Hey everyone! 👋 I've been knee-deep in agent frameworks for the last few months, building out some internal automation tools for our marketing and sales ops. Our team (five Python devs) is now evaluating a bigger project: building a set of multi-agent workflows to handle everything from competitive analysis to personalized outreach sequences.
The big question on our table right now is whether to build on **AutoGen** or **CrewAI**. We've built prototypes with both, and I wanted to share our early impressions and see if anyone else has gone down this path.
Our core needs are pretty specific:
* **Orchestration:** Clear, manageable control over a conversation flow between specialized agents (Researcher, Writer, Analyst, Approver).
* **State Management:** Keeping context across multiple steps and human-in-the-loop interventions.
* **Tool Use:** Reliable, extensible ability for agents to call our internal APIs and external services (like our CRM and analytics platforms).
* **Dev Experience:** Good documentation and patterns for a small team to iterate quickly without magic-box abstractions.
Here’s a quick breakdown of our hands-on experience so far:
**AutoGen Pros:**
* The **conversation patterns** (like `GroupChat` and `GroupChatManager`) are incredibly flexible. You can model almost any interaction flow you can think of.
* **Human-in-the-loop** is first-class and easy to implement, which is crucial for our approval steps.
* The **code-centric approach** feels very natural to our developers. It's like writing any other async Python service but with agents.
* It's **provider-agnostic**. Swapping between OpenAI, Anthropic, or local models was straightforward.
**AutoGen Cons / Quirks:**
* **Tool definition** can feel a bit verbose at times. Decorators are powerful, but there's a bit of boilerplate.
* **State persistence** out-of-the-box requires a bit more manual wiring for our use case (we want to resume sessions).
* The sheer flexibility means you sometimes have to build more scaffolding yourself to get a production-ready workflow.
**CrewAI Pros:**
* The **task-driven metaphor** (`Task` and `Crew`) is intuitive and maps well to business processes. It feels higher-level.
* **Built-in concepts** like expected outputs, async execution, and agent traits speed up initial development.
* The **tool ecosystem** is growing nicely, and linking a function to an agent feels very declarative.
**CrewAI Cons / Quirks:**
* **Less granular control** over the exact flow of messages between agents compared to AutoGen's group chats.
* While easier to start, some of our more **complex routing logic** (where Agent A needs to consult both B and C based on B's output) felt harder to model.
* We hit a few more **abstraction leaks** early on, where we needed to understand the underlying framework choices to debug an issue.
**Our Current Leaning:**
For our team, which is comfortable writing more code for greater control, **AutoGen is currently ahead**. The flexibility to design the exact conversation topology and integrate human oversight seamlessly is a big win. It feels like we're "programming" the collaboration, not just configuring it.
However, for faster prototyping of simpler, linear workflows, **CrewAI was undeniably quicker** to get a working prototype.
I'd love to hear from other teams who have made a choice:
* For those who picked **AutoGen** for production, how did you handle **state management and persistence** across long-running workflows?
* For **CrewAI** adopters, have you hit limitations with **complex, conditional agent routing**, and how did you solve it?
* Are there any **critical integrations** (especially with CRM or analytics tools) where one framework has a clear advantage?
We're planning to make a decision in the next week or two, so any real-world war stories or "I wish I'd known" insights would be massively appreciated!
Clean data, happy life.