sentinel-agent-trust-langchain

LangChain adapter for Sentinel AI — the identity & governance layer for AI agents. Wrap any LangChain tool so that every call is authorized (and audited) by the Sentinel gateway before it runs: your tool code and your agent code stay untouched.

Sentinel is in private beta. For source access, a hosted sandbox organization key, or to collaborate: cosimo@plannest.net

Install

pip install sentinel-agent-trust-langchain

Depends only on the public SDK (sentinel-agent-trust) and langchain-core — no heavyweight extras.

Quickstart

import os
from langchain_core.tools import tool
from sentinel_langchain import SentinelGuard

@tool
def create_refund(customer: str, amount: float) -> str:
    """Issue a refund to a customer."""
    return billing.refund(customer, amount)

guard = SentinelGuard(
    agent_id="agt_...",
    private_key=os.environ["SENTINEL_AGENT_PRIVATE_KEY"],  # base64 Ed25519 seed
    gateway_url="https://your-gateway.example.com",
)

tools = guard.wrap_all([create_refund])   # ← the only change to your agent

# then use the governed tools anywhere LangChain tools go:
# from langchain.agents import create_agent
# agent = create_agent(model, tools)

Every tool call is signed with the agent's Ed25519 key and checked against your policies in real time. The wrapped tool keeps the same name, description, and argument schema — the LLM sees no difference.

What the model sees

Sentinel's decision is translated into an observation the model can act on (the underlying tool is never executed unless the gateway approves):

This mapping is what keeps agents out of retry loops: a policy denial reads as a hard "no", an approval-pending reads as "park it", an outage reads as "try again shortly".

Mapping tools to your policy taxonomy

By default a tool named create_refund is authorized as action tool.create_refund on resource tool://create_refund, so a single policy with action_pattern: "tool.*" governs every wrapped tool. For business-level policies, map tools explicitly:

tools = guard.wrap_all(
    [create_refund, read_contact],
    overrides={
        "create_refund": {"action": "crm.refund.create", "resource": "crm://refunds"},
        "read_contact":  {"action": "crm.contact.read",  "resource": "crm://contacts/*"},
    },
)

…and the policy on the Sentinel side:

{
  "action_pattern": "crm.refund.create",
  "constraints": {"max_amount": 5000, "max_total_amount_per_day": 20000},
  "approval_thresholds": {"amount": 500}
}

Each refund ≤ 5000, at most 20 000/day in total, and a human approves anything from 500 up — enforced outside the model, no prompt engineering.

The tool's parsed arguments are sent as the action payload, so constraints like max_amount apply to the actual amount argument the model chose.

Async

Both paths are supported: tool.invoke(...) and await tool.ainvoke(...). The authorization call runs in a worker thread in the async path, so it never blocks the event loop.

CrewAI and other frameworks

CrewAI (and most agent frameworks) can consume LangChain tools directly, so wrapping with SentinelGuard before handing tools to your crew works today. A native CrewAI adapter is on the roadmap.

Demo

With the repo's demo gateway running (cd gateway && go run .):

pip install -e sdk/python -e sdk/adapters/langchain
python examples/langchain_crm_agent.py

License

Apache 2.0 (the adapter and SDKs). The Sentinel platform is licensed separately under BSL 1.1 — see the repository root.