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I’ve been tinkering with AI frameworks to build a newsroom that delivers consistent, high-quality output, and my latest shift to LangGraph has been a game-changer.
When I first started this project, I was using CrewAI to set up an AI-driven newsroom. It was exciting to see the initial setup come together—agents working as editors, writers, and researchers, churning out content like a well-oiled machine. We had discussed the differences between frameworks like CrewAI, AutoGen, and LangGraph, weighing their strengths and weaknesses previously. CrewAI was great for quickly testing ideas, and I loved how fast I could prototype. But as we moved toward production, I hit a wall. The output wasn’t always consistent—sometimes the formatting was off, or the structure didn’t quite meet the standards I needed. That’s when I decided to pivot to LangGraph, hoping for more control and reliability.
Why LangGraph Feels Like the Right Fit
Switching to LangGraph wasn’t about reinventing the wheel; it was about refining it. The biggest draw for me was the control it offered. With CrewAI, I sometimes felt like I was herding cats—agents would produce content, but the final output could vary in quality or format. LangGraph, on the other hand, gave me a way to define clear workflows through nodes and edges. Each agent (or node) receives a specific instruction, and the edges map out exactly how they pass work to one another.
Another perk is the consistent formatting. In my CrewAI setup, I’d occasionally get outputs that didn’t align with the structure I wanted—think mismatched headings or uneven tone across articles. With LangGraph, I set up an editor agent to handle the final output, ensuring everything follows a standard format. The result? Articles that look polished and professional every time. The output quality feels more stable, which saves me from endless tweaking.
How the Newsroom Looks Now
The structure of my AI newsroom hasn’t changed much from the CrewAI version, which made the transition smoother than I expected. Instead of crews and tasks, I’m now working with nodes and edges in LangGraph. Each node is an agent with a clear job—researching, drafting, or editing—and the edges define the workflow, like a relay race where the baton passes smoothly from one runner to the next. Setting it up was straightforward, and honestly, LangGraph feels just as approachable as CrewAI for implementation.
One of my favorite improvements is how the editor agent now oversees the final output. Now, the editor agent ensures everything fits a standard template, which has been a huge relief. That said, there are still a few minor tweaks on my to-do list, like ensuring the day of the week is correct and verifying the sources for each article. It’s not perfect yet—there’s still some fine-tuning to do—but the consistency is night-and-day compared to before.
And you can observe there are certain minor fix are waiting for me, for example the date of week, and the accurate source of each articles.
You can check more details here: GitHub Repo
A Quick Shoutout to UV
On a bit of a tangent, I have to mention how much I’ve loved using UV for managing my project’s environment. It’s a package management tool that’s made my life so much easier. With UV, I just pick a Python version, initialize the project, add the packages I need, and run my script with a simple “uv run” command. It handles almost everything else, from dependencies to virtual environments. It’s the kind of tool that makes you wonder how you ever got by without it.
Wrapping It Up
Switching to LangGraph has been a small but meaningful step toward building a more reliable AI newsroom. It’s given me the control and consistency I was craving, without overcomplicating the process. If you’re wrestling with inconsistent outputs in your own projects, it might be worth giving LangGraph a try.
