SecureVector SDK for LangChain

Bring the SecureVector local threat monitor's three controls — tool-call permissions, secret / data-leak detection, and threat detection — to every LangChain tool call, with tamper-evident audit logging. One import.

pip install securevector-sdk-langchain

📦 One install — batteries included. pip install securevector-sdk-langchain also installs the local SecureVector app (securevector-ai-monitor): the adapter and the detection engine + tamper-evident audit chain arrive in a single pip install. The SDK is a thin interception layer — the app must be running locally (securevector-app --web) for it to do anything.

🌐 Pointing at your own cloud? Use the lightweight install. If you've deployed SecureVector to your own cloud, you don't need the bundled local app. Install only the adapter on the machine where your agents run, and point it at your deployment:

# lightweight — adapter only, no local app (your env already has langchain)
pip install securevector-sdk-langchain --no-deps

# point at your SecureVector endpoint — all you need for a private (in-VPC) endpoint
export SECUREVECTOR_SDK_APP_URL=https://<your-securevector-endpoint>

# OPTIONAL: only if your endpoint is publicly exposed and gated with an inbound token.
# A private endpoint in your own VPC needs no key. To gate a public one, use a free
# SecureVector cloud account API key or an SVET token — it gates access only; no agent
# data is sent to SecureVector.
export SECUREVECTOR_API_KEY=<SecureVector account key or SVET token>

The adapter then forwards every tool call to your remote deployment instead of a local app. The default pip install securevector-sdk-langchain (no --no-deps) still bundles the app for local use.

Quick start

Enforcement (recommended) — the documented wrap_tool_call middleware, which can cleanly block a tool before it runs:

from securevector_sdk_langchain import secure_middleware
from langchain.agents import create_agent

agent = create_agent(
    model, tools,
    middleware=[secure_middleware(mode="enforce")],
)

A denied tool is short-circuited with a ToolMessage (the model sees a clean "blocked by policy" result) — no exceptions, no crashed runs.

Observe-only logging for legacy AgentExecutor / raw LCEL chains, where the middleware surface isn't available:

from securevector_sdk_langchain import SecureVectorCallbackHandler

chain.invoke(payload, config={"callbacks": [SecureVectorCallbackHandler()]})

Why two paths? LangChain callbacks are an observability surface — they cannot reliably block a tool call. The wrap_tool_call middleware is the documented interception/short-circuit point, so enforcement lives there.

LLM cost tracking — add cost_tracking_middleware() and every model call's token usage (input / output / cached) posts to the app's Cost Tracking, where the pricing table turns it into real dollars (your agent runs on your own API keys) and per-agent budgets apply:

from securevector_sdk_langchain import secure_middleware, cost_tracking_middleware

agent = create_agent(
    model, tools,
    middleware=[secure_middleware(mode="enforce"), cost_tracking_middleware()],
)

Records are attributed to agent_id="langchain-agent" by default — name your agent with cost_tracking_middleware(agent_id="checkout-bot") or SECUREVECTOR_SDK_AGENT_ID. The callback handler captures the same usage via on_llm_end for legacy chains. Cost capture is best-effort: an unreachable app never breaks the agent.

What happens on every tool call

Before a tool runs, the SDK:

1. (a) Permissions — resolves an allow/block verdict for the tool, using the app's own precedence: cloud-pushed synced policy → local override → essential registry → default-allow.
2. (b)+(c) Secret & threat scan — sends the serialized tool input through the app's /analyze pipeline.

After the tool returns, the result is scanned the same way to catch secrets / exfiltration in tool output. Every decision is written to the app's audit chain tagged runtime_kind="langchain".

observe vs enforce

	local app reachable	local app unreachable
observe (default)	log + advisory verdict; tool always runs	tool runs (fail-open)
enforce (opt-in)	tool runs only if the verdict ≠ block	tool denied (fail-closed)

agent = create_agent(model, tools, middleware=[secure_middleware(mode="enforce")])

Enforce mode prints a one-time disclosure to stderr. (Enforcement requires the middleware path; the observe-only callback handler always runs in observe mode.)

Configuration

All optional, via env or install(...) kwargs:

Env var	Default	Meaning
SECUREVECTOR_SDK_APP_URL	http://127.0.0.1:8741	local app base URL
SECUREVECTOR_SDK_MODE	observe	observe or enforce
SECUREVECTOR_SDK_TIMEOUT_MS	3000	per-call verdict timeout
SECUREVECTOR_SDK_RISK_THRESHOLD	70	risk score that blocks in enforce mode
SECUREVECTOR_SDK_AGENT_ID	langchain-agent	agent id shown in Cost Tracking
SECUREVECTOR_SDK_DISABLED	(unset)	set truthy to no-op

Compliance

The tool-call-level, attributed, tamper-evident audit trail this produces is exactly the action-layer logging auditors ask for under EU AI Act Art. 12 / 15. This SDK produces the local evidence; the cloud governance surface turns it into an auditor-ready pack.

Trademarks

SecureVector is the product name of this SDK. LangChain and LangGraph are trademarks of LangChain, Inc. This is an independent, community SDK that integrates with LangChain via its public callback API. It is not affiliated with, sponsored by, or endorsed by LangChain, Inc. The name uses "langchain" only descriptively, to identify the framework this package works with (nominative fair use).

License

Apache-2.0. See LICENSE and NOTICE.