Why We Don't Use LangChain — And You Shouldn't Either

“Can we use LangChain with CastleGuard?”

We get this question a lot—from customers who’ve seen a flashy demo, read a Medium article, or experimented with LangChain in a hackathon. At first glance, it looks like a tempting shortcut: plug-and-play agents, built-in vector search, ready-made pipelines.

But when one of our defense clients tried using LangChain inside their secure CastleGuard deployment, everything broke.

No logs. No observability. No safety layer. The LLM was being called in unsupported ways, bypassing the very controls that make CastleGuard safe and compliant.

That’s when we realized: LangChain isn't just risky—it's incompatible with how secure AI should be deployed.

Here’s why we chose to build on our own secure API instead—and why LangChain doesn’t belong in any production-grade system that values control, observability, and long-term reliability.

🔒 No Observability. No Accountability.

LangChain is a black box. When developers use LangChain to orchestrate prompts, tools, or agents inside CastleGuard, we lose visibility into how the model is being called. That means:

• ❌ No metrics

• ❌ No logs

• ❌ No tracing

Our safeguards, rate limits, and usage analytics—compliance-critical in government and defense—are completely bypassed. In mission-critical deployments, that’s not a minor oversight. It’s a governance failure.

🧨 A Breaking-Change Machine

LangChain evolves quickly. Too quickly. What worked yesterday may not work tomorrow.

Its frequent updates often introduce breaking changes to core components—undocumented, unannounced, and incompatible with earlier versions. That means:

• Engineering teams waste time chasing bugs

• You’re forced to test every new release

• You spend more time fixing workflows than improving them

For secure deployments with strict SLAs and validation requirements, this is unsustainable.

🧠 Workflow > Model

LangChain is LLM-centric. It assumes your entire stack revolves around a model. But CastleGuard customers don’t think in terms of prompts—they think in terms of outcomes:

• Translate this policy document into French (format intact)

• Transcribe this exit interview and summarize it

• Extract insights from this procurement guide

• Enforce role-based access on a sensitive document

These workflows require translation, transcription, document Q&A, metadata handling, and policy enforcement—all supported by CastleGuard’s API, not by LangChain.

🧩 You’ll End Up Using Our API Anyway

LangChain doesn’t replace your backend. It simply adds another layer of abstraction—and risk. You still need to call the CastleGuard API for:

• Secure authentication and user identity

• Role-based access control (RBAC)

• Logging, tracing, and audit reports

• Specialized models like Evia for French-Canadian translation

So why not just use the API from the start?

🧼 Why Our API Wins

By building directly on the CastleGuard API, our customers gain:

• ✅ Total observability (metrics, logs, usage reporting)

• ✅ Full control over execution logic, versions, and tools

• ✅ Modular, scalable components tailored for secure environments

• ✅ Consistency across workflows—from translation to Q&A to coding support

And best of all: it just works. No breaking updates, no unsafe shortcuts.

Final Word

LangChain may be fine for rapid prototyping or research demos. But for regulated, mission-critical environments—like those powered by CastleGuard—it’s a liability.

If you need to deploy secure, productive, and compliant AI inside an air-gapped or on-premise environment, start with the right foundation. Use a platform built for your realities.

We don’t use LangChain—and we strongly recommend you don’t either.