# Langgraph LangGraph is a low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The thread is the Workflow: Durable AI agents without changing Agent code DevFeed: [The thread is the Workflow: Durable AI agents without changing Agent code]() Original publisher: [Read original article]() Author: Greg Haskins Published: 2026-09-03T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [AI Agent](), [Langgraph](), [Code](), [Framework]() Tags: [agent](), [ai-agents](), [community](), [crashes](), [durability](), [langgraph](), [state](), [workflow]() ### AI overview The article explains how Temporal Workflows add durable execution to LangGraph-based AI agents without requiring changes to the agent code. It describes mapping Agent Protocol Threads to Temporal Workflows, Runs to Activities, and checkpoints to Workflow state so executions can recover after worker crashes. ### Source excerpt Learn how Temporal Workflows can make LangGraph-based AI agents durable, preserving threads and checkpoints across crashes without changing agent code. ## Private Preview: DigitalOcean Managed Agents Runtime Services DevFeed: [Private Preview: DigitalOcean Managed Agents Runtime Services]() Original publisher: [Read original article]() Author: Salman Paracha Published: 2026-08-25T19:01:05Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Agent Harness](), [Artificial Intelligence](), [Cloud](), [Claude Code](), [codex](), [Langgraph](), [Command-line interface]() Tags: [agents](), [ai-agents](), [apis](), [claude-code](), [cli](), [cloud](), [codex](), [coding-agents](), [digitalocean](), [gateway](), [harness](), [langgraph](), [preview](), [product-updates](), [services](), [workflows]() ### AI overview DigitalOcean announces Managed Agents Runtime Services (M.A.R.S.) in private preview, combining Harness Runtime and Action Gateway. The managed service provides cloud infrastructure for persistent, scalable agent sessions and governed access to tools, APIs, and SaaS systems. ### Source excerpt AI agents are helping developers, teams, and businesses do more: writing and executing code, conducting research, and running dynamic workflows across systems. But that ability is often bounded by where they run. Close the laptop, and the work stops there. You can't pick it up on another device, hand off to a teammate, or scale it across users. Moving agents to cloud VMs solves part of this problem; developers and companies building agent harness frameworks still have to build a high-fidelity experience that can match a local session, including agent friendly execution environments, session persistence, secure tool access, human-in-the-loop approvals, and observability. Now available in Private Preview, DigitalOcean Managed Agents Runtime Services (M.A.R.S.) provides that infrastructure as a fully managed service. It gives developers, teams, and ISVs a powerful yet lightweight environment for operating coding agents and long-running, multi-tool agentic workflows without building and managing the underlying infrastructure themselves. M.A.R.S. brings together two products: Harness Runtime provides the managed execution environment in which agents run, persist, and scale. Action Gateway gives those same agents governed access to the tools, APIs, and SaaS systems they need to complete real-world work. Rather than requiring you to rebuild your agent around a proprietary framework, M.A.R.S. lets you define an environment template that packages your preferred harness, dependencies, tools, and configuration. Designed to work with Claude Code, Codex CLI, and OpenCode, as well as agents built with LangGraph or CrewAI, giving teams the freedom to choose the agent experience that best fits their needs without being locked into a single harness or framework. Agent sessions that outlive your laptop Harness Runtime helps agent sessions run independently of any local machine. They can start in under a second and resume from a pause in as little as 200 milliseconds while preserving ## Durable, flexible multi-agent systems DevFeed: [Durable, flexible multi-agent systems]() Original publisher: [Read original article]() Author: Melanie Warrick Published: 2026-08-06T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Multi Agent Systems](), [Orchestration](), [systems](), [Langgraph](), [Persistence](), [Google]() Tags: [agents](), [distributed-system](), [google-adk](), [langgraph](), [multi-agent](), [orchestration](), [persistence](), [temporal-voices]() ### AI overview This article describes a multi-agent delivery fleet built with Google ADK and LangGraph, using Temporal for orchestration, persistence, retries, and resumability. It explains how to keep human oversight durable when agents or workers disconnect or when workflows wait for human responses. ### Source excerpt Running a multi-agent delivery fleet on Google ADK and LangGraph, and what it takes to make the human in the loop durable. ## Durable Digest: July highlights DevFeed: [Durable Digest: July highlights]() Original publisher: [Read original article]() Author: Temporal Technologies Published: 2026-07-30T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [releases](), [distributed-systems](), [Cloud Run](), [Langgraph](), [datadog](), [API]() Tags: [announcements](), [api](), [cloud](), [cloud-run](), [datadog](), [distributed-systems](), [explore](), [langgraph](), [releases]() ### AI overview Temporal's July Durable Digest summarizes product releases and updates, including pre-release Serverless Workers for GCP Cloud Run, public-preview LangGraph and LangSmith integrations, generally available billing and metrics APIs, Datadog and Vantage integrations, and improvements to the Worker Status UI. ### Source excerpt Highlights from July include major product releases that make it easier to build and operate durable applications, and enhanced visibility into your Workers. ## LangGraph in production: Temporal's LangGraph Plugin adds Durable Execution DevFeed: [LangGraph in production: Temporal's LangGraph Plugin adds Durable Execution]() Original publisher: [Read original article]() Author: Brian Strauch Published: 2026-07-16T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Langgraph](), [Orchestration](), [Agent Framework](), [human review](), [Python](), [telemetry]() Tags: [agent-framework](), [crash-recovery](), [human-review](), [langgraph](), [orchestration](), [product-news](), [python]() ### AI overview Temporal announces a public-preview LangGraph integration for Python, allowing LangGraph agents to run on Temporal with automatic failure recovery, durable execution, human-in-the-loop waits, and support for long-running work. The announcement also covers LangSmith integrations for Python and TypeScript. ### Source excerpt LangGraph agents now run on Temporal: automatic crash recovery, free human-in-the-loop waits, and full observability via new LangSmith integration. ## Coding Challenge #123 - Database Driven LLM Wiki DevFeed: [Coding Challenge #123 - Database Driven LLM Wiki]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-06-06T08:01:47Z Content type: tutorial Language: en Sources: [Coding Challenges]() Topics: [Wiki](), [LLMs](), [Database](), [Langgraph](), [LangChain](), [Retrieval Augmented Generation (RAG)]() Tags: [coding](), [database](), [knowledge-base](), [langchain](), [langgraph](), [llms](), [rag]() ### AI overview A coding challenge to build a personal LLM wiki that incrementally maintains a structured Markdown knowledge base from curated sources. The proposed implementation uses Oracle AI Database for vector embeddings and full-text indexes, hybrid search for retrieval, LangGraph for workflows, and LangChain for LLM integration. ### Source excerpt This challenge is to build your own database powered LLM Wiki. ## Razorpay Oncall Agent: From 30-Minute Investigations to 90-Second AI Analysis DevFeed: [Razorpay Oncall Agent: From 30-Minute Investigations to 90-Second AI Analysis]() Original publisher: [Read original article]() Author: Anuj Gupta Published: 2026-04-29T06:56:11Z Content type: article Language: en Sources: [Razorpay Engineering - Medium]() Topics: [Artificial Intelligence](), [Langgraph](), [incident](), [SRE](), [observability](), [Large Language Model](), [Retrieval-Augmented Generation]() Tags: [agent](), [ai](), [incident](), [langgraph](), [logs](), [metrics](), [observability](), [retrieval-augmented-generation](), [sre]() ### AI overview Razorpay describes building a multi-agent AI system to automate production incident investigations. The Oncall Agent uses LangGraph, an LLM, alerting tools, and two retrieval-augmented generation systems containing architecture, dependency, and diagnostic runbook context. ### Source excerpt Our on-call engineers were spending 30 minutes investigating every production alert. Here's what happened when we automated it. At 3 AM, alerts don't care about your sleep schedule. When our payment infrastructure threw an error last month, our on-call engineer spent 32 minutes jumping between six different monitoring systems before understanding what was broken. One tool for metrics. Another for logs. Third tool for pod health. And multiple more for infrastructure, deployment history and database health. By the time they identified the root cause (a bad deployment), payment failures had already impacted customers for nearly 40 minutes. This wasn't their fault. They followed our runbook perfectly. The problem was that no single system could tell them "here's what's wrong and why." They had to manually connect dots across disconnected observability tools. That's when we asked ourselves: what if AI could do this investigation for us? The Metric Nobody Optimizes For The SRE world talks endlessly about Mean Time to Detect (how fast you catch problems) and Mean Time to Resolve (how fast you fix them). But there's a critical phase hiding between them: Mean Time to Investigate. MTTI is the gap from "we know it's broken" to "we know what to fix." At Razorpay, this phase was consuming 20-40 minutes per incident. With 15-20 incidents weekly, that's 6-8 hours of engineering time spent doing repetitive investigative work. Worse, the quality was inconsistent. Senior engineers knew exactly which systems to check for payment alerts. Junior engineers sometimes checked irrelevant dashboards or missed critical correlations. The investigation depended entirely on who was on-call that night. What We Built (And Why It Works) Razorpay Oncall Agent is a multi-agent AI system that automates incident investigation. The architecture is built on LangGraph, a framework for creating stateful workflows with conditional logic, and uses LLM as the reasoning engine. Here's how the components work t ## Контроль против гибкости: два подхода к созданию AI-агентов DevFeed: [Контроль против гибкости: два подхода к созданию AI-агентов]() Original publisher: [Read original article]() Author: Mentalitet (red\_mad\_robot) Published: 2025-11-14T17:15:42Z Content type: article Language: ru Sources: [Redmadrobot EN](), [Redmadrobot RU]() Topics: [Artificial Intelligence](), [Large Language Model](), [AI Development](), [Natural language processing](), [Langgraph](), [SDKs](), [OpenAI](), [cursor]() Tags: [agent](), [agents](), [ai](), [cursor](), [development](), [development-kit](), [langgraph](), [llm](), [llm-05dacf5e57d9](), [llm-12323b29df45](), [nlp](), [openai](), [red-mad-robot](), [sdk](), [software-development](), [tag-1605473766c5](), [tag-61cd5a476b1d](), [tag-68e701e78517](), [tag-93edcb27480f](), [tag-b0a411324cb6](), [tag-dd24610a6838]() ### AI overview The article compares code-first and graph-first approaches to building multi-agent systems. It describes how LLMs coordinate tools and other agents, including ReAct, handoff, agent-as-tool, and group-chat patterns, drawing on the author's development experience and documentation from OpenAI and LangGraph. ### Source excerpt Привет! Меня зовут Женя Орлов, я руковожу NLP-разработкой в red_mad_robot. Мы экспериментируем с мультиагентными системами и изучаем, как LLM ведут себя в разных архитектурах. В процессе накопилось много наблюдений и рабочих инсайтов -- хочу поделиться тем, что помогает нам при проектировании AI-агентов. В последнее время внимание инженеров и исследователей привлекают агентные архитектуры -- системы, где LLM координирует работу внутренних инструментов и других моделей. Разберём два основных подхода к построению агентных систем. Читать далее ## From prototype to production-ready agentic AI solution: A use case from Grid Dynamics DevFeed: [From prototype to production-ready agentic AI solution: A use case from Grid Dynamics]() Original publisher: [Read original article]() Author: Eugene Steinberg Published: 2025-09-29T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [AI Agent](), [Artificial Intelligence](), [Langgraph](), [Error Handling](), [retry](), [Redis]() Tags: [ai](), [case-study](), [community](), [error-handling](), [langgraph](), [production](), [redis](), [retry](), [state-management]() ### AI overview A Grid Dynamics case study describes building a deep research agent for a Fortune 500 manufacturer. The team used LangGraph initially, then migrated to Temporal after encountering challenges with error handling, retries, state management, caching, scaling, custom workflows, and human-in-the-loop interactions. ### Source excerpt What happens when a promising AI agent prototype hits the high stakes of a prod environment? Grid Dynamics found out the hard way. ## Сравнение AI-систем для глубокого анализа данных и исследований DevFeed: [Сравнение AI-систем для глубокого анализа данных и исследований]() Original publisher: [Read original article]() Author: kekslop (red\_mad\_robot) Published: 2025-03-18T12:40:03Z Content type: comparison Language: ru Sources: [Redmadrobot EN](), [Redmadrobot RU]() Topics: [Artificial Intelligence](), [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [Langgraph](), [vllm](), [OpenAI](), [Claude]() Tags: [ai](), [ai-6560afdd46fd](), [deepresearch](), [langgraph](), [llm](), [openai](), [rag](), [research](), [sast](), [structured-output](), [tag-1cd610c0e518](), [tag-5e27d5297aca](), [tag-8e9a901cca08](), [tag-baf5012a40ff](), [vllm]() ### AI overview The article compares OpenAI, Grok, Perplexity, and the author's NDT Deep Research system on five analytical tasks, including financial-market analysis, technology research, and identifying promising AI startups. NDT Deep Research uses LangGraph, Tavily, multiple language models, Structured Output, vLLM, retrieval-augmented generation, and source analysis. The author reports that system changes reduced generation costs threefold and latency by 40%. ### Source excerpt Как я сравнил топовые AI-модели для глубокого анализа данных и собственную разработку Привет! Меня зовут Валера Ковальский, я CEO NDT by red_mad_robot. Недавно я протестировал ведущие AI-системы, которые способны проводить глубокие исследования, и делюсь с вами результатами. Читать далее