Time Travel ⏱️

Prerequisites

This guide assumes that you are familiar with LangGraph's checkpoints and states. If not, please review the persistence 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 🔁 and 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:

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:

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:

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:

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:

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: Read the persistence guide for more context on replaying.
• How to View and Update Past Graph State: Step-by-step instructions for working with graph state that demonstrate the replay and fork actions.

Comments