To debug a LangGraph run, give it a persistent checkpoint and a stable thread_id, inspect its latest state and history, then stream node events to find the first transition that differs from what you expected. Once you have isolated the checkpoint, you can replay later work or create a separate state branch—but replay runs those later nodes again, including model calls and external side effects.
Make the run inspectable first
State inspection relies on a checkpointer and thread identity. Compile the graph with a checkpointer, then pass the same thread_id in the configurable run settings when invoking it and inspecting it. The ID lets the checkpointer retrieve the checkpoints for that conversation or workflow. See the LangGraph persistence documentation for current details.
from langgraph.checkpoint.memory import InMemorySaver
checkpointer = InMemorySaver()
graph = builder.compile(checkpointer=checkpointer)
config = {"configurable": {"thread_id": "debug-run-123"}}
result = graph.invoke(inputs, config)InMemorySaver is useful for a local experiment, but its data does not survive process loss. For a deployed graph, choose an appropriate persistence backend; Agent Server deployments manage the persistence infrastructure. If you invoke with one thread ID and inspect with another, you will not be looking at the same run.
Inspect the current snapshot
Call get_state with the run configuration to retrieve a StateSnapshot. Its fields answer different questions: what values are currently stored, what is scheduled next, and what execution details were recorded.
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snapshot = graph.get_state(config)
print(snapshot.values) # channel values at this checkpoint
print(snapshot.next) # node(s) scheduled next; empty means complete
print(snapshot.metadata) # source, writes, and step metadata
print(snapshot.tasks) # task details, including errors or interrupts where presentAn empty snapshot.next indicates that no node is scheduled to execute next at this checkpoint. To inspect a particular point rather than the latest one, include that checkpoint’s ID in the configurable run settings. A current snapshot is a useful symptom, but it cannot by itself show every transition that led to the result.
Walk backward to find the first bad transition
get_state_history returns snapshots in reverse chronological order, with the latest first. Compare neighboring snapshots and look for the earliest one where an expected field disappears, changes unexpectedly, or takes on a malformed value.
history = list(graph.get_state_history(config))
for snapshot in history:
print(snapshot.created_at, snapshot.metadata, snapshot.next, snapshot.values)Check metadata.writes to associate a channel update with the node that produced it, and next to see which node or nodes were scheduled afterward. Snapshots also include checkpoint and parent-checkpoint IDs, which help identify a starting point for replay. The persistence guide documents the snapshot and history APIs at LangGraph persistence.
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Watch a live run at the right level of detail
Streaming is useful when the failure is hard to diagnose after the fact or when you want to see exactly where execution diverges. Choose a stream mode based on the evidence you need. The example below requests node updates and task events:
for chunk in graph.stream(
inputs,
config=config,
stream_mode=["updates", "tasks"],
version="v2",
):
print(chunk)| Mode | What it helps you see | Requirement or use |
|---|---|---|
updates |
State updates emitted by each node. | Use to locate the node that changed a value. |
tasks |
Node task starts and finishes, results, and errors. | Requires a checkpointer; useful for locating a failing task. |
checkpoints |
Saved checkpoint events. | Requires a checkpointer; use to see when state was saved. |
debug |
Node names, full state, and additional runtime metadata. | Use when you need a broad view of execution; combines checkpoint and task events. |
messages |
Streamed language-model tokens and node metadata. | Use when the unexpected behavior appears in model output. |
To include output and namespaces from nested graphs, pass subgraphs=True. The official streaming documentation recommends event streaming for new applications while retaining stream modes for direct runtime event access and selected event output. Check the current documentation when adopting a newer API version.
Check whether reducers explain the state change
If a field looks overwritten, missing, or duplicated, inspect the state schema and its reducer before assuming the model caused the problem. A channel without a reducer replaces its prior value when it receives an update. A reducer defines how the old and new values are combined, so the same update can append, merge, or transform data depending on the channel.
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For message state, add_messages appends new messages and updates an existing message when the incoming message has the same ID. If a message appears to have been replaced rather than added, check its ID and whether the channel uses that reducer. See the LangGraph Graph API documentation for state and reducer details.
Replay from a checkpoint or branch with an update
Replaying from a historical checkpoint skips earlier work and executes later nodes again. That can isolate a fault while keeping prior results fixed, but it is execution—not simply viewing a saved run. Any later LLM calls, API requests, or other side effects may happen again.
For an experiment with altered state, use update_state. It creates a new checkpoint rather than editing the old one, so you can explore a branch while preserving the original checkpoint history. Before replaying production work, identify which downstream nodes have side effects and verify that rerunning them is safe. The persistence guide covers checkpoint access, replay, and state updates.
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Choose node boundaries that make failures visible
If one node performs several operations, it can be difficult to tell which one caused a bad state or error. Split operations when doing so improves visibility, isolates an external service, or lets you apply a different retry strategy. For example, separate retrieval from drafting so you can distinguish poor search results from a generation problem.
Smaller nodes can provide more visible checkpoint boundaries and limit repeated work when restarting. Splitting everything into tiny nodes is not required; use boundaries that make diagnosis or recovery meaningfully clearer. LangChain’s agent-design guidance discusses shaping workflows around these considerations.
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- Transient network or rate-limit failure: apply a retry policy to the node that calls the external service.
- Recoverable tool or parse failure: record the error in graph state and route to a node that can adjust or repair the action.
- Missing user information: use an interrupt to pause for input when the workflow is designed for human resolution.
- Unexpected exception: allow it to surface while debugging rather than swallowing an error when its recovery behavior is unknown.
- Retry exhaustion: route to a recovery or compensation path if the application needs one.
For resume inconsistencies, first verify that the same thread ID is being used, then inspect the last completed checkpoint. If a node is interrupted mid-execution, it restarts from the beginning of that node. Successful task writes from other nodes in the same super-step can be reused when execution resumes. These behaviors are described in the LangGraph persistence documentation and fault-tolerance guidance.
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Understand why an intermediate checkpoint may be missing
LangGraph offers three durability modes, which determine when checkpoint writes are persisted relative to graph execution:
| Mode | Persistence behavior | Trade-off |
|---|---|---|
exit |
Persists when execution exits. | Does not preserve intermediate state for recovery from a mid-run process crash. |
async |
Persists while the next step executes. | A process crash can occur before a checkpoint write completes. |
sync |
Writes a checkpoint before the next step begins. | Trades some performance for higher durability. |
If a snapshot you expected is absent, consider both the configured durability mode and when a process failure occurred. The persistence guide describes these modes and their behavior: LangGraph persistence.
Use LangSmith Studio for a visual run timeline
LangSmith Studio is an optional visual route for debugging graphs available through the Agent Server protocol. In Graph mode, it can show traversed nodes, intermediate states, and time-travel debugging. Chat mode is a simpler chat-testing interface and is supported when the graph state includes or extends MessagesState. Use Studio when a visual timeline helps reveal the fault; direct APIs are better suited to local execution flows and automated diagnostics. See the LangSmith Studio documentation for current requirements.
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