objects

Pass any object with a get_response or get_response_async method to llm= and the agent uses it as its backend instead of the OpenAI default. The tool loop calls llm_instance.get_response(**kwargs) (or its async twin), so those are the two surfaces the agent detects on.

from praisonaiagents import Agent

class MyBackend:
    model = "my-backend"
    def get_response(self, prompt, **kwargs):
        return "answer from my backend"

agent = Agent(instructions="Use my backend.", llm=MyBackend())
agent.start("hello")   # routed to MyBackend, not OpenAI

An async-only backend works too — expose get_response_async and the async tool loop drives it. Either method alone is enough; you do not need both.

from praisonaiagents import Agent

class MyAsyncBackend:
    model = "my-async-backend"
    async def get_response_async(self, prompt, **kwargs):
        return "answer from my async backend"

agent = Agent(instructions="Use my async backend.", llm=MyAsyncBackend())
agent.start("hello")

The agent detects on get_response and get_response_async, so any backend exposing either works — including the ScriptedModel test double from Offline Testing.

Migration

Related