DEV Community: Colleen Harig

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Colleen Harig — Wed, 27 Aug 2025 21:05:28 +0000

Shani Shoham

Aug 27 '25

GPU Container Checkpoint/Restore with CRIUgpu: Zero-Downtime Live Migration for ML Workloads

#gpu #machinelearning #nvidia

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5 min read

Announcing: Native Support for LangChain

Colleen Harig — Thu, 03 Apr 2025 15:42:00 +0000

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:

An AI agent monitors a Slack channel for incident reports
It fetches the most recent runbook and matches the report to a known scenario
It pulls real-time metrics from Grafana
It creates a JIRA ticket with the correct priority and context
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.

12 Must-Have GenAI Products for Faster LLM Development

Colleen Harig — Tue, 18 Feb 2025 16:20:00 +0000

Large language models (LLMs) are transforming how we build intelligent applications, enabling capabilities like real-time automation, data-driven insights, and dynamic interactions. One of the most powerful advancements in this space is function calling, which allows LLMs to interface with external systems, APIs, and platforms, making workflows more dynamic and flexible. However, implementing function calling effectively often requires specialized frameworks and libraries.

We’ve rounded up 12 GenAI frameworks, platforms, and tools, with a focus on how they support function calling to enhance AI-powered solutions. These tools help developers streamline workflows, integrate external systems, and scale AI capabilities with ease. Whether you’re building a chatbot, automating complex processes, or creating intelligent assistants, these libraries provide the functionality you need.

Let’s take a closer look at the tools leading the way in LLM and function-calling innovation.

GenAI Frameworks

LangChain

LangChain is a popular framework for constructing LLM-powered workflows that require sequential and structured processes. It allows developers to create “chains” that link multiple tasks, such as data retrieval, prompt refinement, and response generation. Its modular architecture makes it a preferred choice for building complex applications with minimal coding effort.

Key Features:

Built-in modules for chains, agents, and memory management.
Compatibility with multiple LLM providers and plugins.
Extensive support for data retrieval and embedding-based searches.

Use Cases: Perfect for building applications like intelligent chatbots, automated report generators, and context-aware assistants.

LlamaIndex

LlamaIndex serves as an all-in-one platform for developers aiming to scale and optimize their LLM applications. It simplifies the process of model fine-tuning, deployment, and monitoring, making it especially useful for production-grade solutions. With robust tools for evaluation and testing, LlamaIndex ensures high-quality results while minimizing time-to-market.

Key Features:

Tools for evaluating and iterating on LLM performance.
Advanced scaling solutions for handling increased workloads.
Utilities for quick deployment and inference optimization.

**Use Cases: **Ideal for enterprises building large-scale LLM-driven solutions that require reliable performance in production environments.

‍CrewAI

CrewAI delivers a comprehensive toolkit for developing, managing, and deploying AI agents capable of sophisticated tasks. It provides developers with an intuitive framework for creating agents that integrate with multiple platforms, APIs, and workflows. With advanced monitoring and debugging tools, CrewAI ensures robust and scalable AI solutions.

Key Features:

A suite of tools for creating and managing AI agents.
Compatibility with popular APIs and external services.
Real-time monitoring and debugging capabilities.

**Use Cases: **Best for developers building scalable, production-ready AI agents for customer service, automation, and data analysis.

‍AutoGen

AutoGen introduces a groundbreaking approach to AI application development through its multi-agent conversation framework. By enabling LLMs to work collaboratively as autonomous agents, AutoGen unlocks new possibilities for solving complex tasks. Each agent can specialize in a different aspect of the workflow, resulting in faster and more accurate outputs.

Key Features:

Multi-agent architecture for collaborative problem-solving.
Advanced role assignments for agents with specialized functions.
Flexible integration with external APIs and data sources.

Use Cases: Ideal for building AI-powered assistants, research tools, and collaborative task-solving systems.

GenAI Frameworks with UIs‍

Vellum AI

Vellum AI focuses on the operational lifecycle of LLM products, from prompt design to post-deployment monitoring. It provides developers with tools to compare prompts at scale, refine responses, and maintain model performance over time. With its emphasis on workflow orchestration, Vellum AI is a valuable resource for teams managing multiple LLM-driven projects.

Key Features:

Large-scale prompt evaluation and optimization.
Workflow orchestration for model testing and refinement.
Deployment tools for maintaining consistent model outputs.

Use Cases: Suitable for developers and teams looking to optimize, deploy, and monitor AI-driven products in dynamic environments.

‍LangGraph

LangGraph enables developers to create agentic workflows, where LLMs act as intermediaries to manage and execute tasks. With its emphasis on structured workflows and external tool integration, LangGraph is a powerful addition to the LLM ecosystem, catering to diverse application needs.

Key Features:

Workflow management tools for defining agent interactions.
Extensive integration options for external tools and services.
Support for multi-agent coordination and execution.

Use Cases: Ideal for building LLM-driven applications that require orchestrated agent interactions and structured workflows.

Tool Building Platforms

‍Toolhouse

Toolhouse is an innovative library designed to make function calling with LLMs effortless and highly scalable. By focusing on modularity and ease of use, Toolhouse allows developers to define and execute external functions seamlessly, making it an ideal choice for projects that require dynamic interactions with APIs or external systems.

Key Features:

Intuitive interface for defining function calls directly in workflows.
Pre-built adapters for popular APIs and databases.
Real-time debugging tools for troubleshooting function execution.

Use Cases: Perfect for developers building intelligent assistants, workflow automation tools, or data aggregation systems that rely on smooth function execution.

Gentoro

Gentoro stands out as a next-generation LLM tool library aimed at simplifying enterprise-grade applications’ development. Leveraging its prompt-driven approach, Gentoro enables developers to bridge legacy systems with cutting-edge AI solutions. It emphasizes privacy, security, and minimal manual intervention while delivering highly accurate and adaptive responses. With Gentoro, companies can utilize proprietary and external data sources while automating complex backend operations.

Key Features:

Automatically defines and implements necessary tools and functions based on sample prompts.
Refines LLM responses through real-world feedback loops.
Ensures accuracy by detecting inaccuracies, suggesting fixes, and applying them upon approval.

Use Cases: Ideal for enterprises integrating AI into existing systems for operations such as decision support, automation, and real-time data analytics.

Composio

Composio is a comprehensive development platform that brings modularity and flexibility to LLM-powered workflows. Its extensive compatibility with over 150 external tools and services makes it a favorite for developers building AI agents for real-world applications. Whether you’re managing data pipelines or automating customer interactions, Composio simplifies the authentication, orchestration, and deployment processes.

Key Features:

Supports agentic frameworks like LangChain and AutoGen for building adaptive workflows.
Offers a library of pre-configured tools for common use cases like CRM, productivity, and software development.
Provides a unified interface for managing tool integrations and interactions.

Use Cases: Perfect for developers and businesses creating advanced AI agents that interact with diverse ecosystems and automate complex workflows. (Learn more)

Superface

Superface takes function calling to the next level by offering an automated integration framework specifically designed for connecting LLMs with external systems. Its declarative approach allows developers to specify what they need from an API without worrying about the underlying implementation, making integrations faster and more reliable.

Key Features:

Declarative API interaction for seamless function execution.
Built-in error handling and retry mechanisms.
Support for multi-step workflows involving multiple APIs.

**Use Cases: **Ideal for creating applications that require frequent API interactions, such as e-commerce platforms, real-time data analysis tools, and intelligent workflow managers.

Tools

Browserbase

Browserbase enables LLMs to interact with the web in a more intelligent and autonomous manner. It empowers developers to build applications that leverage browsing as part of their workflows, making tasks like web scraping, automated data collection, and online research much easier. With headless browsing capabilities and robust APIs, it’s a go-to tool for AI-driven web interactions.

Key Features:

Headless browsing for faster and more efficient automation.
Integration with LLM frameworks to interpret and respond to web data.
Enhanced controls for dynamic content handling.

Use Cases: Ideal for building AI agents capable of performing online research, tracking competitor data, or extracting insights from the web.

‍Exa

Exa is designed for data-intensive applications that require a robust bridge between LLMs and large-scale data processing. It offers developers a set of tools to optimize data handling, preprocessing, and transformation. By focusing on efficiency and scalability, Exa addresses challenges in utilizing massive datasets with LLM-powered solutions.

Key Features:

Seamless integration with diverse data sources, including cloud databases and APIs.
Tools for efficient data cleaning and feature extraction tailored to LLM input requirements.
High performance for large-scale data queries and operations.

Use Cases: Particularly suited for industries like finance, healthcare, and e-commerce, where large datasets are essential for decision-making and insights.

Function calling has become a cornerstone of advanced LLM development, enabling seamless interactions between AI models and external systems. The libraries on this list are designed to help you harness this capability, providing the tools to integrate, optimize, and scale your applications effectively.

Whether you’re just starting with function calling or looking to enhance your current workflows, these libraries offer the foundation for smarter, more dynamic AI solutions. Explore their features, experiment with their capabilities, and bring your LLM-powered ideas to life.

DEV Community: Colleen Harig

[Boost]

GPU Container Checkpoint/Restore with CRIUgpu: Zero-Downtime Live Migration for ML Workloads

Announcing: Native Support for LangChain

Why native support for LangChain?

What’s included in Gentoro’s LangChain support?

What are Bridges, Tools, and Services?

Use Case: AI Agent for Production Support

Why this matters

Get started

12 Must-Have GenAI Products for Faster LLM Development

GenAI Frameworks

LangChain

LlamaIndex

‍CrewAI

‍AutoGen

GenAI Frameworks with UIs‍

Vellum AI

‍LangGraph

Tool Building Platforms

‍Toolhouse

Gentoro

Composio

Superface

Tools

Browserbase

‍Exa

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Using MCP Server to Integrate LLMs into Your Systems

Patrick Chan for Gentoro ・ Feb 13