LangChain and LangGraph

pip install 'failproofai-sdk[langgraph]'

import failproofai_sdk

failproofai_sdk.configure(environment="production")
failproofai_sdk.instrument()

with failproofai_sdk.session():
    graph.invoke({"messages": [HumanMessage("...")]})

ChatOpenAI(model="gpt-4o-mini", stream_usage=True)   # ohne dies keine Tokens

import failproofai_sdk
from langchain_core.messages import HumanMessage
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import ToolNode, create_react_agent

failproofai_sdk.configure(environment="production")
failproofai_sdk.instrument()


@tool
def price_of(item: str) -> float:
    """Return the unit price of an item in USD."""
    return {"widget": 42.0, "gadget": 17.5}[item.lower().strip()]


@tool
def stock_of(item: str) -> int:
    """Return the units of an item currently in stock."""
    return {"widget": 120, "gadget": 0}[item.lower().strip()]


tools = ToolNode([price_of, stock_of], handle_tool_errors=True)
graph = create_react_agent(ChatOpenAI(model="gpt-4o-mini"), tools)

with failproofai_sdk.session():
    with failproofai_sdk.agent("analyst", goal="price and stock report"):
        result = graph.invoke({
            "messages": [HumanMessage("Price and stock for widget and gadget?")]
        })

with failproofai_sdk.session():
    with failproofai_sdk.agent("analyst", goal="price and stock report"):
        graph.invoke(...)

with failproofai_sdk.session():
    with failproofai_sdk.agent("supervisor"):
        with failproofai_sdk.agent("researcher"):
            research_graph.invoke(...)
        with failproofai_sdk.agent("writer"):
            writer_graph.invoke(...)

graph.invoke(
    {"messages": [...]},
    config={"metadata": {"failproofai_sdk_session_id": f"chat-{user_id}"}},
)

failproofai_sdk.instrument(
    "langchain",
    session_id=None,          # jeden Run an eine Session-ID pinnen
    include_chains=set(),     # Allowlist für intermediäre Chains als Hook-Paare
    capture_content=True,     # False entfernt Prompts und Completions aus den Payloads
    graph_callbacks=True,     # erstklassige Interrupt- und Resume-Unterstützung, benötigt langgraph 1.2+
)

from langgraph.types import Command, interrupt

def approve(state):
    decision = interrupt({"prompt": "Ship it?", "options": ["yes", "no"]})
    return {"approved": decision == "yes"}

with failproofai_sdk.session():
    graph.invoke(state, config)                    # human_wait, agent_pause
    graph.invoke(Command(resume="yes"), config)    # agent_resume, human_input