A few years ago, choosing an AI framework often meant comparing TensorFlow, PyTorch and a handful of machine learning libraries. In 2026, that approach is too narrow. An AI project may involve training a custom model, using an existing LLM, building a RAG system, connecting AI to enterprise data or developing an agent that performs actions across other systems.
Each of these scenarios requires a different technology layer. PyTorch, Hugging Face Transformers, LangGraph and LlamaIndex may all appear in the same solution, but they address very different problems. That is why the question “Which AI framework is best?” should be replaced with a more useful one: “Which part of the AI system are we building, and what do we actually need the technology to do?”
Key takeaway: there is no single universal “best AI framework”. scikit-learn, PyTorch, Transformers, LlamaIndex and LangGraph operate at different layers of an AI solution. The choice should start with the use case, data, model strategy and production requirements – not with the popularity of a particular tool.
AI frameworks at a glance
Classical ML → scikit-learn
Deep learning / training → PyTorch, TensorFlow/Keras, JAX
Pretrained models / LLMs / multimodal → Hugging Face Transformers
RAG and enterprise data → LlamaIndex
LLM applications and agents → LangChain
Stateful agent orchestration → LangGraph
Agent SDK / ecosystem → Semantic Kernel, OpenAI Agents SDK, Google ADK
What is an AI framework in 2026?
An AI framework is a set of components, abstractions and tools that simplify the development of a particular part of an AI system. Depending on its purpose, it may support model training, inference, data access, RAG, agent orchestration or integration between a model, tools and enterprise applications.
In practice, the term “AI framework” is now used broadly. Frameworks, libraries and SDKs are often grouped together even though they are not technically the same thing. JAX is a numerical computing library, Transformers focuses on pretrained models, while OpenAI Agents SDK provides components for building agentic applications.
Why does this distinction matter? Comparing PyTorch with LangGraph by asking which one is “better” is similar to comparing a database with a frontend framework. Both may exist in the same architecture, but they solve problems at different layers.
Which AI framework should you use for what?
Instead of ranking AI frameworks, it is more useful to identify the problem each technology is designed to solve.
| Technology | Primary use | When to consider it |
|---|---|---|
| scikit-learn | Classical machine learning. | Classification, regression, clustering and tabular data. |
| PyTorch | Deep learning and model training. | Custom models, experimentation and advanced neural networks. |
| TensorFlow / Keras | Machine learning and deep learning. | Existing TensorFlow environments or teams that want a higher-level Keras API. |
| JAX | High-performance numerical computing. | Research, custom algorithms, JIT, autodiff and accelerator-intensive workloads. |
| Transformers | Working with pretrained models. | LLMs, NLP, vision, audio, multimodal AI, inference and fine-tuning. |
| LangChain | LLM applications and agents. | Integrating models, tools and application components. |
| LangGraph | Agent and workflow orchestration. | Stateful workflows, durable execution and human-in-the-loop processes. |
| LlamaIndex | Data, RAG and knowledge applications. | When AI needs to work with enterprise documents and internal data. |
| Semantic Kernel | Integrating models, plugins and agents. | Enterprise applications, particularly in Microsoft and .NET environments. |
| OpenAI Agents SDK | Building agentic applications. | Tools, handoffs, guardrails, sessions and tracing for agents. |
| Google ADK | Agents and multi-agent systems. | Code-first agents, orchestration, tools, evaluation and deployment. |
Important: this is not a ranking. A more complex system may use several of these technologies at the same time – for example, a model handled through Transformers, a RAG-based data layer and a separate orchestration layer for an AI agent.
PyTorch, TensorFlow, Keras, JAX and scikit-learn – when do you actually need an ML framework?
Traditional machine learning frameworks and libraries still play an important role in AI projects. Not every problem requires an LLM or an agentic system.
scikit-learn – classical machine learning
scikit-learn supports supervised and unsupervised learning and a wide range of traditional ML algorithms. It is well suited to use cases involving tabular data, classification, regression, clustering, preprocessing and model selection.
If a business problem can be solved effectively using a classical statistical or machine learning model, introducing an LLM may only increase cost and architectural complexity.
PyTorch – deep learning and greater control over the model
PyTorch remains relevant for teams building and training deep learning models. Its ecosystem includes distributed training and mechanisms for compiling and optimizing model execution, such as torch.compile.
It is worth considering when the team is genuinely working on model architecture or the training process rather than simply consuming a model through an API.
TensorFlow and Keras – no longer the same decision as a few years ago
TensorFlow remains an end-to-end machine learning platform with an ecosystem that extends into production ML workflows. Keras, however, needs to be considered separately: Keras 3 is a multi-backend API and can run on TensorFlow, JAX and PyTorch backends.
This means Keras should no longer be described simply as an easier layer on top of TensorFlow. The decision may involve both the convenience of the API and the backend the team wants to use.
JAX – when transformations and high-performance computing matter
JAX combines array-oriented numerical computing with automatic differentiation, JIT compilation and vectorization. It provides strong control over high-performance computations, although its programming model may require more specialized expertise.
A useful question before comparing PyTorch, TensorFlow and JAX: do we actually need to train our own model? In many enterprise use cases, using an existing model, introducing RAG or connecting AI to an existing workflow may be the better architectural decision.
Hugging Face Transformers – when the project uses pretrained models
Many modern AI projects do not start with building a model from scratch. Instead, teams use pretrained models and adapt the way they are applied to a specific business problem.
The Hugging Face Transformers library supports pretrained models for text, vision, audio and multimodal tasks. It can be used for inference as well as further training or fine-tuning.
Important distinction: PyTorch primarily provides the underlying model and computation framework, Transformers simplifies work with families of pretrained models, while a model provider API may abstract away both of these layers. These are three different architectural decisions.
LangChain and LangGraph – when building an LLM application or AI agent
When a model needs to use tools, execute multiple steps or operate as part of a larger workflow, a model framework alone is not enough. An application and orchestration layer becomes necessary.
LangChainA higher-level layer for building applications and agents that use models, tools and integrations. It is useful when teams want to compose application components quickly. | LangGraphA lower-level orchestration layer for long-running and stateful agent workflows, including persistence, durable execution and human-in-the-loop patterns. |
For a simple use case, an additional orchestration layer may be unnecessary. However, if an agent executes a multi-step process, needs to retain state, wait for human approval or safely resume an interrupted run, the architecture requires more control over the workflow.
LlamaIndex – when the problem starts with data and RAG
If the main question is “How do we connect the model to our organization’s knowledge and data?”, the technology decision should focus on the data and retrieval layer rather than the LLM alone.
LlamaIndex provides tools for RAG, knowledge applications, data connectors and agents working with external data sources. It can therefore be considered when AI needs to work with enterprise documents, knowledge bases and internal systems.
A framework will not fix a data problem. If the organization lacks clear data ownership, access rules, consistent definitions or reliable sources, those issues should be addressed before selecting RAG components. See also our guide to preparing data for AI.
AI agent frameworks and SDKs: Semantic Kernel, OpenAI Agents SDK and Google ADK
A growing number of projects use a dedicated layer for building AI agents. Such frameworks and SDKs may cover not only the agent loop itself, but also tools, task delegation, state, human approval, tracing and multi-agent orchestration.
Semantic Kernel
Semantic Kernel is Microsoft’s open-source development kit for integrating AI models, application code and plugins and for building agentic solutions. It is particularly relevant for teams working in Microsoft and .NET environments, while also supporting Python and Java.
OpenAI Agents SDK
OpenAI Agents SDK provides a relatively small set of abstractions for building agentic applications. Its components include agents, tools, handoffs, guardrails, sessions, human-in-the-loop mechanisms and tracing.
It may be a good fit when a project is built close to the OpenAI stack and the team wants the runtime to handle elements such as the agent loop, tool execution and handoffs instead of implementing them from scratch.
Google Agent Development Kit
Google ADK is an open-source, code-first framework for building agents and multi-agent systems. It covers areas including orchestration, tools, evaluation and deployment. It is optimized for the Google AI ecosystem while being designed to work with other models and deployment environments as well.
Choosing an agent framework should not start with the number of features it offers. A more important question is who will control state, tools, permissions, memory and workflow – and how the solution will be tested and operated after deployment.
For production systems, framework selection is only one part of the problem. See our guide to AgentOps and managing AI agents in production.
How to choose an AI framework: 7 decisions to make before selecting the technology
A framework’s feature list is only one part of the decision. In enterprise projects, its fit with the architecture, team and operating model is often more important.
- What are we actually building?
A classical ML model, custom deep learning model, LLM application, RAG system or AI agent? Each scenario points to a different layer of the technology stack. - Will we train, fine-tune or only run inference?
If the system relies on a model provider API, a training framework may not be part of the application architecture at all. - What data will the solution process?
Tabular data, documents, images, audio and multimodal inputs require different components and different approaches to the data pipeline. - Do we need model-provider portability?
An abstraction layer may make switching providers easier, but it also introduces another component that needs to be developed and maintained. - How will the solution operate in production?
Evaluate deployment, scaling, observability, testing, security, permissions, failure handling and operational cost – not only how quickly the team can build a PoC. - How much abstraction do we actually need?
A framework can significantly speed up development, but each additional layer can make debugging harder and increase dependency on a particular ecosystem. - Will the team be able to maintain this stack in two years?
Consider internal skills, documentation, project maturity, update cadence and how difficult it would be to replace a component later.
Red flag: the team chooses a framework during the PoC stage simply because it makes the demo easy to build, without checking how testing, observability, deployment and production maintenance will work later.
Does every AI project need a sophisticated framework?
No. A framework can solve architectural problems, but it also introduces another dependency into the system.
If an application makes a single model call and returns the result to the user, direct use of an API or SDK may be simpler than introducing a full orchestration layer. Similarly, an agent performing one predictable process does not always need an extensive multi-agent framework.
| Scenario | Possible approach |
|---|---|
| One simple model call. | Direct API / SDK may be enough. |
| Model + several tools. | Lightweight application or agent framework. |
| Long-running process with state and approval. | Orchestration framework with persistence and human-in-the-loop. |
| AI working with internal documents. | RAG / data framework. |
| Training a custom model. | ML / deep learning framework. |
Good architecture does not maximize the number of frameworks. It minimizes the number of components required to solve the problem reliably.
How can Edge One Solutions support an AI project?
At Edge One Solutions, we do not start an AI project by choosing a specific framework. We first analyze the use case, data, integrations, current architecture constraints and how the solution is expected to operate in production.
Depending on the project, we can combine expertise across Artificial Intelligence, Data Engineering, Custom Development, integrations, Testing & QA and DevOps. This allows technology choices to take into account not only development, but also deployment, testing and long-term maintenance.
Building an AI system and unsure which stack to choose?
We can help translate the use case into a solution architecture, select the right AI components and design integrations with enterprise data and existing systems.
Documentation and expert resources
The AI ecosystem changes quickly, so the current feature set and project status should always be verified in the official documentation before selecting a technology.
FAQ – AI frameworks
Summary: start with the problem layer, then choose the AI framework
The modern AI ecosystem extends far beyond TensorFlow and PyTorch. Classical machine learning, deep learning, pretrained models, RAG and agentic systems require different categories of tools that often work together within the same architecture.
Framework selection should therefore begin by defining what the team is actually building, which data the solution will use, whether a custom model needs to be trained, how the system will operate in production and who will maintain it over time.
Only then does it make sense to compare individual technologies. For one project, the right choice may be scikit-learn. For another, PyTorch. For a third, it may be a combination of a pretrained model, RAG and an orchestration framework for an AI agent.
The most useful question is not: “Which AI framework is best?”
The better question is: “Which component solves the problem at a specific layer of our system, and can we operate it reliably in production?”
