# AI use cases for Langgraph (langchain-ai/langgraph)

Repo: [langchain-ai/langgraph](https://github.com/langchain-ai/langgraph) | Use cases: 3 | Page: https://dedupedai.com/use-cases/langchain-ai-langgraph/

LangGraph is a low-level orchestration framework from LangChain for building, managing and deploying long-running, stateful agents. It provides durable execution that resumes from where a run left off, human-in-the-loop checkpoints to inspect and modify agent state, short-term and long-term memory, debugging through LangSmith, and production deployment. It can be used without LangChain, and Deep Agents is a higher-level package built on top of it.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Build an agent that resumes after a crash: https://dedupedai.com/use-cases/langchain-ai-langgraph/build-an-agent-that-resumes-after-failure/
- Pause an agent for human approval before it acts: https://dedupedai.com/use-cases/langchain-ai-langgraph/add-human-approval-to-an-agent/
- Give an agent memory that lasts across sessions: https://dedupedai.com/use-cases/langchain-ai-langgraph/give-an-agent-memory-across-sessions/

## How to build an agent that resumes after a crash with Langgraph

Repo: [langchain-ai/langgraph](https://github.com/langchain-ai/langgraph) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README

Use LangGraph's durable execution so a long agent run picks up exactly where it stopped.

**Who it is for:** A developer whose research or data agent runs for an hour and loses everything when one API call times out.

**The problem:** A long agent loop that dies halfway through has to start again from zero, wasting time and tokens.

**The outcome:** An agent built as a LangGraph graph that persists its progress and resumes from the last step after a failure.

### Steps

1. Install the library with `pip install -U langgraph`
2. Work through the LangGraph Quickstart in the docs to define your agent as a graph of steps
3. Add persistence following the Durable execution guide so each step's state is saved
4. Kill the process mid-run, restart it and confirm it continues from the last saved step
5. Use the Guides for streaming, memory and persistence, and design patterns such as branching and subgraphs

### Prompt to paste into your agent

```text
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.
```

### 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.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/langchain-ai-langgraph/build-an-agent-that-resumes-after-failure/

## How to pause an agent for human approval before it acts with Langgraph

Repo: [langchain-ai/langgraph](https://github.com/langchain-ai/langgraph) | Difficulty: Intermediate | Time: 45 minutes | Basis: From the README

Use LangGraph interrupts to inspect and edit an agent's state before a risky step runs.

**Who it is for:** A team building an agent that sends emails, edits records or spends money and wants a person to sign off first.

**The problem:** Fully autonomous agents can take an action you cannot undo before anyone has looked at it.

**The outcome:** An agent that stops at chosen points, shows its state for review and continues only after a person approves or edits it.

### Steps

1. Install with `pip install -U langgraph`
2. Model the agent as a LangGraph graph, putting the risky action in its own step
3. Follow the Human-in-the-loop guide (interrupts) to pause before that step
4. Inspect and, if needed, modify the agent state at the pause, then resume the run
5. Pair it with persistence so a paused run can wait for a reviewer without losing its place

### Prompt to paste into your agent

```text
With langchain-ai/langgraph, add a human-in-the-loop interrupt before the <risky action> step of my agent. When it pauses, print the pending state, let me edit it, and only resume after I approve.
```

### Caveat

Interrupt syntax lives in the docs, not the README, and it changes between releases, so match the docs to your installed version.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/langchain-ai-langgraph/add-human-approval-to-an-agent/

## How to give an agent memory that lasts across sessions with Langgraph

Repo: [langchain-ai/langgraph](https://github.com/langchain-ai/langgraph) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README

Combine short-term working memory and long-term memory so an assistant remembers users between conversations.

**Who it is for:** A builder of a support or tutoring assistant that forgets every user the moment a session ends.

**The problem:** Stateless agents make users repeat themselves and cannot build on past conversations.

**The outcome:** An agent with working memory for the current task and persistent memory it reads back in later sessions.

### Steps

1. Install with `pip install -U langgraph`
2. Read the Comprehensive memory guide for short-term and long-term memory
3. Keep in-progress reasoning in short-term memory and save durable facts to long-term memory
4. Start a new session for the same user and confirm the agent recalls what it stored
5. Trace a few runs in LangSmith to check what is written to and read from memory

### Prompt to paste into your agent

```text
Using langchain-ai/langgraph, give my <assistant> short-term memory for the current conversation and long-term memory keyed by user, following the Comprehensive memory docs. Demonstrate it remembering a fact from a previous session.
```

### Caveat

LangSmith is a separate LangChain product; the README does not state its pricing, so check before relying on it in production.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/langchain-ai-langgraph/give-an-agent-memory-across-sessions/