Skip to content
All solutions

LangChain in-house or an agent platform? A guide for regulated enterprises

LangChain is an open-source framework: it helps engineers write agent code. Used on its own, everything around that code is yours to build and run, and a regulated enterprise needs a lot around it: deployment inside your perimeter, an audit trail, approvals, budgets, personal data checks and control over models. aixplain provides those as one OS, in our cloud or on your own servers, while your engineers keep writing agents in code with Koder and the Agents SDK.

What you build around a framework

  • Hosting inside your perimeter: on-prem, in your VPC or air-gapped
  • A record of every run that your auditors can follow
  • Approvals before agents send, spend or connect
  • Budgets, rate limits and an off switch
  • Checks for personal data, jailbreaks and unsafe content
  • Model choice, and switching models without rewrites

What the aixplain OS gives you

Those pieces come built in and work together. Inspectors check each input and output, every agent has an owner, a budget and an off switch, and every run records who ran it, each tool call, approvals, cost and errors.

Your engineers keep their code

Engineers build agents in their own code with Koder and the Agents SDK, then deploy them on aixplain. Teams outside engineering describe the work, and Omni creates the agent. The same policies apply to every agent, however it was created.

When a framework alone still fits

One team with one use case and no audit requirements can do well with a framework alone. Each agent you add after that needs the same controls, built and kept up by your team.

Common questions

Can we run aixplain inside our own environment?

Yes, with Enterprise: on-prem, in your VPC or air-gapped. Your agents deploy and run there too.

How do we prove what an agent did?

Every run records who ran it, each tool call, every decision or approval, and the cost. Inspectors log what they checked or changed. Every agent has an owner from the start, so your auditors can trace each result to a person.

Which models can we use?

GPT, Gemini, Qwen, Llama, ALLaM or Jais. Switch without rebuilding your agents.

Start with one process

Bring one process and your security questions. We’ll show how it runs in your environment.