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Full Description
Stop LLM applications from breaking in production. Build deterministic pipelines, enforce strict tool contracts, engineer high-signal context for RAG, and orchestrate resilient multi-agent workflows using two foundational frameworks: Haystack for pipelines and LangGraph for low-level agent orchestration.
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Key Features
Design reproducible LLM pipelines using typed components and strict tool contracts
Build resilient multi-agent systems with LangGraph and modular microservices
Evaluate and monitor pipeline performance with Ragas and Weights & Biases
Book DescriptionModern LLM applications often break in production due to brittle pipelines, loose tool definitions, and noisy context. This book shows you how to build production-ready, context-aware systems using Haystack and LangGraph. You'll learn to design deterministic pipelines with strict tool contracts and deploy them as microservices. Through structured context engineering, you'll orchestrate reliable agent workflows and move beyond simple prompt-based interactions.
You'll start by understanding LLM behavior—tokens, embeddings, and transformer models—and see how prompt engineering has evolved into a full context engineering discipline. Then, you'll build retrieval-augmented generation (RAG) pipelines with retrievers, rankers, and custom components using Haystack's graph-based architecture. You'll also create knowledge graphs, synthesize unstructured data, and evaluate system behavior using Ragas and Weights & Biases. In LangGraph, you'll orchestrate agents with supervisor-worker patterns, typed state machines, retries, fallbacks, and safety guardrails.
By the end of the book, you'll have the skills to design scalable, testable LLM pipelines and multi-agent systems that remain robust as the AI ecosystem evolves.
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What you will learn
Build structured retrieval pipelines with Haystack
Apply context engineering to improve agent performance
Serve pipelines as LangGraph-compatible microservices
Use LangGraph to orchestrate multi-agent workflows
Deploy REST APIs using FastAPI and Hayhooks
Track cost and quality with Ragas and Weights & Biases
Implement retries, circuit breakers, and observability
Design sovereign agents for high-volume local execution
Who this book is forLLM engineers, NLP developers, and data scientists looking to build production-grade pipelines, agentic workflows, or RAG systems. Ideal for tech leads looking to move beyond prototypes to scalable, testable solutions, as well as teams modernizing legacy NLP pipelines into orchestration-ready microservices. Proficiency in Python and familiarity with core NLP concepts are recommended.
Contents
Table of Contents
Introduction to Natural Language Processing Pipelines
Diving Deep into Large Language Models
Introduction to Haystack by deepset
Bringing Components Together - Haystack Pipelines for Different Use Cases
Haystack Pipeline Development with Custom Components
Building Reproducible and Production-Ready RAG Systems
Deploying Haystack-Based Applications
Hands-on Projects
Future Trends and Beyond
Epilogue: The Architecture of Agentic AI
