google-adk-llm-agent

name	google-adk-llm-agent
description	Create Google ADK LlmAgent (also aliased as Agent). Use when building a single AI agent with model, instruction, tools, and configuration. Covers all LlmAgent parameters, instruction providers, output schemas, and include_contents options.

Google ADK — LlmAgent Reference

Import

from google.adk.agents import Agent  # Alias for LlmAgent
from google.adk.agents import LlmAgent  # Full name

Basic Agent

root_agent = Agent(
    name="my_agent",
    model="gemini-2.5-flash",
    description="What this agent does (used by parent for delegation).",
    instruction="You are a helpful assistant that answers questions.",
    tools=[my_tool_function],
)

All LlmAgent Parameters

Parameter	Type	Description
name	str	Unique agent identifier (required)
model	str	Model ID like "gemini-2.5-flash" (required)
instruction	str | Callable	System prompt or dynamic instruction provider
description	str	Used by parent agent for delegation decisions
tools	list	List of tool functions, BaseTool instances, or toolsets
sub_agents	list	Child agents for multi-agent systems
output_key	str	State key to store agent's final output
output_schema	type	Pydantic model or dict for structured output
include_contents	str	"default" or "none" — controls conversation history visibility
before_agent_callback	Callable	Called before agent runs
after_agent_callback	Callable	Called after agent completes
before_tool_callback	Callable	(tool, args, tool_context) -> Optional[dict] — called before tool
after_tool_callback	Callable	(tool, args, tool_context, response) -> Optional[dict] — called after tool
generate_content_config	GenerateContentConfig	Advanced model configuration
code_executor	CodeExecutor	Built-in code execution capability
planner	BasePlanner	Enable planning/thinking (e.g. BuiltInPlanner)
context_cache_config	ContextCacheConfig	Cache context for efficiency

Dynamic Instruction Provider

from google.adk.agents.readonly_context import ReadonlyContext

def my_instruction(readonly_context: ReadonlyContext) -> str:
    user_name = readonly_context.state.get("user_name", "user")
    return f"You are a helpful assistant for {user_name}. Be concise."

root_agent = Agent(
    name="personalized_agent",
    model="gemini-2.5-flash",
    instruction=my_instruction,
    tools=[],
)

Structured Output (output_schema)

from pydantic import BaseModel

class AnalysisResult(BaseModel):
    summary: str
    sentiment: str
    confidence: float

agent = Agent(
    name="analyzer",
    model="gemini-2.5-flash",
    instruction="Analyze the given text and return structured results.",
    output_schema=AnalysisResult,
)

include_contents Options

Value	Behavior
"default"	Agent sees relevant conversation history (default)
"none"	Agent sees NO prior conversation — only instruction + current input

summary_agent = Agent(
    name="summarizer",
    model="gemini-2.5-flash",
    instruction="Summarize the provided text.",
    include_contents="none",  # Fresh context each time
)

output_key — Storing Results in State

research_agent = Agent(
    name="researcher",
    model="gemini-2.5-flash",
    instruction="Research the given topic.",
    output_key="research_output",  # Stored in state["research_output"]
)

Generate Content Config

from google.genai import types

agent = Agent(
    name="creative_agent",
    model="gemini-2.5-flash",
    instruction="Write creative stories.",
    generate_content_config=types.GenerateContentConfig(
        temperature=0.9,
        top_p=0.95,
        max_output_tokens=2048,
    ),
)

Code Executor

from google.adk.code_executors import BuiltInCodeExecutor

agent = Agent(
    name="coder",
    model="gemini-2.5-flash",
    instruction="Write and execute Python code to solve problems.",
    code_executor=BuiltInCodeExecutor(),
)

Key Conventions

• name must be a valid Python identifier (letters, digits, underscores)
• description is critical for multi-agent delegation — be specific
• instruction can be a string or a callable that returns a string
• Tools are plain Python functions with docstrings (auto-converted to FunctionTool)
• The module-level variable MUST be named root_agent for CLI discovery

Related Skills

• google-adk-function-tool — Creating custom tools for agents
• google-adk-callbacks — Lifecycle hooks (before/after agent and tool)
• google-adk-multi-agent — LLM-routed multi-agent systems
• google-adk-workflow-agents — Deterministic orchestration (Sequential, Parallel, Loop)