The article walks through five progressively sophisticated RAG architectures—Naive, Hybrid, Graph, Advanced, and Agentic—showing how to implement each with LangGraph and LlamaIndex, when to choose them, and the trade‑offs in performance, correctness, and scalability.
LangGraph is an open‑source framework that replaces linear LLM pipelines with graph‑based, stateful agents, offering loops, conditional branching, persistent checkpoints, human‑in‑the‑loop support, and built‑in monitoring, enabling complex multi‑step workflows that scale from simple chatbots to enterprise‑grade AI assistants.
The article examines why Loop Engineering is giving way to Graph Engineering, detailing the five‑layer evolution, structural flaws of single‑loop systems, the advantages of graph‑based multi‑agent orchestration, real‑world examples, cost‑benefit analysis, and practical guidance on when to adopt graph engineering.
This article compares LangGraph, Google ADK 2.0, and LlamaIndex on how they model, scope, update, persist, interrupt, and concurrently control state in agent workflows, providing concrete code snippets, tables, and practical guidance for selecting the right framework.
The article explains why agents fail due to missing memory and workflow management, introduces Loop Engineering’s six‑step control model, shows its limits for complex multi‑system processes, and presents LangGraph‑based graph engineering recipes—including SQL repair loops, evidence‑gated RAG, and human‑in‑the‑loop checkpoint recovery—to build reliable, auditable enterprise agents.
Multi‑agent workflows often deadlock or drift because their agents behave like distributed nodes, so treating them as a distributed system reveals classic failure modes—deadlocks, state pollution, lack of timeouts, and missing idempotency—allowing proven engineering practices to keep AI pipelines reliable.
This article explains the distinction between control state and data state in AI agent frameworks, tracing the concept from Turing machines through operating systems, databases, and compilers, and shows how LangGraph separates these states via a three‑layer architecture, code examples, and design guidelines.
Graph Engineering isn’t a brand‑new technology but a shift from single‑agent loops to a network of specialized nodes, edges, and shared state that lets multiple AI agents collaborate, run in parallel, and avoid context decay, with practical LangGraph examples, pros, cons, and when to adopt it.
The article explains that Graph Engineering isn’t a brand‑new concept but an evolution of Prompt, Loop, and Harness engineering, detailing how LangGraph has been used for three years, the core components of graph‑based agents, practical patterns, pitfalls, and when to choose graphs over other approaches.
The Argonne National Laboratory team introduces ChemGraph, an LLM‑driven agent for computational chemistry, and evaluates it across 13 benchmark tasks, showing that small models excel on simple tasks while larger models and multi‑agent designs dramatically improve performance on complex molecular simulations.
This article presents a comprehensive, production‑ready guide for building end‑to‑end observability and quantitative evaluation of LLM‑powered RAG/Agent systems using the open‑source Langfuse platform together with the RAGAS benchmark, covering architecture, installation, code instrumentation, dataset management, metric collection, and best‑practice recommendations.
This article explains how to use LangGraph to create a hybrid RAG agent that dynamically selects between vector, graph, web, or direct LLM retrieval, detailing the router, grader, rewriter, generator, and hallucination‑checking components along with a complete Python implementation.
The article breaks down AI orchestration as a management layer that routes tasks, maintains state, executes tools, handles retries, and coordinates multiple agents, comparing frameworks like LangGraph and CrewAI while highlighting practical pitfalls and best‑practice advice for building reliable multi‑step AI workflows.
This article demonstrates how to use LangGraph to build a state‑machine‑based hybrid RAG agent that routes each query to the most suitable retriever—vector similarity, graph traversal, or web search—through a Router, and then validates answers with grading, rewriting, generation, and hallucination‑checking components.
The article chronicles how a marketing activity that required ten system switches and over forty manual fields was transformed by replacing simple AI‑assisted form filling with a two‑stage Agent architecture and an aggregated workbench, detailing the architectural choices, trade‑offs, and practical lessons learned.
The article analyzes five often‑overlooked pitfalls that emerge when scaling enterprise AI agents to production—mis‑chosen orchestration architecture, lack of tool‑level circuit breaking, state‑splitting among multiple agents, absent evaluation frameworks, and unclear security boundaries—offering concrete causes and practical mitigation strategies.
The article distills three Bright Data webinars, detailing how to overcome traditional web‑crawling challenges with an adaptive Crawler API, integrate the Model Context Protocol (MCP) for human‑like browser control, and build a LangGraph‑powered AI search engine while addressing compliance, billing, and scaling considerations.
The article walks through the challenges of large‑scale web crawling, explains Bright Data’s adaptive Crawler API and MCP protocol, discusses compliance and proxy strategies, and shows how to build a next‑generation AI search engine with LangGraph and Python tool integration.
The article outlines ten core concepts—State, Node, Chain vs Graph, Routing, Retrieval, Structured Output, Streaming, Memory, Checkpointing, and Human‑in‑the‑Loop—explaining why they are crucial for building reliable, scalable AI agents and showing concrete Python examples for each.
This article explains why a single AI agent often falls short for complex tasks, outlines the benefits of multi‑agent collaboration, compares common architecture patterns, and provides hands‑on examples using AutoGen, CrewAI, and LangGraph, followed by a real‑world customer‑service team case and best‑practice guidelines.
This 2026 guide breaks down seven decision layers for building production agents, explains the four primary constraints—latency budget, audit traceability, model portability, and language stack—and compares leading open‑source toolkits with concrete benchmarks, migration costs, and integration trade‑offs.
The article presents a production‑grade, six‑layer architecture for an AI assistant that unifies LangGraph state orchestration, industrial‑strength RAG pipelines, multi‑level memory management, and the Model Context Protocol (MCP), addressing integration fragmentation, fault tolerance, observability, and security to enable scalable enterprise deployments.
This article walks through constructing a lightweight YAML‑based DSL workflow engine on Spring AI 1.1.2 and Ollama, showing how to define state graphs, register tools, parse and execute nodes—including conditional edges, while loops, and parallel branches—without external orchestration tools.
The article clarifies that LangGraph, OpenClaw, and Hermes are not interchangeable products but three separate layers of agent development—Orchestration, User‑Facing entry, and Self‑evolving runtime—each solving different problems, with distinct use‑cases, strengths, and trade‑offs.
Blade AI, the new open‑source intelligent layer for ChaosBlade, lets SREs describe fault scenarios in natural language and automatically handles target discovery, safety checks, execution, verification, and recovery, reducing a typical 20‑30 minute chaos experiment to a few seconds and enabling daily resilience testing.
The article walks through a systematic performance map of LangChain agents and demonstrates concrete latency, token‑usage, and concurrency optimizations—streaming responses, Redis caching, model routing, prompt trimming, context summarisation, dynamic tool selection, parallel graph nodes and batch processing—showing real‑world gains of up to 75% lower latency, 71% fewer tokens and a 217% throughput increase.
The article analyzes five common agent architecture paradigms, explains their strengths and weaknesses, recommends suitable frameworks for each, and provides a five‑step decision process to help teams select the most appropriate architecture for their business needs.
The article classifies agent failures into transient, structural, and semantic types, compares how Claude Code, OpenAI Codex, and Google Gemini CLI agents handle errors, and shows how LangGraph implements robust retry policies, fallback routing, and human‑in‑the‑loop handoff with concrete code examples and best‑practice guidelines.
The article analyzes why binding all tools to an LLM agent is costly and error‑prone, presents benchmark data showing token usage dropping six‑fold and error rates falling by up to five times with dynamic selection, and details three practical strategies—vector retrieval, LLM routing, and rule‑semantic hybrid—along with implementation tips, description engineering, multi‑turn handling, and common pitfalls.
This article breaks down LLM semantic routing as a classifier, compares keyword, embedding, and LLM‑based routes, provides full TypeScript implementations, introduces hybrid routing for speed and accuracy, and covers production‑grade observability and dynamic configuration to avoid common pitfalls.
The article analyzes how AI agent architecture choices—single‑agent versus multi‑agent, ReAct, plan‑and‑execute, orchestrator‑worker, hierarchical teams, reflection, and HITL—affect cost, reliability, and scalability, providing quantitative trade‑offs and industry examples to guide workload‑specific selection.
This guide outlines a six‑stage, 17‑week roadmap for becoming a production‑ready AI agent engineer by 2026, detailing essential skills such as LangGraph orchestration, Claude Agent SDK scheduling, context‑engineering primitives, evaluation pipelines, and curated free resources while warning against over‑hyped frameworks.
This article examines the evolution of large‑model agents, outlines four development stages, compares workflow, collaborative, and evolutionary frameworks, details core components such as perception, memory, planning, tools, and reflection, and explains how a progressive, loop‑based architecture can be applied across verticals like research, code generation, and complex workflow automation.
The article analyzes the shortcomings of traditional one‑shot RAG pipelines, introduces four Agentic RAG patterns that let an LLM‑driven agent control retrieval strategy, source selection, query rewriting and retry limits, and provides concrete TypeScript implementations with LangGraph, code snippets, and practical pitfalls.
This article walks through constructing a multi‑agent trading platform—analysts, researchers, traders, risk managers, and a portfolio manager—using LangChain, LangGraph, and LLMs (gpt‑4o, gpt‑4o‑mini), with real‑time data tools, shared and long‑term memory, ReAct loops, structured debates, and a final executable trade proposal.
The article explains why the old LangChain memory classes are deprecated, breaks down the new 1.x memory architecture into three independent components, and shows how to replace ConversationSummaryBufferMemory with a concise six‑line agent setup that supports multi‑user isolation, persistence, and summarization middleware.
The article analyzes multi‑agent collaboration as the core evolution of Agentic AI, presenting 2026 success cases from JP Morgan, enterprise onboarding, supply‑chain orchestration, and customer support, while dissecting failure patterns, governance risks, and recommended frameworks such as CrewAI, LangGraph, and AutoGen.
The article compares LangChain, LangGraph, and Deep Agents, outlining their roles, core differences, strengths, and weaknesses, and then maps typical LLM‑agent development scenarios to the most suitable framework, providing a concise decision matrix for developers.
This article walks through a lightweight, runnable knowledge‑base question‑answering skeleton built with Deep Agents, explains its current capabilities and limitations, shows the project structure and core code, and outlines a step‑by‑step upgrade path toward a production‑grade RAG system.
The article explains how to transition Deep Agents from a local CLI debugging tool to production-ready callable services, comparing CLI, Python SDK, LangGraph service, and ACP, and provides practical code examples, deployment steps, and a pre‑launch checklist for secure enterprise integration.
The article explains how Deep Agents separates long‑term rule storage (memory) from tool‑level boundaries (permissions), details backend persistence options, shows concrete configuration examples, and outlines best practices for safely managing team code assistants.
This article explains the Swarm collaboration model in LangGraph, contrasting it with Supervisor, detailing the handoff tool that atomically updates the active_agent state and routes control, and provides a complete travel‑booking example, custom handoff creation, common pitfalls, and best‑practice tips.
When a single LLM agent can’t handle research, writing, and fact‑checking simultaneously, the article breaks down four multi‑agent collaboration patterns—Supervisor, Swarm, Pipeline, and Mesh—detailing their architectures, code examples, pros, cons, suitable scenarios, and common pitfalls to help you pick the best fit.
The article analyzes the fast‑changing AI‑agent landscape, proposes five concrete criteria for filtering new technologies, outlines essential concepts such as context engineering, tool design, scheduler‑subagent patterns, evaluation frameworks, and recommends a stable 2026 tech stack while warning against hype‑driven tools.
The article details ten production‑grade pitfalls encountered when using LangGraph—ranging from misusing thread IDs and unbounded state growth to uncaught tool errors, infinite loops, concurrency conflicts, subgraph field mismatches, HITL timeouts, and misconfigured LangSmith tracing—each illustrated with concrete code, root‑cause analysis, and concrete remediation steps.
This article compares LangChain and LangGraph by implementing the same three‑stage code‑review pipeline with both frameworks, showing how LangChain offers a simple linear flow while LangGraph provides state‑machine orchestration for loops, conditional branches, and retries, and explains when each approach is preferable.
The article explains why streaming output is essential for responsive LLM agents, compares batch and streaming latency, details the five LangGraph streamMode options with code examples, shows how to combine them, and lists common pitfalls to avoid runtime errors and poor user experience.
The article compares LangGraph, create_agent, and Deep Agents—three abstraction layers in the LangChain ecosystem—explaining their hierarchy, trade‑offs, code examples, suitable scenarios, and common pitfalls to help developers pick the right tool for building AI assistants.
This article provides a detailed, step‑by‑step analysis of DeerFlow—an open‑source Super Agent Harness—covering its design philosophy versus traditional frameworks, core architecture layers, key services such as Gateway API, LangGraph Server and Sandbox, the long‑horizon agent features, skills system, deployment options, and real‑world enterprise case studies, all illustrated with diagrams and code snippets.
This article explains why single‑agent LLM pipelines fail when many tools are attached, introduces the Supervisor pattern that separates routing and execution across specialized agents, compares Tool‑Calling and Handoff approaches, provides a complete TypeScript implementation—including hierarchical supervisors—and lists five common pitfalls with concrete fixes.
The article examines why single‑agent setups fail, introduces the Supervisor pattern for clear responsibility separation, compares Tool‑Calling and Handoff approaches, provides a complete TypeScript implementation, explores hierarchical supervisors, and outlines five common pitfalls with concrete fixes.
This article provides a detailed, question‑and‑answer style walkthrough of LangChain and LangGraph, covering their core concepts, components, workflow patterns, memory mechanisms, LCEL syntax, graph construction, conditional edges, loops, multi‑agent collaboration, persistence, and a comparison with LlamaIndex, offering concrete code examples and practical insights for AI interview preparation.
This tutorial walks through the RAG Agent architecture, core components, Python implementation, continuous dialogue handling, integration with a real vector store, and a performance comparison with pure RAG, demonstrating how to enable AI to retrieve and answer from a private knowledge base.
This article walks through constructing a LangGraph‑based code‑assistant that generates code, automatically checks syntax and execution, iteratively fixes errors, and finalizes output, illustrating the full workflow, state definition, node implementations, graph assembly, and sample runs.
This article explains how to use LangGraph's checkpoint and memory features together with a state‑machine router to build a chatbot that retains full conversation history, compresses long contexts, and dynamically switches between chat and task modes across multiple turns.
This article explains how LangGraph's conditional edges let AI workflows dynamically choose the next step based on state, contrasting them with fixed edges, and provides step‑by‑step Python examples—including a router function, RAG retrieval routing, retry handling, and nested conditional logic.
This article explains how LangGraph's interrupt() and Command mechanisms enable safe human‑in‑the‑loop approvals for risky AI actions such as data deletion, financial transfers, and email sending, balancing security with efficiency through a clear pause‑resume workflow.
The article explains why streaming output is needed for LLMs, describes LangGraph's five stream modes, provides minimal code examples, compares the modes, shows asynchronous streaming with FastAPI, and outlines a practical streaming chatbot workflow.
This article walks through moving LangGraph from development to production by configuring persistent checkpoints with PostgresSaver or RedisSaver, using stream_mode for flexible streaming outputs, deploying the LangGraph Server via CLI or Docker Compose, handling retries, monitoring state, and visualizing the overall architecture.
This article explains LangGraph's checkpoint persistence, detailing its data structure, the role of thread_id for multi‑session isolation, the three available checkpointer backends, and how to use checkpoints for conversation memory, resumable workflows, and manual state updates, while highlighting common pitfalls.
This article explains why AI agents need human‑in‑the‑loop checkpoints, introduces the interrupt() coroutine that freezes graph execution, shows how to embed it in tools and full graphs, compares three intervention modes, and provides production‑grade tips and common pitfalls.
This article explains why subgraphs are needed for complex tasks, shows how to define a subgraph as a node, use named channels for data flow, isolate namespaces, reuse compiled subgraphs, add checkpoints, and presents a complete multi‑agent orchestration example in Python using LangGraph.
The article explains LangGraph’s checkpoint system that lets agents retain dialogue context, detailing the InMemorySaver for development and PostgresSaver for production, how to use checkpointer.put/get, thread_id for session isolation, manual state manipulation, and time‑travel replay, with full Python examples.
This tutorial explains the ReAct (Reason‑Act‑Observe) loop in LangGraph, shows how to control execution branches with conditional edges, provides a full hand‑written agent example, demonstrates the convenience of the prebuilt create_react_agent, and covers multi‑turn dialogue, streaming output, and loop‑count limits.
This article walks through LangGraph's three‑step tool‑calling workflow, shows how to define tools with the @tool decorator, demonstrates executing calls via ToolNode, provides a complete agent example, explains bind_tools, shares best‑practice guidelines, streaming execution, and a concise recap.
This article explains LangGraph’s two state‑update modes—overwrite and merge—shows how to use Annotated with custom reducers such as operator.add or add_messages, demonstrates when reducers run, and provides full Python examples, including persistence with checkpointers and custom merge functions.
This article explains how LangGraph’s graph‑based StateGraph lets you model agent workflows visually, contrasting it with LangChain’s high‑level API, detailing the three core components, showing complete Python examples, and highlighting benefits such as easier debugging, extensibility, and checkpoint support.
This article walks through building a production‑ready ReAct loop in 2026, replacing fragile string‑based tool parsing with native JSON tool calls, persisting state via LangGraph and Postgres, structuring evidence collection, handling errors, and addressing loop‑termination and cost‑control challenges.
This article explains the concept of Conditional Edge in LangGraph, shows how to add conditional edges with three parameters, demonstrates rule‑based, multi‑branch, and loop routing patterns, compares rule‑based versus LLM‑based routing, provides a complete customer‑service agent example, and lists common pitfalls and best‑practice checklists.
LangGraph’s state management hinges on three core concepts—Channel as the storage unit, Annotation as the declarative API for Channels, and Reducer as the pure function that merges updates—understanding their interactions resolves common bugs, enables custom state schemas, and ensures correct concurrent node updates.
The article compares LangChain and LangGraph by implementing the same three‑stage code‑review pipeline with identical agents and Gemini 2.5 Flash calls, showing when a linear toolkit suffices and when a state‑machine orchestrator becomes necessary.
This article dissects LangGraph's core execution engine, showing how it transforms LLM calls into a state‑machine workflow with mutable State, Nodes, Edges, Reducers, a scheduler loop, conditional branching, and parallel fan‑out/fan‑in execution.
This guide presents a step‑by‑step roadmap for becoming an Agentic AI engineer in 2026, covering Python fundamentals, LLM concepts, framework selection, advanced memory management, tool integration, production deployment, and interview preparation with concrete examples and best‑practice recommendations.
The article systematically dissects how Harness’s enterprise‑grade Super Agent architecture leverages LangChain’s component library and LangGraph’s execution engine, detailing dependency relationships, source‑level integration, and a real‑world multimodal customer‑service agent case.
This tutorial walks through creating a LangChain‑based AI agent by covering model integration, tool definition with @tool, short‑ and long‑term memory handling via checkpointers and vector stores, and assembling everything with create_agent, middleware, and code examples for a functional travel assistant.
This article provides a practical selection guide for developers building AI agents in 2026, dissecting the design, core components, strengths, and limitations of four major frameworks—LangChain, LlamaIndex, LangGraph, and AutoGen—while offering use‑case recommendations, code examples, and a decision‑tree to help choose the most suitable tool.
The article walks through a medical triage AI Agent built with LangChain, LangGraph, and LangSmith, showing how the framework supplies core model and tool interfaces, how graph‑based orchestration manages complex branching, loops and human‑in‑the‑loop steps, and how tracing and evaluation prove reliability for product managers.
This article introduces the Tree of Thoughts (ToT) reasoning framework, explains its search‑tree based workflow, demonstrates a full implementation with LangGraph to solve the classic wolf‑goat‑cabbage puzzle, and compares its reliability against a simple Chain‑of‑Thought approach.
This article compares a rigid sequential multi‑agent pipeline with a flexible blackboard architecture, showing how shared memory and a dynamic controller let specialist AI agents cooperate opportunistically, obey conditional user instructions, and achieve higher efficiency and instruction‑following scores.
This article dissects DeerFlow 2.0’s architecture, detailing its TypeScript‑React frontend, Python‑LangGraph backend, FastAPI interface, the deerflow‑harness core, agent and skill scheduling mechanisms, three collaboration modes, and how it compares to OpenClaw.
This article walks through creating a LangGraph workflow in Python, first using OpenAI’s GPT‑5‑nano model, then swapping to Alibaba’s Qwen 3.5‑plus model, showing how to suppress warnings, filter out thinking responses, visualize the graph, and troubleshoot common errors, all without any prior AI coding experience.
This article introduces the PEV (Planner‑Executor‑Verifier) architecture, explains why AI agents need a verification step to avoid blindly trusting faulty tool outputs, demonstrates a full implementation with LangGraph, compares its robustness to a naïve baseline, and discusses its advantages, limitations, and suitable use cases.
The article analyzes the rise of multi‑agent collaboration frameworks as the core infrastructure of Agentic AI in 2026, compares CrewAI, AutoGen, LangGraph and OpenAI Swarm on usability, production capability, strengths, weaknesses and market share, provides code examples, expert insights and a practical adoption roadmap.
DeerFlow 2.0, the open‑source Agent harness from ByteDance that quickly amassed over 48 000 GitHub stars, is dissected across five dimensions—sub‑agents, sandbox isolation, long‑term memory, Skill ecosystem, and MCP integration—to explain its architecture, deployment workflow, real‑world use cases, and the community’s mixed enthusiasm.
This article dissects the shortcomings of single‑agent LLM pipelines, introduces the Supervisor‑based Multi‑Agent architecture with LangGraph, demonstrates parallel task execution, robust error handling, and result merging, and provides concrete interview guidance backed by real performance data.
This article explains the planning architecture for AI agents, contrasts it with the ReAct approach, provides step‑by‑step Python code using LangChain and LangGraph, evaluates both methods on task completion and process efficiency, and discusses when each architecture is most suitable.
The article introduces metacognitive agents that equip AI with a self‑model to evaluate confidence, domain relevance, tool availability, and risk before acting, demonstrating a LangGraph‑based medical triage assistant with code, workflow, safety advantages, and practical test results.
This step‑by‑step tutorial shows how to create a functional AI development‑assistant agent using Spring Boot, LangChain4j, and LangGraph, covering environment setup, tool, state, and workflow definitions, API exposure, testing, Docker packaging, and optional enhancements.
This article introduces the Reflection mode—a generate‑critique‑refine loop that enables large language models to self‑review and improve generated code, demonstrates a full implementation with Nebius AI Studio and LangGraph, and evaluates the approach with concrete Fibonacci examples and quantitative scoring.
This article introduces the background and key advantages of the DeepAgents framework, explains its four core capabilities—task planning, context management, sub‑agent generation, and long‑term memory—and provides a step‑by‑step code example that builds a complex AI agent with just a few lines of Python.
This article analyzes the six pillars of context engineering for large language models, focusing on compression techniques, extractive vs. abstractive methods, the LLMLingua toolkit, dynamic orchestration with routing and agentic RAG, and how LangGraph enables sophisticated agent‑driven workflows.
This article explains why large language models alone can hallucinate, introduces the tool‑using agent architecture, and provides a step‑by‑step Python tutorial using LangChain, LangGraph, and Tavily to create, run, and evaluate a real‑time web‑search capable AI assistant.
This article introduces the ReAct (Reason + Act) agent pattern, explains its reasoning‑action‑observation loop, shows how to build a basic single‑call agent and a full ReAct agent with LangGraph, compares their performance on a multi‑step query, and provides a quantitative evaluation highlighting ReAct’s advantages and trade‑offs.
Deep Agents, an open‑source framework built on LangGraph, bundles planning, file‑system tools, sub‑agent coordination and context management into a ready‑to‑run AI agent that can be launched with three lines of Python code and fully customized for diverse applications.
This article walks through the design and implementation of a production‑ready Web AI agent that uses FastAPI as the HTTP layer, LangGraph to orchestrate multi‑step reasoning, and MCP to expose external tools, showing how to manage state, integrate multiple LLM providers, and extend the system with persistence, rate‑limiting, and monitoring.
OpenMAIC, an open‑source multi‑agent platform from Tsinghua, lets users upload a PDF or topic and automatically creates a full virtual classroom—including AI professor, AI students, slides, quizzes, and a whiteboard for step‑by‑step problem solving—using LangGraph orchestration and support for major LLMs.
This article updates the comparison of LangGraph and Semantic Kernel for Python AI agents, outlines their recent releases, explains their core architectural models, shows side‑by‑side code examples, and provides concrete decision criteria for choosing the appropriate framework.
This article chronicles the step‑by‑step development of a game‑data analysis agent, detailing three architectural versions, the challenges of domain terminology, LLM uncertainty, permission granularity, and the engineering solutions—including LangGraph, Dify, custom prompts, state management, security checks, token optimization, and deployment within an internal network.
The article explains how enterprises can overcome the "massive data, scarce insight" paradox in e‑commerce by adopting a multi‑agent architecture built on LangGraph and Amazon Bedrock AgentCore, detailing the system’s layered design, state management, end‑to‑end QBR report generation, and production‑grade deployment steps.
This article explains how to integrate durable long‑term memory into LangGraph agents, covering memory types, their coordination, limitations of native LangGraph storage, and a step‑by‑step implementation using Trustcall’s schema‑driven extractors for both user profiles and paper collections.
