When you look at the GenAI ecosystem right now, there are dozens of orchestration frameworks popping up every month. LangChain, CrewAI, AutoGen, you name it. They all have the same promise: spin up intelligent agents, chain together reasoning, call external tools, and you've got a working "AI application." And honestly, they're great at what they set out to do — fast prototyping. If you're a developer or a startup hacking on something new, LangChain will get you from zero to demo in a weekend.
But that's not the problem we set out to solve. Pyrana was built for enterprises. And enterprises don't just need demos — they need systems that are production-grade, observable, governed, and trusted. That's where the gap lies.
The Enterprise Wall
Here's the pattern we've seen over and over: a team builds a prototype with LangChain or CrewAI, gets some cool results, and then tries to scale it to production. Almost immediately they hit the wall.
- Stability and scale: Distributed workloads, long-running jobs, retries, fault tolerance. LangChain doesn't come with production-hardened orchestration; you're left bolting it on yourself.
- Governance and permissions: Enterprises need audit logs, RBAC, throttling, encryption, compliance frameworks. These aren't "nice to haves" — they're mandatory.
- Observability: In production, you need to know why something failed, where it failed, and how often it fails. LangChain has some tracing integrations, but at enterprise scale, you need full observability pipelines.
- Integration debt: Real enterprise workflows require deep integration with legacy systems, databases, ERP platforms, CRMs, and compliance pipelines. Plug-and-play chains won't cut it.
These aren't unsolved problems. They're just different problems than what LangChain and CrewAI were built for. Which means if you try to go to production with them, you end up reinventing infrastructure anyway.
Why We Rebuilt It
Pyrana isn't a "wrapper" on top of LLMs. It's an orchestration layer built from day one with enterprise DNA.
- Resilient orchestration: We treat AI workflows like any other distributed system — retries, checkpoints, async task management, and safe rollbacks.
- Governance built in: RBAC, compliance hooks, throttling, and enterprise authentication aren't add-ons, they're part of the core.
- Observability as first-class: Every task, every step, every agent call is tracked, logged, and available for real-time monitoring.
- Scalable by design: Whether it's one decision agent running for a BU or thousands running across an enterprise, the system is designed to handle load without breaking.
That's the gap: it's not that LangChain is "bad" — it's just solving a different problem. For enterprises, the orchestration layer isn't about prototyping quickly. It's about running reliably.
The Counterpoint
You could argue that starting with LangChain is still worth it for speed, and you'd be right — for early R&D, it's a fantastic playground. But once you move beyond the sandbox, the cost of retrofitting governance, observability, and scale is enormous.
That's why we didn't build on LangChain. We built Pyrana. Because the last thing an enterprise wants is to be running its core AI infrastructure on a framework that was never designed for production in the first place.