You are designing a 4 agent research crew: a planner, two parallel searchers, and a synthesizer. The workflow needs to be open ended (the planner can spawn extra searches based on early findings). Should you build it on AutoGen or CrewAI? Pick one and defend the choice on at least three concrete axes.
AutoGen fits open-ended research because its GroupChat is transcript-driven and can spawn extra turns; CrewAI's Tasks are fixed-shape at design time.
Think of two ways to run a research team. CrewAI is like handing each teammate a printed checklist on Monday. Everyone knows their tasks, they run through them in order, and Friday's the deadline. Great when the work is predictable. AutoGen is like sitting everyone around a conference table with a whiteboard. The planner says 'Alice, search for X', hears the result, then decides on the spot to ask 'Bob, dig into the angle Alice just found'. Nothing was decided in advance. The meeting just keeps going until someone says 'we have enough, write it up'. The research crew in this question keeps changing its mind mid-flight, so you want the conference room, not the checklist.
Concept explanation~2 min read
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Concept explanation~2 min read
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Multi-agent framework selection is one of the few choices in an LLM stack that you cannot easily revisit. By the time a crew is in production, your prompts, tools, observability, and team's mental model are all wrapped around the framework's primitives. The right question to ask in the interview is not 'which framework is best'. It is 'what shape is the workload, and which framework's primitives express that shape natively'.
For the workload described, a planner with two searchers and a synthesizer, where the planner can spawn extra searches based on early findings, the shape is conversational and adaptive. The next move is a function of what the previous moves returned. AutoGen models this directly; CrewAI models it indirectly and forces you to fight the framework. The rest of this dive walks through the four axes that decide it.
Task-shaped vs transcript-shaped: the framing that decides everything
CrewAI's core abstraction is the Task. You define a Task with a description, an expected output, and the Agent that owns it. You assemble Tasks into a Crew with a Process. Sequential runs them in order, Hierarchical adds a manager LLM that picks which Task to run next from a fixed pool.
The key constraint: the pool of Tasks is decided at construction time. The Hierarchical Process can re-order or skip Tasks, but it cannot manufacture new ones mid-run. If your workload genuinely needs 'and now run one more search the planner just thought of', you are either pre-allocating extra Tasks that may go unused, or using the Hierarchical manager's planning prompt to fake task creation through clever phrasing. Both fragile.
AutoGen's core abstraction is the GroupChat. A set of Agents share a transcript. After every message, a select_speaker policy returns the next Agent. The chat continues until is_termination_msg fires on the last message. There is no Task list. Every move the crew makes is a regular message in the chat, including 'run another search'. The workload's adaptivity is a property of the framework, not something you have to engineer around.
Situations where this technique stops working.
2–4 min · Everything important, quickly.
| Axis | CrewAI | AutoGen |
|---|---|---|
| Work model | Agents + predefined Task list | Agents sharing a GroupChat transcript |
| Control flow | Sequential or Hierarchical Process | select_speaker policy after each turn |
| Termination | Task list exhausted | is_termination_msg predicate on content |
| Tool execution | Tools attached to Agent or Task | UserProxyAgent turn inside the chat |
| Best fit | Fixed-shape pipelines | Open-ended, planner-driven crews |
| Cost of bad fit | Manager LLM hacks to fake adaptivity | Looping or early-stop if predicates are sloppy |
Real products, models, and research that use this idea.
- Microsoft's AutoGen Studio ships sample GroupChats for open-ended research crews with planner and searcher roles.
- CrewAI's official Sequential and Hierarchical Process examples target fixed-shape pipelines like marketing-brief generation and content review.
What an interviewer would ask next. Try answering before peeking at the approach.
QHow would your design change if the crew needed to pause for human approval mid-run?
Talk about checkpointing and human in the loop. AutoGen supports human input via UserProxyAgent; LangGraph adds typed checkpointers and interrupts; CrewAI's HITL story is thinner. Frame the answer around what 'pause' actually means: stash transcript, await human verdict, resume.
Red flags & common mistakes
The phrases that signal junior thinking. Click to expand.
Red flags & common mistakes
The phrases that signal junior thinking. Click to expand.
Picking CrewAI because it sounds simpler and then bolting on a hierarchical Process with a manager LLM to fake mid-run task creation. At which point you have rebuilt AutoGen badly.
60 second bullets to scan on the way to the call.
Task-shaped vs transcript-shaped framework lens
How CrewAI Sequential vs Hierarchical Process resolves Tasks
Primary sources. Browse if you want the original framing.
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