Looking at enterprise agent systems. Need something that actually works without costing a fortune.
BabyAGI is open source, which is good. But is it actually stable for real delegation tasks? Autogen is from Microsoft, so probably expensive and locked down. Heard both can get complex fast.
Who has real experience with either for basic agent workflows? Need to know actual setup time, hidden API costs, and if the "enterprise" features are just bloat.
I'm a principal engineer at a mid-sized logistics platform (300-500 person org) where we run mixed Python/Go stacks, and we've been running experimental agent systems for dynamic pricing and routing since last year. We've deployed both BabyAGI and Autogen in test environments to evaluate them for automating our exception handling workflows.
Core comparison:
1. **Initial setup to a working agent loop**: BabyAGI you can get a basic sequential task agent running in about 30-45 minutes; it's just a Python script with a vector DB (we used Chroma). Autogen required closer to 4 hours to configure the `GroupChatManager` and get the agents talking reliably without infinite loops, due to its heavier abstraction layer.
2. **Hidden API cost structure**: The primary cost is LLM calls. BabyAGI's default execution loop is naive and can hammer the LLM with re-planning steps, leading to runaway costs if you don't implement a strict iteration cap. In a test, a simple 5-task job used 38 LLM calls. Autogen's group chat is more structured but uses multiple agent roles (UserProxy, Assistant, etc.) that each generate calls. For the same job, Autogen used 22 calls but each had longer context (more tokens). Net cost was within 10-15% for us, with Autogen slightly cheaper on GPT-4 due to fewer, more consolidated turns.
3. **Where it breaks / latency**: BabyAGI's `TaskCreationChain` becomes a bottleneck when the task queue exceeds 10-15 items; we saw latency spikes from 2s to 12s average response time. Autogen's breakdown is conversational deadlock; agents can get stuck in "I defer to you" loops, requiring custom termination conditions. We had to add a watchdog timer.
4. **Enterprise integration effort**: Autogen has baked-in support for code execution, function calling, and human-in-the-loop interrupts which took a day to integrate with our internal API. BabyAGI required us to write that orchestration layer ourselves, which added roughly 3-4 days of development. However, BabyAGI's simplicity meant our custom code was easier to debug and own.
My pick for basic agent workflows is BabyAGI if your team has Python maturity and you need something transparent and controllable within a week. For a use case requiring built-in safety guards, multi-agent debate, and official support, Autogen is the choice. To make the call clean, tell us the average number of delegation steps per workflow and whether you have in-house LLM ops to manage cost explosions.
--perf