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Planners inject planning instructions into the system prompt before each LLM call and can post-process the response. 
from langchain_adk import BasePlanner, ReadonlyContext, LlmRequest, LlmResponse

class MyPlanner(BasePlanner):
    def build_planning_instruction(
        self, ctx: ReadonlyContext, request: LlmRequest
    ) -> str | None:
        return "Think step by step before acting. Plan before calling tools."

    def process_planning_response(
        self, ctx: ReadonlyContext, response: LlmResponse
    ) -> LlmResponse | None:
        return None  # no post-processing needed

​
PlanReActPlanner

A built-in planner that enforces structured planning tags — the agent must emit a /*PLANNING*/ block before reasoning and a /*FINAL_ANSWER*/ block to conclude. 
from langchain_adk import PlanReActPlanner, LlmAgent

agent = LlmAgent(
    name="PlanningAgent",
    llm=llm,
    tools=[...],
    planner=PlanReActPlanner(),
)

​
TaskPlanner

Maintains a task board in ctx.state and injects its current status into the system prompt. Pairs with ManageTasksTool so the agent can create, update, complete, and list tasks. 
from langchain_adk import TaskPlanner, LlmAgent

planner = TaskPlanner()

agent = LlmAgent(
    name="ProjectAgent",
    llm=llm,
    tools=[planner.get_manage_tasks_tool()],
    planner=planner,
    instructions=(
        "Track your work with manage_tasks. "
        "Initialize tasks at the start. Mark each complete when done."
    ),
)
The agent can call manage_tasks with actions: initialize, list, create, update, complete, remove.