--- title: Building Distributed Agents with LangGraph + Temporal description: A Temporal Technologies solutions architect demonstrates how combining Temporal with LangGraph solves the pain points of running AI agents in distributed cloud... canonical: https://daily.dev/posts/building-distributed-agents-with-langgraph-temporal-nip0y2pbv twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Building Distributed Agents with LangGraph + Temporal | daily.dev og:description: A Temporal Technologies solutions architect demonstrates how combining Temporal with LangGraph solves the pain points of running AI agents in distributed cloud... og:url: https://daily.dev/posts/building-distributed-agents-with-langgraph-temporal-nip0y2pbv og:image: https://api.daily.dev/og/posts/nIP0Y2PBv.png og:image:alt: Building Distributed Agents with LangGraph + Temporal og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Building Distributed Agents with LangGraph + Temporal **[Temporal](https://daily.dev/sources/temporal)** · 11 min read · 0 upvotes · 0 comments ## Summary A Temporal Technologies solutions architect demonstrates how combining Temporal with LangGraph solves the pain points of running AI agents in distributed cloud environments, such as losing state on crashes, needing to manage checkpoint databases, and handling human-in-the-loop interruptions. A live demo compares a standalone LangGraph customer-support agent (which gets stuck on API failures and requires manual re-triggering) against the same agent wrapped in a Temporal workflow (which automatically retries, checkpoints progress, and survives worker crashes without losing state or incurring extra LLM costs). The presenter argues this requires minimal code (under 100 lines) and removes the need to manage queues, state databases, or durability infrastructure. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How can I make a LangGraph agent recover automatically from a crash without losing its state or re-running LLM calls? Wrapping the LangGraph application inside a Temporal workflow lets Temporal automatically save and load workflow state at each step of the agent loop, persisting it across multiple nodes using Raft consensus. If a worker crashes or an API call fails, Temporal resumes from the last checkpoint without re-incurring LLM costs, using a configured retry policy instead of manual re-triggering. _daily.dev surfaces practical patterns like this for engineers hardening agent pipelines against crashes and API failures._ ### What problems occur when a LangGraph agent that uses a local file-based checkpointer is deployed to a distributed cloud environment? There is no reliable local file system in the cloud, so retries may land on a different machine without access to the original checkpoint storage. This creates additional complexity around distributed file systems, load balancers, DNS/certificates, authentication, client-versus-agent sessions, and recovering from cloud outages, all of which typically require standing up and operating a separate state database. _Developers weighing agent infrastructure trade-offs can track approaches like this on daily.dev._ ## Similar posts on daily.dev - [From prototype to production-ready agentic AI solution: A use case from Grid Dynamics](https://daily.dev/posts/from-prototype-to-production-ready-agentic-ai-solution-a-use-case-from-grid-dynamics-macljegxn) · Temporal · 1 upvotes · 0 comments - [Durable, flexible multi-agent systems](https://daily.dev/posts/durable-flexible-multi-agent-systems-megbkitj3) · Temporal · 1 upvotes · 0 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#distributed-systems](https://daily.dev/tags/distributed-systems), [#langgraph](https://daily.dev/tags/langgraph) [View this post on daily.dev](https://daily.dev/posts/building-distributed-agents-with-langgraph-temporal-nip0y2pbv) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Building Distributed Agents with LangGraph + Temporal","url":"https://daily.dev/posts/building-distributed-agents-with-langgraph-temporal-nip0y2pbv","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/building-distributed-agents-with-langgraph-temporal-nip0y2pbv"},"datePublished":"2026-08-14T23:06:05.192Z","dateModified":"2026-08-15T08:01:10.558Z","description":"A Temporal Technologies solutions architect demonstrates how combining Temporal with LangGraph solves the pain points of running AI agents in distributed cloud...","image":"https://i.ytimg.com/vi/jCHWup7-m8o/sddefault.jpg","thumbnailUrl":"https://i.ytimg.com/vi/jCHWup7-m8o/sddefault.jpg","isAccessibleForFree":true,"articleSection":"Temporal","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Temporal","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/65290ca7e3ab46a7b1df76f2757800c2","url":"https://daily.dev/sources/temporal"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/building-distributed-agents-with-langgraph-temporal-nip0y2pbv","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"ai-agents,distributed-systems,langgraph","timeRequired":"PT11M","video":{"@type":"VideoObject","name":"Building Distributed Agents with LangGraph + Temporal","description":"A Temporal Technologies solutions architect demonstrates how combining Temporal with LangGraph solves the pain points of running AI agents in distributed cloud...","thumbnailUrl":"https://i.ytimg.com/vi/jCHWup7-m8o/sddefault.jpg","uploadDate":"2026-08-14T23:06:05.192Z","duration":"PT11M","url":"https://api.daily.dev/r/nIP0Y2PBv","embedUrl":"https://www.youtube.com/embed/jCHWup7-m8o"}} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Temporal","item":"https://daily.dev/sources/temporal"},{"@type":"ListItem","position":3,"name":"Building Distributed Agents with LangGraph + Temporal"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/building-distributed-agents-with-langgraph-temporal-nip0y2pbv#faq","mainEntity":[{"@type":"Question","name":"How can I make a LangGraph agent recover automatically from a crash without losing its state or re-running LLM calls?","acceptedAnswer":{"@type":"Answer","text":"Wrapping the LangGraph application inside a Temporal workflow lets Temporal automatically save and load workflow state at each step of the agent loop, persisting it across multiple nodes using Raft consensus. 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