12 Must-Have GenAI Products for Faster LLM Development

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:

1. Built-in modules for chains, agents, and memory management.
2. Compatibility with multiple LLM providers and plugins.
3. 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:

1. Tools for evaluating and iterating on LLM performance.
2. Advanced scaling solutions for handling increased workloads.
3. 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:

1. A suite of tools for creating and managing AI agents.
2. Compatibility with popular APIs and external services.
3. 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:

1. Multi-agent architecture for collaborative problem-solving.
2. Advanced role assignments for agents with specialized functions.
3. 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:

1. Large-scale prompt evaluation and optimization.
2. Workflow orchestration for model testing and refinement.
3. 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:

1. Workflow management tools for defining agent interactions.
2. Extensive integration options for external tools and services.
3. 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:

1. Intuitive interface for defining function calls directly in workflows.
2. Pre-built adapters for popular APIs and databases.
3. 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:

1. Automatically defines and implements necessary tools and functions based on sample prompts.
2. Refines LLM responses through real-world feedback loops.
3. 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:

1. Supports agentic frameworks like LangChain and AutoGen for building adaptive workflows.
2. Offers a library of pre-configured tools for common use cases like CRM, productivity, and software development.
3. 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:

1. Declarative API interaction for seamless function execution.
2. Built-in error handling and retry mechanisms.
3. 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:

1. Headless browsing for faster and more efficient automation.
2. Integration with LLM frameworks to interpret and respond to web data.
3. 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:

1. Seamless integration with diverse data sources, including cloud databases and APIs.
2. Tools for efficient data cleaning and feature extraction tailored to LLM input requirements.
3. 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.