11 Python Libraries Every AI Engineer Should Know

With LLMs and generative AI going mainstream, AI engineering is becoming all the more relevant. And so is the role of the AI engineer.

So what do you need to build useful AI applications? Well, you need a toolkit that spans model interaction, orchestration, data management, and more. In this article, we’ll go over Python libraries and framework you’ll need in your AI engineering toolkit, covering the following:

• Integrating LLMs in your application
• Orchestration frameworks
• Vector stores and data management
• Monitoring and observability

Let’s get started.

1. Hugging Face Transformers

 
What it’s for: Hugging Face Transformers library is the swiss army knife for working with pre-trained models and NLP tasks. It is a comprehensive NLP toolkit that democratizes access to transformer models. It is a unified platform for downloading, using, and fine-tuning pre-trained models and makes state-of-the-art NLP accessible to developers without requiring deep ML expertise.

Key Features

• Massive model hub with thousands of shared models
• Unified API for different architectures (BERT, GPT, T5, and much more)
• Pipeline abstraction for quick task implementation
• Native PyTorch and TensorFlow support

Learning Resource: Hugging Face NLP Course

2. Ollama

 
What it’s for: Ollama is a framework for running and managing open-source LLMs locally. It simplifies the process of running models like Llama and Mistral on your own hardware, handling the complexity of model quantization and deployment.

Key Features

• Simple CLI/API for running models like Llama, Mistral
• Custom model fine-tuning with Modelfiles
• Easy model pulling and version management
• Built-in model quantization

Learning Resource: Ollama Course – Build AI Apps Locally

3. OpenAI Python SDK

 
What it’s for: The OpenAI Python SDK is the official toolkit for integrating OpenAI’s language models into Python applications. It provides a programmatic interface to interact with GPT models, handling all the underlying API communication and token management complexities.

Key Features

• Clean Python SDK for all OpenAI APIs
• Streaming responses support
• Function calling capabilities
• Token counting utilities

Learning Resource: The official developer quickstart guide

4. Anthropic SDK

 
What it’s for: The Anthropic Python SDK is a specialized client library for integration with Claude and other Anthropic models. It provides a clean interface for chat-based applications and complex completions, with built-in support for streaming and system prompts.

Key Features

• Messages API for chat completions
• Streaming support
• System prompt handling
• Multiple model support (Claude 3 family)

Learning Resource: Anthropic Python SDK

5. LangChain

 
What it’s for: LangChain is a framework that helps developers build LLM applications. It provides abstractions and tools to combine LLMs with other sources of computation or knowledge.

Key Features

• Chain and agent abstractions for workflow building
• Built-in memory systems for context management
• Document loaders for multiple formats
• Vectorstore integrations for semantic search
• Modular prompt management system

Learning Resource: LangChain for LLM Application Development – DeepLearning.AI

6. LlamaIndex

 
What it’s for: LlamaIndex is a framework specifically designed to help developers connect custom data with LLMs. It provides the infrastructure for ingesting, structuring, and accessing private or domain-specific data in LLM applications.

Key Features

• Data connectors for various sources (PDF, SQL, etc.)
• Built-in RAG (Retrieval Augmented Generation) patterns
• Query engines for different retrieval strategies
• Structured output parsing
• Evaluation framework for RAG pipelines

Learning Resource: Building Agentic RAG with LlamaIndex – DeepLearning.AI

7. SQLAlchemy

 
What it’s for: SQLAlchemy is a SQL toolkit and ORM (Object Relational Mapper) for Python. It abstracts database operations into Python code, making database interactions more pythonic and maintainable.

Key Features

•   Powerful ORM for database interaction
•   Support for multiple SQL databases
•   Connection pooling and engine management
•   Schema migrations with Alembic
•   Complex query building with Python syntax

Learning Resource: SQLAlchemy Unified Tutorial

8. ChromaDB

 
What it’s for: ChromaDB is an open-source embeddings database for AI applications. It provides efficient storage and retrieval of vector embeddings. Great for semantic search and AI-powered information retrieval systems.

Key Features

• Simple API for storing and querying embeddings
• Multiple persistence options (in-memory, parquet, sqlite)
• Direct integration with popular LLM frameworks
• Built-in embedding functions

Learning Resource: Getting Started – Chroma Docs

9. Weaviate

 
What it’s for: Weaviate is a cloud-native vector search engine that enables semantic search across multiple data types. It’s designed to handle large-scale vector operations efficiently while providing rich querying capabilities through GraphQL. You can use the  Python client library Weaviate

Key Features

• GraphQL-based querying
• Multi-modal data support (text, images, etc.)
• Real-time vector search
• CRUD operations with vectors
• Built-in backup and restore

Learning Resource: 101T Work with: Text data | Weaviate, 101V Work with: Your own vectors | Weaviate

10. Weights & Biases

 
What it’s for: Weights & Biases is an ML experiment tracking and model monitoring platform. It helps teams monitor, compare, and improve machine learning models by providing comprehensive logging and visualization capabilities.

Key Features

• Experiment tracking with automatic logging
• Model performance visualization
• Dataset versioning and tracking
• System metrics monitoring (GPU, CPU, memory)
• Integration with major ML frameworks

Learning Resource: Effective MLOps: Model Development

11. LangSmith

 
What it’s for: LangSmith is a production monitoring and evaluation platform for LLM applications. It provides insights into LLM interactions, helping you understand, debug, and optimize LLM-powered applications in production.

Key Features

• Trace visualization for LLM chains
• Prompt/response logging and analysis
• Dataset creation from production traffic
• A/B testing for prompts and models
• Cost and latency tracking
• Direct integration with LangChain

Learning Resource: Introduction to LangSmith

Wrapping Up

 
That’s all for now. You can think of this collection as a toolkit for modern AI engineering. You can start building production-grade LLM applications and use these as needed.

The most effective engineers understand not just individual libraries, but how to use them to solve relevant problems. We encourage you to experiment with these tools. There may be changes, new frameworks may become popular. But the fundamental patterns these libraries address will remain relevant.

As you continue developing AI applications, however, remember that ongoing learning and community engagement are super important, too. Happy coding and learning!
 
 

Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.