Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sairam0424/ag-bash --skill langgraph-human-in-the-loopgit clone --depth 1 https://github.com/sairam0424/ag-bashWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sairam0424/ag-bash/langgraph-human-in-the-loop)<a href="https://agentmods.dev/skills/sairam0424/ag-bash/langgraph-human-in-the-loop"><img src="https://agentmods.dev/badge/skills/sairam0424/ag-bash/langgraph-human-in-the-loop/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sairam0424/ag-bash/langgraph-human-in-the-loop"><img src="https://agentmods.dev/badge/skills/sairam0424/ag-bash/langgraph-human-in-the-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.03896 |
| Opus 5 | $0.00028 | $0.01948 |
| Sonnet 5 | $0.00011 | $0.00779 |
| Haiku 4.5 | $0.00006 | $0.00390 |
Grade A, and why
langgraph-human-in-the-loop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to langgraph-human-in-the-loop — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 533 lines — stays where its author put it; the contents beside it link to each section on GitHub.
interrupt(value)— pauses execution, surfaces a value to the callerCommand(resume=value)— resumes execution, providing the value back tointerrupt()- Checkpointer — required to save state while paused
- Thread ID — required to identify which paused execution to resume
Requirements
Three things are required for interrupts to work:
- Checkpointer — compile with
checkpointer=InMemorySaver()(dev) orPostgresSaver(prod) - Thread ID — pass
{"configurable": {"thread_id": "..."}}to everyinvoke/streamcall - JSON-serializable payload — the value passed to
interrupt()must be JSON-serializable
Basic Interrupt + Resume
interrupt(value) pauses the graph. The value surfaces in the result under __interrupt__. Command(resume=value) resumes — the resume value becomes the return value of interrupt().
Critical: when the graph resumes, the node restarts from the beginning — all code before interrupt() re-runs.
class State(TypedDict): approved: bool
def approval_node(state: State): # Pause and ask for approval approved = interrupt("Do you approve this action?") # When resumed, Command(resume=...) returns that value here return {"approved": approved}
checkpointer = InMemorySaver() graph = ( StateGraph(State) .add_node("approval", approval_node) .add_edge(START, "approval") .add_edge("approval", END) .compile(checkpointer=checkpointer) )
config = {"configurable": {"thread_id": "thread-1"}}
Initial run — hits interrupt and pauses
result = graph.invoke({"approved": False}, config) print(result["interrupt"])
[Interrupt(value='Do you approve this action?')]
Resume with the human's response
result = graph.invoke(Command(resume=True), config) print(result["approved"]) # True
</python>
<typescript>
Pause execution for human review and resume with Command.
```typescript
import { interrupt, Command, MemorySaver, StateGraph, StateSchema, START, END } from "@langchain/langgraph";
import { z } from "zod";
const State = new StateSchema({
approved: z.boolean().default(false),
});
const approvalNode = async (state: typeof State.State) => {
// Pause and ask for approval
const approved = interrupt("Do you approve this action?");
// When resumed, Command({ resume }) returns that value here
return { approved };
};
const checkpointer = new MemorySaver();
const graph = new StateGraph(State)
.addNode("approval", approvalNode)
.addEdge(START, "approval")
.addEdge("approval", END)
.compile({ checkpointer });
const config = { configurable: { thread_id: "thread-1" } };
// Initial run — hits interrupt and pauses
let result = await graph.invoke({ approved: false }, config);
console.log(result.__interrupt__);
// [{ value: 'Do you approve this action?', ... }]
// Resume with the human's response
result = await graph.invoke(new Command({ resume: true }), config);
console.log(result.approved); // true
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 533 lines · 55 tokens per session scan A 3e43a99257a8
langgraph-human-in-the-loop is a skill published in the GitHub repository sairam0424/ag-bash (0 stars, last pushed 6d ago), licensed Apache-2.0. It adds 55 tokens to every session and 3,896 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to langgraph-human-in-the-loop, differing in 0 lines, and is treated as a copy.
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