Crew Planning

Crew planning is an optional phase that runs before your crew executes its tasks. CrewAI uses an internal AgentPlanner to produce step-by-step plans from the crew’s context. Those plans are injected into each task’s description, so every agent sees structured guidance instead of only the raw task text.

Enabling planning

Pass planning=True on the Crew constructor. By default, planning uses gpt-4o-mini. To use a different model for the planning call, set planning_llm (for example "gpt-4o").

from crewai import Crew, Process
 
crew = Crew(
    agents=[researcher, analyst, writer],
    tasks=[research_task, analysis_task, writing_task],
    process=Process.sequential,
    planning=True,
    planning_llm="gpt-4o",
)

What happens under the hood

Roughly:

1. Inputs — agents, tasks, process, and other crew configuration are available to the planner.
2. AgentPlanner — consumes that crew-wide picture and generates plans (ordered steps and rationale aligned with your tasks).
3. Injection — those plans are merged into task descriptions so each step’s agent receives the planner’s breakdown as part of what they must follow.

So planning is not a separate user-facing API you call manually; it is wired into kickoff (and related run paths) when planning=True.

With vs without planning

When to use it

Turn planning on when:

• Work is multi-step and complex (research → analysis → writing, pipelines with dependencies, etc.).
• You care about consistency across agents and tasks.
• You want deliberate ordering and decomposition reflected in what each agent sees, not only in your initial task strings.

Cost and latency

Planning triggers an extra LLM call (or planner work, depending on version) before normal task execution. Expect a small latency bump and additional tokens for that planning step—usually modest compared to a long crew run, but non-zero.

Key takeaways

• planning=True enables an AgentPlanner that runs before task execution.
• Plans are injected into task descriptions, not kept only in logs.
• planning_llm overrides the default planner model (default gpt-4o-mini).
• Use planning for complex, multi-step crews where structure and consistency matter.
• Budget for slightly higher latency and token usage from the planning call.