Top AI Frameworks and LLM Libraries in 2025

Everyone's talking about AI, everyone wants to use it, and businesses are racing to integrate it. But if you're thinking about building your AI solution, one of the first big decisions you'll make is choosing the right tools. Specifically, you'll need to decide on an LLM framework and the supporting libraries to make your AI project work as it should.

On the other hand, if you go for the wrong framework, you'll waste time, enjoy unnecessary complexity, or hit a dead end when you scale. So, we're here to break it all down in a simple way. Let’s start with a basic overview of the most popular AI frameworks and libraries.

What Is an LLM Framework?

Before we dive into picking the best one, let's get clear on what a large language model framework actually is. It is a set of tools and components that helps developers create, optimize, and deploy large language models (LLMs). It takes care of important tasks like managing AI models, connecting to data, and keeping responses accurate and relevant.

There are different types of frameworks, and each does a specific job:

• Orchestration frameworks connect AI models, data, and APIs to build things like chatbots and virtual assistants.
• Data integration frameworks make AI understand and use different types of data, such as documents, databases, or web content.
• Search and retrieval frameworks make it easy for AI to find and pull up the right information.

So, an LLM framework is the foundation of an AI app, which lets developers focus on something more amazing than the basics.

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What Is an AI Framework?

Some people confuse the terms LLM and AI, using them interchangeably, but that's not right. An AI framework is also a powerful toolkit that gives a structured environment to create and manage general artificial intelligence models. Just imagine, if you had to build everything from scratch: designing the infrastructure, figuring out the algorithms, and managing endless lines of code, it'd take forever, and you'd probably burn out by the time you got something functional.

AI frameworks help you avoid all these consequences. Here are a few of the best types:

1. Deep learning frameworks train large models, run sophisticated computations, and optimize performance to deliver top-tier results.
2. Reinforcement learning frameworks make your AI model improve over time by rewarding it for successful actions and guiding it toward better decisions.
3. Computer vision frameworks give the tools to analyze images, detect objects, or recognize faces.

In general, you'd like to use an AI framework for several reasons:

1. They speed up development. AI frameworks come packed with pre-built modules and functionalities, so developers can bypass months of manual coding and dive right into the creative process. Instead of building things from scratch, you plug in the parts you need and focus on creating something groundbreaking.
2. They optimize performance. An AI framework handles huge computational tasks without crashing or slowing down.
3. They make AI accessible. You don't need to be an AI expert to create smart applications. These frameworks are user-friendly and allow even juniors to harness the power of artificial intelligence without complicated math or machine learning theory.

How do gen AI frameworks differ from libraries? The difference between frameworks and libraries is a little smudged since both offer tons of helpful features. Frameworks usually give you a more organized, structured, and "built-in" way to create an app, kind of like a blueprint. On the other hand, libraries are like toolboxes full of ready-to-use pieces of reusable code that you can pick up when you need them for specific tasks.

Now, let's compare some of the most popular large language model frameworks and see what makes each one useful.

Comparing LLM Frameworks

Not all frameworks are the same: some focus on flexibility, and others specialize in search, chatbot applications, or information retrieval. We'll break down three of the biggest players often listed among the top LLM frameworks: LlamaIndex, Langchain, and Haystack.

LlamaIndex

LlamaIndex is the friendly middleman that helps your LLMs talk to all the different types of data on the web and outside. Whether it's structured data from SQL databases or unstructured data like PDFs and APIs, LlamaIndex is there to help LLMs make sense of it all.

Why Use It

If your AI application needs to pull insights from many sources of data, LlamaIndex is what you need. It connects the dots between your AI models and the complex data systems you're working with.

When It's Most Useful

If you're tackling projects that need organizing, indexing, querying, and extracting insights from different types of business data and large documents, LlamaIndex will be a nice choice. It bridges the gap between the raw data and the model's understanding, so the process is faster and more accurate.

One popular use case for LlamaIndex is in financial services. Financial institutions often look to pull valuable insights from everywhere like PDFs of financial reports, structured data from SQL databases, and even live stock market APIs. LlamaIndex is one of the best LLM frameworks that lets companies organize and query this data in a way that can go to an AI model to generate financial forecasts or customer insights. 

Key Features:

• organizes and queries both structured data (SQL databases) and unstructured data (PDFs, APIs);
• extracts insights from a wide range of data sources;
• helps LLMs understand and process diverse data types in a unified way;
• can work well for retrieving documents and augmenting knowledge;
• scales well for industries like finance, where you need to analyze both structured and unstructured business data;
• works well with vector databases like FAISS and ChromaDB;
• integrates with LangChain.

Langchain

Langchain is another notable framework for LLM usage. It's very flexible and lets developers connect and orchestrate various modular components of an AI application. Langchain ties everything together: models, data sources, external APIs, and more.

Why Use It

Langchain is ideal for simplifying the development of AI apps, especially if you're creating something interactive or conversational. It's your top choice for chatbots, automation systems, and applications that rely on knowledge retrieval.

When It's Most Useful

A great real-world example of Langchain in action is customer service chatbots. Companies build AI assistants that are multi-functional with LangChain, so they answer customer queries and access external APIs to check product availability and order status and even book appointments for the customer.

Key Features:

• chains multiple LLM calls together;
• integrates with vector databases effectively compared to various Langchain competitors;
• supports tools like OpenAI, Hugging Face, and Pinecone.

Haystack

Haystack is among the best AI frameworks used for LLM search and retrieval. It makes finding and pulling up specific pieces of information from vast amounts of data much faster. Haystack often powers search engines and Q&A systems.

Why Use It

If your AI application needs to sift through large amounts of text data and deliver quick, relevant answers, Haystack has the tools for you. It excels at building systems that search, retrieve, and summarize information on demand, such as making an AI-based semantic search app.

When It's Most Useful

One of the most impressive real-world uses of Haystack is in legal research. For example, a law firm can use Haystack to develop a search engine that scans thousands of legal documents, case studies, and court rulings to allocate the most relevant information for a particular case. With this system, legal teams can find the right documents and summarize key points at the same time, saving hours of manual research.

Key Features:

• fast querying over large data sets;
• dynamic indexing and querying;
• supports full-text and geospatial queries;
• handles millions of documents efficiently.

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5 Popular LLM Libraries

Now that we've covered LLM frameworks, let's talk about libraries. While popular AI frameworks used in large language models help structure your AI project, LLM libraries provide all the building blocks. They're used to train, fine-tune, and deploy AI models. Here are five essential libraries and how they fit into the AI development process.

PyTorch

PyTorch is one of the most popular AI libraries for deep learning. It's an open-source machine learning framework for easy building and training deep neural networks. In essence, it's one of the fundamental pillars hat are used for developing LLMs like BERT and GPT.

Why Use It

PyTorch stands out because it's developer-friendly, highly flexible, and suitable for research and production applications. It lets you build complex models and is appreciated for its dynamic computational graph. When working with LLMs, this means that custom architectures and fine-tuning on specific datasets will be likely required.

When It's Most Useful

PyTorch is perfect for teams looking for a powerful deep-learning library and community support. Moreover, it is applicable when fine-tuning pre-trained LLMs like OpenAI's GPT or Meta's LLaMA on domain-specific tasks like summarizing legal documents, generating text, analyzing sentiment, building AI chatbots, or machine translation.

It's also popular for developing and training deep learning models behind autonomous vehicles. These models process massive amounts of visual and sensor data in real-time to make decisions like detecting pedestrians, recognizing road signs, and avoiding obstacles. Additionally, researchers apply PyTorch for tokenizing text data, training transformer-based architecture, and optimizing models for high-performance inference.

Key Features:

• flexible and on-the-fly graph building;
• dynamic debugging during model training;
• extensive support for neural networks;
• leveraging GPUs and TPUs for efficient LLM training;
• optimized for parallel processing.

Hugging Face (Including Transformers)

Hugging Face gives pre-trained AI models and tools for natural language processing (NLP). The Transformers library, a part of Hugging Face, is particularly powerful with a vast collection of pre-trained models ready for fine-tuning and deployment. Hugging Face also simplifies the process of integrating and using LLMs, including GPT and BERT for creating various solutions including chatbots and content generation apps.

Why Use It

You can take advantage of the state-of-the-art models for many tasks like text generation, translation, and sentiment analysis right out of the box. This visibly cuts down the necessary time and effort for training the models from scratch when it comes to tasks like creating personalized customer interactions.

When It's Most Useful

Hugging Face is perfect for teams that want to train custom LLMs and deploy AI solutions quickly with minimal effort. Its tools answer customer queries, give recommendations, and even resolve issues through automated conversations. It's applicable for niche apps too, say, for fine-tuning existing models on specific datasets for cases like medical report summarization.

Key Features:

• supports hundreds of pre-trained models;
• is compatible with TensorFlow, PyTorch, and JAX, simplifying integration;
• Hugging Face Hub provides easy model sharing with users, including fine-tuned LLMs.

TensorFlow

TensorFlow is a robust and highly scalable open-source AI library developed by Google. It's best for building and deploying large-scale deep learning models, capable of handling complex neural networks and large datasets. It's a good choice for those looking for scalable LLM solutions, including those dealing with translation, content creation, and chatbots.

Why Use It

TensorFlow is a go-to choice for training deep learning models, running inference, or deploying machine learning solutions in production environments. It's flexible, supports a variety of hardware accelerators, and integrates well with cloud computing platforms.

When It's Most Useful

TensorFlow is ideal for teams working on high-performance, large-scale AI applications that handle massive datasets, train complex models, and achieve high throughput. If you're building models for real-time applications or scaling up machine learning pipelines in industries like healthcare, finance, or e-commerce, TensorFlow should be a key player in your toolkit. It's a particularly useful library for apps that require rapid inference times, which could be the case for virtual assistants that need to process user queries and generate real-time responses.

Key Features:

• supports end-to-end machine learning workflows;
• helps optimize the entire LLM pipeline's performance;
• works on CPUs, GPUs, and mobile devices, making integration simpler in various environments;
• simplified high-level API for neural networks, aiding faster prototyping and LLM architecture iteration.

Keras

Keras is an open-source deep-learning library with a high-level interface for building neural networks, and it runs on top of TensorFlow. It gives you a simplified, user-friendly interface to design and experiment with deep learning models.

Why Use It

Keras has an intuitive, high-level API that makes it much easier to prototype, build, and test neural networks. Since Keras runs on top of TensorFlow, it inherits all the performance benefits of TensorFlow, but with a much more accessible interface. It's a popular library applied for large language model development, letting people make prototypes quicker and iterate complex architectures.

When It's Most Useful

Keras is best suited for beginners, researchers, or teams that want to prototype and experiment with deep learning models without getting bogged down by low-level technical complexity. It's also great if you need to build and test models before moving on to more complex implementations. It can be handy if you're fine-tuning pre-trained models on certain datasets that speed up the creation of various apps like content generation, virtual assistants, chatbots, or summarization tools.

Key Features:

• has a user-friendly design that makes it easy for developers to use and extend the library;
• Good for experimenting with different LLM architectures and configurations;
• works with TensorFlow backend;
• access to many pre-trained models.

vLLM

vLLM is a library that optimizes how LLMs handle data, improving performance without using up too much memory. This can be a valuable tool if you're working with large-scale AI models which often require substantial memory resources. This library may conserve system resources and boost the interference efficiency and serving processes.

Why Use It

It's great for handling larger models without your system slowing down and LLM inference with high throughput. If you're working with big AI models, vLLM will make them run with maximum resources saved.

It is also capable of optimizing memory usage, which helps LLMs operate better even when there are heavy loads. This can be highly beneficial for apps where latency can hinder the user experience such as solutions requiring quick, real-time processing.

When It's Most Useful

vLLM is perfect for scaling LLMs in production environments where speed is the top priority. For example, if you're running a service that gives real-time AI responses or manages large-scale data processing, vLLM will make your models perform at their best without crashing or lagging. Likewise, it can simultaneously handle multiple requests without making performance worse. This can be a good solution for tools like automated customer support, where quick response times are crucial.

Key Features:

• uses an innovative memory allocation algorithm, PagedAttention, for optimized GPU memory usage, it can efficiently manage attention keys and values efficiently, which brings memory fragmentation to the minimum and throughput to the maximum;
• can serve LLMs like LLaMA, Falcon, and Mistral effectively and without worries about performance bottlenecks;
• supports OpenAI-compatible APIs for easy integration.

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How to Choose the Right Option Among the Top AI Frameworks and Libraries

Making up your mind regarding which tools to add to your stack is tricky, especially if you're new to AI. Feel free to refer to these tables as your cheat sheet whenever you're stuck in your decision-making process.

How to Choose from the Generative AI Frameworks

Giving a brief overview, mind these main points when considering frameworks:

• LlamaIndex is great if your project needs to handle diverse data sources and ensure your LLM can query and process that data, it's also a good choice among AI chatbot frameworks.
• Langchain shines when you're building dynamic, interactive applications that need a high level of flexibility.
• Haystack is your best choice when your AI needs to perform advanced search and retrieval tasks.

How to Choose an LLM Library

There are many options as you consider various AI tools and frameworks, and it's often tough to choose the best tool for your project. So, first, think about these key factors: 

• Project goals. Choose based on your use case, like Langchain for building apps, Hugging Face for NLP, TensorFlow for big-scale AI, etc.
• Scalability. Need to handle large amounts of data? Go for TensorFlow or vLLM.
• Ease of use. If you're new to AI, Keras and Hugging Face are more user-friendly. Advanced developers may prefer PyTorch for flexibility.
• Performance. vLLM is optimized for efficiency, while TensorFlow is perfect for large-scale applications.
• Community support. Libraries like TensorFlow and PyTorch have vast communities, making it easier to find resources and support.

Why Build from Scratch? Explore LLM Frameworks

LLM frameworks like LlamaIndex, Langchain, and Haystack structure and optimize AI applications. Meanwhile, LLM libraries like PyTorch, Hugging Face, TensorFlow, Keras, and vLLM give the core tools for training and running AI models.

However, no single option is the best because it all depends on your needs. So, take some time to learn more about what's available for you and how it fits your project.

Still stuck? At Upsilon, we use the latest and most tested generative AI frameworks and libraries in our projects, so we'll be glad to offer you our gen AI development services to assist in bringing your project to life. Feel free to reach out, and we'll pick the optimal technology stack for your case!