#LangGraph
71 related articles
Building an AI Directory Content Pipeline with LangGraph and Contentful
Analyzing Monogram's AI directory content pipeline for Quicknode: a 5-node LangGraph workflow combining Tavily retrieval, GPT-4o generation, and Contentful category validation for 60+ structured fields.
Getting Started with LangGraph: From Agent Pain Points to Low-Level Orchestration Principles
A deep dive into LangGraph's low-level orchestration principles, comparing the hierarchy and use cases of LangChain Agent and Deep Agent to explain why fixed-workflow tasks need LangGraph rather than relying solely on Agents.
OpenUI 1.0: Letting LangGraph Agents Generate Interactive Interfaces Directly
OpenUI 1.0 is released: an MIT open-source, model-agnostic generative UI framework with LangChain/LangGraph integration, letting AI agents generate interactive interfaces directly.
Why Is LangGraph Supervisor So Token-Hungry? A Hop-by-Hop Cost Breakdown
A deep dive into why the LangGraph Supervisor pattern burns so many tokens: it's re-invoked after every worker, and context inflates hop by hop. We analyze cost structure via langgraph-supervisor source code and offer five verifiable optimization directions plus hop-by-hop measurement methods.
LangGraph + FastAPI + SSE: A Practical Guide to Building Streaming AI Agents
A hands-on tutorial on building streaming AI agents with LangGraph, FastAPI, and SSE: covering separation of agent orchestration and API transport, Open-Meteo tool integration, SSE streaming responses, and production gaps in persistence, idempotency, and observability.
LangChain or LangGraph? Let create_age…
LangChain or LangGraph? Let create_agent's Return Value Decide
In LangChain 1.4.3, create_agent actually returns a LangGraph graph. Five runnable scripts compare create_agent, StateGraph, and agent-as-node, with selection rules and common error fixes.
Building a Golden Hour Walk Planner with a 1B Model: Let AI Handle Language, Let Code Handle Math
A local 1B-model walk planner that keeps models on language and code on computation, using LangGraph pipelines and double-bracket place name validation to fight hallucinations. Includes qwen2.5 vs llama3.2 benchmarks, all under 0.5GB RAM.
LangGraph in Production: Five Years of Hard-Won Agent Architecture Experience
A 5-year Agent developer open-sources a production-grade LangGraph reference project, sharing lessons on architecture evolution from conditional trees to graph state machines.
Andrew Ng's Agent Course Highlights: A Practical Path from Single Agents to Full Automation
Key insights from Andrew Ng's AI Agents in LangGraph course: 5 agentic workflow design patterns, building ReAct agents from scratch, LangGraph nodes and state, Agentic Search, persistence, human-in-the-loop, and multi-agent architectures.
Building a Self-Hosted GitLab AI Review Agent with LangGraph.js: Real-World Lessons from Production
A developer's LangGraph.js GitLab AI Agent has reviewed 1,000+ MRs in production with a 2/3 adoption rate. Key lessons: middleware over prompts, Critic filtering, Qwen self-hosting, and per-project memory.
Where Should File Artifacts Live in LangGraph's Pause-and-Resume Workflows?
Where should generated files live in LangGraph pause-resume workflows? This article examines the boundary between graph state and durable output, explaining the immutable revisions + lightweight manifest pattern and a four-layer storage strategy.
Why Do Unattended AI Agents Redo Completed Work? LangGraph's Context Trap
A developer shares how their LangGraph Agent redid completed nodes during unattended runs. Learn why context accumulation — not model drift — is the real culprit, and how active context collapse fixes it.
AI Agent Engineer in Practice: Prompt Injection Defense and LangGraph Production Debugging
A deep technical interview breakdown for AI Agent engineers: LangGraph node/edge design, MCP tool trust models, multi-layer Prompt injection defense, and real production incident diagnosis.
LangGraph 1.0 Hands-On Tutorial: Building Persistent AI Agents from Scratch
A deep dive into the LangGraph 1.0 tutorial series — covering durable execution, memory management, Human in the Loop, and multi-agent systems across six lessons.
Two Years as an AI Agent Engineer: A Real Interview Breakdown on Token Costs, LangGraph, and Production Incidents
A real foundation-tier AI Agent engineer interview breakdown: LangGraph nodes & cost traps, MCP protocol, multi-agent architecture, token cost engineering, and a research agent death-loop incident post-mortem.
From Chain to Graph: Why LangGraph Has Become the Engineering Foundation for AI Applications
Why is the shift from Chain to Graph inevitable? This article breaks down LangGraph's three core elements, compares chain vs. graph across four dimensions, and explains its value as the engineering foundation for AI applications.
LangGraph + MCP + Medical RAG: A Practical Introduction to Enterprise-Grade Multi-Agent Systems
Turing College instructor Loulan breaks down enterprise-grade multi-agent development using LangGraph, LangChain, and MCP through a real medical RAG project.
langgraph-jev: Building Type-Safe Decision Nodes for LangGraph
langgraph-jev wraps TypeSafe's Jev API as typed LangGraph decision nodes, offering typed I/O, confidence scoring, and deterministic routing for reliable AI agent workflows.
New VS Code Extension: Visualize and Debug Your LangGraph Agent Workflows
Agentic Engineering Extension is a VS Code plugin that renders LangGraph and CrewAI agent codebases as interactive graphs, with real execution replay, bidirectional code editing, and built-in evaluation.
LangGraph Agent Memory Staleness: How to Prevent Agents from Making Decisions on Outdated Data
LangGraph persistent memory silently uses stale facts when source data changes. This article breaks down the root cause, compares full rebuild vs TTL vs proactive invalidation, and offers practical engineering solutions.