Defining Agents

Decorator-Based Agents

from astromesh_adk import agent

@agent(name="assistant", model="openai/gpt-4o", description="General assistant")
async def assistant(ctx):
    """You are a helpful assistant."""
    return None  # Use default orchestration

The docstring becomes the system prompt. Return None to run the agent’s orchestration pattern, or return a string to answer directly without calling the model. (ctx.run_default() is not wired by the runner and raises.)

Class-Based Agents

from astromesh_adk import Agent, RunContext

class MyAgent(Agent):
    """You are a planning assistant."""

    name = "custom"
    model = "ollama/llama3"
    pattern = "plan_and_execute"

    async def on_before_run(self, ctx: RunContext):
        """Called before execution."""

    async def on_after_run(self, ctx: RunContext, result):
        """Called after execution."""

The class docstring is the system prompt. on_before_run and on_after_run are called by the runner; system_prompt_fn and on_tool_call exist on the base class but the runner does not call them yet.

Configuration Options

Parameter	Type	Description
name	str	Agent identifier
model	str	Provider/model (e.g., "openai/gpt-4o")
description	str	Shown when the agent is used as a tool. Defaults to name
tools	list	Tools available to the agent
pattern	str	Orchestration: react (default), plan_and_execute, parallel_fan_out, pipeline. An unknown value falls back to react. supervisor, swarm and parallel are team patterns
max_iterations	int	Orchestration iteration cap. Default 10
memory	str | dict	Conversational memory, built by the core factory — only redis is supported, with its URL: {"conversational": {"backend": "redis", "connection": {"url": "redis://..."}}} and the core redis extra. Any other backend makes the run raise
guardrails	dict	Input/output guardrails
fallback_model	str	Fallback provider/model
routing	str	Routing strategy. Default cost_optimized
model_config	dict	Extra model parameters