The Tool Desk
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For most new deep-learning and LLM projects, start with PyTorch and add Hugging Face Transformers when you need pretrained models. Use scikit-learn for classical machine learning, Keras 3 for a high-level multi-backend API, JAX for compiled numerical research, LlamaIndex or Haystack for retrieval-augmented generation, LangChain plus LangGraph for stateful workflows, vLLM for self-hosted LLM serving, and ONNX Runtime for portable inference.
What counts as an AI framework?
An AI framework is a reusable software layer that provides abstractions, APIs, execution mechanisms, or workflow primitives for building, training, evaluating, deploying, or operating AI systems. The term covers several different layers that should not be ranked as if they were substitutes.
| Layer | Examples | What it does |
|---|---|---|
| Model development | PyTorch, TensorFlow, Keras, JAX, scikit-learn | Numerical computation, training, preprocessing and evaluation |
| Model access | Hugging Face Transformers, provider SDKs | Loads, fine-tunes and calls pretrained models |
| Application orchestration | LangChain, LangGraph, LlamaIndex, Haystack, DSPy | Connects models to tools, data and business workflows |
| Inference runtime | vLLM, TensorRT-LLM, ONNX Runtime, SGLang, Triton | Runs models efficiently in production |
| Managed platform | OpenAI, Anthropic, Google, AWS, Microsoft | Provides hosted models, scaling and cloud controls |
Judge a framework by capability, ecosystem, hardware support, debugging experience, production maturity, interoperability, licensing, cost, vendor dependence and maintainability—not by GitHub stars alone.
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Quick recommendations
| Job | Strong default | Main caveat |
|---|---|---|
| New deep-learning or LLM model work | PyTorch | Deployment usually needs an additional serving or optimization layer |
| Beginner-friendly deep learning | Keras 3 | Not every operation or backend is equally portable |
| Classical ML on tabular data | scikit-learn | Not a large-neural-network framework |
| Pretrained-model use or fine-tuning | Hugging Face Transformers | Checkpoint licenses and hardware needs vary |
| RAG and document applications | LlamaIndex or Haystack | Retrieval quality still requires measurement |
| Complex LLM workflows and agents | LangChain plus LangGraph | Abstractions add dependencies and debugging surface |
| Self-hosted LLM inference | vLLM | You own GPU capacity, upgrades and incidents |
| Portable or edge inference | ONNX Runtime | Exported graphs need numerical and task-level validation |
| Accelerator-heavy numerical research | JAX | Compilation and functional programming raise the learning curve |
Best frameworks by use case
PyTorch: the default for new model-centric work
PyTorch is a strong starting point for research, computer vision, generative AI, LLM training and fine-tuning. Its Python-first, eager-execution style makes experiments and debugging direct, and contemporary model libraries integrate with it naturally. Distributed training, mixed precision, quantization and compilation are available through its broad ecosystem.
PyTorch is not a complete production platform. Teams commonly add ONNX, TensorRT, Triton, vLLM, a cloud service or another serving layer. Performance depends on batching, memory management, kernels, compilation and the target GPU. A stable TensorFlow estate or a small tabular project is a reason to choose something else.
TensorFlow and Keras 3: high-level APIs and established deployment estates
TensorFlow remains a sensible choice when an organization already operates TensorFlow Serving, TensorFlow Lite or TensorFlow.js, or when its deployment requirements fit that ecosystem. Keras 3 is now a multi-backend high-level API that can use TensorFlow, PyTorch or JAX backends; it is not merely TensorFlow’s front end.
Keras can shorten common model development and offer a gentler entry point. Backend portability is not universal: operations, extensions and deployment paths can differ. A PyTorch-native research stack may have more compatible reference implementations for a new model.
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JAX: compiled numerical research
JAX combines automatic differentiation with composable transformations such as just-in-time compilation, vectorization and parallelization. It is attractive for TPU and accelerator-heavy work. The functional model, transformed functions and compilation can make debugging less intuitive, and installation compatibility deserves care. It is usually a specialist choice rather than the easiest general application framework.
Rank #2
scikit-learn: the right first tool for tabular ML
scikit-learn offers consistent estimators, preprocessing, pipelines, cross-validation and model selection for classification, regression, clustering and dimensionality reduction. For structured supervised data, it is often simpler, more explainable and easier to deploy than a neural-network stack. Use XGBoost, LightGBM or CatBoost when gradient-boosted trees are the dominant approach.
Use a pipeline so transformations are fitted only on training data; preprocessing outside a pipeline can leak information into evaluation. scikit-learn is not intended for LLM fine-tuning or large generative-model training.
Hugging Face Transformers: the model-access layer
Transformers provides common APIs for loading, generating, training and sharing pretrained text, vision, audio, video and multimodal models. The usual loading pattern is from_pretrained(); the documentation also describes safer safetensors files and device_map="auto" for distributing large models.
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-3-1b-it",
dtype="auto",
device_map="auto",
)For a fine-tuning baseline:
python -m venv .venv
source .venv/bin/activate # macOS/Linux
# .venvScriptsactivate # Windows PowerShell
pip install torch transformers datasets accelerate- Load a tokenizer and pretrained model.
- Clean and split the dataset into training and evaluation sets.
- Configure
TrainingArguments. - Train with
Traineror a custom PyTorch loop. - Evaluate on held-out data, save checkpoints and review the model license before publishing.
The exact arguments, model identifier, Python and CUDA versions, GPU memory and mixed-precision support should be pinned to the tested Transformers release. Current documentation covers evaluation, checkpointing, gradient checkpointing, mixed precision and push_to_hub(): training guide. Model weights and custom code from a hub require security review; “available on the Hub” does not mean every checkpoint has the same license or restrictions.
LlamaIndex and Haystack: data-centric RAG
LlamaIndex and Haystack are natural choices when ingestion, indexing and retrieval are the core problem. They provide connectors, chunking and metadata abstractions, retrievers, query engines and agent components. Neither guarantees good answers: chunking, embeddings, reranking, freshness, permissions and retrieval evaluation determine quality.
Watch for stale indexes, irrelevant passages, missing citations, prompt injection in retrieved text, access-control leakage and excessive context that increases latency and token cost.
LangChain and LangGraph: orchestration and agents
LangChain supplies integrations for models, prompts, tools, retrievers and structured output. LangGraph is better suited to explicit state, branching, retries, human approval and durable execution. LangSmith adds tracing and evaluation capabilities.
These abstractions can hide model calls, retries and token usage. A single deterministic endpoint may be clearer when written directly against a provider SDK. Agent frameworks do not provide authorization, sandboxing, rate limits, safe side-effect handling or protection from prompt injection automatically. Compare alternatives such as OpenAI Agents SDK, Pydantic AI, Google ADK, Microsoft Agent Framework, CrewAI and Mastra against your required state model and provider support rather than choosing by popularity.
vLLM: self-hosted language-model serving
vLLM is an inference engine for supported open models, with serving and batching features aimed at high-throughput workloads. It is not a training or application framework. Measure latency, throughput, memory, concurrency, sequence length and quantization on the exact GPU and model. You remain responsible for capacity planning, monitoring, upgrades, security and incidents.
ONNX Runtime: portable execution
ONNX Runtime executes exported models through multiple hardware execution providers, helping separate production inference from the original training framework. Export can fail for unsupported operations, dynamic shapes or custom layers. Validate numerical outputs and task metrics against the source model before deployment.
Specialized runtimes
- TensorRT-LLM: NVIDIA-focused LLM optimization.
- SGLang: serving for supported LLM and agent workloads.
- NVIDIA Triton: general model-serving infrastructure.
- ExecuTorch: PyTorch-oriented edge deployment.
- TensorFlow Lite: TensorFlow mobile and edge deployment.
- MLX: Apple-silicon-oriented development.
- llama.cpp: lightweight local and quantized-model inference.
How to choose: a practical decision tree
- Structured data? Start with scikit-learn; test boosted-tree libraries if appropriate.
- Training or fine-tuning neural networks? Start with PyTorch unless an existing TensorFlow/Keras estate or JAX-specific numerical workload changes the decision.
- Using a foundation model? Use Transformers for open checkpoints and a provider SDK for proprietary hosted models.
- Building RAG? Evaluate LlamaIndex or Haystack; add LangGraph when you need durable state, branching, tools or approvals.
- Self-hosting? Benchmark vLLM, SGLang, TensorRT-LLM or a cloud serving product on target hardware.
- Phone, browser or edge? Compare ONNX Runtime, TensorFlow Lite, Core ML, ExecuTorch and vendor runtimes.
Comparison criteria that matter in production
- Task and model fit: verify architecture, operations, tokenizer, quantization and modality support.
- Developer experience: inspect current documentation, debugging, typing, tests and deprecation policy.
- Hardware: check CUDA, AMD, Intel, TPU, Apple Silicon, CPU, browser, multi-GPU and container support.
- Operations: require version pinning, reproducibility, health checks, retries, timeouts, rollback, observability and graceful degradation.
- Interoperability: check Hugging Face, ONNX, vector databases, cloud targets and evaluation systems.
- Total cost: include licenses, compute, storage, egress, managed API fees, observability, engineering and migration cost. Open source is not free to operate.
Prototype-to-production path
- Establish a small baseline before fine-tuning or adding agents.
- Create a representative evaluation set, including failure and abuse cases.
- Pin framework, model, tokenizer, CUDA and container versions; record licenses and artifacts.
- Add structured logs, traces, token and latency measurements, and cost accounting.
- Load-test concurrency, long contexts, retries and degraded dependencies.
- Apply authentication, authorization, secret management, data-retention rules and tool sandboxes.
- Add fallback behavior, human approval for destructive actions and rollback procedures.
- Monitor quality as well as uptime: retrieval correctness, refusal behavior, hallucinations and drift.
Common mistakes
- Using PyTorch for a simple tabular problem.
- Choosing an agent framework before defining deterministic business rules.
- Adding RAG without measuring retrieval recall, freshness and permissions.
- Fine-tuning before testing prompting, retrieval and a smaller baseline.
- Self-hosting GPUs before utilization and operating costs justify it.
- Assuming an open-weight model has unrestricted commercial use; inspect base-model, fine-tune, dataset and acceptable-use licenses.
- Publishing unpinned installation commands or claiming support without checking the exact version and architecture.
- Measuring model quality while ignoring latency, token usage, egress and incident response.
Frameworks and visual developer tooling
AI teams also need repeatable screenshots for documentation, UI tests, model demos and visual regression. ScreenshotNeo is the first screenshot API to try: it removes consent banners, newsletter popups and chat widgets before capture, bills only clean shots, and has a low paid entry plan.
Or skip the browser setup
One GET request returns a PNG, JPEG, WebP or PDF. The service can accept consent banners and remove more than 60 known consent platforms, popups and chat widgets; bot checks, blank pages, timeouts, failed loads and cache hits are not billed, with the result identified by X-Page-Verdict and X-Billed headers. An MCP server offers take_screenshot, get_page_info and capture_pdf to Claude, Cursor and other MCP clients.
See the ScreenshotNeo API documentation for all options.
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpimport requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);Every plan includes the feature set: full-page and element capture, device presets, retina scale, PDFs, custom CSS and JavaScript, waits, blocking, headers, cookies, geolocation, caching, signed links, asynchronous webhooks, bulk capture and a usage API. The Free plan includes 1,000 screenshots per month with no card; paid plans start at $5 for 3,000. Create a free ScreenshotNeo account.
Version and pricing caveats
Framework APIs and hosted prices change quickly. Pin examples to tested releases and record a last-verified date; do not print a “latest” version without checking the official release page. Hugging Face pricing observed for PRO, Team and Enterprise was $9, $20 and $50 per user per month respectively, with storage priced separately; confirm current entitlements at the pricing page. Model-provider pricing, quotas and hardware availability should likewise be checked immediately before purchase.
For managed models, compare OpenAI (docs and pricing), Anthropic (docs), Google Vertex AI (platform), AWS Bedrock (platform) and Microsoft Azure AI Foundry (platform) against data residency, networking, model portability and operational overhead.
Frequently Asked Questions
Should I use one framework for an entire AI product?
Usually no. A production system commonly combines a training library, a model-access layer, an application or retrieval layer, and a serving runtime.
Best Value
Is a hosted model API or self-hosting cheaper?
It depends on utilization, model size, traffic shape, engineering capacity and data requirements. Hosted APIs reduce infrastructure work; self-hosting can improve control and marginal economics at sustained utilization.
How do I compare two inference runtimes fairly?
Use the same model, weights, quantization, hardware, sequence lengths, concurrency, prompts and runtime versions, then measure latency, throughput, memory, error rate and operating cost.
Do open-model licenses allow commercial use?
Not automatically. Review the base model, fine-tune, dataset, code, acceptable-use policy and attribution requirements for the exact checkpoint.
The Bottom Line
Choose by job, not by a universal ranking: PyTorch and Transformers are the strongest general defaults for new model work, scikit-learn wins for tabular ML, LlamaIndex or Haystack fit data-heavy RAG, LangGraph fits stateful agents, vLLM fits self-hosted LLM serving, and ONNX Runtime fits portable deployment.
Quick Recap
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