Code langgraph Ops Untested graph free

Monitoring Check — LangGraph

Turn a status paste or log snippet into a short health note: what changed, severity, and a reversible next step. (LangGraph stateful graph with a human checkpoint.)

Id: langgraph/monitoring · Slug: monitoring · Status: Untested · Untested on this machine — recipe reviewed for safety (no secrets, no destructive commands, no auto-spend). Mark tested after you run it locally.

What is this

Code for LangGraph. Turn a status paste or log snippet into a short health note: what changed, severity, and a reversible next step. (LangGraph stateful graph with a human checkpoint.)

How this runtime fits

This LangGraph implementation is a graph-style runtime for the parent AI Team. Agent orchestration and multi-agent workflow language fit when nodes split work and gates; it is not automatically identical to every multi-agent system.

Parent AI Team: monitoring · What is an AI Team?

What it does

Turn a status paste or log snippet into a short health note: what changed, severity, and a reversible next step. (LangGraph stateful graph with a human checkpoint.)

Who for

Builders working on Ops jobs who can run LangGraph themselves.

Need to run

You will run this locally or on infra you control.
No production credentials in prompts or committed files.
Python 3.10+
LangGraph in a venv
Local model binder

How to use

Install LangGraph in a venv (pin versions).
Wire produce() to your local model client.
Run the file once with Example in.
Keep human_gate approval defaulting to False; enable revise only intentionally.

Limitations

Not a substitute for professional legal, medical, or investment advice.
Outputs can be wrong; human review required before publish or spend.
Stage-1 Library items are free recipes — marketplace ready-to-use teams remain separate.
Do not attach billing cloud LLM nodes without a human budget check.

Test status

Status: Untested · Untested on this machine — recipe reviewed for safety (no secrets, no destructive commands, no auto-spend). Mark tested after you run it locally.

Files

graph_monitoring.py — LangGraph nodes + interrupt
README.md — Run notes

Full prompt / config / code

# graph_monitoring.py — Monitoring Check (LangGraph sketch)
# pip install langgraph langchain-core (pin yourself)
# Use a local chat model binder; do not embed secrets.

from typing import TypedDict, Literal
from langgraph.graph import StateGraph, END

SYSTEM = """You are an ops monitoring note-taker. Goal: short health note from a status paste.

RULES:
- Severity: info|warn|critical based only on provided signals.
- Prefer reversible next steps. No destructive commands.
- No auto-remediation that spends money or deletes data.
- If signal is insufficient, say so.

OUTPUT:
1) Status one-liner
2) What changed
3) Severity + evidence
4) Reversible next step
5) Owner / wait condition"""

class State(TypedDict):
    user_input: str
    draft: str
    approved: bool
    notes: str

def produce(state: State) -> State:
    # Pseudo: call your local model with SYSTEM + state["user_input"]
    draft = "[MODEL OUTPUT PLACEHOLDER — wire your local LLM here]\n" + state["user_input"][:500]
    return {**state, "draft": draft, "notes": "awaiting human"}

def human_gate(state: State) -> State:
    # In real use: interrupt / input() / UI approval.
    # Default False so nothing auto-publishes.
    return {**state, "approved": False}

def route_after_gate(state: State) -> Literal["done", "revise"]:
    return "done" if state.get("approved") else "done"  # stage-1: always end after gate

g = StateGraph(State)
g.add_node("produce", produce)
g.add_node("human_gate", human_gate)
g.set_entry_point("produce")
g.add_edge("produce", "human_gate")
g.add_conditional_edges("human_gate", route_after_gate, {"done": END, "revise": "produce"})
app = g.compile()

if __name__ == "__main__":
    out = app.invoke({"user_input": "PASTE_EXAMPLE_IN", "draft": "", "approved": False, "notes": ""})
    print(out)

Example in

Status paste:
- netlify deploy: ok
- checkout function p95: 1800ms (was 400ms)
- error rate 2.1% (was 0.3%)
Window: last 30m

Example out

Status: checkout latency + errors elevated
Changed: p95 400→1800ms; errors 0.3→2.1%
Severity: warn (not confirmed outage)
Next: check last deploy diff; freeze new deploys if rising
Wait: 15m trend

Model / runtime notes

Graph includes an explicit human checkpoint. Stage-1 ends after gate (no infinite revise loop).

Canonical Team

Parent: monitoring · implementation_id: langgraph/monitoring

GitHub source

teams/monitoring/langgraph · commit eb87e8e049fdf9908e438305ff827c7b617505b5 · license: free-use-at-own-risk

Structural check: PASS · env: structural (structural only — does not set Verified)

Related Library items

Related marketplace Team pages

Soft thematic links only — not identity merges. Marketplace Teams ≠ Library AI Team records.

Recurring Price Check (marketplace Team page — separate entity)
Evidence Gate Check (marketplace Team page — separate entity)