Build LangGraph 1.0 human-in-the-loop approval flows with `interrupt_before` /
`interrupt_after` and `Command(resume=...)` — JSON-serializable state, clean
resume semantics, and UI wiring for approval decisions. Use when adding an
approval gate before an expensive tool call, wiring a Slack/web UI for agent
approvals, or debugging a graph that crashes on interrupt.
Trigger with "langgraph human in loop", "langgraph interrupt_before",
"langgraph approval flow", "Command resume", "langgraph HITL".
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SKILL.md
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name
langchain-langgraph-human-in-loop
description
Build LangGraph 1.0 human-in-the-loop approval flows with `interrupt_before` /
`interrupt_after` and `Command(resume=...)` — JSON-serializable state, clean
resume semantics, and UI wiring for approval decisions. Use when adding an
approval gate before an expensive tool call, wiring a Slack/web UI for agent
approvals, or debugging a graph that crashes on interrupt.
Trigger with "langgraph human in loop", "langgraph interrupt_before",
"langgraph approval flow", "Command resume", "langgraph HITL".
A team adds interrupt_before=["send_email"] to require a human approval
before the email goes out. First integration test crashes at the interrupt
boundary with:
TypeError: Object of type datetime is not JSON serializable
The culprit is two nodes upstream: a classify node stashed
"received_at": datetime.utcnow() into state. Every node-level unit test
passed because node completion does not serialize state — only the
checkpointer does, and only at supersteps that include an interrupt. The
failure is invisible until interrupt time (P17).
A week later the resume path ships. The human reviews the draft, clicks
"approve with edits," and the backend runs:
The prior 47 messages vanish. messages was typed as plain
list[AnyMessage] with no reducer, so update replaces the field instead of
appending (P18).
This skill covers: three interrupt styles (interrupt_before,
interrupt_after, inline interrupt()), the JSON-only state invariant with
a pre-interrupt scanner, the Command(resume=...) /
Command(update=..., resume=...) contract, an approval UI wire format
(GET pending / POST decision with optimistic concurrency), safe-cancellation
routing to END, and the tradeoff between native interrupts and a separate
approval service. Pin: langgraph 1.0.x, langgraph-checkpoint 2.0.x.
Pain-catalog anchors: P17, P18 (adjacent: P16, P20).
Prerequisites
Python 3.10+
langgraph >= 1.0, < 2.0
A checkpointer: MemorySaver (dev), PostgresSaver (prod), or SqliteSaver (single-box)
A thread_id contract at the app boundary (see langchain-langgraph-checkpointing)
Familiarity with langchain-langgraph-basics — nodes, edges, TypedDict state with reducers
Instructions
Step 1 — Choose the interrupt style
LangGraph 1.0 exposes three interrupt mechanisms. They are not interchangeable.
Style
Syntax
Use when
interrupt_before=[node]
compile(interrupt_before=["send_email"])
Review inputs before an irreversible tool. Graph pauses before node runs. State shown is the input.
interrupt_after=[node]
compile(interrupt_after=["draft_email"])
Review output of a node (e.g., an LLM draft). Graph pauses after node completes.
Inline interrupt()
Inside a node: decision = interrupt({"kind": "..."})
Structured prompt mid-node with custom payload. Most flexible; lives in node code.
Rule of thumb: prefer interrupt_before for hard gates (tool must not run
without approval). Use interrupt_after for review loops (draft → approve →
send). Use inline interrupt() when the prompt varies on intermediate
computation.
Typical interrupt round-trip latency in production is 50-300 ms from
pause to checkpoint write (local Postgres) plus UI time; budget 1-5 s
total for a Slack-based approval. Checkpoint row sizes average 2-20 KB on
small graphs and cap at ~1 MB on PostgresSaver before historical
checkpoints need pruning.
See Interrupt Decision Tree for full
criteria, multiple-interrupt-per-graph patterns, and the interrupt-vs-tool
comparison.
Step 2 — Enforce the JSON-serializable state invariant (P17)
Checkpointers serialize state to JSON on every superstep. Any non-JSON type
raises TypeError at the interrupt boundary — not at the offending node.
Canonical offenders:
Type
Fix
datetime / date
dt.isoformat() — ISO 8601 string
bytes
base64.b64encode(b).decode()
set
sorted(s)
Pydantic BaseModel with non-primitive fields
.model_dump(mode="json")
Custom classes
dataclasses.asdict(obj) or vars(obj)
numpy.ndarray
.tolist()
decimal.Decimal
str(d) or float(d) (lossy)
float("nan") / float("inf")
None (JSON forbids them; some savers crash on allow_nan=False)
Ship a pre-interrupt scanner in dev and CI:
import json
from typing importAnyclassNonSerializableStateError(TypeError):
"""Raised when state contains values the checkpointer cannot serialize."""defassert_state_is_json_serializable(state: dict[str, Any], *, path: str = "state") -> None:
"""Walk state depth-first and raise a typed error naming the offending key path."""
_walk(state, path)
def_walk(v: Any, path: str) -> None:
if v isNoneorisinstance(v, (bool, int, float, str)):
returnifisinstance(v, list):
for i, item inenumerate(v):
_walk(item, f"{path}[{i}]")
returnifisinstance(v, dict):
for k, val in v.items():
_walk(val, f"{path}.{k}")
returnraise NonSerializableStateError(
f"{path} is {type(v).__name__}, not JSON-serializable. "f"Convert at node boundary."
)
Call assert_state_is_json_serializable(state) at the end of every node
preceding an interrupt-flagged node, or attach as LangGraph middleware. In
CI, run the full graph to interrupt against a fixture that exercises every
branch — the only way to catch P17 before prod.
See State Serialization for Interrupts
for the full forbidden-types list, the Pydantic-in-state pattern, and the
integration-test harness.
Step 3 — The resume contract
Two shapes. They are not equivalent.
from langgraph.types import Command
# Shape A — resume only: human approved as-is
graph.invoke(Command(resume="approved"), config)
# Shape B — update + resume: human edited state mid-graph
graph.invoke(
Command(update={"recipient": "new@example.com"}, resume="approved"),
config,
)
resume="..." is the value returned from inline interrupt() inside the
node (if any). For interrupt_before / interrupt_after, no node reads
resume, but the checkpoint records it for audit.
update={...} merges into state via the reducer declared in the TypedDict.
Without a reducer, update replaces the field (P18). Always annotate
list and dict state:
from typing import Annotated, TypedDict
from langchain_core.messages import AnyMessage
from langgraph.graph.message import add_messages
classAgentState(TypedDict):
messages: Annotated[list[AnyMessage], add_messages] # append, not replace
approvals: Annotated[list[dict], lambda l, r: l + r] # custom append reducer
draft: Annotated[dict, lambda l, r: {**l, **r}] # dict merge reducer
last_decision: str# scalar: replace is fine
See Resume Patterns for the five canonical
resume shapes (plain approve, approve with edits, reject to END, partial
approval, inline-interrupt structured return), the reducer cookbook, and the
audit-log write order.
POST /approvals/<thread-id>/decision applies the decision:
{"decision":"approve" | "reject" | "edit","edits":{"recipient":"corrected@example.com"},"approver":"jeremy@intentsolutions.io","reason":"Verified against ticket INT-4821","expected_checkpoint_id":"01JABC...","idempotency_key":"c2f5e8a0-..."}
Optimistic concurrency (the expected_checkpoint_id check) matters the
moment two approvers open the same thread in two browser tabs. Without it,
the second click silently overwrites the first. Return 409 Conflict on
mismatch; UI refreshes.
See Approval UI Wiring for the full
HTTP contract with status codes, FastAPI implementation, Slack Block Kit
mapping, state-diff redaction, and an audit-log schema compatible with SOC2
evidence requirements.
Step 5 — Safe cancellation: route to END on reject
When the human rejects, the gated node must NOT execute. Two clean patterns:
Pattern A — conditional edge after the interrupted node (preferred):
from langgraph.graph import END
defroute_after_approval(state: AgentState) -> str:
if state.get("last_decision") == "rejected":
return END
return"send_email"
builder.add_conditional_edges("await_approval", route_after_approval, {
"send_email": "send_email",
END: END,
})
Prefer Pattern A in production: graph topology stays the source of truth,
audit replays work without the UI. Always log the rejection to the checkpoint
via Command(update={"last_decision": "rejected", "reject_reason": ...})
BEFORE routing to END — otherwise the audit trail lives only in the UI DB.
Step 6 — Native interrupts vs a separate approval service
Dimension
LangGraph interrupts
Separate approval service
Latency
50-300 ms pause + human time
Human time + queue latency
State coherence
Single source of truth (checkpoint)
Two systems to reconcile
Concurrency
Checkpoint-based optimistic locking
Whatever the queue provides
Multi-graph
Per-graph, per-thread
Centralized policy engine
Observability
get_state() + checkpoint history
Separate audit system
Failure mode
JSON-serialization at interrupt (P17)
Network partition between services
Best for
Single LangGraph app, 1-10 approval types, <1k/day
Multi-app enterprise, complex RBAC, 10k+/day
Single LangGraph app with fewer than a dozen approval types: native
interrupts are simpler and more reliable. Cross-app approval platform with
escalations, delegations, and SLAs: run a dedicated service and call it from
a tool, not from an interrupt.
Output
Graph compiled with explicit interrupt_before / interrupt_after lists, or inline interrupt() calls where payload structure matters
TypedDict state with explicit reducers on every list and dict field
Pre-interrupt state scanner attached as middleware or called at node exits; raises NonSerializableStateError with a key path
Approval HTTP endpoints: GET pending with state diffs, POST decision with expected_checkpoint_id optimistic-concurrency check and idempotency_key dedupe
Rejection routes to END via conditional edge (Pattern A) with last_decision recorded in state for audit
Audit log written BEFORE state mutation with approver, reason, thread_id, checkpoint_id_before, checkpoint_id_after
Error Handling
Error
Cause
Fix
TypeError: Object of type datetime is not JSON serializable at interrupt
Non-JSON value in state (P17)
Convert at node boundary; add pre-interrupt scanner in CI
Resume with Command(update={"messages": [new]}) loses history
messages field missing reducer (P18)
Annotate as Annotated[list[AnyMessage], add_messages]
ValueError: Thread ... has no interrupted nodes on resume
Graph already ran to completion, or thread_id mismatch
Call graph.get_state(config) first; assert snapshot.next is non-empty
Human clicks approve, nothing happens
Missing checkpointer on compile() — interrupts require persistence
graph.compile(checkpointer=MemorySaver() or PostgresSaver(...))
Two approvers both click approve, second one's edits win silently
No optimistic concurrency
Include expected_checkpoint_id in POST body; return 409 on mismatch
KeyError: 'configurable' at resume
config dict missing thread_id
config = {"configurable": {"thread_id": tid}} — required by every checkpointer
Approval UI shows stale state after another approver acted
Cached GET /pending response
Cache-Control: no-store on the pending endpoint
Graph halts silently after reject
Conditional edge router returned value not in path_map
Include END in path_map; assert router output in keyset
Examples
Approval gate before an expensive tool
Email-sending agent that must not send without approval. State carries
draft: {to, subject, body}, graph compiles with
interrupt_before=["send_email"], resume either invokes the send tool or
routes to END on reject. See
Resume Patterns for the full worked example
including audit-log write order.
Partial approval — approve one argument, edit another
Human accepts the recipient but rewrites the subject. Resume is
Command(update={"draft": {**state["draft"], "subject": new_subject}}, resume="approved").
Note the spread — without it the draft is replaced. Scalar dicts replace by
default; declare a dict reducer to merge partials cleanly. See
Resume Patterns.
Inline interrupt() with a custom payload
Inside a validate_purchase node, the model has decided to buy three items
at USD 450 total. The node calls
decision = interrupt({"kind": "confirm_purchase", "items": items, "total": 450})
and the UI reads the payload to render a rich confirmation dialog. On resume,
decision is whatever the UI sent via
Command(resume={"approved": True, "notes": "..."}). See
Interrupt Decision Tree.
Slack-driven approval
GET /pending feeds a cron that posts Block Kit messages with approve/reject
buttons. Button callback POSTs to /decision. Slack's interaction payload
carries user.id, which becomes approver in the audit log. See
Approval UI Wiring for the Block Kit
template and signing-secret validation.
