Production-Grade Python, LangChain & LangGraph

Description

Welcome to the Leanpub Launch video for Production-Grade Python, LangChain & LangGraph: A deployment-first handbook for developers who know Python syntax but have not shipped Python https://leanpub.com/production-grade-python-langchain-and-langgraph by Fiodar Sazanavets! 0:00 Fiodar introduces himself, his .NET/.NET background, and how he transitioned into AI engineering 0:50 How LangChain emerged alongside early LLM releases and became central to AI engineering in Python 1:35 The book's origin: compiling notes to guide software engineers into production Python and AI engineering 2:24 Book structure overview: production Python packaging, CI/CD, testing, then LangChain and LangGraph 3:12 Distinction between LangChain (single-agent logic) and LangGraph (multi-agent orchestration with graphs) 3:59 Real-world example: an industrial multi-agent platform routing questions about wind turbines 4:50 How LLM-generated Python code handles complex calculations in an isolated container instead of letting the LLM do math 5:38 Sandboxed container execution: isolating generated Python code alongside time series data for safe calculation 6:10 Why books written from personal learning journeys often produce the most practical and valuable guides 6:24 Closing summary of the book's target audience and deployment-first approach About the Book Learn the engineering skills behind one of software’s most valuable emerging fields AI engineering is rapidly becoming one of the most commercially valuable areas of software development. Companies are moving beyond simple chatbot prototypes and looking for engineers who can build reliable AI-powered applications, agentic workflows, retrieval systems, model integrations, and production infrastructure. That creates strong demand for developers who understand both software engineering fundamentals and modern AI application frameworks. Python sits at the center of this ecosystem. It is the dominant language across AI, machine learning, data engineering, model tooling, and an increasing share of agent development. LangChain and LangGraph add the abstractions needed to build applications around large language models, tools, retrieval, state, and increasingly autonomous workflows. These skills also occupy an interesting position in the changing software market. AI can automate more routine implementation work, but organizations still need engineers who can design the systems around AI: integration, orchestration, reliability, evaluation, security, deployment, observability, and production control. Learning to build the systems that use AI is therefore a compelling way to move toward the part of software engineering that AI itself is helping to create. Production-Grade Python, LangChain & LangGraph is designed to take you there. The book assumes that you already know basic Python syntax. Instead of teaching loops, functions, and classes again, it focuses on the engineering knowledge required to turn Python code into real production software. You will learn: How Python actually executes code through CPython, bytecode, modules, and processes Modern project structure with pyproject.toml Virtual environments, dependency management, locking, packaging, wheels, and reproducible builds FastAPI and production API design Configuration and secrets management Async programming and concurrency Database access, transactions, migrations, and persistence Caching and external service integration Error handling, retries, timeouts, and idempotency Structured logging, metrics, tracing, and health checks Unit, integration, API, and system testing Containers, CI/CD, and production deployment Security practices for backend and AI applications The second half applies those engineering foundations to modern AI engineering with LangChain and LangGraph. You will learn how to build: LLM-powered applications Structured model interactions Tool-using agents Retrieval-Augmented Generation systems Stateful workflows Conditional and parallel execution Persistent agent state Durable workflows Human-in-the-loop approval Streaming applications Long-running agent processes Multi-step and multi-agent systems Testable and observable AI workflows The emphasis throughout is on production engineering, not demos. Calling an LLM is easy. Building a system around it that remains secure, reliable, observable, recoverable, and maintainable is where much of the real engineering value lies. A complete production-style support-agent application brings the concepts together using FastAPI, SQLAlchemy, LangChain, LangGraph, persistence, authentication, observability, human approval, and safe execution of irreversible actions. The book also includes a companion code repository containing the complete application, runnable examples, tests, deployment configuration, and every code sample used throughout the manuscript. If you already know basic Python and want to position yourself for the growing field of AI engineering, this book gives you the production foundations and agent-development skills needed to move beyond prototypes and build systems organizations can actually depend on. About the Author Fiodar Sazanavets is a senior software engineer specializing in AI systems, distributed applications, and data-intensive software. A former Microsoft engineer and fourt-time Microsoft MVP, he has more than a decade of professional experience designing and building production systems across a wide range of industries. His core areas of expertise include Python, .NET, cloud technologies, distributed systems, and modern AI engineering. Throughout his career, he has worked on systems ranging from railway passenger information platforms and distributed IoT clusters to e-commerce applications and financial transaction-processing systems. He has also led engineering teams, mentored developers, and helped organizations translate complex technical requirements into reliable software that solves real business problems. Alongside his engineering work, Fiodar is passionate about technical education. He writes books, creates online courses, mentors developers, and regularly publishes practical software engineering content. His teaching focuses on the skills developers need to build real-world systems: architecture, reliability, cloud engineering, AI, and production software development. He writes about software engineering and AI at fiodar.substack.com/. Follow the author here! https://x.com/FSazanavets Thank you for watching, please like and leave a comment, we'd love to hear from you! Please Subscribe and Follow! 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