openwright-langgraph 0.3.0

LangGraph source connector for OpenWright — capture agent decisions, tools, and human-in-the-loop approvals as evidence.

openwright-langgraph

A LangGraph source connector for OpenWright: capture a LangGraph lending/underwriting agent's runtime behavior as signed, control-mapped evidence — tool and model calls automatically, plus the wedge controls (human-in-the-loop approval and the credit decision).

from openwright.connectors import load
conn = load("openwright.source_connectors", "langgraph")

with conn.instrument(client, thread_id="loan-42") as run:
    graph.invoke(state, config={"callbacks": run.callbacks,
                                "configurable": {"thread_id": "loan-42"}})
    # when the reviewer approves and you resume the graph:
    appr = run.record_human_approval(reviewer="alice@bank", rationale="reviewed KYC")
    run.record_decision(output="APPROVED", risk_classification="high",
                        approval_ref=appr.event_id, control="art-14-human-oversight")

A human-approved high-risk decision yields Art-14 satisfied; an un-approved one is flagged. Payloads are hashed (sha256: references) — no raw prompts/PII enter the ledger. Built on LangChain's callback API + the OpenWright SDK; no crypto is reimplemented. Not affiliated with LangChain/LangGraph.