#Planning

Planning mode tells the session to produce a structured plan before it starts calling tools. Use it for multi-step work (refactors, release reviews, audits) where you want the agent to decompose the goal first instead of jumping straight into edits.

Set it through the session option planningMode (Node) / planning_mode (Python). The accepted values are:

"auto" is the default structured pre-analysis path.

import { Agent } from '@a3s-lab/code';

const agent = await Agent.create('agent.acl');
const session = agent.session('/repo', {
  planningMode: 'auto',
});

const result = await session.send(
  'Plan and complete the release-readiness review.',
);
console.log(result.text);
console.log(`${result.toolCallsCount} tool calls executed`);

session.close();

from a3s_code import Agent, SessionOptions

agent = Agent.create("agent.acl")
opts = SessionOptions()
opts.planning_mode = "auto"
session = agent.session("/repo", opts)

result = session.send("Plan and complete the release-readiness review.")
print(result.text)
print(f"{result.tool_calls_count} tool calls executed")

session.close()

Planning state is attached to the run, so a host UI can render a task list from run events and update completion as the agent works. Planning organizes the work; verification commands still provide the completion evidence.

Runnable session examples ship at crates/code/sdk/node/examples/orchestration/parallel-pipeline.mjs and crates/code/sdk/python/examples/orchestration_workflow.py.