Blog Posts

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Announcing: Native Support for LangChain
Build production-ready AI agents faster with Gentoro’s native LangChain support—no more glue code, flaky APIs, or auth headaches.
What Are Agentic AI Tools?
Agentic tools let AI act beyond text generation, using inferred invocation to interact with real-world applications. Learn how MCP simplifies AI-tool connections.
LangChain: From Chains to Threads
LangChain made AI development easier, but as applications evolve, its limitations are showing. Explore what’s next for AI frameworks beyond chain-based models.
Vibe Coding: The New Way We Create and Interact with Technology
Vibe coding, powered by generative AI, is redefining software creation and interaction. Learn how this paradigm shift is transforming development and user experience.
Rethinking LangChain in the Agentic AI World
LangChain is powerful but manually intensive. What if agentic AI handled the complexity? Explore how directive programming could redefine AI development.
Introducing Model Context Protocol (MCP) Support for Gentoro
Discover how Gentoro’s support for Model Context Protocol (MCP) simplifies AI tool integration, enabling smarter workflows with Claude Desktop and more.
LLM Function-Calling vs. Model Context Protocol (MCP)
Explore how function-calling and MCP revolutionize enterprise workflows by simplifying LLM usage and showcasing their unique roles in development.
Using MCP Server to Integrate LLMs into Your Systems
Learn how MCP servers streamline enterprise LLM development, overcome framework hurdles, and power scalable, efficient generative AI applications with ease.
LLM Function-Calling Performance: API- vs User-Aligned
Discover how API-aligned and user-aligned function designs influence LLM performance, optimizing outcomes in function-calling tasks.
Building Bridges: Connecting LLMs with Enterprise Systems
Uncover the technical hurdles developers encounter when connecting LLMs with enterprise systems. From API design to security, this blog addresses it all.
Contextual Function-Calling: Reducing Hidden Costs in LLM Function-Calling Systems
Understand the hidden token costs of OpenAI's function-calling feature and how Contextual Function-Calling can reduce expenses in LLM applications.
Simplifying Data Extraction with OpenAI JSON Mode and Schemas
Discover how to tackle LLM output formatting challenges with JSON mode and DTOs, ensuring more reliable ChatGPT responses for application development.
Why Function-Calling GenAI Must Be Built by AI, Not Manually Coded
Learn why AI should build function-calling systems dynamically instead of manual coding, and how to future-proof these systems against LLM updates and changes.
User-Aligned Functions to Improve LLM-to-API Function-Calling Accuracy
Explore function-calling in LLMs, its challenges in API integration, and how User-Aligned Functions can bridge the gap between user requests and system APIs.
Charting a New Path: Announcing Early Access of Gentoro, LLM to Enterprise Bridge
Introducing Gentoro, a middleware solution that enables AI agents to securely interact with enterprise systems while simplifying development and ensuring compliance.
Function-based RAG: Extending LLMs Beyond Static Knowledge Bases
Learn how Retrieval-Augmented Generation (RAG) enhances LLMs by connecting them to external data sources, enabling real-time data access and improved responses.

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