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docs: concepts durable execution (#3355)
Conceptual page for durable execution
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Eugene Yurtsev
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# Durable Execution
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**Durable execution** is a technique in which a process or workflow saves its progress at key points, allowing it to pause and later resume exactly where it left off. This is particularly useful in scenarios that require [human-in-the-loop](./human_in_the_loop.md), where users can inspect, validate, or modify the process before continuing, and in long-running tasks that might encounter interruptions or errors (e.g., calls to an LLM timing out). By preserving completed work, durable execution enables a process to resume without reprocessing previous steps -- even after a significant delay (e.g., a week later).
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LangGraph's built-in [persistence](./persistence.md) layer provides durable execution for workflows, ensuring that the state of each execution step is saved to a durable store. This capability guarantees that if a workflow is interrupted -- whether by a system failure or for [human-in-the-loop](./human_in_the_loop.md) interactions -- it can be resumed from its last recorded state.
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!!! tip
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If you are using LangGraph with a checkpointer, you already have durable execution enabled. You can pause and resume workflows at any point, even after interruptions or failures.
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To make the most of durable execution, ensure that your workflow is designed to be [deterministic](#determinism-and-consistent-replay) and [idempotent](#idempotency) and wrap any side effects or non-deterministic operations inside [tasks](./functional_api.md#task). You can use [tasks](./functional_api.md#task) from both the [StateGraph (Graph API)](./low_level.md) and the [Functional API](./functional_api.md).
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## Requirements
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To leverage durable execution in LangGraph, you need to:
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1. Enable [persistence](./persistence.md) in your workflow by specifying a [checkpointer](./persistence.md#checkpointer-libraries) that will save workflow progress.
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2. Specify a [thread identifier](./persistence.md#threads) when executing a workflow. This will track the execution history for a particular instance of the workflow.
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3. Wrap any non-deterministic operations (e.g., random number generation) or operations with side effects (e.g., file writes, API calls) inside [tasks][langgraph.func.task] to ensure that when a workflow is resumed, these operations are not repeated for the particular run, and instead their results are retrieved from the persistence layer. For more information, see [Determinism and Consistent Replay](#determinism-and-consistent-replay).
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## Determinism and Consistent Replay
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When you resume a workflow run, the code does **NOT** resume from the **same line of code** where execution stopped; instead, it will identify an appropriate [starting point](#starting-points-for-resuming-workflows) from which to pick up where it left off. This means that the workflow will replay all steps from the [starting point](#starting-points-for-resuming-workflows) until it reaches the point where it was stopped.
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As a result, when you are writing a workflow for durable execution, you must wrap any non-deterministic operations (e.g., random number generation) and any operations with side effects (e.g., file writes, API calls) inside [tasks](./functional_api.md#task) or [nodes](./low_level.md#nodes).
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To ensure that your workflow is deterministic and can be consistently replayed, follow these guidelines:
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- **Avoid Repeating Work**: If a [node](./low_level.md#nodes) contains multiple operations with side effects (e.g., logging, file writes, or network calls), wrap each operation in a separate **task**. This ensures that when the workflow is resumed, the operations are not repeated, and their results are retrieved from the persistence layer.
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- **Encapsulate Non-Deterministic Operations:** Wrap any code that might yield non-deterministic results (e.g., random number generation) inside **tasks** or **nodes**. This ensures that, upon resumption, the workflow follows the exact recorded sequence of steps with the same outcomes.
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- **Use Idempotent Operations**: When possible ensure that side effects (e.g., API calls, file writes) are idempotent. This means that if an operation is retried after a failure in the workflow, it will have the same effect as the first time it was executed. This is particularly important for operations that result in data writes. In the event that a **task** starts but fails to complete successfully, the workflow's resumption will re-run the **task**, relying on recorded outcomes to maintain consistency. Use idempotency keys or verify existing results to avoid unintended duplication, ensuring a smooth and predictable workflow execution.
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For some examples of pitfalls to avoid, see the [Common Pitfalls](./functional_api.md#common-pitfalls) section in the functional API, which shows
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how to structure your code using **tasks** to avoid these issues. The same principles apply to the [StateGraph (Graph API)][langgraph.graph.state.StateGraph].
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## Using tasks in nodes
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If a [node](./low_level.md#nodes) contains multiple operations, you may find it easier to convert each operation into a **task** rather than refactor the operations into individual nodes.
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=== "Original"
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```python
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from typing import NotRequired
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from typing_extensions import TypedDict
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import uuid
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from langgraph.checkpoint.memory import MemorySaver
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from langgraph.graph import StateGraph, START, END
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import requests
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# Define a TypedDict to represent the state
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class State(TypedDict):
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url: str
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result: NotRequired[str]
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def call_api(state: State):
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"""Example node that makes an API request."""
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# highlight-next-line
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result = requests.get(state['url']).text[:100] # Side-effect
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return {
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"result": result
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}
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# Create a StateGraph builder and add a node for the call_api function
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builder = StateGraph(State)
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builder.add_node("call_api", call_api)
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# Connect the start and end nodes to the call_api node
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builder.add_edge(START, "call_api")
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builder.add_edge("call_api", END)
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# Specify a checkpointer
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checkpointer = MemorySaver()
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# Compile the graph with the checkpointer
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graph = builder.compile(checkpointer=checkpointer)
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# Define a config with a thread ID.
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thread_id = uuid.uuid4()
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config = {"configurable": {"thread_id": thread_id}}
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# Invoke the graph
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graph.invoke({"url": "https://www.example.com"}, config)
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```
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=== "With task"
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```python
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from typing import NotRequired
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from typing_extensions import TypedDict
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import uuid
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from langgraph.checkpoint.memory import MemorySaver
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from langgraph.func import task
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from langgraph.graph import StateGraph, START, END
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import requests
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# Define a TypedDict to represent the state
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class State(TypedDict):
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urls: list[str]
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result: NotRequired[list[str]]
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@task
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def _make_request(url: str):
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"""Make a request."""
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# highlight-next-line
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return requests.get(url).text[:100]
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def call_api(state: State):
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"""Example node that makes an API request."""
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# highlight-next-line
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requests = [_make_request(url) for url in state['urls']]
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results = [request.result() for request in requests]
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return {
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"results": results
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}
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# Create a StateGraph builder and add a node for the call_api function
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builder = StateGraph(State)
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builder.add_node("call_api", call_api)
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# Connect the start and end nodes to the call_api node
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builder.add_edge(START, "call_api")
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builder.add_edge("call_api", END)
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# Specify a checkpointer
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checkpointer = MemorySaver()
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# Compile the graph with the checkpointer
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graph = builder.compile(checkpointer=checkpointer)
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# Define a config with a thread ID.
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thread_id = uuid.uuid4()
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config = {"configurable": {"thread_id": thread_id}}
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# Invoke the graph
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graph.invoke({"urls": ["https://www.example.com"]}, config)
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```
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## Resuming Workflows
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Once you have enabled durable execution in your workflow, you can resume execution for the following scenarios:
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- **Pausing and Resuming Workflows:** Use the [interrupt][langgraph.types.interrupt] function to pause a workflow at specific points and the [Command][langgraph.types.Command] primitive to resume it with updated state. See [**Human-in-the-Loop**](./human_in_the_loop.md) for more details.
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- **Recovering from Failures:** Automatically resume workflows from the last successful checkpoint after an exception (e.g., LLM provider outage). This involves executing the workflow with the same thread identifier by providing it with a `None` as the input value (see this [example](./functional_api.md#resuming-after-an-error) with the functional API).
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## Starting Points for Resuming Workflows
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* If you're using a [StateGraph (Graph API)][langgraph.graph.state.StateGraph], the starting point is the beginning of the [**node**](./low_level.md#nodes) where execution stopped.
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* If you're making a subgraph call inside a node, the starting point will be the **parent** node that called the subgraph that was halted.
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Inside the subgraph, the starting point will be the specific [**node**](./low_level.md#nodes) where execution stopped.
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* If you're using the Functional API, the starting point is the beginning of the [**entrypoint**](./functional_api.md#entrypoint) where execution stopped.
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@@ -829,7 +829,8 @@ from langgraph.checkpoint.memory import MemorySaver
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from langgraph.func import entrypoint, task
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from langgraph.types import StreamWriter
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# Global variable to track the number of attempts
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# This variable is just used for demonstration purposes to simulate a network failure.
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# It's not something you will have in your actual code.
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attempts = 0
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@task()
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@@ -29,6 +29,7 @@ The conceptual guide does not cover step-by-step instructions or specific implem
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- [Memory](memory.md): Memory in AI applications refers to the ability to process, store, and effectively recall information from past interactions. With memory, your agents can learn from feedback and adapt to users' preferences.
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- [Streaming](streaming.md): Streaming is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
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- [Functional API](functional_api.md): `@entrypoint` and `@task` decorators that allow you to add LangGraph functionality to an existing codebase.
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- [Durable Execution](durable_execution.md): LangGraph's built-in [persistence](./persistence.md) layer provides durable execution for workflows, ensuring that the state of each execution step is saved to a durable store.
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- [FAQ](faq.md): Frequently asked questions about LangGraph.
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## LangGraph Platform
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