Looking at deploying AI agents in production. Benchmarks I've seen are mostly marketing fluff. Need tools that handle real load, have proper observability, and don't break the bank.
AgentGPT gets discussed here. For actual production, what's the stack?
* Throughput under sustained concurrent sessions?
* Latency p99 for complex agent chains?
* Cost per 1k operations with real-world token usage?
* Kubernetes operator or just a Docker container?
My shortlist for evaluation includes AutoGen, LangChain, and maybe CrewAI. Where does AgentGPT fit? Show me the numbers or the deployment YAML, not the hype.
Good questions. I've stress-tested AgentGPT's open-source framework for prototyping, but I'd be cautious about putting it into production as-is.
For your throughput and latency asks, the bigger bottleneck will be your underlying LLM API calls, not the AgentGPT orchestration layer itself. The framework doesn't include built-in connection pooling or advanced queuing for concurrent sessions. You'd need to add that.
On cost, you can calculate the framework's overhead: it's mostly the extra "agent" prompting tokens it injects between LLM calls. I've measured an extra 8-12% token usage on medium-complexity chains versus a barebones script. That adds up fast at scale. For deployment, it's just a Docker container; no Kubernetes operator or built-in observability beyond basic logs.
If LangChain and AutoGen are on your shortlist, AgentGPT sits a tier below them for production readiness. It's great for fast conceptual demos, but you'll be building the scaling and monitoring pieces yourself.