Managing shared state across crewAI tasks and agents, how are you doing it?

[Arpanwanwe]

I have been using crewAI for Agent Workflow based on Role (Planner, Researcher, Executor, Reviewer), and it has been functioning well for structured task handoffs. Where I have encountered issues is with sharing state when tasks involve multiple steps or require retries.

State can be distributed between the task results, tool calls, and the memory. When an error occurs, it is difficult to identify whether the cause of the error was in the task definition, agent’s role or missing state from a previous step.

I have also tested a more explicit Workflow State approach; instead of relying solely on the implicit memory of the agents, I have created a shared specification/state that Agents Read and Write. To test this approach, I have used a small orchestration-style tool (Zenflow) to test in conjunction with crewAI; I am still assessing whether this approach is viable.

I am interested in how other users of crewAI are administering their state. Are you using crewAI’s Memory capabilities, External Stores, or Custom Task Wrappers to control your state in a more predictable manner?