LangChain in Action

Artificial intelligence has evolved beyond chatbots and static decision trees. Enterprise agents today need to be able to see images, understand spoken language, remember context over sessions, and reason through multi-step tasks without human intervention. LangChain has emerged as the leading open-source framework for combining these capabilities into production-ready systems.

This book is a hands-on guide to building multi-modal, context-aware AI agents. Readers start by designing reusable LangChain workflows and selecting the right language models, then move into building multimodal pipelines that handle text, images, and audio together. From there, the book covers vision-enabled agents powered by GPT-4o and CLIP, voice assistants built with Whisper and Azure Speech, and agents with persistent memory that maintain context across sessions. Later chapters tackle emotion-aware interactions, retrieval-augmented generation with hybrid search, and knowledge graph fusion for advanced multi-hop reasoning. The book also explores autonomous agents that execute real-world tasks and provides a practical guide to multi-agent planning, collaboration, and evaluation.

By the end of this book, readers will be equipped to design and deploy production-grade AI agents that handle real-world complexity, agents that see, hear, remember, reason, and act with intelligence and precision.

WHAT YOU WILL LEARN
● Build modular LangChain workflows with reusable components
● Architect multimodal pipelines for text, image, and audio
● Implement speech recognition and TTS for voice-enabled agents
● Design agents with persistent memory and cross-session context
● Build RAG systems grounded in domain-specific knowledge bases
● Deploy and evaluate autonomous agents for real-world tasks

WHO THIS BOOK IS FOR
This book is for software engineers, solution architects, and AI practitioners building production-grade agents. Python proficiency and basic familiarity with LLMs or REST APIs are required, as you will transition from simple setups to complex, multi-modal, and autonomous multi-agent systems.

1. Building Your First LangChain Agent
2. Designing Modular and Reusable LangChain Workflows
3. LLM Reasoning and Agent Intelligence
4. Building Multimodal LangChain Pipelines
5. Building Vision-enabled Agents with Visual Intelligence
6. Building Conversational Agents with Voice Intelligence
7. Developing Context-Aware Agents with Persistent Memory
8. Designing Emotion-Aware Agents
9. Building Retrieval-Enhanced Agents
10. Fusing Knowledge Networks for Advanced Reasoning
11. Creating Autonomous Agents that Perform Real-World Tasks
12. Planning, Collaboration, and Evaluating Autonomous Agents

Deepak Kamboj is a senior software engineer and AI solution architect at Microsoft with over 25 years of experience in enterprise software, AI-powered automation, and developer productivity. He specializes in agentic workflows, Model Context Protocol (MCP) servers, multi-agent systems, and generative AI applications, delivering measurable organizational value.

At Microsoft, Deepak has helped multiple teams adopt modern Playwright-based test automation and has built AI-driven test generation and self-healing pipelines to improve engineering efficiency. He is a Copilot Champion, a Microsoft Hackathon participant and advisor, and a recognized accessibility champion.

Deepak holds a B.Tech. in computer science and engineering and is an active voice in the developer community through his LinkedIn newsletter AI Weekly, his GitHub projects, and speaking engagements focused on practical AI, automation at scale, and developer experience.