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. Youll learn to design deterministic pipelines with strict tool contracts and deploy them as microservices. Through structured context engineering, youll orchestrate reliable agent workflows and move beyond simple prompt-based interactions. You'll start by understanding LLM behaviortokens, embeddings, and transformer modelsand 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 Haystacks graph-based architecture. Youll also create knowledge graphs, synthesize unstructured data, and evaluate system behavior using Ragas and Weights & Biases. In LangGraph, youll orchestrate agents with supervisor-worker patterns, typed state machines, retries, fallbacks, and safety guardrails. By the end of the book, youll 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.
