Hey everyone, I've been deep in the weeds this week benchmarking role-playing agents for some customer journey simulation work. Specifically, I've been comparing **AutoGen** and **Camel** head-to-head on a concrete task: simulating a series of 10 back-and-forth conversations between a "buyer" agent and a "sales rep" agent.
My primary metric was **execution speed**, because when you're iterating on revenue models or lead-scoring logic, you need quick feedback loops.
Here's the setup and what I found:
**The Task:**
* Create two agents with defined roles and a seed instruction to start a negotiation about a software contract.
* Run 10 full conversational turns between them.
* I used the same OpenAI GPT-4 model configuration for both frameworks to keep it fair.
**The Raw Results:**
* **AutoGen** (using `GroupChat` with a `GroupChatManager`): **~62 seconds**
* **Camel** (using the `RolePlaying` scenario): **~22 seconds**
That's a pretty significant difference for the same core task. Camel was nearly 3x faster in this controlled run.
**My Analysis & Why It Matters for RevOps:**
This speed advantage for Camel likely comes down to architectural simplicity for this specific use case. AutoGen's `GroupChat` is incredibly powerful for complex, multi-agent workflows where you might have a facilitator, dynamic speaker selection, or tool calls—think a simulation with a buyer, sales rep, and a product expert agent. That extra capability adds overhead.
For research where you need high-volume, straightforward agent-to-agent dialogue (like stress-testing sales scripts or qualifying question paths), Camel's lighter-weight approach seems more efficient.
However, for most of my forecasting and pipeline management sims, I need that extra orchestration. So my takeaway is:
* Use **Camel** for rapid, high-volume conversational research on a fixed dyad.
* Use **AutoGen** when your simulation requires more complex agent coordination, human-in-the-loop, or tool integration (like pulling data from a CRM).
Has anyone else run similar comparisons? I'm particularly curious if you've measured performance with different LLM backends or more agents in the mix.
Happy revving
Senior Integration Architect at a mid-market SaaS company in the HR tech space. I run event-driven customer data syncs in production daily, moving payloads from our product to Salesforce, Marketo, and a data warehouse, so I'm deep on orchestration logic and API performance.
Based on my experience evaluating both for workflow automation, here's a concrete breakdown:
1. **Integration and Maintenance Overhead** - AutoGen requires more explicit wiring for production-grade observability. You'll add 40-50 lines of custom logging and error handling per agent group to get parity with our ELK stack. Camel's role-playing scenario is more contained, so the same logging took about 15 lines of wrapper code.
2. **State Management and Cost** - For multi-turn conversations, AutoGen's `GroupChat` can silently re-invoke the LLM for speaker selection after each turn, which increases token usage. In my last shop, we saw a consistent 12-18% higher OpenAI cost on comparable AutoGen flows versus a hand-rolled Camel orchestration, purely from meta-prompt overhead.
3. **Latency Under Load** - Your speed test matches my scaling tests. With 10 concurrent simulated chat loops, Camel held around 22-25 seconds per loop. AutoGen latency scaled linearly to about 90 seconds per loop, as the group chat manager became a contention point. This isn't a flaw, it's architectural: AutoGen is designed for more complex, multi-agent debate.
4. **Production Readiness** - Camel's `RolePlaying` is a specialized tool for a specific pattern (two agents talking). AutoGen is a broader framework; if your use case expands beyond dyadic conversation to include tool-calling, human-in-the-loop, or external data fetches mid-conversation, the initial 3x speed penalty can be worth it. Migrating from Camel to AutoGen later would be a full rewrite.
I'd pick Camel for your described use case of running high-volume, repetitive customer journey simulations where the conversation pattern is fixed. The performance benefit directly improves iteration speed. If your research will later require the sales rep agent to break from the script, query a CRM, or involve a third "support" agent, then AutoGen is the better long-term bet. To decide, tell us: do you need to call external APIs during the agent dialogue, and is this simulation purely for internal analysis or will it evolve into a customer-facing feature?
Interesting point about the logging overhead. I hadn't considered that, since I usually just watch the agent console output for my small experiments. Your cost note on AutoGen's speaker selection makes sense too - I saw some extra tokens in my logs but hadn't quantified it.
Do you think that 12-18% cost delta is consistent across different role definitions? Like if you used a more complex "product manager" vs "engineer" scenario instead of buyer/seller?