{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "langchain-ai-langgraph--build-an-agent-that-resumes-after-failure",
 "repo": "langchain-ai/langgraph",
 "repo_url": "https://github.com/langchain-ai/langgraph",
 "repo_slug": "langchain-ai-langgraph",
 "title": "Build an agent that resumes after a crash",
 "page_title": "How to build an agent that resumes after a crash with Langgraph",
 "summary": "Use LangGraph's durable execution so a long agent run picks up exactly where it stopped.",
 "who": "A developer whose research or data agent runs for an hour and loses everything when one API call times out.",
 "problem": "A long agent loop that dies halfway through has to start again from zero, wasting time and tokens.",
 "outcome": "An agent built as a LangGraph graph that persists its progress and resumes from the last step after a failure.",
 "steps": [
  "Install the library with `pip install -U langgraph`",
  "Work through the LangGraph Quickstart in the docs to define your agent as a graph of steps",
  "Add persistence following the Durable execution guide so each step's state is saved",
  "Kill the process mid-run, restart it and confirm it continues from the last saved step",
  "Use the Guides for streaming, memory and persistence, and design patterns such as branching and subgraphs"
 ],
 "agent_prompt": "Using langchain-ai/langgraph, build a <describe agent> as a graph with durable execution, following the official Quickstart and Durable execution docs. Then show me that if I stop the process halfway, rerunning it resumes from the last completed step instead of starting over.",
 "agents": [
  "claude-code",
  "codex",
  "cursor"
 ],
 "difficulty": "Intermediate",
 "time": "1 hour",
 "basis": "readme",
 "caveat": "The README links out to the docs for all code, so plan to read the Quickstart and Durable execution pages; LangGraph is low level and you write the graph yourself.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "python",
 "requirements": [
  "Python and pip",
  "An LLM provider for your agent's model calls",
  "Optional: a LangSmith account for tracing and the LangSmith Deployment platform",
  "For JavaScript or TypeScript, the separate LangGraph.js library"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/langchain-ai-langgraph/build-an-agent-that-resumes-after-failure/",
 "md": "https://dedupedai.com/use-cases/langchain-ai-langgraph/build-an-agent-that-resumes-after-failure/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/langchain-ai-langgraph--build-an-agent-that-resumes-after-failure.json",
 "api_version": "v1"
}