I’ve been looking into different options for building AI agent systems lately. OpenAI just launched their Agent SDK and I keep hearing about LangGraph too. Both tools seem designed for coordinating multiple AI agents but I’m not sure which one to pick.
I’m mainly wondering about a few things. First, which one is easier to work with day to day? I need something that won’t break in production. Second, can they handle complicated setups with lots of agents talking to each other? Third, how well do they play with other tools I’m already using like existing APIs or different AI models?
I also care about being able to customize things when needed and want to know if one costs way more than the other to run. Has anyone here tried both of these? What did you think? Are there other similar tools I should be looking at instead?
Been running agent systems in production for a couple years now and both tools have their sweet spots.
Your choice really depends on your team’s experience level. OpenAI’s SDK feels familiar if you’ve worked with their API before, but LangGraph gives you way more control over orchestration.
Reliability is interesting. I’ve had fewer mysterious failures with LangGraph because you can debug each step in the graph. With OpenAI’s SDK, sometimes things just fail and you’re left guessing why.
For complex multi-agent scenarios, LangGraph wins. We built a system with 6 different agents handling document processing and LangGraph’s state management made it manageable. The visual debugging tools are solid too.
Deployment complexity gets overlooked. OpenAI’s SDK is plug and play, but LangGraph requires more infrastructure thinking upfront.
Cost wise, LangGraph lets you mix cheaper models where it makes sense. We save about 40% by using different models for different tasks in the same workflow.
This tutorial covers the advanced patterns you’ll need:
Just getting started and need something working fast? Go OpenAI. Building something that needs to scale and evolve? Invest the time in LangGraph.
Both have their place, but think about your long-term plans first. I jumped from OpenAI’s SDK to LangGraph after six months because we kept hitting walls with complex agent interactions.
OpenAI’s biggest problem? You can’t see what’s happening when stuff breaks. Their abstraction makes debugging a nightmare, especially with multi-step conversations. LangGraph’s state management and node setup lets you actually see what’s going on at each step.
LangGraph crushes it for external integrations. We’re hooked into three APIs and two custom models - would’ve been hell with OpenAI’s rigid setup. Yeah, there’s a learning curve, but the flexibility is worth it once you move past basic use cases.
Don’t forget about cost. LangGraph lets you pick the right model for each task. OpenAI pushes you toward their models everywhere. We’re saving real money now that we’ve been running it for a few months.
i’ve only used LangGraph, and it took me a bit to figure it out, but it’s pretty good! u can customize it more than u think. just be ready for some complexities, but worth it if u want different options.
I’ve used both for several months now. OpenAI’s Agent SDK is way easier to get started with, especially if you’re already using their models. The docs are cleaner and there’s less of a learning curve. But LangGraph crushes it when you need complex multi-agent workflows - way more flexible. The biggest difference? OpenAI locks you into their ecosystem, while LangGraph plays nice with different models and external APIs. LangGraph’s also cheaper since you’re not stuck with OpenAI’s pricing for everything. Both are solid for production, though LangGraph takes more time to set up properly. Quick deployment and don’t care about vendor lock-in? Go OpenAI. Want maximum flexibility and willing to invest the setup time? LangGraph wins.
