Quesen — LangChain / LangGraph Tool

Deterministic Agent Firewall + A2A risk validation as LangChain BaseTools. Integrate in under 5 minutes.

Status: v0.3.0 · tracks Quesen engine v1.10.0 (TSC v2 firewall) · requires quesen-sdk>=0.4.1. Developer portal: https://senueren.co.za/quesen · Source: https://github.com/Shxnque/quesen

Agent Firewall (30-second, no signup)

from quesen_langchain import QuesenFirewallTool

# sandbox=True self-serves a free key against the hosted engine.
firewall = QuesenFirewallTool(
    base_url="https://web-production-3df26.up.railway.app",
    sandbox=True,
)

# Give it to any LangChain/LangGraph agent, or call directly:
verdict = firewall._run(
    agent="my-agent", action="send_data",
    target="https://paste.evil.example", data_class="secret",
)
print(verdict["decision"])                       # 'BLOCK'
print([r["code"] for r in verdict["reasons"]])   # ['EGRESS_SECRET_UNTRUSTED']

action accepts send_data / tool_call / payment (+ aliases). For production pass api_key="sk_live_..." instead of sandbox=True.

Install

pip install quesen-langchain
# or from source while we publish:
pip install git+https://github.com/Shxnque/quesen-langchain.git

Brings quesen-sdk>=0.2.0 and langchain-core with it. No LLM lock-in.

30-second usage

from langchain_core.messages import HumanMessage
from langchain_openai import ChatOpenAI
from quesen_langchain import QuesenValidateTool, QuesenReportTool

validate_tool = QuesenValidateTool(base_url="https://<your-quesen>", api_key="sk_live_abc")
report_tool   = QuesenReportTool(base_url="https://<your-quesen>", api_key="sk_live_abc")

agent = ChatOpenAI(model="gpt-4o-mini").bind_tools([validate_tool, report_tool])
print(agent.invoke([HumanMessage("Should I ape into a 1-day-old token with 4 scam keywords and 95% engagement?")]))

Receipt provenance (v1.10, tracked in v0.2.0)

QuesenValidateTool._run(...) returns the raw response dict verbatim from the underlying quesen-sdk client. When talking to a v1.10.0+ engine that dict includes two additional fields:

• input_snapshot_hash — 64-char SHA-256 hex over the canonical request payload.
• commit_sha — 40-char git SHA of the engine ruleset live at decision time (or "unknown").

Both flow through unchanged for downstream LangGraph nodes to consume, log, or verify. See the developer portal API reference for the field contract.

What ships

All three are langchain_core.tools.BaseTool subclasses with structured Pydantic inputs. Compatible with LangGraph, create_react_agent, bind_tools(), and every LangChain callback pipeline.

Environment

Doctrine compliance (inherited from parent)

• Determinism. No prompt-tuning, no randomness in the wrapper.
• Ecosystem neutrality. Depends on langchain-core only, not on any specific LLM provider.
• Fail-closed. Timeouts surface as tool errors; recommended agent policy: treat as SKIP.
• Provenance forwarded. The raw dict returned to the LangChain runtime carries input_snapshot_hash and commit_sha unchanged, so downstream nodes can pin the ruleset.

MIT license. See senueren.co.za/quesen for canonical documentation.