arcjet

Arcjet is the runtime security platform that ships in your AI code. Detect prompt injection, authorize agent tool calls, redact sensitive data, and block bots and abuse. Real-time security building blocks you call inside your app, before an action happens.

This is the Python SDK for Arcjet — use arcjet /
arcjet_sync for request protection (FastAPI, Flask, Django route handlers)
and arcjet.guard for guard protection (AI agent tool calls, MCP servers,
background jobs).

Why Arcjet?

Your app's AI features and agents take real actions, calling tools, reading data, hitting APIs. Arcjet runs inside that code and lets you enforce security on each action in real time, then audit what happened.

Getting started

Install the Arcjet CLI

The CLI is used to log in, manage site keys, and install protection skills.

Homebrew (macOS and Linux):

brew install arcjet/tap/arcjet

npx (Node.js) — run any command without installing:

npx @arcjet/cli <command>

Or download a binary for
macOS (Apple Silicon, Intel), Linux (x86_64, arm64), and Windows (x86_64,
arm64).

Examples below use the arcjet binary. If you installed via npx, replace
arcjet with npx @arcjet/cli.

Quick setup with an AI agent

1. Log in with the CLI:

   arcjet auth login
   

2. Install the Arcjet skill:

   npx skills add arcjet/skills
   

3. Tell your agent what to protect — it handles the rest.

Manual setup

1. Log in with the CLI (or at app.arcjet.com):

   arcjet auth login
   

2. pip install arcjet (or uv add arcjet)
3. Get your site key:

   arcjet sites get-key
   

   Or copy it from the Arcjet dashboard.
4. Set ARCJET_KEY=ajkey_yourkey in .env
5. Protect a route — see the AI protection example or
   individual feature examples below.

Get help

Join our Discord server or reach out for
support.

• Documentation — full reference and guides
• Examples — FastAPI
  and Flask example apps, including LangChain integration
• Blueprints — recipes for common security
  patterns

Quick start

Note: Examples below use FastAPI (async). For Flask and other sync
frameworks, use arcjet_sync instead of arcjet. The API is identical — see
Async vs. sync client.

Protect an AI chat endpoint with prompt injection detection, token budget rate
limiting, and bot protection:

# main.py
import os
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
from pydantic import BaseModel

from arcjet import (
    arcjet,        # async client — use arcjet_sync for Flask and other sync frameworks
    detect_bot,
    detect_prompt_injection,
    detect_sensitive_info,
    shield,
    token_bucket,
    Mode,
    SensitiveInfoEntityType,
)

app = FastAPI()

arcjet_key = os.getenv("ARCJET_KEY")
if not arcjet_key:
    raise RuntimeError(
        "ARCJET_KEY is required. Get one with: arcjet sites get-key"
        " or from https://app.arcjet.com"
    )

# Create a single Arcjet instance and reuse it across requests.
# Use arcjet_sync instead if you are using Flask or another sync framework.
aj = arcjet(
    key=arcjet_key,
    rules=[
        # Detect and block prompt injection attacks in user messages
        detect_prompt_injection(mode=Mode.LIVE),
        # Block sensitive data (e.g. credit cards, PII) from reaching your LLM
        detect_sensitive_info(
            mode=Mode.LIVE,
            deny=[
                SensitiveInfoEntityType.CREDIT_CARD_NUMBER,
                SensitiveInfoEntityType.EMAIL,
                SensitiveInfoEntityType.PHONE_NUMBER,
            ],
        ),
        # Rate limit by token budget — refill 100 tokens every 60 seconds
        token_bucket(
            characteristics=["userId"],
            mode=Mode.LIVE,
            refill_rate=100,
            interval=60,
            capacity=1000,
        ),
        # Block automated clients and scrapers from your AI endpoints
        detect_bot(
            mode=Mode.LIVE,
            allow=[],  # empty = block all bots
        ),
        # Protect against common web attacks (SQLi, XSS, etc.)
        shield(mode=Mode.LIVE),
    ],
)


class ChatRequest(BaseModel):
    message: str


@app.post("/chat")
async def chat(request: Request, body: ChatRequest):
    userId = "user_123"  # replace with real user ID from session

    decision = await aj.protect(
        request,
        requested=5,  # tokens consumed per request
        characteristics={"userId": userId},
        detect_prompt_injection_message=body.message,  # scan for prompt injection
        sensitive_info_value=body.message,  # scan for PII
    )

    if decision.is_denied():
        status = 429 if decision.reason_v2.type == "RATE_LIMIT" else 403
        return JSONResponse({"error": "Denied"}, status_code=status)

    # Safe to pass body.message to your LLM
    return {"reply": "..."}

Features

Feature	Request (arcjet)	Guard (arcjet.guard)
Rate Limiting	✅	✅
Prompt Injection Detection	✅	✅
Sensitive Information Detection	✅	✅
Bot Protection	✅	—
Shield WAF	✅	—
Email Validation	✅	—
Request Filters	✅	—
IP Analysis	✅	—
Custom Rules	—	✅

• 🔒 Prompt Injection Detection — detect and block
  prompt injection attacks before they reach your LLM.
• 🤖 Bot Protection — stop scrapers, credential stuffers, and
  AI crawlers from abusing your endpoints.
• 🛑 Rate Limiting — token bucket, fixed window, and sliding
  window algorithms; model AI token budgets per user.
• 🕵️ Sensitive Information Detection — block
  PII, credit cards, and custom patterns from entering your AI pipeline.
• 🛡️ Shield WAF — protect against SQL injection, XSS, and other
  common web attacks.
• 📧 Email Validation — block disposable, invalid, and
  undeliverable addresses at signup.
• 📝 Signup Form Protection —
  combines bot protection, email validation, and rate limiting to protect your
  signup forms.
• 🎯 Request Filters — expression-based rules on IP, path,
  headers, and custom fields.
• 🌐 IP Analysis — geolocation, ASN, VPN, proxy, Tor, and hosting
  detection included with every request.
• 🧩 Arcjet Guard — lower-level API for AI agent tool calls and
  background tasks where there is no HTTP request.

Which features do I need?

If your app has...	Recommended features
LLM / AI chat endpoints	Prompt injection + sensitive info + token bucket rate limit + bot protection + shield
AI agent tool calls	Arcjet Guard — rate limiting + prompt injection + sensitive info + custom rules
Public API	Rate limiting + bot protection + shield
Signup / login forms	Email validation + bot protection + rate limiting (or signup protection)
Internal / admin routes	Shield + request filters (country, VPN/proxy blocking)
Any web application	Shield + bot protection (good baseline for all apps)

All features can be combined in a single Arcjet instance. Rules are evaluated
together — if any rule denies the request, decision.is_denied() returns
True. Use Mode.DRY_RUN on individual rules to test them before enforcing.

Installation

Install from PyPI with
uv:

# With a uv project
uv add arcjet

# With an existing pip managed project
uv pip install arcjet

Or with pip:

pip install arcjet

Prompt injection detection

Detect and block prompt injection attacks — attempts by users to hijack your
LLM's behavior through crafted input — before they reach your model.

FastAPI

from arcjet import arcjet, detect_prompt_injection, Mode

aj = arcjet(
    key=arcjet_key,
    rules=[
        detect_prompt_injection(mode=Mode.LIVE),
    ],
)

@app.post("/chat")
async def chat(request: Request, body: ChatRequest):
    decision = await aj.protect(
        request,
        detect_prompt_injection_message=body.message,
    )

    if decision.is_denied():
        return JSONResponse({"error": "Prompt injection detected"}, status_code=403)

    # safe to pass body.message to your LLM

Flask

from arcjet import arcjet_sync, detect_prompt_injection, Mode

aj = arcjet_sync(
    key=arcjet_key,
    rules=[
        detect_prompt_injection(mode=Mode.LIVE),
    ],
)

@app.route("/chat", methods=["POST"])
def chat():
    body = request.get_json()
    decision = aj.protect(request, detect_prompt_injection_message=body["message"])

    if decision.is_denied():
        return jsonify(error="Prompt injection detected"), 403

    # safe to pass body["message"] to your LLM

You can tune the detection sensitivity with the threshold parameter (0.0–1.0,
default 0.5). Higher values require stronger signals to trigger a denial,
reducing false positives but potentially missing subtle attacks:

detect_prompt_injection(mode=Mode.LIVE, threshold=0.8)

See the Prompt Injection docs for
more details.

Bot protection

Manage traffic from automated clients. Block scrapers, credential stuffers, and
AI crawlers, while allowing legitimate bots like search engines and monitors.

FastAPI

from arcjet import arcjet, detect_bot, Mode, BotCategory

aj = arcjet(
    key=arcjet_key,
    rules=[
        detect_bot(
            mode=Mode.LIVE,
            allow=[
                BotCategory.SEARCH_ENGINE,  # Google, Bing, etc.
                # BotCategory.MONITOR,      # Uptime monitoring
                # BotCategory.PREVIEW,      # Link previews (Slack, Discord)
                # "OPENAI_CRAWLER_SEARCH",  # Allow OpenAI crawler
            ],
        ),
    ],
)

@app.get("/")
async def index(request: Request):
    decision = await aj.protect(request)

    if decision.is_denied():
        return JSONResponse({"error": "Bot detected"}, status_code=403)

    return {"message": "Hello world"}

Flask

from arcjet import arcjet_sync, detect_bot, is_spoofed_bot, Mode, BotCategory

aj = arcjet_sync(
    key=arcjet_key,
    rules=[
        detect_bot(mode=Mode.LIVE, allow=[BotCategory.SEARCH_ENGINE]),
    ],
)

@app.route("/")
def index():
    decision = aj.protect(request)

    if decision.is_denied():
        return jsonify(error="Bot detected"), 403

    if any(is_spoofed_bot(r) for r in decision.results):
        return jsonify(error="Spoofed bot"), 403

    return jsonify(message="Hello world")

Bot categories

Configure rules using categories
or specific bot identifiers:

detect_bot(
    mode=Mode.LIVE,
    allow=[
        BotCategory.SEARCH_ENGINE,
        "OPENAI_CRAWLER_SEARCH",
    ],
)

Available categories: ACADEMIC, ADVERTISING, AI, AMAZON,
ARCHIVE, BOTNET, FEEDFETCHER, GOOGLE, META, MICROSOFT,
MONITOR, OPTIMIZER, PREVIEW, PROGRAMMATIC, SEARCH_ENGINE,
SLACK, SOCIAL, TOOL, UNKNOWN, VERCEL, YAHOO. Use
BotCategory.<NAME> in Python or pass the string directly. You can also
allow or deny specific bots by name.

If you specify an allow list, all other bots are denied. An empty allow list
blocks all bots. The reverse applies for deny lists.

Verified vs. spoofed bots

Bots claiming to be well-known crawlers (e.g. Googlebot) are verified against
their known IP ranges. Use is_spoofed_bot() to check:

from arcjet import is_spoofed_bot

if any(is_spoofed_bot(r) for r in decision.results):
    return jsonify(error="Spoofed bot"), 403

See the Bot Protection docs for
more details.

Rate limiting

Limit request rates per IP, user, or any custom characteristic. Arcjet supports
token bucket, fixed window, and sliding window algorithms. Token buckets are
ideal for controlling AI token budgets — set capacity to the max tokens a user
can spend, refill_rate to how many tokens are restored per interval, and
deduct tokens per request via requested in protect(). The interval accepts
seconds as a number. Use characteristics to track limits per user instead of
per IP.

Token bucket (recommended for AI)

Rate limits track by IP address by default. To track per user, declare the key
name in characteristics on the rule, then pass the actual value in
protect():

from arcjet import arcjet, token_bucket, Mode

aj = arcjet(
    key=arcjet_key,
    rules=[
        token_bucket(
            characteristics=["userId"],  # or ["ip.src"] for IP-based
            mode=Mode.LIVE,
            refill_rate=100,   # tokens added per interval
            interval=60,       # interval in seconds
            capacity=1000,     # maximum tokens per bucket
        ),
    ],
)

@app.post("/chat")
async def chat(request: Request):
    decision = await aj.protect(
        request,
        requested=5,  # tokens consumed by this request
        characteristics={"userId": "user_123"},
    )

    if decision.is_denied():
        return JSONResponse({"error": "Rate limited"}, status_code=429)

Fixed window

from arcjet import arcjet, fixed_window, Mode

aj = arcjet(
    key=arcjet_key,
    rules=[
        fixed_window(mode=Mode.LIVE, window=60, max=100),
    ],
)

Sliding window

from arcjet import arcjet, sliding_window, Mode

aj = arcjet(
    key=arcjet_key,
    rules=[
        sliding_window(mode=Mode.LIVE, interval=60, max=100),
    ],
)

See the Rate Limiting docs for more
details.

Sensitive information detection

Detect and block PII in request content before it reaches your LLM or data
store. The default (local WebAssembly) backend detects EMAIL, PHONE_NUMBER,
IP_ADDRESS, and CREDIT_CARD_NUMBER. You can provide a custom detect
callback for additional patterns, or the optional on-device Rampart backend
(see below) for names, addresses, and government/financial identifiers.

from arcjet import arcjet, detect_sensitive_info, SensitiveInfoEntityType, Mode

aj = arcjet(
    key=arcjet_key,
    rules=[
        detect_sensitive_info(
            mode=Mode.LIVE,
            deny=[
                SensitiveInfoEntityType.EMAIL,
                SensitiveInfoEntityType.CREDIT_CARD_NUMBER,
            ],
        ),
    ],
)

# Pass the content to scan with each protect() call
decision = await aj.protect(request, sensitive_info_value="User input to scan")

You can supplement built-in detectors with a custom detect callback:

def my_detect(tokens: list[str]) -> list[str | None]:
    return ["CUSTOM_PII" if "secret" in t.lower() else None for t in tokens]

rules = [
    detect_sensitive_info(
        mode=Mode.LIVE,
        deny=["CUSTOM_PII"],
        detect=my_detect,
    ),
]

On-device Rampart backend (more entity types)

The default backend detects the four types above. To detect names, addresses,
and government/financial identifiers, install the optional
arcjet[sensitive-info-rampart] extra and pass its backend to the rule. It
runs the on-device Rampart
NER model entirely locally, so no data leaves your environment:

pip install "arcjet[sensitive-info-rampart]"

from arcjet import arcjet, detect_sensitive_info, Mode
from arcjet_sensitive_info_rampart import rampart

aj = arcjet(
    key=arcjet_key,
    rules=[
        detect_sensitive_info(
            mode=Mode.LIVE,
            deny=["EMAIL", "GIVEN_NAME", "SURNAME", "STREET_NAME", "SSN"],
            backend=rampart(),
        ),
    ],
)

The backend adds these entity types: GIVEN_NAME, SURNAME, SSN, URL,
TAX_ID, BANK_ACCOUNT, ROUTING_NUMBER, GOVERNMENT_ID, PASSPORT,
DRIVERS_LICENSE, BUILDING_NUMBER, STREET_NAME, SECONDARY_ADDRESS,
CITY, STATE, ZIP_CODE. Listing one of these without a supporting
backend (or a custom detect function) raises, since the default engine can
never match it. The bundled model loads once on first use and is reused for
every request. See the
arcjet-sensitive-info-rampart README and
the examples/fastapi-rampart example.

See the Sensitive Information docs for
more details.

Shield WAF

Protect against common web attacks including SQL injection, XSS, path
traversal, and other OWASP Top 10 threats. No additional configuration
needed — Shield analyzes request patterns automatically.

from arcjet import arcjet, shield, Mode

aj = arcjet(
    key=arcjet_key,
    rules=[
        shield(mode=Mode.LIVE),
    ],
)

See the Shield docs for more details.

Email validation

Prevent users from signing up with disposable, invalid, or undeliverable email
addresses. Deny types: DISPOSABLE, FREE, INVALID, NO_MX_RECORDS,
NO_GRAVATAR.

from arcjet import arcjet, validate_email, EmailType, Mode

aj = arcjet(
    key=arcjet_key,
    rules=[
        validate_email(
            mode=Mode.LIVE,
            deny=[
                EmailType.DISPOSABLE,
                EmailType.INVALID,
                EmailType.NO_MX_RECORDS,
            ],
        ),
    ],
)

# Pass the email with each protect() call
decision = await aj.protect(request, email="[email protected]")

See the Email Validation docs for
more details.

Request filters

Filter requests using expression-based rules against request properties (IP
address, headers, path, HTTP method, and custom local fields).

Block by country

Restrict access to specific countries — useful for licensing, compliance, or
regional rollouts. The allow list denies all countries not listed:

from arcjet import arcjet, filter_request, Mode

aj = arcjet(
    key=arcjet_key,
    rules=[
        # Allow only US traffic — all other countries are denied
        filter_request(
            mode=Mode.LIVE,
            allow=['ip.src.country eq "US"'],
        ),
    ],
)

@app.get("/")
async def index(request: Request):
    decision = await aj.protect(request)

    if decision.is_denied():
        return JSONResponse({"error": "Access restricted in your region"}, status_code=403)

To restrict to a specific state or province, combine country and region:

filter_request(
    mode=Mode.LIVE,
    # Allow only California — useful for state-level compliance e.g. CCPA testing
    allow=['ip.src.country eq "US" and ip.src.region eq "California"'],
)

Block VPN and proxy traffic

Prevent anonymized traffic from accessing sensitive endpoints — useful for
fraud prevention, enforcing geo-restrictions, and reducing abuse:

from arcjet import arcjet, filter_request, Mode

aj = arcjet(
    key=arcjet_key,
    rules=[
        filter_request(
            mode=Mode.LIVE,
            deny=[
                "ip.src.vpn",    # VPN services
                "ip.src.proxy",  # Open proxies
                "ip.src.tor",    # Tor exit nodes
            ],
        ),
    ],
)

For cases where you want to allow some anonymized traffic (e.g. Apple Private
Relay) but still log or handle it differently, use decision.ip helpers after
calling protect():

decision = await aj.protect(request)

if decision.ip.is_vpn() or decision.ip.is_tor():
    return JSONResponse({"error": "VPN traffic not allowed"}, status_code=403)

ip = decision.ip_details
if ip and ip.is_relay:
    # Privacy relay (e.g. Apple Private Relay) — lower risk than a VPN
    pass  # allow through with custom handling

Custom local fields

Pass arbitrary values from your application for use in filter expressions:

decision = await aj.protect(
    request,
    filter_local={"userId": current_user.id, "plan": current_user.plan},
)

These are then available as local.userId and local.plan in expressions:

filter_request(
    mode=Mode.LIVE,
    deny=['local.plan eq "free" and ip.src.country ne "US"'],
)

See the Request Filters docs,
IP Geolocation blueprint,
and VPN/Proxy Detection blueprint
for more details.

IP analysis

Arcjet returns IP metadata with every decision — no extra API calls needed.

# High-level helpers
if decision.ip.is_hosting():
    # likely a cloud/hosting provider — often suspicious for bots
    return JSONResponse({"error": "Hosting IP blocked"}, status_code=403)

if decision.ip.is_vpn() or decision.ip.is_proxy() or decision.ip.is_tor():
    # apply your policy for anonymized traffic
    pass

# Typed field access
ip = decision.ip_details
if ip:
    print(ip.city, ip.country_name)   # geolocation
    print(ip.asn, ip.asn_name)        # ASN / network
    print(ip.is_vpn, ip.is_hosting)   # reputation

Available fields include geolocation (latitude, longitude, city,
region, country, continent), network (asn, asn_name, asn_domain,
asn_type, asn_country), and reputation (is_vpn, is_proxy, is_tor,
is_hosting, is_relay).

LangChain example

Arcjet works with any Python code, including LangChain agents and chains. In this
example, we protect a LangChain agent's chat endpoint with Arcjet to prevent
prompt injection, block bots, prevent sensitive data leakage, and enforce token
budgets before invoking the agent.

FastAPI + LangChain

from arcjet import arcjet, detect_bot, detect_prompt_injection, detect_sensitive_info, token_bucket, Mode, SensitiveInfoEntityType

aj = arcjet(
    key=arcjet_key,
    rules=[
        detect_prompt_injection(mode=Mode.LIVE),
        detect_sensitive_info(
            mode=Mode.LIVE,
            deny=[
                SensitiveInfoEntityType.EMAIL,
                SensitiveInfoEntityType.CREDIT_CARD_NUMBER,
                SensitiveInfoEntityType.PHONE_NUMBER,
            ],
        ),
        detect_bot(mode=Mode.LIVE, allow=["CURL"]),
        token_bucket(characteristics=["userId"], mode=Mode.LIVE, refill_rate=5, interval=10, capacity=10),
    ],
)

@app.post("/chat")
async def chat(request: Request, body: ChatRequest):
    decision = await aj.protect(
        request,
        requested=5,
        characteristics={"userId": "user_123"},
        detect_prompt_injection_message=body.message,  # scan for prompt injection
        sensitive_info_value=body.message,  # scan for PII before sending to LLM
    )

    if decision.is_denied():
        status = 429 if decision.reason_v2.type == "RATE_LIMIT" else 403
        return JSONResponse({"error": "Denied"}, status_code=status)

    reply = await chain.ainvoke({"message": body.message})
    return {"reply": reply}

Flask + LangChain

from arcjet import arcjet_sync, detect_bot, detect_prompt_injection, detect_sensitive_info, token_bucket, Mode, SensitiveInfoEntityType

aj = arcjet_sync(
    key=arcjet_key,
    rules=[
        detect_prompt_injection(mode=Mode.LIVE),
        detect_sensitive_info(
            mode=Mode.LIVE,
            deny=[
                SensitiveInfoEntityType.EMAIL,
                SensitiveInfoEntityType.CREDIT_CARD_NUMBER,
                SensitiveInfoEntityType.PHONE_NUMBER,
            ],
        ),
        detect_bot(mode=Mode.LIVE, allow=["CURL"]),
        token_bucket(characteristics=["userId"], mode=Mode.LIVE, refill_rate=5, interval=10, capacity=10),
    ],
)

@app.post("/chat")
def chat():
    body = request.get_json()
    message = body.get("message", "") if body else ""

    decision = aj.protect(
        request,
        requested=5,
        characteristics={"userId": "user_123"},
        detect_prompt_injection_message=message,  # scan for prompt injection
        sensitive_info_value=message,  # scan for PII before sending to LLM
    )

    if decision.is_denied():
        status = 429 if decision.reason_v2.type == "RATE_LIMIT" else 403
        return jsonify(error="Denied"), status

    reply = chain.invoke({"message": message})
    return jsonify(reply=reply)

Arcjet Guard

arcjet.guard is a lower-level API designed for AI agent tool calls and
background tasks where there is no HTTP request object. It gives you
fine-grained, per-call control over rate limiting, prompt injection detection,
sensitive information detection, and custom rules.

How it differs from arcjet / arcjet_sync

	arcjet / arcjet_sync	arcjet.guard
Designed for	HTTP request protection	AI agent tool calls, background jobs
Request object	Required (protect(request, ...))	Not needed
Rule binding	Rules configured once, input via protect() kwargs	Rules configured as classes, called with input per invocation
Rate limit key	IP or characteristics dict	Explicit key string (SHA-256 hashed before sending)
Custom rules	Not supported	LocalCustomRule with typed config/input/data

Installation

arcjet.guard is included in the arcjet package — no extra install required.

Quick start

import os
from arcjet.guard import (
    launch_arcjet,       # async — use launch_arcjet_sync for sync frameworks
    TokenBucket,
    DetectPromptInjection,
    LocalDetectSensitiveInfo,
)

arcjet_key = os.getenv("ARCJET_KEY")
if not arcjet_key:
    raise RuntimeError("ARCJET_KEY is required")

# Create a single guard client and reuse it.
aj = launch_arcjet(key=arcjet_key)

# Configure rules once at startup
user_limit = TokenBucket(
    refill_rate=100,
    interval_seconds=60,
    max_tokens=1000,
)
prompt_scan = DetectPromptInjection()
sensitive = LocalDetectSensitiveInfo(deny=["EMAIL", "CREDIT_CARD_NUMBER"])

# At call time, bind input and guard
async def handle_tool_call(user_id: str, message: str):
    decision = await aj.guard(
        label="tools.weather",
        rules=[
            user_limit(key=user_id, requested=5),
            prompt_scan(message),
            sensitive(message),
        ],
    )

    if decision.conclusion == "DENY":
        raise RuntimeError(f"Blocked: {decision.reason}")

    # safe to proceed

Sync usage

For Flask, Django, or other sync frameworks, use launch_arcjet_sync:

from arcjet.guard import launch_arcjet_sync, TokenBucket

aj = launch_arcjet_sync(key=arcjet_key)
user_limit = TokenBucket(refill_rate=10, interval_seconds=60, max_tokens=100)

def handle_tool_call(user_id: str):
    decision = aj.guard(
        label="tools.weather",
        rules=[user_limit(key=user_id)],
    )

    if decision.conclusion == "DENY":
        raise RuntimeError("Rate limited")

Rate limiting

Token bucket, fixed window, and sliding window algorithms are available.
Configure the rule once, then call it with a key (and optional requested
token count) for each invocation:

Token bucket

from arcjet.guard import TokenBucket

user_limit = TokenBucket(
    refill_rate=100,      # tokens added per interval
    interval_seconds=60,  # seconds between refills
    max_tokens=1000,      # maximum bucket capacity
)

# At call time:
decision = await aj.guard(
    label="tools.weather",
    rules=[user_limit(key=user_id, requested=5)],
)

Fixed window

from arcjet.guard import FixedWindow

team_limit = FixedWindow(
    max_requests=1000,
    window_seconds=3600,
)

decision = await aj.guard(
    label="api.search",
    rules=[team_limit(key=team_id)],
)

Sliding window

from arcjet.guard import SlidingWindow

api_limit = SlidingWindow(
    max_requests=500,
    interval_seconds=60,
)

decision = await aj.guard(
    label="api.query",
    rules=[api_limit(key=user_id)],
)

Prompt injection detection

from arcjet.guard import DetectPromptInjection

prompt_scan = DetectPromptInjection()

decision = await aj.guard(
    label="tools.weather",
    rules=[prompt_scan(user_message)],
)

if decision.conclusion == "DENY":
    print("Prompt injection detected")

Sensitive information detection

Detects PII locally — the raw text never leaves the SDK. The default backend
detects EMAIL, PHONE_NUMBER, IP_ADDRESS, CREDIT_CARD_NUMBER.

from arcjet.guard import LocalDetectSensitiveInfo

sensitive = LocalDetectSensitiveInfo(
    deny=["EMAIL", "CREDIT_CARD_NUMBER"],
)

decision = await aj.guard(
    label="tools.send_email",
    rules=[sensitive(user_input)],
)

For additional entity types (names, addresses, SSN, etc.), install
arcjet[sensitive-info-rampart] and pass the on-device Rampart backend:

from arcjet.guard import LocalDetectSensitiveInfo
from arcjet_sensitive_info_rampart import rampart

sensitive = LocalDetectSensitiveInfo(deny=["GIVEN_NAME", "SSN"], backend=rampart())

Listing a backend-only type without a supporting backend raises.

Custom rules

Define typed custom rules that run locally. Subclass LocalCustomRule and
override evaluate (sync) or evaluate_async (async):

from typing import TypedDict
from arcjet.guard import LocalCustomRule, CustomEvaluateResult

class TopicConfig(TypedDict):
    blocked_topic: str

class TopicInput(TypedDict):
    topic: str

class TopicData(TypedDict):
    matched: str

class TopicBlockRule(LocalCustomRule[TopicConfig, TopicInput, TopicData]):
    def evaluate(
        self,
        config: TopicConfig,
        input: TopicInput,
    ) -> CustomEvaluateResult:
        if input["topic"] == config["blocked_topic"]:
            return CustomEvaluateResult(
                conclusion="DENY",
                data={"matched": input["topic"]},
            )
        return CustomEvaluateResult(conclusion="ALLOW")

rule = TopicBlockRule(config={"blocked_topic": "weapons"})
inp = rule(data={"topic": user_topic})
decision = await aj.guard(rules=[inp], label="content")

# Access typed result
r = inp.result(decision)
if r and r.conclusion == "DENY":
    print(f"Blocked topic: {r.data['matched']}")

Per-rule results

Both the configured rule and the bound input provide typed result accessors:

user_limit = TokenBucket(refill_rate=10, interval_seconds=60, max_tokens=100)
inp = user_limit(key=user_id, requested=5)

decision = await aj.guard(label="tools.weather", rules=[inp])

# From the bound input (matches exact invocation)
r = inp.result(decision)
if r:
    print(r.remaining_tokens, r.max_tokens)

# From the configured rule (matches all invocations of this rule)
r = user_limit.result(decision)

# Check only denied results
denied = inp.denied_result(decision)
if denied:
    print(f"Rate limited — resets at {denied.reset_at_unix_seconds}")

Decision API

decision = await aj.guard(label="tools.weather", rules=[...])

# Layer 1: conclusion and reason
decision.conclusion   # "ALLOW" or "DENY"
decision.reason       # "RATE_LIMIT", "PROMPT_INJECTION", "SENSITIVE_INFO", "CUSTOM", "ERROR", etc.

# Layer 2: error/warning detection
decision.has_failed_open()  # True if ALLOW only because a rule/decision could not be processed (fail-closed gate)
decision.error_results()  # Results that errored (rules or the decision that could not be processed)
decision.warnings           # Decision-level diagnostics (e.g. an invalid metadata key that was stripped)

# Layer 3: per-rule results (see "Per-rule results" above)
for result in decision.results:
    print(result.type, result.conclusion)

guard() parameter reference

Parameter	Type	Description
rules	Sequence[RuleWithInput]	Bound rule inputs (required)
label	str	Label identifying this guard call (required)
metadata	Metadata | None	Structured metadata — see Metadata
correlation_id	str | None	Opaque id correlating this call with other guard()/protect() calls

Metadata

guard(), protect(), and every guard rule accept metadata: a mapping of
string keys to any JSON-serializable value, including nested objects and
arrays. It is attached to the decision for correlation and analytics.

decision = await aj.guard(
    label="tools.weather",
    rules=[user_limit(key=user_id)],
    metadata={
        "user": {"id": user_id, "plan": "pro"},
        "tool_name": "get_weather",
        "duration_ms": 160,
        "success": True,
    },
)

Each top-level value is JSON-encoded by the SDK and stored verbatim, so exact
integers and value formatting survive. Server-enforced limits:

Limit	Value	Over the limit
Top-level keys	128	Extra keys dropped
Serialized bytes / value	4 KiB	That key dropped
Nesting depth / value	10	That key dropped
Key names	letters, digits, -, ., _	That key dropped

Nothing here can fail a call or change a decision — metadata is excluded from
fingerprinting and from the decision cache key. Every dropped key is reported:
server-side drops arrive on decision.warnings, one per key. Keys the SDK itself
could not encode (a datetime, a set, NaN, a circular reference) are collected
into a single warning naming them all, added to decision.warnings and reported
to the server. For protect(), which has no warnings channel on its Decision,
that warning is logged at WARNING instead.

Metadata is untrusted and is not redacted — do not put secrets or PII in it.

Some limits are the SDK's own, not the server's. The SDK drops keys once one
request's metadata exceeds 768 KiB in total (keys plus JSON-encoded values,
counted before compression). That ceiling sits well above anything the server
would accept — its own caps allow roughly 512 KiB in a single map — and exists
only so oversized metadata cannot push a request past the 1 MiB protocol limit,
where it would be rejected outright and fail open.

Two behaviours differ between the Python and JavaScript SDKs:

• Integer precision. Python integers are arbitrary-precision and are sent
  verbatim, so a value past 2^53 survives exactly. The JavaScript SDK cannot do
  this — its numbers are IEEE-754 doubles before they reach the wire — so send
  such values as strings if both SDKs must agree.
• Objects with a toJSON() method, including JavaScript Date, are
  serialized by that method in the JS SDK. Python has no equivalent protocol, so
  a datetime (or any other non-JSON type) is dropped with a warning. Convert
  explicitly — datetime.isoformat() — if both SDKs must agree.

Rule-level metadata is merged with guard()-level metadata shallowly: a
duplicate key's whole value is replaced, never deep-merged.

DRY_RUN mode

All guard rules accept a mode parameter. Use "DRY_RUN" to evaluate rules
without blocking:

user_limit = TokenBucket(
    refill_rate=10,
    interval_seconds=60,
    max_tokens=100,
    mode="DRY_RUN",
)

Best practices

Single-instance pattern

Create one Arcjet client at startup and reuse it across all requests:

# Good — one instance, created once at startup
aj = arcjet(key=arcjet_key, rules=[...])

# Bad — new instance per request wastes resources
@app.get("/")
async def index(request: Request):
    aj = arcjet(key=arcjet_key, rules=[...])  # don't do this

DRY_RUN mode for testing

Use Mode.DRY_RUN to test rules without blocking traffic. Decisions are logged
but requests are allowed through:

aj = arcjet(
    key=arcjet_key,
    rules=[
        detect_bot(mode=Mode.DRY_RUN, allow=[]),
        token_bucket(mode=Mode.DRY_RUN, refill_rate=5, interval=10, capacity=10),
    ],
)

Proxy configuration

When running behind a load balancer or reverse proxy, configure trusted IPs so
Arcjet resolves the real client IP from X-Forwarded-For:

aj = arcjet(
    key=arcjet_key,
    rules=[...],
    proxies=["10.0.0.0/8", "192.168.0.1"],
)

Outbound HTTP proxy

If your environment requires outbound requests to the Arcjet API to go through a
forward proxy (e.g. Squid), set the standard proxy environment variables. The
SDK honors them automatically — no code changes required:

export HTTPS_PROXY="http://proxy.example.com:3128"
# Optional: comma-separated hosts that should bypass the proxy
export NO_PROXY="decide.arcjet.com,localhost"

HTTP_PROXY, HTTPS_PROXY, and NO_PROXY are all supported (the lower-case
variants work too). Because the Arcjet API is reached over HTTPS, HTTPS_PROXY
is the relevant variable for proxying Arcjet traffic. NO_PROXY accepts a
comma-separated list of hostnames to bypass, or * to disable proxying
entirely. Since Arcjet is reached by hostname, list the hostname (e.g.
decide.arcjet.com) to bypass it.

Async vs. sync client

Use arcjet (async) with FastAPI and other async frameworks. Use arcjet_sync
with Flask and other sync frameworks:

from arcjet import arcjet, arcjet_sync

# Async — for FastAPI, Starlette, etc.
aj_async = arcjet(key=arcjet_key, rules=[...])
decision = await aj_async.protect(request)

# Sync — for Flask, Django, etc.
aj_sync = arcjet_sync(key=arcjet_key, rules=[...])
decision = aj_sync.protect(request)

protect() parameter reference

All parameters are optional keyword arguments passed alongside the request:

Parameter	Type	Used by
requested	int	Token bucket rate limit
characteristics	dict[str, Any]	Rate limiting (pass values for keys declared in rule config)
detect_prompt_injection_message	str	Prompt injection detection
sensitive_info_value	str	Sensitive info detection
email	str	Email validation
filter_local	dict[str, str]	Request filters (local.* fields)
metadata	Metadata	Structured metadata — see Metadata
correlation_id	str	Opaque id correlating this call with other protect()/guard() calls
ip_src	str	Manual IP override (advanced)

Decision response

decision = await aj.protect(request)

# Top-level checks
decision.is_denied()     # True if any rule denied the request
decision.is_allowed()    # True if all rules allowed the request
decision.is_error()      # True if Arcjet encountered an error (fails open)

# reason_v2.type values: "BOT", "RATE_LIMIT", "SHIELD", "EMAIL", "ERROR", "FILTER"
if decision.reason_v2.type == "RATE_LIMIT":
    print(decision.reason_v2.remaining)  # tokens/requests remaining
elif decision.reason_v2.type == "BOT":
    print(decision.reason_v2.denied)     # list of denied bot names
    print(decision.reason_v2.spoofed)    # list of spoofed bot names

# Per-rule results (for granular handling)
for result in decision.results:
    print(result.reason_v2.type, result.is_denied())

Error handling

Arcjet is designed to fail open — if the service is unavailable, requests are
allowed through. Check for errors explicitly if your use case requires it:

decision = await aj.protect(request)

if decision.is_error():
    # Arcjet service error — fail open or apply fallback policy
    pass
elif decision.is_denied():
    return JSONResponse({"error": "Denied"}, status_code=403)

Support

This repository follows the Arcjet Support
Policy.

Security

This repository follows the Arcjet Security
Policy.

Compatibility

Packages maintained in this repository are compatible with Python 3.10 and
above.

License

Licensed under the Apache License, Version
2.0.