LangChain has quickly become the go-to framework for building powerful, multi-step AI agents. Whether you’re constructing decision trees, implementing dynamic workflows, or just wiring up an LLM to call tools—LangChain has made building AI applications much more modular and composable.

But building AI agents that actually work in production? That’s still hard.

Not because the LLM doesn’t know what to do, but because the infrastructure around it—the glue code, the authentication logic, the flaky integrations—is still your problem. And it slows down every LangChain project.

At Gentoro, we’ve been working on a solution to this problem. Today, we’re excited to announce native support for LangChain within Gentoro.

Now LangChain developers can focus entirely on reasoning and workflows—while Gentoro handles all the painful bits behind the scenes.

Why native support for LangChain?

LangChain gave us a common language for building agents: chains, tools, and graphs. But building real-world AI agents still means dealing with brittle APIs, expiring credentials, inconsistent schemas, and the never-ending complexity of enterprise systems.

Gentoro’s mission is to abstract all of that away by enabling developers to:

• Automatically generate tools from custom APIs or services
• Manages the full tool lifecycle from hosting to testing and execution, powered by LLMs
• Provide standardized access to services via MCP
• Dynamically call tools from your LangChain agent using native SDKs
• Add new tools or services without writing integration code
• Securely manage credentials, auth flows, and key rotation

All of this plugs directly into LangChain or LangGraph, so you can stop managing infrastructure and start building value.

What’s included in Gentoro’s LangChain support?

Gentoro’s LangChain support includes both protocol-level compatibility and SDK-level integrations. You can build tools and services using Gentoro’s platform, and call them from LangChain in two ways:

1. MCP (Model Context Protocol) Support‍

MCP is a vendor-neutral communication layer that standardizes how LangChain agents interact with enterprise systems, much like HTTP enables web communication. MCP abstracts away the complexities of authentication, authorization, and data exchange, ensuring secure and efficient interactions.

2. Native SDK Support

For use cases requiring deeper integration, Gentoro provides Python and TypeScript SDKs, allowing developers to customize workflows, enhance data processing, and optimize interactions between LangChain agents and enterprise services.

3. Multi-Language and Framework Support

Whether you’re working in Python or TypeScript, Gentoro offers a flexible integration path, making it easier to deploy AI-powered workflows across a variety of enterprise environments.

What are Bridges, Tools, and Services?

In Gentoro, we use a few key concepts to model how AI agents interact with the outside world:

• Bridges are environments where your tools live. Think of a Bridge as a collection of capabilities your agent can call at runtime.
• Services are the APIs or platforms you’re connecting to—like Slack, JIRA, or Grafana. Gentoro handles the authentication and connection logic for you.
• Tools are the individual functions you expose to your AI agent. These can be generated automatically by Gentoro, written in code, or defined with natural language.

These concepts map directly to how LangChain thinks about agents and tool use. You can create LangGraph nodes that call Gentoro tools dynamically—based on LLM reasoning—or explicitly when your agent knows exactly what to do next.

Use Case: AI Agent for Production Support

One of the most powerful applications of Gentoro with LangChain is building AI agents for production support.

Imagine this:

1. An AI agent monitors a Slack channel for incident reports
2. It fetches the most recent runbook and matches the report to a known scenario
3. It pulls real-time metrics from Grafana
4. It creates a JIRA ticket with the correct priority and context
5. It notifies the right team via Slack with a summary of the issue

All of that is possible today with LangChain and Gentoro—and it takes minutes to configure.

We built this agent live and documented the whole process. The best part? The logic stays clean and readable because Gentoro handles the integrations under the hood.

Why this matters

LangChain made agent workflows easier. Gentoro makes them real.

The next wave of GenAI applications aren’t flashy chatbots—they’re be useful, reliable agents doing real work. Monitoring systems, summarizing dashboards, answering support tickets, triggering automation. These agents need access to real-world tools—and they need to be trusted to operate safely in production.

Gentoro is here to power that layer.

With our LangChain support, you get:

• Speed: Build agents in hours, not weeks
• Stability: Rely on enterprise-grade infrastructure
• Simplicity: No more writing glue code
• Scalability: Add new tools and services without rebuilding anything

Get started

Ready to try Gentoro with your LangChain agent?

• Explore the Gentoro docs
• View the documentation for the Production Support example
• Reach out to us—we’d love to hear what you’re building

Let LangChain define the agent’s logic. Let Gentoro power everything else.