LangGraph

Table of Contents Introduction From Classic RAG to Agentic RAG 2.1. What Is Retrieval‑Augmented Generation? 2.2. Limitations of the Classic Pipeline 2.3. The “Agentic” Paradigm Shift Why LangGraph 2.0? 3.1. Core Concepts: Nodes, Edges, and State 3.2. Built‑in Agentic Patterns 3.3. Compatibility with LangChain & LlamaIndex Designing an Autonomous Knowledge Retrieval System 4.1. High‑Level Architecture 4.2. Defining the Graph Nodes 4.3. State Management & Loop Control Step‑by‑Step Implementation 5.1. Environment Setup 5.2. Creating the Retrieval Node 5.3. Building the Reasoning Agent 5.4. Putting It All Together: The LangGraph 5.5. Running a Sample Query Advanced Agentic Behaviors 6.1. Self‑Critique & Re‑asking 6.2. Tool‑Use: Dynamic Source Selection & Summarization 6.3. Memory & Long‑Term Context Evaluation & Monitoring 7.1. Metrics for Autonomous RAG 7.2. Observability with LangGraph Tracing Deployment Considerations 8.1. Scalable Vector Stores 8.2. Serverless vs. Containerized Execution 8.3. Cost‑Effective LLM Calls Best Practices & Common Pitfalls Conclusion Resources Introduction Retrieval‑Augmented Generation (RAG) has become the de‑facto standard for building knowledge‑aware language‑model applications. By coupling a large language model (LLM) with an external knowledge store, developers can overcome the hallucination problem and answer domain‑specific questions with up‑to‑date facts. ...

Introduction Retrieval‑Augmented Generation (RAG) has become the de‑facto baseline for many knowledge‑intensive applications—question answering, summarisation, and data‑driven code generation. While RAG excels at pulling relevant context from external sources and feeding it into a language model, it remains fundamentally reactive: the model receives a prompt, produces an answer, and stops. For many research‑oriented tasks, a single forward pass is insufficient. Consider a scientist who must: Identify a gap in the literature. Gather and synthesise relevant papers, datasets, and code. Design experiments, run simulations, and iteratively refine hypotheses. Document findings in a reproducible format. These steps require autonomous planning, dynamic tool usage, and continuous feedback loops—behaviours that go beyond classic RAG pipelines. Enter LangGraph, an open‑source framework that lets developers compose LLM‑driven workflows as directed graphs, and local LLM serving (e.g., Ollama, LM Studio, or self‑hosted vLLM) that offers deterministic, privacy‑preserving inference. Together, they enable the creation of autonomous research agents that can reason, act, and learn without human intervention. ...

Introduction Enterprises are under relentless pressure to turn data into actions faster than ever before. Traditional rule‑based automation pipelines struggle to keep up with the nuance, variability, and sheer volume of modern business processes—think customer‑support tickets, contract analysis, supply‑chain alerts, or knowledge‑base retrieval. Enter autonomous AI agents: self‑directed software entities that can reason, retrieve relevant information, and take actions without constant human supervision. When combined with LangGraph, a graph‑oriented orchestration library for large language models (LLMs), and vector search, a scalable similarity‑search technique for embedding‑based data, these agents become powerful engines for enterprise workflows. ...

The landscape of Artificial Intelligence is shifting from simple, stateless chat interfaces to complex, autonomous agentic workflows. While single-agent systems can handle basic tasks, production-grade applications often require a “team” of specialized agents working together. This is where Multi-Agent Orchestration becomes critical. In this guide, we will explore how to master multi-agent systems using LangGraph, a library built on top of LangChain designed specifically for building stateful, multi-actor applications with LLMs. ...