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docs: document interrupt & HIL
- Document interrupt reference - Update conceptual guides for HIL - Split time-travel conceptual guide - Split breakpoints into separate conceptual guide - Update relevant how-tos - Update how-to index page for HIL with more information and recommendations - New how-to for multi turn conversation
This commit is contained in:
Eugene Yurtsev
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|
||||
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
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,132 @@
|
||||
# Breakpoints
|
||||
|
||||
Breakpoints pause graph execution at specific points and enable stepping through execution step by step. Breakpoints are powered by LangGraph's [**persistence layer**](./persistence.md), which saves the state after each graph step. Breakpoints can also be used to enable [**human-in-the-loop**](./human_in_the_loop.md) workflows, though we recommend using the [`interrupt` function](./human_in_the_loop.md#interrupt) for this purpose.
|
||||
|
||||
## Requirements
|
||||
|
||||
To use breakpoints, you will need to:
|
||||
|
||||
1. [**Specify a checkpointer**](persistence.md#checkpoints) to save the graph state after each step.
|
||||
2. [**Set breakpoints**](#setting-breakpoints) to specify where execution should pause.
|
||||
3. **Run the graph** with a [**thread ID**](./persistence.md#threads) to pause execution at the breakpoint.
|
||||
4. **Resume execution** using `invoke`/`ainvoke`/`stream`/`astream` (see [**The `Command` primitive**](./human_in_the_loop.md#the-command-primitive)).
|
||||
|
||||
## Setting breakpoints
|
||||
|
||||
There are two places where you can set breakpoints:
|
||||
|
||||
1. **Before** or **after** a node executes by setting breakpoints at **compile time** or **run time**. We call these [**static breakpoints**](#static-breakpoints).
|
||||
2. **Inside** a node using the [`NodeInterrupt` exception](#nodeinterrupt-exception).
|
||||
|
||||
### Static breakpoints
|
||||
|
||||
Static breakpoints are triggered either **before** or **after** a node executes. You can set static breakpoints by specifying `interrupt_before` and `interrupt_after` at **"compile" time** or **run time**.
|
||||
|
||||
=== "Compile time"
|
||||
|
||||
```python
|
||||
graph = graph_builder.compile(
|
||||
interrupt_before=["node_a"],
|
||||
interrupt_after=["node_b", "node_c"],
|
||||
checkpointer=..., # Specify a checkpointer
|
||||
)
|
||||
|
||||
thread_config = {
|
||||
"configurable": {
|
||||
"thread_id": "some_thread"
|
||||
}
|
||||
}
|
||||
|
||||
# Run the graph until the breakpoint
|
||||
graph.invoke(inputs, config=thread_config)
|
||||
|
||||
# Optionally update the graph state based on user input
|
||||
graph.update_state(update, config=thread_config)
|
||||
|
||||
# Resume the graph
|
||||
graph.invoke(None, config=thread_config)
|
||||
```
|
||||
|
||||
=== "Run time"
|
||||
|
||||
```python
|
||||
graph.invoke(
|
||||
inputs,
|
||||
config={"configurable": {"thread_id": "some_thread"}},
|
||||
interrupt_before=["node_a"],
|
||||
interrupt_after=["node_b", "node_c"]
|
||||
)
|
||||
|
||||
thread_config = {
|
||||
"configurable": {
|
||||
"thread_id": "some_thread"
|
||||
}
|
||||
}
|
||||
|
||||
# Run the graph until the breakpoint
|
||||
graph.invoke(inputs, config=thread_config)
|
||||
|
||||
# Optionally update the graph state based on user input
|
||||
graph.update_state(update, config=thread_config)
|
||||
|
||||
# Resume the graph
|
||||
graph.invoke(None, config=thread_config)
|
||||
```
|
||||
|
||||
!!! note
|
||||
|
||||
You cannot set static breakpoints at runtime for **sub-graphs**.
|
||||
If you have a sub-graph, you must set the breakpoints at compilation time.
|
||||
|
||||
Static breakpoints can be especially useful for debugging if you want to step through the graph execution one
|
||||
node at a time or if you want to pause the graph execution at specific nodes.
|
||||
|
||||
### `NodeInterrupt` exception
|
||||
|
||||
We recommend that you [**use the `interrupt` function instead**](#the-interrupt-function) of the `NodeInterrupt` exception if you're trying to implement
|
||||
[human-in-the-loop](./human_in_the_loop.md) workflows. The `interrupt` function is easier to use and more flexible.
|
||||
|
||||
??? node "`NodeInterrupt` exception"
|
||||
|
||||
The developer can define some *condition* that must be met for a breakpoint to be triggered. This concept of [dynamic breakpoints](./low_level.md#dynamic-breakpoints) is useful when the developer wants to halt the graph under *a particular condition*. This uses a `NodeInterrupt`, which is a special type of exception that can be raised from within a node based upon some condition. As an example, we can define a dynamic breakpoint that triggers when the `input` is longer than 5 characters.
|
||||
|
||||
```python
|
||||
def my_node(state: State) -> State:
|
||||
if len(state['input']) > 5:
|
||||
raise NodeInterrupt(f"Received input that is longer than 5 characters: {state['input']}")
|
||||
|
||||
return state
|
||||
```
|
||||
|
||||
|
||||
Let's assume we run the graph with an input that triggers the dynamic breakpoint and then attempt to resume the graph execution simply by passing in `None` for the input.
|
||||
|
||||
```python
|
||||
# Attempt to continue the graph execution with no change to state after we hit the dynamic breakpoint
|
||||
for event in graph.stream(None, thread_config, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
The graph will *interrupt* again because this node will be *re-run* with the same graph state. We need to change the graph state such that the condition that triggers the dynamic breakpoint is no longer met. So, we can simply edit the graph state to an input that meets the condition of our dynamic breakpoint (< 5 characters) and re-run the node.
|
||||
|
||||
```python
|
||||
# Update the state to pass the dynamic breakpoint
|
||||
graph.update_state(config=thread_config, values={"input": "foo"})
|
||||
for event in graph.stream(None, thread_config, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
Alternatively, what if we want to keep our current input and skip the node (`my_node`) that performs the check? To do this, we can simply perform the graph update with `as_node="my_node"` and pass in `None` for the values. This will make no update the graph state, but run the update as `my_node`, effectively skipping the node and bypassing the dynamic breakpoint.
|
||||
|
||||
```python
|
||||
# This update will skip the node `my_node` altogether
|
||||
graph.update_state(config=thread_config, values=None, as_node="my_node")
|
||||
for event in graph.stream(None, thread_config, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
## Additional Resources 📚
|
||||
|
||||
- [**Conceptual Guide: Persistence**](persistence.md): Read the persistence guide for more context about persistence.
|
||||
- [**Conceptual Guide: Human-in-the-loop**](human_in_the_loop.md): Read the human-in-the-loop guide for more context on integrating human feedback into LangGraph applications using breakpoints.
|
||||
- [**How to View and Update Past Graph State**](../how-tos/human_in_the_loop/time-travel.ipynb): Step-by-step instructions for working with graph state that demonstrate the **replay** and **fork** actions.
|
||||
@@ -1,322 +1,541 @@
|
||||
# Human-in-the-loop
|
||||
|
||||
Human-in-the-loop (or "on-the-loop") enhances agent capabilities through several common user interaction patterns.
|
||||
!!! tip "This guide uses the new `interrupt` function."
|
||||
|
||||
Common interaction patterns include:
|
||||
As of LangGraph 0.2.57, the recommended way to set breakpoints is using the [`interrupt` function][langgraph.types.interrupt] as it simplifies **human-in-the-loop** patterns.
|
||||
|
||||
(1) `Approval` - We can interrupt our agent, surface the current state to a user, and allow the user to accept an action.
|
||||
If you're looking for the previous version of this conceptual guide, which relied on static breakpoints and `NodeInterrupt` exception, it is available [here](v0-human-in-the-loop.md).
|
||||
|
||||
(2) `Editing` - We can interrupt our agent, surface the current state to a user, and allow the user to edit the agent state.
|
||||
A **human-in-the-loop** (or "on-the-loop") workflow integrates human input into automated processes, allowing for decisions, validation, or corrections at key stages. This is especially useful in **LLM-based applications**, where the underlying model may generate occasional inaccuracies. In low-error-tolerance scenarios like compliance, decision-making, or content generation, human involvement ensures reliability by enabling review, correction, or override of model outputs.
|
||||
|
||||
(3) `Input` - We can explicitly create a graph node to collect human input and pass that input directly to the agent state.
|
||||
|
||||
Use-cases for these interaction patterns include:
|
||||
## Use cases
|
||||
|
||||
(1) `Reviewing tool calls` - We can interrupt an agent to review and edit the results of tool calls.
|
||||
Key use cases for **human-in-the-loop** workflows in LLM-based applications include:
|
||||
|
||||
(2) `Time Travel` - We can manually re-play and / or fork past actions of an agent.
|
||||
1. [**🛠️ Reviewing tool calls**](#review-tool-calls): Humans can review, edit, or approve tool calls requested by the LLM before tool execution.
|
||||
2. **✅ Validating LLM outputs**: Humans can review, edit, or approve content generated by the LLM.
|
||||
3. **💡 Providing context**: Enable the LLM to explicitly request human input for clarification or additional details or to support multi-turn conversations.
|
||||
|
||||
## Persistence
|
||||
## `interrupt`
|
||||
|
||||
All of these interaction patterns are enabled by LangGraph's built-in [persistence](./persistence.md) layer, which will write a checkpoint of the graph state at each step. Persistence allows the graph to stop so that a human can review and / or edit the current state of the graph and then resume with the human's input.
|
||||
|
||||
### Breakpoints
|
||||
|
||||
Adding a [breakpoint](./low_level.md#breakpoints) a specific location in the graph flow is one way to enable human-in-the-loop. In this case, the developer knows *where* in the workflow human input is needed and simply places a breakpoint prior to or following that particular graph node.
|
||||
|
||||
Here, we compile our graph with a checkpointer and a breakpoint at the node we want to interrupt before, `step_for_human_in_the_loop`. We then perform one of the above interaction patterns, which will create a new checkpoint if a human edits the graph state. The new checkpoint is saved to the `thread` and we can resume the graph execution from there by passing in `None` as the input.
|
||||
The [`interrupt` function][langgraph.types.interrupt] in LangGraph enables human-in-the-loop workflows by pausing the graph at a specific node, presenting information to a human, and resuming the graph with their input. This function is useful for tasks like approvals, edits, or collecting additional input. The [`interrupt` function][langgraph.types.interrupt] is used in conjunction with the [`Command`](../reference/types.md#langgraph.types.Command) object to resume the graph with a value provided by the human.
|
||||
|
||||
```python
|
||||
# Compile our graph with a checkpointer and a breakpoint before "step_for_human_in_the_loop"
|
||||
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["step_for_human_in_the_loop"])
|
||||
from langgraph.types import interrupt
|
||||
|
||||
# Run the graph up to the breakpoint
|
||||
thread_config = {"configurable": {"thread_id": "1"}}
|
||||
for event in graph.stream(inputs, thread_config, stream_mode="values"):
|
||||
print(event)
|
||||
def human_node(state: State):
|
||||
value = interrupt(
|
||||
# Any JSON serializable value to surface to the human.
|
||||
# For example, a question or a piece of text or a set of keys in the state
|
||||
some_data
|
||||
)
|
||||
...
|
||||
# Update the state with the human's input or route the graph based on the input.
|
||||
...
|
||||
|
||||
graph = graph_builder.compile(
|
||||
checkpointer=checkpointer # Required for `interrupt` to work
|
||||
)
|
||||
|
||||
# Run the graph until the interrupt
|
||||
thread_config = {"configurable": {"thread_id": "some_id"}}
|
||||
graph.invoke(some_input, config=thread_config)
|
||||
|
||||
# Perform some action that requires human in the loop
|
||||
|
||||
# Continue the graph execution from the current checkpoint
|
||||
for event in graph.stream(None, thread_config, stream_mode="values"):
|
||||
print(event)
|
||||
# Resume the graph with the human's input
|
||||
graph.invoke(Command(resume=value_from_human), config=thread_config)
|
||||
```
|
||||
|
||||
### Dynamic Breakpoints
|
||||
## Requirements
|
||||
|
||||
Alternatively, the developer can define some *condition* that must be met for a breakpoint to be triggered. This concept of [dynamic breakpoints](./low_level.md#dynamic-breakpoints) is useful when the developer wants to halt the graph under *a particular condition*. This uses a `NodeInterrupt`, which is a special type of exception that can be raised from within a node based upon some condition. As an example, we can define a dynamic breakpoint that triggers when the `input` is longer than 5 characters.
|
||||
To use `interrupt` in your graph, you need to:
|
||||
|
||||
1. [**Specify a checkpointer**](persistence.md#checkpoints) to save the graph state after each step.
|
||||
2. **Call `interrupt()`** in the appropriate place. See the [Design Patterns](#design-patterns) section for examples.
|
||||
3. **Run the graph** with a [**thread ID**](./persistence.md#threads) until the `interrupt` is hit.
|
||||
4. **Resume execution** using `invoke`/`ainvoke`/`stream`/`astream` (see [**The `Command` primitive**](#the-command-primitive)).
|
||||
|
||||
## Design Patterns
|
||||
|
||||
There are typically three different **actions** that you can do with a human-in-the-loop workflow:
|
||||
|
||||
1. **Approve or Reject**: Pause the graph before a critical step, such as an API call, to review and approve the action. If the action is rejected, you can prevent the graph from executing the step, and potentially take an alternative action. This pattern often involve **routing** the graph based on the human's input.
|
||||
2. **Edit Graph State**: Pause the graph to review and edit the graph state. This is useful for correcting mistakes or updating the state with additional information. This pattern often involves **updating** the state with the human's input.
|
||||
3. **Get Input**: Explicitly request human input at a particular step in the graph. This is useful for collecting additional information or context to inform the agent's decision-making process or for supporting **multi-turn conversations**.
|
||||
|
||||
Below we show different design patterns that can be implemented using these **actions**.
|
||||
|
||||
### Approve or Reject
|
||||
|
||||
<figure markdown="1">
|
||||
{: style="max-height:400px"}
|
||||
<figcaption>Depending on the human's approval or rejection, the graph can proceed with the action or take an alternative path.</figcaption>
|
||||
</figure>
|
||||
|
||||
Pause the graph before a critical step, such as an API call, to review and approve the action. If the action is rejected, you can prevent the graph from executing the step, and potentially take an alternative action.
|
||||
|
||||
```python
|
||||
def my_node(state: State) -> State:
|
||||
if len(state['input']) > 5:
|
||||
raise NodeInterrupt(f"Received input that is longer than 5 characters: {state['input']}")
|
||||
return state
|
||||
|
||||
from typing import Literal
|
||||
from langgraph.types import interrupt, Command
|
||||
|
||||
def human_approval(state: State) -> Command[Literal["some_node", "another_node"]]:
|
||||
is_approved = interrupt(
|
||||
{
|
||||
"question": "Is this correct?",
|
||||
# Surface the output that should be
|
||||
# reviewed and approved by the human.
|
||||
"llm_output": state["llm_output"]
|
||||
}
|
||||
)
|
||||
|
||||
if is_approved:
|
||||
return Command(goto="some_node")
|
||||
else:
|
||||
return Command(goto="another_node")
|
||||
|
||||
# Add the node to the graph in an appropriate location
|
||||
# and connect it to the relevant nodes.
|
||||
graph_builder.add_node("human_approval", human_approval)
|
||||
graph = graph_builder.compile(checkpointer=checkpointer)
|
||||
|
||||
# After running the graph and hitting the interrupt, the graph will pause.
|
||||
# Resume it with either an approval or rejection.
|
||||
thread_config = {"configurable": {"thread_id": "some_id"}}
|
||||
graph.invoke(Command(resume=True), config=thread_config)
|
||||
```
|
||||
|
||||
Let's assume we run the graph with an input that triggers the dynamic breakpoint and then attempt to resume the graph execution simply by passing in `None` for the input.
|
||||
See [how to review tool calls](../how-tos/human_in_the_loop/review-tool-calls.ipynb) for a more detailed example.
|
||||
|
||||
### Review & Edit State
|
||||
|
||||
<figure markdown="1">
|
||||
{: style="max-height:400px"}
|
||||
<figcaption>A human can review and edit the state of the graph. This is useful for correcting mistakes or updating the state with additional information.
|
||||
</figcaption>
|
||||
</figure>
|
||||
|
||||
```python
|
||||
# Attempt to continue the graph execution with no change to state after we hit the dynamic breakpoint
|
||||
for event in graph.stream(None, thread_config, stream_mode="values"):
|
||||
print(event)
|
||||
from langgraph.types import interrupt
|
||||
|
||||
def human_editing(state: State):
|
||||
...
|
||||
result = interrupt(
|
||||
# Interrupt information to surface to the client.
|
||||
# Can be any JSON serializable value.
|
||||
{
|
||||
"task": "Review the output from the LLM and make any necessary edits.",
|
||||
"llm_generated_summary": state["llm_generated_summary"]
|
||||
}
|
||||
)
|
||||
|
||||
# Update the state with the edited text
|
||||
return {
|
||||
"llm_generated_summary": result["edited_text"]
|
||||
}
|
||||
|
||||
# Add the node to the graph in an appropriate location
|
||||
# and connect it to the relevant nodes.
|
||||
graph_builder.add_node("human_editing", human_editing)
|
||||
graph = graph_builder.compile(checkpointer=checkpointer)
|
||||
|
||||
...
|
||||
|
||||
# After running the graph and hitting the interrupt, the graph will pause.
|
||||
# Resume it with the edited text.
|
||||
thread_config = {"configurable": {"thread_id": "some_id"}}
|
||||
graph.invoke(
|
||||
Command(resume={"edited_text": "The edited text"}),
|
||||
config=thread_config
|
||||
)
|
||||
```
|
||||
|
||||
The graph will *interrupt* again because this node will be *re-run* with the same graph state. We need to change the graph state such that the condition that triggers the dynamic breakpoint is no longer met. So, we can simply edit the graph state to an input that meets the condition of our dynamic breakpoint (< 5 characters) and re-run the node.
|
||||
See [How to wait for user input using interrupt](../how-tos/human_in_the_loop/wait-user-input.ipynb) for a more detailed example.
|
||||
|
||||
```python
|
||||
# Update the state to pass the dynamic breakpoint
|
||||
graph.update_state(config=thread_config, values={"input": "foo"})
|
||||
for event in graph.stream(None, thread_config, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
### Review Tool Calls
|
||||
|
||||
Alternatively, what if we want to keep our current input and skip the node (`my_node`) that performs the check? To do this, we can simply perform the graph update with `as_node="my_node"` and pass in `None` for the values. This will make no update the graph state, but run the update as `my_node`, effectively skipping the node and bypassing the dynamic breakpoint.
|
||||
<figure markdown="1">
|
||||
{: style="max-height:400px"}
|
||||
<figcaption>A human can review and edit the output from the LLM before proceeding. This is particularly
|
||||
critical in applications where the tool calls requested by the LLM may be sensitive or require human oversight.
|
||||
</figcaption>
|
||||
</figure>
|
||||
|
||||
```python
|
||||
# This update will skip the node `my_node` altogether
|
||||
graph.update_state(config=thread_config, values=None, as_node="my_node")
|
||||
for event in graph.stream(None, thread_config, stream_mode="values"):
|
||||
print(event)
|
||||
def human_review_node(state) -> Command[Literal["call_llm", "run_tool"]]:
|
||||
# This is the value we'll be providing via Command(resume=<human_review>)
|
||||
human_review = interrupt(
|
||||
{
|
||||
"question": "Is this correct?",
|
||||
# Surface tool calls for review
|
||||
"tool_call": tool_call
|
||||
}
|
||||
)
|
||||
|
||||
review_action, review_data = human_review
|
||||
|
||||
# Approve the tool call and continue
|
||||
if review_action == "continue":
|
||||
return Command(goto="run_tool")
|
||||
|
||||
# Modify the tool call manually and then continue
|
||||
elif review_action == "update":
|
||||
...
|
||||
updated_msg = get_updated_msg(review_data)
|
||||
# Remember that to modify an existing message you will need
|
||||
# to pass the message with a matching ID.
|
||||
return Command(goto="run_tool", update={"messages": [updated_message]})
|
||||
|
||||
# Give natural language feedback, and then pass that back to the agent
|
||||
elif review_action == "feedback":
|
||||
...
|
||||
feedback_msg = get_feedback_msg(review_data)
|
||||
return Command(goto="call_llm", update={"messages": [feedback_msg]})
|
||||
```
|
||||
|
||||
See [our guide](../how-tos/human_in_the_loop/dynamic_breakpoints.ipynb) for a detailed how-to on doing this!
|
||||
See [how to review tool calls](../how-tos/human_in_the_loop/review-tool-calls.ipynb) for a more detailed example.
|
||||
|
||||
## Interaction Patterns
|
||||
### Multi-turn conversation
|
||||
|
||||
### Approval
|
||||
<figure markdown="1">
|
||||
{: style="max-height:400px"}
|
||||
<figcaption>A <strong>multi-turn conversation</strong> architecture where an <strong>agent</strong> and <strong>human node</strong> cycle back and forth until the agent decides to hand off the conversation to another agent or another part of the system.
|
||||
</figcaption>
|
||||
</figure>
|
||||
|
||||
|
||||
A **multi-turn conversation** involves multiple back-and-forth interactions between an agent and a human, which can allow the agent to gather additional information from the human in a conversational manner.
|
||||
|
||||
Sometimes we want to approve certain steps in our agent's execution.
|
||||
|
||||
We can interrupt our agent at a [breakpoint](./low_level.md#breakpoints) prior to the step that we want to approve.
|
||||
This design pattern is useful in an LLM application consisting of [multiple agents](./multi_agent.md). One or more agents may need to carry out multi-turn conversations with a human, where the human provides input or feedback at different stages of the conversation. For simplicity, the agent implementation below is illustrated as a single node, but in reality
|
||||
it may be part of a larger graph consisting of multiple nodes and include a conditional edge.
|
||||
|
||||
This is generally recommend for sensitive actions (e.g., using external APIs or writing to a database).
|
||||
|
||||
With persistence, we can surface the current agent state as well as the next step to a user for review and approval.
|
||||
|
||||
If approved, the graph resumes execution from the last saved checkpoint, which is saved to the `thread`:
|
||||
=== "Using a human node per agent"
|
||||
|
||||
In this pattern, each agent has its own human node for collecting user input.
|
||||
This can be achieved by either naming the human nodes with unique names (e.g., "human for agent 1", "human for agent 2") or by
|
||||
using subgraphs where a subgraph contains a human node and an agent node.
|
||||
|
||||
```python
|
||||
from langgraph.types import interrupt
|
||||
|
||||
def human_input(state: State):
|
||||
human_message = interrupt("human_input")
|
||||
return {
|
||||
"messages": [
|
||||
{
|
||||
"role": "human",
|
||||
"content": human_message
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
def agent(state: State):
|
||||
# Agent logic
|
||||
...
|
||||
|
||||
graph_builder.add_node("human_input", human_input)
|
||||
graph_builder.add_edge("human_input", "agent")
|
||||
graph = graph_builder.compile(checkpointer=checkpointer)
|
||||
|
||||
# After running the graph and hitting the interrupt, the graph will pause.
|
||||
# Resume it with the human's input.
|
||||
graph.invoke(
|
||||
Command(resume="hello!"),
|
||||
config=thread_config
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
=== "Sharing human node across multiple agents"
|
||||
|
||||
In this pattern, a single human node is used to collect user input for multiple agents. The active agent is determined from the state, so after human input is collected, the graph can route to the correct agent.
|
||||
|
||||
```python
|
||||
from langgraph.types import interrupt
|
||||
|
||||
def human_node(state: MessagesState) -> Command[Literal["agent_1", "agent_2", ...]]:
|
||||
"""A node for collecting user input."""
|
||||
user_input = interrupt(value="Ready for user input.")
|
||||
|
||||
# Determine the **active agent** from the state, so
|
||||
# we can route to the correct agent after collecting input.
|
||||
# For example, add a field to the state or use the last active agent.
|
||||
# or fill in `name` attribute of AI messages generated by the agents.
|
||||
active_agent = ...
|
||||
|
||||
return Command(
|
||||
update={
|
||||
"messages": [{
|
||||
"role": "human",
|
||||
"content": user_input,
|
||||
}]
|
||||
},
|
||||
goto=active_agent,
|
||||
)
|
||||
```
|
||||
|
||||
See [how to implement multi-turn conversations](../how-tos/multi-agent-multi-turn-convo.ipynb) for a more detailed example.
|
||||
|
||||
### Validating human input
|
||||
|
||||
If you need to validate the input provided by the human within the graph itself (rather than on the client side), you can achieve this by using multiple interrupt calls within a single node.
|
||||
|
||||
```python
|
||||
# Compile our graph with a checkpointer and a breakpoint before the step to approve
|
||||
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["node_2"])
|
||||
from langgraph.types import interrupt
|
||||
|
||||
# Run the graph up to the breakpoint
|
||||
for event in graph.stream(inputs, thread, stream_mode="values"):
|
||||
print(event)
|
||||
|
||||
# ... Get human approval ...
|
||||
def human_node(state: State):
|
||||
"""Human node with validation."""
|
||||
question = "What is your age?"
|
||||
|
||||
# If approved, continue the graph execution from the last saved checkpoint
|
||||
for event in graph.stream(None, thread, stream_mode="values"):
|
||||
print(event)
|
||||
while True:
|
||||
answer = interrupt(question)
|
||||
|
||||
# Validate answer, if the answer isn't valid ask for input again.
|
||||
if not isinstance(answer, int) or answer < 0:
|
||||
question = f"'{answer} is not a valid age. What is your age?"
|
||||
answer = None
|
||||
continue
|
||||
else:
|
||||
# If the answer is valid, we can proceed.
|
||||
break
|
||||
|
||||
print(f"The human in the loop is {answer} years old.")
|
||||
return {
|
||||
"age": answer
|
||||
}
|
||||
```
|
||||
|
||||
See [our guide](../how-tos/human_in_the_loop/breakpoints.ipynb) for a detailed how-to on doing this!
|
||||
## The `Command` primitive
|
||||
|
||||
### Editing
|
||||
When using the `interrupt` function, the graph will pause at the interrupt and wait for user input.
|
||||
|
||||
|
||||
Graph execution can be resumed using the [Command](../reference/types.md#langgraph.types.Command) primitive which can be passed through the `invoke`, `ainvoke`, `stream` or `astream` methods.
|
||||
|
||||
Sometimes we want to review and edit the agent's state.
|
||||
|
||||
As with approval, we can interrupt our agent at a [breakpoint](./low_level.md#breakpoints) prior to the step we want to check.
|
||||
|
||||
We can surface the current state to a user and allow the user to edit the agent state.
|
||||
|
||||
This can, for example, be used to correct the agent if it made a mistake (e.g., see the section on tool calling below).
|
||||
The `Command` primitive provides several options to control and modify the graph's state during resumption:
|
||||
|
||||
We can edit the graph state by forking the current checkpoint, which is saved to the `thread`.
|
||||
1. **Pass a value to the `interrupt`**: Provide data, such as a user's response, to the graph using `Command(resume=value)`. Execution resumes from the beginning of the node where the `interrupt` was used, however, this time the `interrupt(...)` call will return the value passed in the `Command(resume=value)` instead of pausing the graph.
|
||||
|
||||
We can then proceed with the graph from our forked checkpoint as done before.
|
||||
```python
|
||||
# Resume graph execution with the user's input.
|
||||
graph.invoke(Command(resume={"age": "25"}), thread_config)
|
||||
```
|
||||
|
||||
2. **Update the graph state**: Modify the graph state using `Command(update=update)`. Note that resumption starts from the beginning of the node where the `interrupt` was used. Execution resumes from the beginning of the node where the `interrupt` was used, but with the updated state.
|
||||
|
||||
```python
|
||||
# Update the graph state and resume.
|
||||
# You must provide a `resume` value if using an `interrupt`.
|
||||
graph.invoke(Command(update={"foo": "bar"}, resume="Let's go!!!"), thread_config)
|
||||
```
|
||||
|
||||
By leveraging `Command`, you can resume graph execution, handle user inputs, and dynamically adjust the graph's state.
|
||||
|
||||
## Using with `invoke` and `ainvoke`
|
||||
|
||||
When you use `stream` or `astream` to run the graph, you will receive an `Interrupt` event that let you know the `interrupt` was triggered.
|
||||
|
||||
`invoke` and `ainvoke` do not return the interrupt information. To access this information, you must use the [get_state](../reference/graphs.md#langgraph.graph.graph.CompiledGraph.get_state) method to retrieve the graph state after calling `invoke` or `ainvoke`.
|
||||
|
||||
```python
|
||||
# Compile our graph with a checkpointer and a breakpoint before the step to review
|
||||
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["node_2"])
|
||||
|
||||
# Run the graph up to the breakpoint
|
||||
for event in graph.stream(inputs, thread, stream_mode="values"):
|
||||
print(event)
|
||||
|
||||
# Review the state, decide to edit it, and create a forked checkpoint with the new state
|
||||
graph.update_state(thread, {"state": "new state"})
|
||||
|
||||
# Continue the graph execution from the forked checkpoint
|
||||
for event in graph.stream(None, thread, stream_mode="values"):
|
||||
print(event)
|
||||
# Run the graph up to the interrupt
|
||||
result = graph.invoke(inputs, thread_config)
|
||||
# Get the graph state to get interrupt information.
|
||||
state = graph.get_state(thread_config)
|
||||
# Print the state values
|
||||
print(state.values)
|
||||
# Print the pending tasks
|
||||
print(state.tasks)
|
||||
# Resume the graph with the user's input.
|
||||
graph.invoke(Command(resume={"age": "25"}), thread_config)
|
||||
```
|
||||
|
||||
See [this guide](../how-tos/human_in_the_loop/edit-graph-state.ipynb) for a detailed how-to on doing this!
|
||||
```pycon
|
||||
{'foo': 'bar'} # State values
|
||||
(
|
||||
PregelTask(
|
||||
id='5d8ffc92-8011-0c9b-8b59-9d3545b7e553',
|
||||
name='node_foo',
|
||||
path=('__pregel_pull', 'node_foo'),
|
||||
error=None,
|
||||
interrupts=(Interrupt(value='value_in_interrupt', resumable=True, ns=['node_foo:5d8ffc92-8011-0c9b-8b59-9d3545b7e553'], when='during'),), state=None,
|
||||
result=None
|
||||
),
|
||||
) # Pending tasks. interrupts
|
||||
```
|
||||
|
||||
### Input
|
||||
## How does resuming from an interrupt work?
|
||||
|
||||
|
||||
!!! warning
|
||||
|
||||
Sometimes we want to explicitly get human input at a particular step in the graph.
|
||||
|
||||
We can create a graph node designated for this (e.g., `human_input` in our example diagram).
|
||||
|
||||
As with approval and editing, we can interrupt our agent at a [breakpoint](./low_level.md#breakpoints) prior to this node.
|
||||
|
||||
We can then perform a state update that includes the human input, just as we did with editing state.
|
||||
Resuming from an `interrupt` is **different** from Python's `input()` function, where execution resumes from the exact point where the `input()` function was called.
|
||||
|
||||
But, we add one thing:
|
||||
A critical aspect of using `interrupt` is understanding how resuming works. When you resume execution after an `interrupt`, graph execution starts from the **beginning** of the **graph node** where the last `interrupt` was triggered.
|
||||
|
||||
We can use `as_node=human_input` with the state update to specify that the state update *should be treated as a node*.
|
||||
|
||||
The is subtle, but important:
|
||||
|
||||
With editing, the user makes a decision about whether or not to edit the graph state.
|
||||
|
||||
With input, we explicitly define a node in our graph for collecting human input!
|
||||
|
||||
The state update with the human input then runs *as this node*.
|
||||
**All** code from the beginning of the node to the `interrupt` will be re-executed.
|
||||
|
||||
```python
|
||||
# Compile our graph with a checkpointer and a breakpoint before the step to to collect human input
|
||||
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["human_input"])
|
||||
|
||||
# Run the graph up to the breakpoint
|
||||
for event in graph.stream(inputs, thread, stream_mode="values"):
|
||||
print(event)
|
||||
|
||||
# Update the state with the user input as if it was the human_input node
|
||||
graph.update_state(thread, {"user_input": user_input}, as_node="human_input")
|
||||
|
||||
# Continue the graph execution from the checkpoint created by the human_input node
|
||||
for event in graph.stream(None, thread, stream_mode="values"):
|
||||
print(event)
|
||||
counter = 0
|
||||
def node(state: State):
|
||||
# All the code from the beginning of the node to the interrupt will be re-executed
|
||||
# when the graph resumes.
|
||||
global counter
|
||||
counter += 1
|
||||
print(f"> Entered the node: {counter} # of times")
|
||||
# Pause the graph and wait for user input.
|
||||
answer = interrupt()
|
||||
print("The value of counter is:", counter)
|
||||
...
|
||||
```
|
||||
|
||||
See [this guide](../how-tos/human_in_the_loop/wait-user-input.ipynb) for a detailed how-to on doing this!
|
||||
Upon **resuming** the graph, the counter will be incremented a second time, resulting in the following output:
|
||||
|
||||
## Use-cases
|
||||
```pycon
|
||||
> Entered the node: 2 # of times
|
||||
The value of counter is: 2
|
||||
```
|
||||
|
||||
### Reviewing Tool Calls
|
||||
## Common Pitfalls
|
||||
|
||||
Some user interaction patterns combine the above ideas.
|
||||
### Side-effects
|
||||
|
||||
For example, many agents use [tool calling](https://python.langchain.com/docs/how_to/tool_calling/) to make decisions.
|
||||
Place code with side effects, such as API calls, **after** the `interrupt` to avoid duplication, as these are re-triggered every time the node is resumed.
|
||||
|
||||
Tool calling presents a challenge because the agent must get two things right:
|
||||
=== "Side effects before interrupt (BAD)"
|
||||
|
||||
(1) The name of the tool to call
|
||||
This code will re-execute the API call another time when the node is resumed from
|
||||
the `interrupt`.
|
||||
|
||||
(2) The arguments to pass to the tool
|
||||
This can be problematic if the API call is not idempotent or is just expensive.
|
||||
|
||||
Even if the tool call is correct, we may also want to apply discretion:
|
||||
```python
|
||||
from langgraph.types import interrupt
|
||||
|
||||
(3) The tool call may be a sensitive operation that we want to approve
|
||||
def human_node(state: State):
|
||||
"""Human node with validation."""
|
||||
api_call(...) # This code will be re-executed when the node is resumed.
|
||||
answer = interrupt(question)
|
||||
```
|
||||
|
||||
With these points in mind, we can combine the above ideas to create a human-in-the-loop review of a tool call.
|
||||
=== "Side effects after interrupt (OK)"
|
||||
|
||||
```python
|
||||
from langgraph.types import interrupt
|
||||
|
||||
def human_node(state: State):
|
||||
"""Human node with validation."""
|
||||
|
||||
answer = interrupt(question)
|
||||
|
||||
api_call(answer) # OK as it's after the interrupt
|
||||
```
|
||||
|
||||
=== "Side effects in a separate node (OK)"
|
||||
|
||||
```python
|
||||
from langgraph.types import interrupt
|
||||
|
||||
def human_node(state: State):
|
||||
"""Human node with validation."""
|
||||
|
||||
answer = interrupt(question)
|
||||
|
||||
return {
|
||||
"answer": answer
|
||||
}
|
||||
|
||||
def api_call_node(state: State):
|
||||
api_call(...) # OK as it's in a separate node
|
||||
```
|
||||
|
||||
### Subgraphs called as functions
|
||||
|
||||
|
||||
**Subgraphs**: If you're invoking a subgraph [as a function](low_level.md#as-a-function), the **parent** graph will be re-run from the **beginning of the node** where the subgraph was invoked.
|
||||
|
||||
```python
|
||||
# Compile our graph with a checkpointer and a breakpoint before the step to to review the tool call from the LLM
|
||||
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["human_review"])
|
||||
|
||||
# Run the graph up to the breakpoint
|
||||
for event in graph.stream(inputs, thread, stream_mode="values"):
|
||||
print(event)
|
||||
|
||||
# Review the tool call and update it, if needed, as the human_review node
|
||||
graph.update_state(thread, {"tool_call": "updated tool call"}, as_node="human_review")
|
||||
|
||||
# Otherwise, approve the tool call and proceed with the graph execution with no edits
|
||||
|
||||
# Continue the graph execution from either:
|
||||
# (1) the forked checkpoint created by human_review or
|
||||
# (2) the checkpoint saved when the tool call was originally made (no edits in human_review)
|
||||
for event in graph.stream(None, thread, stream_mode="values"):
|
||||
print(event)
|
||||
def some_node(state: State):
|
||||
some_code() # <-- This code will be re-executed when the subgraph is resumed.
|
||||
# Using a subgraph as a function.
|
||||
# The subgraph has an `interrupt` call
|
||||
subgraph_result = subgraph.invoke(some_input)
|
||||
...
|
||||
```
|
||||
|
||||
See [this guide](../how-tos/human_in_the_loop/review-tool-calls.ipynb) for a detailed how-to on doing this!
|
||||
|
||||
### Time Travel
|
||||
### Using multiple interrupts
|
||||
|
||||
When working with agents, we often want closely examine their decision making process:
|
||||
Using multiple interrupts within a **single** node can be helpful for patterns like [validating human input](#validating-human-input). However, using multiple interrupts in the same node can lead to unexpected behavior if not handled carefully.
|
||||
|
||||
(1) Even when they arrive a desired final result, the reasoning that led to that result is often important to examine.
|
||||
When a node contains multiple interrupt calls, LangGraph keeps a list of resume values specific to the task executing the node. Whenever execution resumes, it starts at the beginning of the node. For each interrupt encountered, LangGraph checks if a matching value exists in the task's resume list. Matching is **strictly index-based**, so the order of interrupt calls within the node is critical.
|
||||
|
||||
(2) When agents make mistakes, it is often valuable to understand why.
|
||||
To avoid issues, refrain from dynamically changing the node's structure between executions. This includes adding, removing, or reordering interrupt calls, as such changes can result in mismatched indices. These problems often arise from unconventional patterns, such as mutating state via `Command(resume=..., update=SOME_STATE_MUTATION)` or relying on global variables to modify the node’s structure dynamically.
|
||||
|
||||
(3) In either of the above cases, it is useful to manually explore alternative decision making paths.
|
||||
??? "Example of incorrect code"
|
||||
|
||||
Collectively, we call these debugging concepts `time-travel` and they are composed of `replaying` and `forking`.
|
||||
```python
|
||||
import uuid
|
||||
from typing import TypedDict, Optional
|
||||
|
||||
#### Replaying
|
||||
from langgraph.graph import StateGraph
|
||||
from langgraph.constants import START
|
||||
from langgraph.types import interrupt, Command
|
||||
from langgraph.checkpoint.memory import MemorySaver
|
||||
|
||||
|
||||
|
||||
Sometimes we want to simply replay past actions of an agent.
|
||||
|
||||
Above, we showed the case of executing an agent from the current state (or checkpoint) of the graph.
|
||||
class State(TypedDict):
|
||||
"""The graph state."""
|
||||
|
||||
We by simply passing in `None` for the input with a `thread`.
|
||||
age: Optional[str]
|
||||
name: Optional[str]
|
||||
|
||||
```
|
||||
thread = {"configurable": {"thread_id": "1"}}
|
||||
for event in graph.stream(None, thread, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
Now, we can modify this to replay past actions from a *specific* checkpoint by passing in the checkpoint ID.
|
||||
def human_node(state: State):
|
||||
if not state.get('name'):
|
||||
name = interrupt("what is your name?")
|
||||
else:
|
||||
name = "N/A"
|
||||
|
||||
To get a specific checkpoint ID, we can easily get all of the checkpoints in the thread and filter to the one we want.
|
||||
if not state.get('age'):
|
||||
age = interrupt("what is your age?")
|
||||
else:
|
||||
age = "N/A"
|
||||
|
||||
print(f"Name: {name}. Age: {age}")
|
||||
|
||||
return {
|
||||
"age": age,
|
||||
"name": name,
|
||||
}
|
||||
|
||||
```python
|
||||
all_checkpoints = []
|
||||
for state in app.get_state_history(thread):
|
||||
all_checkpoints.append(state)
|
||||
```
|
||||
|
||||
Each checkpoint has a unique ID, which we can use to replay from a specific checkpoint.
|
||||
builder = StateGraph(State)
|
||||
builder.add_node("human_node", human_node)
|
||||
builder.add_edge(START, "human_node")
|
||||
|
||||
Assume from reviewing the checkpoints that we want to replay from one, `xxx`.
|
||||
# A checkpointer must be enabled for interrupts to work!
|
||||
checkpointer = MemorySaver()
|
||||
graph = builder.compile(checkpointer=checkpointer)
|
||||
|
||||
We just pass in the checkpoint ID when we run the graph.
|
||||
config = {
|
||||
"configurable": {
|
||||
"thread_id": uuid.uuid4(),
|
||||
}
|
||||
}
|
||||
|
||||
```python
|
||||
config = {'configurable': {'thread_id': '1', 'checkpoint_id': 'xxx'}}
|
||||
for event in graph.stream(None, config, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
Importantly, the graph knows which checkpoints have been previously executed.
|
||||
for chunk in graph.stream({"age": None, "name": None}, config):
|
||||
print(chunk)
|
||||
|
||||
So, it will re-play any previously executed nodes rather than re-executing them.
|
||||
for chunk in graph.stream(Command(resume="John", update={"name": "foo"}), config):
|
||||
print(chunk)
|
||||
```
|
||||
|
||||
See [this additional conceptual guide](https://langchain-ai.github.io/langgraph/concepts/persistence/#replay) for related context on replaying.
|
||||
```pycon
|
||||
{'__interrupt__': (Interrupt(value='what is your name?', resumable=True, ns=['human_node:3a007ef9-c30d-c357-1ec1-86a1a70d8fba'], when='during'),)}
|
||||
Name: N/A. Age: John
|
||||
{'human_node': {'age': 'John', 'name': 'N/A'}}
|
||||
```
|
||||
|
||||
See see [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for a detailed how-to on doing time-travel!
|
||||
## Additional Resources 📚
|
||||
|
||||
#### Forking
|
||||
|
||||
|
||||
|
||||
Sometimes we want to fork past actions of an agent, and explore different paths through the graph.
|
||||
|
||||
`Editing`, as discussed above, is *exactly* how we do this for the *current* state of the graph!
|
||||
|
||||
But, what if we want to fork *past* states of the graph?
|
||||
|
||||
For example, let's say we want to edit a particular checkpoint, `xxx`.
|
||||
|
||||
We pass this `checkpoint_id` when we update the state of the graph.
|
||||
|
||||
```python
|
||||
config = {"configurable": {"thread_id": "1", "checkpoint_id": "xxx"}}
|
||||
graph.update_state(config, {"state": "updated state"}, )
|
||||
```
|
||||
|
||||
This creates a new forked checkpoint, `xxx-fork`, which we can then run the graph from.
|
||||
|
||||
```python
|
||||
config = {'configurable': {'thread_id': '1', 'checkpoint_id': 'xxx-fork'}}
|
||||
for event in graph.stream(None, config, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
See [this additional conceptual guide](https://langchain-ai.github.io/langgraph/concepts/persistence/#update-state) for related context on forking.
|
||||
|
||||
See see [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for a detailed how-to on doing time-travel!
|
||||
- [**Conceptual Guide: Persistence**](persistence.md#replay): Read the persistence guide for more context on replaying.
|
||||
- [**How to Guides: Human-in-the-loop**](../how-tos/index.md#human-in-the-loop): Learn how to implement human-in-the-loop workflows in LangGraph.
|
||||
- [**How to implement multi-turn conversations**](../how-tos/multi-agent-multi-turn-convo.ipynb): Learn how to implement multi-turn conversations in LangGraph.
|
||||
|
||||
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|
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|
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|
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|
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@@ -24,7 +24,9 @@ The conceptual guide does not cover step-by-step instructions or specific implem
|
||||
- [LangGraph Glossary](low_level.md): LangGraph workflows are designed as graphs, with nodes representing different components and edges representing the flow of information between them. This guide provides an overview of the key concepts associated with LangGraph graph primitives.
|
||||
- [Common Agentic Patterns](agentic_concepts.md): An agent uses an LLM to pick its own control flow to solve more complex problems! Agents are a key building block in many LLM applications. This guide explains the different types of agent architectures and how they can be used to control the flow of an application.
|
||||
- [Multi-Agent Systems](multi_agent.md): Complex LLM applications can often be broken down into multiple agents, each responsible for a different part of the application. This guide explains common patterns for building multi-agent systems.
|
||||
- [Breakpoints](breakpoints.md): Breakpoints allow pausing the execution of a graph at specific points. Breakpoints allow stepping through graph execution for debugging purposes.
|
||||
- [Human-in-the-Loop](human_in_the_loop.md): Explains different ways of integrating human feedback into a LangGraph application.
|
||||
- [Time Travel](time-travel.md): Time travel allows you to replay past actions in your LangGraph application to explore alternative paths and debug issues.
|
||||
- [Persistence](persistence.md): LangGraph has a built-in persistence layer, implemented through checkpointers. This persistence layer helps to support powerful capabilities like human-in-the-loop, memory, time travel, and fault-tolerance.
|
||||
- [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.
|
||||
- [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.
|
||||
|
||||
@@ -383,6 +383,10 @@ def lookup_user_info(tool_call_id: Annotated[str, InjectedToolCallId], config: R
|
||||
|
||||
If you are using tools that update state via `Command`, we recommend using prebuilt [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] which automatically handles tools returning `Command` objects and propagates them to the graph state. If you're writing a custom node that calls tools, you would need to manually propagate `Command` objects returned by the tools as the update from node.
|
||||
|
||||
### Human-in-the-loop
|
||||
|
||||
`Command` is an important part of human-in-the-loop workflows: when using `interrupt()` to collect user input, `Command` is then used to supply the input and resume execution via `Command(resume="User input")`. Check out [this conceptual guide](./human_in_the_loop.md) for more information.
|
||||
|
||||
## Persistence
|
||||
|
||||
LangGraph provides built-in persistence for your agent's state using [checkpointers][langgraph.checkpoint.base.BaseCheckpointSaver]. Checkpointers save snapshots of the graph state at every superstep, allowing resumption at any time. This enables features like human-in-the-loop interactions, memory management, and fault-tolerance. You can even directly manipulate a graph's state after its execution using the
|
||||
@@ -448,35 +452,32 @@ graph.invoke(inputs, config={"recursion_limit": 5, "configurable":{"llm": "anthr
|
||||
|
||||
Read [this how-to](https://langchain-ai.github.io/langgraph/how-tos/recursion-limit/) to learn more about how the recursion limit works.
|
||||
|
||||
## `interrupt`
|
||||
|
||||
Use the [interrupt](../reference/types.md/#langgraph.types.interrupt) function to **pause** the graph at specific points to collect user input. The `interrupt` function surfaces interrupt information to the client, allowing the developer to collect user input, validate the graph state, or make decisions before resuming execution.
|
||||
|
||||
```python
|
||||
from langgraph.types import interrupt
|
||||
|
||||
def human_approval_node(state: State):
|
||||
...
|
||||
answer = interrupt(
|
||||
# This value will be sent to the client.
|
||||
# It can be any JSON serializable value.
|
||||
{"question": "is it ok to continue?"},
|
||||
)
|
||||
...
|
||||
```
|
||||
|
||||
Resuming the graph is done by passing a [`Command`](#command) object to the graph with the `resume` key set to the value returned by the `interrupt` function.
|
||||
|
||||
Read more about how the `interrupt` is used for **human-in-the-loop** workflows in the [Human-in-the-loop conceptual guide](./human_in_the_loop.md).
|
||||
|
||||
## Breakpoints
|
||||
|
||||
It can often be useful to set breakpoints before or after certain nodes execute. This can be used to wait for human approval before continuing. These can be set when you ["compile" a graph](#compiling-your-graph). You can set breakpoints either _before_ a node executes (using `interrupt_before`) or after a node executes (using `interrupt_after`.)
|
||||
Breakpoints pause graph execution at specific points and enable stepping through execution step by step. Breakpoints are powered by LangGraph's [**persistence layer**](./persistence.md), which saves the state after each graph step. Breakpoints can also be used to enable [**human-in-the-loop**](./human_in_the_loop.md) workflows, though we recommend using the [`interrupt` function](#interrupt-function) for this purpose.
|
||||
|
||||
You **MUST** use a [checkpointer](./persistence.md) when using breakpoints. This is because your graph needs to be able to resume execution.
|
||||
|
||||
In order to resume execution, you can just invoke your graph with `None` as the input.
|
||||
|
||||
```python
|
||||
# Initial run of graph
|
||||
graph.invoke(inputs, config=config)
|
||||
|
||||
# Let's assume it hit a breakpoint somewhere, you can then resume by passing in None
|
||||
graph.invoke(None, config=config)
|
||||
```
|
||||
|
||||
See [this guide](../how-tos/human_in_the_loop/breakpoints.ipynb) for a full walkthrough of how to add breakpoints.
|
||||
|
||||
### Dynamic Breakpoints
|
||||
|
||||
It may be helpful to **dynamically** interrupt the graph from inside a given node based on some condition. In `LangGraph` you can do so by using `NodeInterrupt` -- a special exception that can be raised from inside a node.
|
||||
|
||||
```python
|
||||
def my_node(state: State) -> State:
|
||||
if len(state['input']) > 5:
|
||||
raise NodeInterrupt(f"Received input that is longer than 5 characters: {state['input']}")
|
||||
|
||||
return state
|
||||
```
|
||||
Read more about breakpoints in the [Breakpoints conceptual guide](./breakpoints.md).
|
||||
|
||||
## Subgraphs
|
||||
|
||||
@@ -517,7 +518,7 @@ The simplest way to create subgraph nodes is by using a [compiled subgraph](#com
|
||||
If you pass extra keys to the subgraph node (i.e., in addition to the shared keys), they will be ignored by the subgraph node. Similarly, if you return extra keys from the subgraph, they will be ignored by the parent graph.
|
||||
|
||||
```python
|
||||
from langgraph.graph import START, StateGraph
|
||||
from langgraph.graph import StateGraph
|
||||
from typing import TypedDict
|
||||
|
||||
class State(TypedDict):
|
||||
|
||||
@@ -471,7 +471,7 @@ Second, checkpointers allow for ["memory"](agentic_concepts.md#memory) between i
|
||||
|
||||
### Time Travel
|
||||
|
||||
Third, checkpointers allow for ["time travel"](../how-tos/human_in_the_loop/time-travel.ipynb), allowing users to replay prior graph executions to review and / or debug specific graph steps. In addition, checkpointers make it possible to fork the graph state at arbitrary checkpoints to explore alternative trajectories.
|
||||
Third, checkpointers allow for ["time travel"](time-travel.md), allowing users to replay prior graph executions to review and / or debug specific graph steps. In addition, checkpointers make it possible to fork the graph state at arbitrary checkpoints to explore alternative trajectories.
|
||||
|
||||
### Fault-tolerance
|
||||
|
||||
|
||||
@@ -0,0 +1,72 @@
|
||||
# Time Travel ⏱️
|
||||
|
||||
!!! note "Prerequisites"
|
||||
|
||||
This guide assumes that you are familiar with LangGraph's checkpoints and states. If not, please review the [persistence](./persistence.md) concept first.
|
||||
|
||||
|
||||
When working with non-deterministic systems that make model-based decisions (e.g., agents powered by LLMs), it can be useful to examine their decision-making process in detail:
|
||||
|
||||
1. 🤔 **Understand Reasoning**: Analyze the steps that led to a successful result.
|
||||
2. 🐞 **Debug Mistakes**: Identify where and why errors occurred.
|
||||
3. 🔍 **Explore Alternatives**: Test different paths to uncover better solutions.
|
||||
|
||||
We call these debugging techniques **Time Travel**, composed of two key actions: [**Replaying**](#replaying) 🔁 and [**Forking**](#forking) 🔀 .
|
||||
|
||||
## Replaying
|
||||
|
||||
|
||||
|
||||
Replaying allows us to revisit and reproduce an agent's past actions. This can be done either from the current state (or checkpoint) of the graph or from a specific checkpoint.
|
||||
|
||||
To replay from the current state, simply pass `None` as the input along with a `thread`:
|
||||
|
||||
```python
|
||||
thread = {"configurable": {"thread_id": "1"}}
|
||||
for event in graph.stream(None, thread, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
To replay actions from a specific checkpoint, start by retrieving all checkpoints for the thread:
|
||||
|
||||
```python
|
||||
all_checkpoints = []
|
||||
for state in graph.get_state_history(thread):
|
||||
all_checkpoints.append(state)
|
||||
```
|
||||
|
||||
Each checkpoint has a unique ID. After identifying the desired checkpoint, for instance, `xyz`, include its ID in the configuration:
|
||||
|
||||
```python
|
||||
config = {'configurable': {'thread_id': '1', 'checkpoint_id': 'xyz'}}
|
||||
for event in graph.stream(None, config, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
The graph efficiently replays previously executed nodes instead of re-executing them, leveraging its awareness of prior checkpoint executions.
|
||||
|
||||
## Forking
|
||||
|
||||
|
||||
|
||||
Forking allows you to revisit an agent's past actions and explore alternative paths within the graph.
|
||||
|
||||
To edit a specific checkpoint, such as `xyz`, provide its `checkpoint_id` when updating the graph's state:
|
||||
|
||||
```python
|
||||
config = {"configurable": {"thread_id": "1", "checkpoint_id": "xyz"}}
|
||||
graph.update_state(config, {"state": "updated state"})
|
||||
```
|
||||
|
||||
This creates a new forked checkpoint, xyz-fork, from which you can continue running the graph:
|
||||
|
||||
```python
|
||||
config = {'configurable': {'thread_id': '1', 'checkpoint_id': 'xyz-fork'}}
|
||||
for event in graph.stream(None, config, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
## Additional Resources 📚
|
||||
|
||||
- [**Conceptual Guide: Persistence**](https://langchain-ai.github.io/langgraph/concepts/persistence/#replay): Read the persistence guide for more context on replaying.
|
||||
- [**How to View and Update Past Graph State**](../how-tos/human_in_the_loop/time-travel.ipynb): Step-by-step instructions for working with graph state that demonstrate the **replay** and **fork** actions.
|
||||
@@ -0,0 +1,329 @@
|
||||
# Human-in-the-loop
|
||||
|
||||
!!! note "Use the `interrupt` function instead."
|
||||
|
||||
As of LangGraph 0.2.57, the recommended way to set breakpoints is using the [`interrupt` function][langgraph.types.interrupt] as it simplifies **human-in-the-loop** patterns.
|
||||
|
||||
Please see the revised [human-in-the-loop guide](./human_in_the_loop.md) for the latest version that uses the `interrupt` function.
|
||||
|
||||
|
||||
Human-in-the-loop (or "on-the-loop") enhances agent capabilities through several common user interaction patterns.
|
||||
|
||||
Common interaction patterns include:
|
||||
|
||||
(1) `Approval` - We can interrupt our agent, surface the current state to a user, and allow the user to accept an action.
|
||||
|
||||
(2) `Editing` - We can interrupt our agent, surface the current state to a user, and allow the user to edit the agent state.
|
||||
|
||||
(3) `Input` - We can explicitly create a graph node to collect human input and pass that input directly to the agent state.
|
||||
|
||||
Use-cases for these interaction patterns include:
|
||||
|
||||
(1) `Reviewing tool calls` - We can interrupt an agent to review and edit the results of tool calls.
|
||||
|
||||
(2) `Time Travel` - We can manually re-play and / or fork past actions of an agent.
|
||||
|
||||
## Persistence
|
||||
|
||||
All of these interaction patterns are enabled by LangGraph's built-in [persistence](./persistence.md) layer, which will write a checkpoint of the graph state at each step. Persistence allows the graph to stop so that a human can review and / or edit the current state of the graph and then resume with the human's input.
|
||||
|
||||
### Breakpoints
|
||||
|
||||
Adding a [breakpoint](./breakpoints.md) a specific location in the graph flow is one way to enable human-in-the-loop. In this case, the developer knows *where* in the workflow human input is needed and simply places a breakpoint prior to or following that particular graph node.
|
||||
|
||||
Here, we compile our graph with a checkpointer and a breakpoint at the node we want to interrupt before, `step_for_human_in_the_loop`. We then perform one of the above interaction patterns, which will create a new checkpoint if a human edits the graph state. The new checkpoint is saved to the `thread` and we can resume the graph execution from there by passing in `None` as the input.
|
||||
|
||||
```python
|
||||
# Compile our graph with a checkpointer and a breakpoint before "step_for_human_in_the_loop"
|
||||
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["step_for_human_in_the_loop"])
|
||||
|
||||
# Run the graph up to the breakpoint
|
||||
thread_config = {"configurable": {"thread_id": "1"}}
|
||||
for event in graph.stream(inputs, thread_config, stream_mode="values"):
|
||||
print(event)
|
||||
|
||||
# Perform some action that requires human in the loop
|
||||
|
||||
# Continue the graph execution from the current checkpoint
|
||||
for event in graph.stream(None, thread_config, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
### Dynamic Breakpoints
|
||||
|
||||
Alternatively, the developer can define some *condition* that must be met for a breakpoint to be triggered. This concept of [dynamic breakpoints](./breakpoints.md) is useful when the developer wants to halt the graph under *a particular condition*. This uses a `NodeInterrupt`, which is a special type of exception that can be raised from within a node based upon some condition. As an example, we can define a dynamic breakpoint that triggers when the `input` is longer than 5 characters.
|
||||
|
||||
```python
|
||||
def my_node(state: State) -> State:
|
||||
if len(state['input']) > 5:
|
||||
raise NodeInterrupt(f"Received input that is longer than 5 characters: {state['input']}")
|
||||
return state
|
||||
```
|
||||
|
||||
Let's assume we run the graph with an input that triggers the dynamic breakpoint and then attempt to resume the graph execution simply by passing in `None` for the input.
|
||||
|
||||
```python
|
||||
# Attempt to continue the graph execution with no change to state after we hit the dynamic breakpoint
|
||||
for event in graph.stream(None, thread_config, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
The graph will *interrupt* again because this node will be *re-run* with the same graph state. We need to change the graph state such that the condition that triggers the dynamic breakpoint is no longer met. So, we can simply edit the graph state to an input that meets the condition of our dynamic breakpoint (< 5 characters) and re-run the node.
|
||||
|
||||
```python
|
||||
# Update the state to pass the dynamic breakpoint
|
||||
graph.update_state(config=thread_config, values={"input": "foo"})
|
||||
for event in graph.stream(None, thread_config, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
Alternatively, what if we want to keep our current input and skip the node (`my_node`) that performs the check? To do this, we can simply perform the graph update with `as_node="my_node"` and pass in `None` for the values. This will make no update the graph state, but run the update as `my_node`, effectively skipping the node and bypassing the dynamic breakpoint.
|
||||
|
||||
```python
|
||||
# This update will skip the node `my_node` altogether
|
||||
graph.update_state(config=thread_config, values=None, as_node="my_node")
|
||||
for event in graph.stream(None, thread_config, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
See [our guide](../how-tos/human_in_the_loop/dynamic_breakpoints.ipynb) for a detailed how-to on doing this!
|
||||
|
||||
## Interaction Patterns
|
||||
|
||||
### Approval
|
||||
|
||||
|
||||
|
||||
Sometimes we want to approve certain steps in our agent's execution.
|
||||
|
||||
We can interrupt our agent at a [breakpoint](./breakpoints.md) prior to the step that we want to approve.
|
||||
|
||||
This is generally recommend for sensitive actions (e.g., using external APIs or writing to a database).
|
||||
|
||||
With persistence, we can surface the current agent state as well as the next step to a user for review and approval.
|
||||
|
||||
If approved, the graph resumes execution from the last saved checkpoint, which is saved to the `thread`:
|
||||
|
||||
```python
|
||||
# Compile our graph with a checkpointer and a breakpoint before the step to approve
|
||||
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["node_2"])
|
||||
|
||||
# Run the graph up to the breakpoint
|
||||
for event in graph.stream(inputs, thread, stream_mode="values"):
|
||||
print(event)
|
||||
|
||||
# ... Get human approval ...
|
||||
|
||||
# If approved, continue the graph execution from the last saved checkpoint
|
||||
for event in graph.stream(None, thread, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
See [our guide](../how-tos/human_in_the_loop/breakpoints.ipynb) for a detailed how-to on doing this!
|
||||
|
||||
### Editing
|
||||
|
||||
|
||||
|
||||
Sometimes we want to review and edit the agent's state.
|
||||
|
||||
As with approval, we can interrupt our agent at a [breakpoint](./breakpoints.md) prior to the step we want to check.
|
||||
|
||||
We can surface the current state to a user and allow the user to edit the agent state.
|
||||
|
||||
This can, for example, be used to correct the agent if it made a mistake (e.g., see the section on tool calling below).
|
||||
|
||||
We can edit the graph state by forking the current checkpoint, which is saved to the `thread`.
|
||||
|
||||
We can then proceed with the graph from our forked checkpoint as done before.
|
||||
|
||||
```python
|
||||
# Compile our graph with a checkpointer and a breakpoint before the step to review
|
||||
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["node_2"])
|
||||
|
||||
# Run the graph up to the breakpoint
|
||||
for event in graph.stream(inputs, thread, stream_mode="values"):
|
||||
print(event)
|
||||
|
||||
# Review the state, decide to edit it, and create a forked checkpoint with the new state
|
||||
graph.update_state(thread, {"state": "new state"})
|
||||
|
||||
# Continue the graph execution from the forked checkpoint
|
||||
for event in graph.stream(None, thread, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
See [this guide](../how-tos/human_in_the_loop/edit-graph-state.ipynb) for a detailed how-to on doing this!
|
||||
|
||||
### Input
|
||||
|
||||
|
||||
|
||||
Sometimes we want to explicitly get human input at a particular step in the graph.
|
||||
|
||||
We can create a graph node designated for this (e.g., `human_input` in our example diagram).
|
||||
|
||||
As with approval and editing, we can interrupt our agent at a [breakpoint](./breakpoints.md) prior to this node.
|
||||
|
||||
We can then perform a state update that includes the human input, just as we did with editing state.
|
||||
|
||||
But, we add one thing:
|
||||
|
||||
We can use `as_node=human_input` with the state update to specify that the state update *should be treated as a node*.
|
||||
|
||||
The is subtle, but important:
|
||||
|
||||
With editing, the user makes a decision about whether or not to edit the graph state.
|
||||
|
||||
With input, we explicitly define a node in our graph for collecting human input!
|
||||
|
||||
The state update with the human input then runs *as this node*.
|
||||
|
||||
```python
|
||||
# Compile our graph with a checkpointer and a breakpoint before the step to to collect human input
|
||||
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["human_input"])
|
||||
|
||||
# Run the graph up to the breakpoint
|
||||
for event in graph.stream(inputs, thread, stream_mode="values"):
|
||||
print(event)
|
||||
|
||||
# Update the state with the user input as if it was the human_input node
|
||||
graph.update_state(thread, {"user_input": user_input}, as_node="human_input")
|
||||
|
||||
# Continue the graph execution from the checkpoint created by the human_input node
|
||||
for event in graph.stream(None, thread, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
See [this guide](../how-tos/human_in_the_loop/wait-user-input.ipynb) for a detailed how-to on doing this!
|
||||
|
||||
## Use-cases
|
||||
|
||||
### Reviewing Tool Calls
|
||||
|
||||
Some user interaction patterns combine the above ideas.
|
||||
|
||||
For example, many agents use [tool calling](https://python.langchain.com/docs/how_to/tool_calling/) to make decisions.
|
||||
|
||||
Tool calling presents a challenge because the agent must get two things right:
|
||||
|
||||
(1) The name of the tool to call
|
||||
|
||||
(2) The arguments to pass to the tool
|
||||
|
||||
Even if the tool call is correct, we may also want to apply discretion:
|
||||
|
||||
(3) The tool call may be a sensitive operation that we want to approve
|
||||
|
||||
With these points in mind, we can combine the above ideas to create a human-in-the-loop review of a tool call.
|
||||
|
||||
```python
|
||||
# Compile our graph with a checkpointer and a breakpoint before the step to to review the tool call from the LLM
|
||||
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["human_review"])
|
||||
|
||||
# Run the graph up to the breakpoint
|
||||
for event in graph.stream(inputs, thread, stream_mode="values"):
|
||||
print(event)
|
||||
|
||||
# Review the tool call and update it, if needed, as the human_review node
|
||||
graph.update_state(thread, {"tool_call": "updated tool call"}, as_node="human_review")
|
||||
|
||||
# Otherwise, approve the tool call and proceed with the graph execution with no edits
|
||||
|
||||
# Continue the graph execution from either:
|
||||
# (1) the forked checkpoint created by human_review or
|
||||
# (2) the checkpoint saved when the tool call was originally made (no edits in human_review)
|
||||
for event in graph.stream(None, thread, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
See [this guide](../how-tos/human_in_the_loop/review-tool-calls.ipynb) for a detailed how-to on doing this!
|
||||
|
||||
### Time Travel
|
||||
|
||||
When working with agents, we often want closely examine their decision making process:
|
||||
|
||||
(1) Even when they arrive a desired final result, the reasoning that led to that result is often important to examine.
|
||||
|
||||
(2) When agents make mistakes, it is often valuable to understand why.
|
||||
|
||||
(3) In either of the above cases, it is useful to manually explore alternative decision making paths.
|
||||
|
||||
Collectively, we call these debugging concepts `time-travel` and they are composed of `replaying` and `forking`.
|
||||
|
||||
#### Replaying
|
||||
|
||||
|
||||
|
||||
Sometimes we want to simply replay past actions of an agent.
|
||||
|
||||
Above, we showed the case of executing an agent from the current state (or checkpoint) of the graph.
|
||||
|
||||
We by simply passing in `None` for the input with a `thread`.
|
||||
|
||||
```
|
||||
thread = {"configurable": {"thread_id": "1"}}
|
||||
for event in graph.stream(None, thread, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
Now, we can modify this to replay past actions from a *specific* checkpoint by passing in the checkpoint ID.
|
||||
|
||||
To get a specific checkpoint ID, we can easily get all of the checkpoints in the thread and filter to the one we want.
|
||||
|
||||
```python
|
||||
all_checkpoints = []
|
||||
for state in app.get_state_history(thread):
|
||||
all_checkpoints.append(state)
|
||||
```
|
||||
|
||||
Each checkpoint has a unique ID, which we can use to replay from a specific checkpoint.
|
||||
|
||||
Assume from reviewing the checkpoints that we want to replay from one, `xxx`.
|
||||
|
||||
We just pass in the checkpoint ID when we run the graph.
|
||||
|
||||
```python
|
||||
config = {'configurable': {'thread_id': '1', 'checkpoint_id': 'xxx'}}
|
||||
for event in graph.stream(None, config, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
Importantly, the graph knows which checkpoints have been previously executed.
|
||||
|
||||
So, it will re-play any previously executed nodes rather than re-executing them.
|
||||
|
||||
See [this additional conceptual guide](https://langchain-ai.github.io/langgraph/concepts/persistence/#replay) for related context on replaying.
|
||||
|
||||
See see [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for a detailed how-to on doing time-travel!
|
||||
|
||||
#### Forking
|
||||
|
||||
|
||||
|
||||
Sometimes we want to fork past actions of an agent, and explore different paths through the graph.
|
||||
|
||||
`Editing`, as discussed above, is *exactly* how we do this for the *current* state of the graph!
|
||||
|
||||
But, what if we want to fork *past* states of the graph?
|
||||
|
||||
For example, let's say we want to edit a particular checkpoint, `xxx`.
|
||||
|
||||
We pass this `checkpoint_id` when we update the state of the graph.
|
||||
|
||||
```python
|
||||
config = {"configurable": {"thread_id": "1", "checkpoint_id": "xxx"}}
|
||||
graph.update_state(config, {"state": "updated state"}, )
|
||||
```
|
||||
|
||||
This creates a new forked checkpoint, `xxx-fork`, which we can then run the graph from.
|
||||
|
||||
```python
|
||||
config = {'configurable': {'thread_id': '1', 'checkpoint_id': 'xxx-fork'}}
|
||||
for event in graph.stream(None, config, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
See [this additional conceptual guide](https://langchain-ai.github.io/langgraph/concepts/persistence/#update-state) for related context on forking.
|
||||
|
||||
See [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for a detailed how-to on doing time-travel!
|
||||
@@ -12,6 +12,14 @@
|
||||
"source": [
|
||||
"# How to add breakpoints\n",
|
||||
"\n",
|
||||
"!!! tip \"Prerequisites\"\n",
|
||||
"\n",
|
||||
" This guide assumes familiarity with the following concepts:\n",
|
||||
"\n",
|
||||
" * [Breakpoints](../../../concepts/breakpoints)\n",
|
||||
" * [LangGraph Glossary](../../../concepts/low_level)\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). [Breakpoints](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) are a common HIL interaction pattern, allowing the graph to stop at specific steps and seek human approval before proceeding (e.g., for sensitive actions). \n",
|
||||
"\n",
|
||||
"Breakpoints are built on top of LangGraph [checkpoints](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer), which save the graph's state after each node execution. Checkpoints are saved in [threads](https://langchain-ai.github.io/langgraph/concepts/low_level/#threads) that preserve graph state and can be accessed after a graph has finished execution. This allows for graph execution to pause at specific points, await human approval, and then resume execution from the last checkpoint.\n",
|
||||
@@ -467,7 +475,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.8"
|
||||
"version": "3.11.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -1,24 +1,32 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"id": "ee54cde3-7e4d-43f4-b921-e7141ea0f19e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to add dynamic breakpoints"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "607849c6-4b8c-4e06-ad9c-758bb5a08e86",
|
||||
"id": "b7d5f6a5-9e59-43e4-a4b6-8ada6dace691",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to add dynamic breakpoints with `NodeInterrupt`\n",
|
||||
"\n",
|
||||
"!!! note\n",
|
||||
"\n",
|
||||
" For **human-in-the-loop** workflows use the new [`interrupt()`](../../../reference/types/#langgraph.types.interrupt) function for **human-in-the-loop** workflows. Please review the [Human-in-the-loop conceptual guide](../../../concepts/human_in_the_loop) for more information about design patterns with `interrupt`.\n",
|
||||
"\n",
|
||||
"!!! tip \"Prerequisites\"\n",
|
||||
"\n",
|
||||
" This guide assumes familiarity with the following concepts:\n",
|
||||
"\n",
|
||||
" * [Breakpoints](../../../concepts/breakpoints)\n",
|
||||
" * [LangGraph Glossary](../../../concepts/low_level)\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). [Breakpoints](https://langchain-ai.github.io/langgraph/concepts/low_level/#breakpoints) are a common HIL interaction pattern, allowing the graph to stop at specific steps and seek human approval before proceeding (e.g., for sensitive actions).\n",
|
||||
"\n",
|
||||
"In LangGraph you can add breakpoints before / after a node is executed. But oftentimes it may be helpful to **dynamically** interrupt the graph from inside a given node based on some condition. When doing so, it may also be helpful to include information about **why** that interrupt was raised.\n",
|
||||
"\n",
|
||||
"This guide shows how you can dynamically interrupt the graph using `NodeInterrupt` -- a special exception that can be raised from inside a node. Let's see it in action!\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"First, let's install the required packages"
|
||||
@@ -430,7 +438,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.9"
|
||||
"version": "3.11.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -12,6 +12,12 @@
|
||||
"source": [
|
||||
"# How to edit graph state\n",
|
||||
"\n",
|
||||
"!!! tip \"Prerequisites\"\n",
|
||||
"\n",
|
||||
" * [Human-in-the-loop](../../../concepts/human_in_the_loop)\n",
|
||||
" * [Breakpoints](../../../concepts/breakpoints)\n",
|
||||
" * [LangGraph Glossary](../../../concepts/low_level)\n",
|
||||
"\n",
|
||||
"Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). Manually updating the graph state a common HIL interaction pattern, allowing the human to edit actions (e.g., what tool is being called or how it is being called).\n",
|
||||
"\n",
|
||||
"We can implement this in LangGraph using a [breakpoint](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): breakpoints allow us to interrupt graph execution before a specific step. At this breakpoint, we can manually update the graph state and then resume from that spot to continue. \n",
|
||||
@@ -554,7 +560,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.8"
|
||||
"version": "3.11.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -7,6 +7,15 @@
|
||||
"source": [
|
||||
"# How to view and update past graph state\n",
|
||||
"\n",
|
||||
"!!! tip \"Prerequisites\"\n",
|
||||
"\n",
|
||||
" This guide assumes familiarity with the following concepts:\n",
|
||||
"\n",
|
||||
" * [Time Travel](../../../concepts/time-travel)\n",
|
||||
" * [Breakpoints](../../../concepts/breakpoints)\n",
|
||||
" * [LangGraph Glossary](../../../concepts/low_level)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Once you start [checkpointing](../../persistence) your graphs, you can easily **get** or **update** the state of the agent at any point in time. This permits a few things:\n",
|
||||
"\n",
|
||||
"1. You can surface a state during an interrupt to a user to let them accept an action.\n",
|
||||
@@ -589,7 +598,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.9"
|
||||
"version": "3.11.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -48,12 +48,24 @@ LangGraph makes it easy to manage conversation [memory](../concepts/memory.md) i
|
||||
[Human-in-the-loop](../concepts/human_in_the_loop.md) functionality allows
|
||||
you to involve humans in the decision-making process of your graph. These how-to guides show how to implement human-in-the-loop workflows in your graph.
|
||||
|
||||
- [How to add breakpoints](human_in_the_loop/breakpoints.ipynb)
|
||||
- [How to add dynamic breakpoints](human_in_the_loop/dynamic_breakpoints.ipynb)
|
||||
- [How to edit graph state](human_in_the_loop/edit-graph-state.ipynb)
|
||||
- [How to wait for user input](human_in_the_loop/wait-user-input.ipynb)
|
||||
|
||||
Key workflows:
|
||||
|
||||
- [How to wait for user input](human_in_the_loop/wait-user-input.ipynb): A basic example that shows how to implement a human-in-the-loop workflow in your graph using the `interrupt` function.
|
||||
- [How to review tool calls](human_in_the_loop/review-tool-calls.ipynb): Incorporate human-in-the-loop for reviewing/editing/accepting tool call requests before they executed using the `interrupt` function.
|
||||
|
||||
|
||||
Other methods:
|
||||
|
||||
- [How to add static breakpoints](human_in_the_loop/breakpoints.ipynb): Use for debugging purposes. For [**human-in-the-loop**](../concepts/human_in_the_loop.md) workflows, we recommend the [`interrupt` function][langgraph.types.interrupt] instead.
|
||||
- [How to edit graph state](human_in_the_loop/edit-graph-state.ipynb): Edit graph state using `graph.update_state` method. Use this if implementing a **human-in-the-loop** workflow via **static breakpoints**.
|
||||
- [How to add dynamic breakpoints with `NodeInterrupt`](human_in_the_loop/dynamic_breakpoints.ipynb): **Not recommended**: Use the [`interrupt` function](../concepts/human_in_the_loop.md) instead.
|
||||
|
||||
### Time Travel
|
||||
|
||||
[Time travel](../concepts/time-travel.md) allows you to replay past actions in your LangGraph application to explore alternative paths and debug issues. These how-to guides show how to use time travel in your graph.
|
||||
|
||||
- [How to view and update past graph state](human_in_the_loop/time-travel.ipynb)
|
||||
- [How to review tool calls](human_in_the_loop/review-tool-calls.ipynb)
|
||||
|
||||
### Streaming
|
||||
|
||||
@@ -94,7 +106,10 @@ These how-to guides show common patterns for tool calling with LangGraph:
|
||||
|
||||
### Multi-agent
|
||||
|
||||
[Multi-agent systems](../concepts/multi_agent.md) are useful to break down complex LLM applications into multiple agents, each responsible for a different part of the application. These how-to guides show how to implement multi-agent systems in LangGraph:
|
||||
|
||||
- [How to build a multi-agent network](multi-agent-network.ipynb)
|
||||
- [How to add multi-turn conversation in a multi-agent application](multi-agent-multi-turn-convo.ipynb)
|
||||
|
||||
See the [multi-agent tutorials](../tutorials/index.md#multi-agent-systems) for implementations of other multi-agent architectures.
|
||||
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -347,6 +347,99 @@ class PregelScratchpad(TypedDict, total=False):
|
||||
|
||||
|
||||
def interrupt(value: Any) -> Any:
|
||||
"""Interrupt the graph with a resumable exception from within a node.
|
||||
|
||||
The `interrupt` function enables human-in-the-loop workflows by pausing graph
|
||||
execution and surfacing a value to the client. This value can communicate context
|
||||
or request input required to resume execution.
|
||||
|
||||
In a given node, the first invocation of this function raises a `GraphInterrupt`
|
||||
exception, halting execution. The provided `value` is included with the exception
|
||||
and sent to the client executing the graph.
|
||||
|
||||
A client resuming the graph must use the [`Command`][langgraph.types.Command]
|
||||
primitive to specify a value for the interrupt and continue execution.
|
||||
The graph resumes from the start of the node, **re-executing** all logic.
|
||||
|
||||
If a node contains multiple `interrupt` calls, LangGraph matches resume values
|
||||
to interrupts based on their order in the node. This list of resume values
|
||||
is scoped to the specific task executing the node and is not shared across tasks.
|
||||
|
||||
To use an `interrupt`, you must enable a checkpointer, as the feature relies
|
||||
on persisting the graph state.
|
||||
|
||||
Example:
|
||||
```python
|
||||
import uuid
|
||||
from typing import TypedDict, Optional
|
||||
|
||||
from langgraph.checkpoint.memory import MemorySaver
|
||||
from langgraph.constants import START
|
||||
from langgraph.graph import StateGraph
|
||||
from langgraph.types import interrupt
|
||||
|
||||
|
||||
class State(TypedDict):
|
||||
\"\"\"The graph state.\"\"\"
|
||||
|
||||
foo: str
|
||||
human_value: Optional[str]
|
||||
\"\"\"Human value will be updated using an interrupt.\"\"\"
|
||||
|
||||
|
||||
def node(state: State):
|
||||
answer = interrupt(
|
||||
# This value will be sent to the client
|
||||
# as part of the interrupt information.
|
||||
\"what is your age?\"
|
||||
)
|
||||
print(f\"> Received an input from the interrupt: {answer}\")
|
||||
return {\"human_value\": answer}
|
||||
|
||||
|
||||
builder = StateGraph(State)
|
||||
builder.add_node(\"node\", node)
|
||||
builder.add_edge(START, \"node\")
|
||||
|
||||
# A checkpointer must be enabled for interrupts to work!
|
||||
checkpointer = MemorySaver()
|
||||
graph = builder.compile(checkpointer=checkpointer)
|
||||
|
||||
config = {
|
||||
\"configurable\": {
|
||||
\"thread_id\": uuid.uuid4(),
|
||||
}
|
||||
}
|
||||
|
||||
for chunk in graph.stream({\"foo\": \"abc\"}, config):
|
||||
print(chunk)
|
||||
```
|
||||
|
||||
```pycon
|
||||
{'__interrupt__': (Interrupt(value='what is your age?', resumable=True, ns=['node:62e598fa-8653-9d6d-2046-a70203020e37'], when='during'),)}
|
||||
```
|
||||
|
||||
```python
|
||||
command = Command(resume=\"some input from a human!!!\")
|
||||
|
||||
for chunk in graph.stream(Command(resume=\"some input from a human!!!\"), config):
|
||||
print(chunk)
|
||||
```
|
||||
|
||||
```pycon
|
||||
Received an input from the interrupt: some input from a human!!!
|
||||
{'node': {'human_value': 'some input from a human!!!'}}
|
||||
```
|
||||
|
||||
Args:
|
||||
value: The value to surface to the client when the graph is interrupted.
|
||||
|
||||
Returns:
|
||||
Any: On subsequent invocations within the same node (same task to be precise), returns the value provided during the first invocation
|
||||
|
||||
Raises:
|
||||
GraphInterrupt: On the first invocation within the node, halts execution and surfaces the provided value to the client.
|
||||
"""
|
||||
from langgraph.constants import (
|
||||
CONFIG_KEY_CHECKPOINT_NS,
|
||||
CONFIG_KEY_SCRATCHPAD,
|
||||
|
||||
Reference in New Issue
Block a user