arcjet-py

<a href="https://arcjet.com" target="_arcjet-home"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://arcjet.com/logo/arcjet-dark-lockup-voyage-horizontal.svg"> <img src="https://arcjet.com/logo/arcjet-light-lockup-voyage-horizontal.svg" alt="Arcjet Logo" height="128" width="auto"> </picture> </a> # arcjet <p> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://img.shields.io/pypi/v/arcjet?style=flat-square&label=%E2%9C%A6Aj&labelColor=000000&color=5C5866"> <img alt="PyPI badge" src="https://img.shields.io/pypi/v/arcjet?style=flat-square&label=%E2%9C%A6Aj&labelColor=ECE6F0&color=ECE6F0"> </picture> </p> [Arcjet](https://arcjet.com) 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](https://arcjet.com) — 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):** ```sh brew install arcjet/tap/arcjet ``` **npx (Node.js)** — run any command without installing: ```sh npx @arcjet/cli <command> ``` **Or [download a binary](https://github.com/arcjet/arcjet-cli/releases)** 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: ```sh arcjet auth login ``` 2. Install the Arcjet skill: ```sh 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`](https://app.arcjet.com)): ```sh arcjet auth login ``` 2. `pip install arcjet` (or `uv add arcjet`) 3. **Get your site key:** ```sh arcjet sites get-key ``` Or copy it from the [Arcjet dashboard](https://app.arcjet.com). 4. Set `ARCJET_KEY=ajkey_yourkey` in `.env` 5. Protect a route — see the [AI protection example](#quick-start) or individual [feature examples](#features) below. ### Get help [Join our Discord server](https://arcjet.com/discord) or [reach out for support](https://docs.arcjet.com/support). - [Documentation](https://docs.arcjet.com) — full reference and guides - [Examples](https://github.com/arcjet/arcjet-py/tree/main/examples) — FastAPI and Flask example apps, including [CrewAI Guard](./examples/fastapi-crewai-guard), [OpenAI Agents Guard](./examples/fastapi-openai-agents-guard), and [Google ADK Guard](./examples/fastapi-google-adk-guard) integration - [Blueprints](https://docs.arcjet.com/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](#async-vs-sync-client). Protect an AI chat endpoint with prompt injection detection, token budget rate limiting, and bot protection: ```py # 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 | ✅ | ✅ | | Content Moderation | — | ✅ | | Sensitive Information Detection | ✅ | ✅ | | Bot Protection | ✅ | — | | Shield WAF | ✅ | — | | Email Validation | ✅ | — | | Request Filters | ✅ | — | | IP Analysis | ✅ | — | | Custom Rules | — | ✅ | | Capture (visibility events) | — | ✅ | - 🔒 [Prompt Injection Detection](#prompt-injection-detection) — detect and block prompt injection attacks before they reach your LLM. - 🚫 [Content Moderation](#content-moderation) — detect harmful content in Guard tool-call and job inputs. - 🤖 [Bot Protection](#bot-protection) — stop scrapers, credential stuffers, and AI crawlers from abusing your endpoints. - 🛑 [Rate Limiting](#rate-limiting) — token bucket, fixed window, and sliding window algorithms; model AI token budgets per user. - 🕵️ [Sensitive Information Detection](#sensitive-information-detection) — block PII, credit cards, and custom patterns from entering your AI pipeline. - 🛡️ [Shield WAF](#shield-waf) — protect against SQL injection, XSS, and other common web attacks. - 📧 [Email Validation](#email-validation) — block disposable, invalid, and undeliverable addresses at signup. - 📝 [Signup Form Protection](https://docs.arcjet.com/signup-protection) — combines bot protection, email validation, and rate limiting to protect your signup forms. - 🎯 [Request Filters](#request-filters) — expression-based rules on IP, path, headers, and custom fields. - 🌐 [IP Analysis](#ip-analysis) — geolocation, ASN, VPN, proxy, Tor, and hosting detection included with every request. - 🧩 [Arcjet Guard](#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](#arcjet-guard) — rate limiting + prompt injection + content moderation + sensitive info + custom rules | | Public API | Rate limiting + bot protection + shield | | Signup / login forms | Email validation + bot protection + rate limiting (or [signup protection](https://docs.arcjet.com/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 > [!WARNING] > Arcjet may use `X-Forwarded-For` when it cannot get a public client IP from > the framework request. Clients can spoof this header if they can reach your > application directly or your proxy preserves client-supplied values. Arcjet > continues protecting the request, but logs one warning for the lifetime of > each Arcjet client instance and marks the source as `unverified-header` in the > `client_ip_provenance` debug facet. > > In production, make the application reachable only through a proxy that > overwrites or safely appends `X-Forwarded-For`, and list every trusted hop in > `proxies`. Invalid proxy entries and combining `proxies` with manual IP > detection are rejected when the client is created; malformed `ip_src` values > are rejected before protection. Trusting `0.0.0.0/0` or `::/0` emits a > configuration warning. To inspect the > selected address before protection, call `aj.client_ip_details(request)` and > check its `ip`, `provenance`, `verified`, and `header` fields. If your > application determines the address itself, set > `disable_automatic_ip_detection=True` and pass that validated value as > `ip_src` to both `client_ip_details()` and `protect()`. Install [from PyPI](https://pypi.org/project/arcjet/) with [uv](https://docs.astral.sh/uv/): ```shell # With a uv project uv add arcjet # With an existing pip managed project uv pip install arcjet ``` Or with pip: ```shell pip install arcjet ``` Prefer a glibc Linux image (Debian/Ubuntu slim) in containers. Alpine/musl is not a supported install target — see [Compatibility](#compatibility). ## 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 ```py 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 ```py 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 ``` See the [Prompt Injection docs](https://docs.arcjet.com/prompt-injection) 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 ```py 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 ```py 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](https://docs.arcjet.com/bot-protection/identifying-bots#bot-categories) or [specific bot identifiers](https://github.com/arcjet/well-known-bots): ```py detect_bot( mode=Mode.LIVE, allow=[ BotCategory.SEARCH_ENGINE, "OPENAI_CRAWLER_SEARCH", ], ) ``` Available categories: `ACADEMIC`, `ADVERTISING`, `AI`, `AMAZON`, `APPLE`, `ARCHIVE`, `BOTNET`, `FEEDFETCHER`, `GOOGLE`, `META`, `MICROSOFT`, `MONITOR`, `OPTIMIZER`, `PREVIEW`, `PROGRAMMATIC`, `SEARCH_ENGINE`, `SLACK`, `SOCIAL`, `TOOL`, `UNKNOWN`, `VERCEL`, `WEBHOOK`, `YAHOO`. Use `BotCategory.<NAME>` in Python or pass the string directly. You can also allow or deny [specific bots by name](https://arcjet.com/bot-list). 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: ```py 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](https://docs.arcjet.com/bot-protection) 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()`: ```py 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 ```py from arcjet import arcjet, fixed_window, Mode aj = arcjet( key=arcjet_key, rules=[ fixed_window(mode=Mode.LIVE, window=60, max=100), ], ) ``` ### Sliding window ```py 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](https://docs.arcjet.com/rate-limiting) 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. ```py 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: ```py 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](https://huggingface.co/nationaldesignstudio/rampart) NER model entirely locally, so no data leaves your environment: ```sh pip install "arcjet[sensitive-info-rampart]" ``` ```py 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](./sensitive-info-rampart/README.md) and the [`examples/fastapi-rampart`](./examples/fastapi-rampart) example. See the [Sensitive Information docs](https://docs.arcjet.com/sensitive-info) 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. ```py from arcjet import arcjet, shield, Mode aj = arcjet( key=arcjet_key, rules=[ shield(mode=Mode.LIVE), ], ) ``` See the [Shield docs](https://docs.arcjet.com/shield) 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`. ```py 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="user@example.com") ``` See the [Email Validation docs](https://docs.arcjet.com/email-validation) 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: ```py 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: ```py 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: ```py 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()`: ```py 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: ```py 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: ```py filter_request( mode=Mode.LIVE, deny=['local.plan eq "free" and ip.src.country ne "US"'], ) ``` See the [Request Filters docs](https://docs.arcjet.com/filters), [IP Geolocation blueprint](https://docs.arcjet.com/blueprints/ip-geolocation), and [VPN/Proxy Detection blueprint](https://docs.arcjet.com/blueprints/vpn-proxy-detection) for more details. ## IP analysis Arcjet returns IP metadata with every decision — no extra API calls needed. ```py # 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 if ip.threat: # optional threat intelligence threat = ip.threat print(threat.risk_level, threat.confidence, threat.reputation) print(threat.is_safe, threat.network_types, threat.activities) print(threat.entities, threat.entity_name, threat.service) ``` 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`). Threat intelligence provides `risk_level`, `confidence`, `reputation`, `is_safe`, `network_types`, `activities`, `entities`, `entity_name`, and `service`. `decision.ip_details` and its `threat` field are optional because metadata or threat intelligence may not be available for every IP. ## 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 ```py 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 ```py 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, content moderation, 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 ```py import os from arcjet.guard import ( launch_arcjet, # async — use launch_arcjet_sync for sync frameworks TokenBucket, DetectPromptInjection, ModerateContent, 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. # Guard never reads ARCJET_KEY itself — pass key= explicitly (same as JS Guard). 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() moderate = ModerateContent() 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), moderate(message), sensitive(message), ], ) if decision.conclusion == "DENY": raise RuntimeError(f"Blocked: {decision.reason}") if decision.has_failed_open(): raise RuntimeError("Guard unavailable; refusing to run the tool") # Allowed by a fully evaluated policy; safe to proceed. ``` ### Guard surfaces, extras, and propagation Arcjet Guard protects code that has no HTTP request — a tool call, a worker, an MCP handler. Which surface you want depends on what you are holding when the effect happens. | You have | Use | Needs | Blocks a call? | | --- | --- | --- | --- | | Any Python callable — a worker, an MCP handler, a job | `guard_action` / `guard_action_sync` | `arcjet` | Yes | | A LangChain `BaseTool` you call yourself | `guard_tool` | `arcjet[langchain]` | Yes | | An agent built with `create_agent`, whose tool calls the model chooses | `ArcjetMiddleware` | `arcjet[langchain-agents]` | Yes | | A chain or agent you want to *observe* | `ArcjetCaptureHandler` | `arcjet[langchain]` | No — records only | | A CrewAI crew / LiteAgent / MCP or crew-injected tool | `register_arcjet_hooks` | your own `crewai` | Yes — via `HookAborted` | | A CrewAI `BaseTool` you call yourself | `guard_tool` (`arcjet.guard.crewai`) | your own `crewai` | Yes | | An OpenAI Agents authored `FunctionTool` | `guard_tool` (`arcjet.guard.openai_agents`) | `arcjet[openai-agents]` | Yes — via `reject_content` | | A Claude Agent SDK authored `@tool` / `SdkMcpTool` | `guard_tool` (`arcjet.guard.claude_agent_sdk`) | `arcjet[claude-agent-sdk]` | Yes — via `is_error` tool result | | Unwrapped Claude built-ins / MCP, or inbound user text | `guard_hooks` (`arcjet.guard.claude_agent_sdk`) | `arcjet[claude-agent-sdk]` | Yes — `PreToolUse` deny / `UserPromptSubmit` block | | A Claude Managed Agents custom tool (`agent.custom_tool_use`) | `guard_custom_tool` (`arcjet.guard.claude_managed_agents`) | `arcjet[claude-managed-agents]` | Yes — `user.custom_tool_result` (does not run the tool) | | Claude Managed Agents inbound `user.message` / `initial_events` | `guard_events` (`arcjet.guard.claude_managed_agents`) | `arcjet[claude-managed-agents]` | Yes — does not call `sessions.events.send` | | A Strands Agents authored `@tool` | `guard_tool` (`arcjet.guard.strands_agents`) | `arcjet[strands-agents]` | Yes — error-status JSON `ArcjetDenialResult` | | Unwrapped Strands MCP / vended / built-ins | `guard_hooks` (`arcjet.guard.strands_agents`) | `arcjet[strands-agents]` | Yes — `BeforeToolCallEvent.cancel_tool` | | A Google ADK `LlmAgent` tool call | `guard_tool` (`arcjet.guard.google_adk`) | `arcjet[google-adk]` | Yes — skip dict with `arcjetDenied` | | Unwrapped Google ADK tools on a Runner | `guard_plugin` (`arcjet.guard.google_adk`) | `arcjet[google-adk]` | Yes — `BasePlugin.before_tool_callback` skip dict | Two rules of thumb. If you can name the tool at wiring time, `guard_tool` is the smaller change — it returns something that *is* the tool, so nothing downstream changes. If the model picks the tool and you want one policy per tool name, use the middleware. They compose: a guarded tool called from inside a guarded agent evaluates once per policy, and both land on the same Sequence. `ArcjetCaptureHandler` never blocks anything. LangChain's callback dispatch ignores what a handler returns, so a callback *cannot* deny a call — it is the right home for capture and the wrong home for policy. **Installation extras** unlock specific surfaces: - **`arcjet[langchain]`** — `guard_tool` and the capture handlers (`ArcjetCaptureHandler` and `ArcjetAsyncCaptureHandler`). Depends on `langchain-core` only — no LangGraph. - **`arcjet[langchain-agents]`** — adds `ArcjetMiddleware`, and pulls in LangChain and LangGraph. - **`arcjet[openai-agents]`** — `guard_tool` and `openai_agents_context` for an authored OpenAI Agents `FunctionTool` (`openai-agents>=0.19.0,<1`). Independent of LangChain and CrewAI. Docs: [`/guards/openai-agents-py/`](https://docs.arcjet.com/guards/openai-agents-py/) — not the JS page [`/guards/openai-agents/`](https://docs.arcjet.com/guards/openai-agents/). - **`arcjet[claude-agent-sdk]`** — `guard_tool`, `guard_hooks`, and `claude_agent_context` for the Claude Agent SDK (`claude-agent-sdk>=0.2.127,<1`). Independent of LangChain, CrewAI, and OpenAI Agents. Docs: [`/guards/claude-agent-sdk-py/`](https://docs.arcjet.com/guards/claude-agent-sdk-py/) — not the JS page [`/guards/claude-agent-sdk/`](https://docs.arcjet.com/guards/claude-agent-sdk/). - **`arcjet[claude-managed-agents]`** — `guard_custom_tool`, `guard_events`, and `claude_managed_agents_context` for hosted Claude Managed Agents (`anthropic>=0.92.0,<2`, beta `managed-agents-2026-04-01`). Independent of LangChain, CrewAI, OpenAI Agents, and the Claude Agent SDK. Shared JS+Python docs: [`/guards/claude-managed-agents/`](https://docs.arcjet.com/guards/claude-managed-agents/). - **`arcjet[strands-agents]`** — `guard_tool`, `guard_hooks`, and `strands_agent_context` for Python Strands Agents (`strands-agents>=1.11.0,<2`, the first 1.x with `BeforeToolCallEvent.cancel_tool`). Independent of LangChain, CrewAI, OpenAI Agents, the Claude Agent SDK, and Claude Managed Agents. The default extra set does not pull chromadb. Docs: [`/guards/strands-agents-py/`](https://docs.arcjet.com/guards/strands-agents-py/) — not the JS page [`/guards/strands-agents/`](https://docs.arcjet.com/guards/strands-agents/). - **`arcjet[google-adk]`** — `guard_tool`, `guard_plugin`, and `google_adk_context` for Python Google ADK (`google-adk>=2.0.0,<3`). Independent of LangChain, CrewAI, OpenAI Agents, the Claude Agent SDK, Claude Managed Agents, and Strands Agents. The default extra set does not pull chromadb. Docs: [`/guards/google-adk-py/`](https://docs.arcjet.com/guards/google-adk-py/) — not the JS page [`/guards/google-adk/`](https://docs.arcjet.com/guards/google-adk/). There is deliberately **no `arcjet[crewai]` extra**. `arcjet.guard.crewai` works against the `crewai` you install yourself (`>=1.15.3,<2`, where `@on` and `HookAborted` landed; CrewAI requires Python `>=3.10,<3.14`). Arcjet does not depend on it because `crewai` hard-depends on `chromadb~=1.1.0`, and every `chromadb` from 1.0.0 to 1.5.9 carries an unpatched critical RCE ([CVE-2026-45829](https://nvd.nist.gov/vuln/detail/CVE-2026-45829)) — an Arcjet install should not be what puts that in your environment. The floor is enforced at `register_arcjet_hooks()` instead, so a too-old CrewAI is reported where you can act on it. These are separate because LangGraph is large and optional; many applications use tools without agents. Importing `arcjet.guard.langchain` never loads LangGraph — that happens only when you reference `ArcjetMiddleware` or `ToolPolicy`, and without the extra installed you get an error naming it. Everything comes from one import path: ```py from arcjet.guard.langchain import ( ArcjetCaptureHandler, ArcjetMiddleware, ToolPolicy, guard_tool, ) ``` **Fail-closed default** — The *checkpoint surfaces* (`guard_action`, `guard_action_sync`, `guard_tool`, `guard_hooks`, `guard_custom_tool`, `guard_events`, `ArcjetMiddleware`, `register_arcjet_hooks`) default to denying when evaluation is unavailable. This is Arcjet's one documented divergence from the platform-wide fail-open convention. Note the scope: the core `guard()` call still fails **open**, returning an ALLOW for which `has_failed_open()` is `True`, as does the request protection API (`arcjet` / `arcjet_sync`). It is the surfaces that wrap a consequential effect which fail closed: - **`on_guard_error="deny"`** (default) — If Guard is unavailable or evaluation fails, the guarded action blocks with `ArcjetUnavailableError`. - **`on_guard_error="allow"`** — Opt out: if Guard is unavailable, the action runs anyway. Use this only where availability matters more than enforcement. A `DENY` conclusion always blocks, with `ArcjetDeniedError`, regardless of `on_guard_error`. The two errors are deliberately distinct: a denial means policy evaluated and said no, while unavailability means the check never happened. `on_guard_error` opts out of failing closed on an *unevaluated* policy — never out of an evaluated denial. **Correlation and Sequences** — Trace requests across boundaries using a correlation ID: ```py from arcjet.guard import arcjet_sequence, current_correlation_id # Open a sequence with an explicit ID with arcjet_sequence(correlation_id="request-id-123"): # All guard() and capture() calls inside see this ID payload = {"correlation_id": current_correlation_id()} # "request-id-123" # Pass payload to a worker... # Outside the sequence, ID is None assert current_correlation_id() is None # Worker resumes the sequence by reopening it. Nothing is inherited across a # broker or a bare thread, so the ID has to arrive as data. def worker(payload): with arcjet_sequence(correlation_id=payload["correlation_id"]): # Every decision and capture in here lands on the originating # request's Sequence rather than starting one of its own. send_the_thing() ``` Precedence for ID selection: explicit `correlation_id` argument → `RunnableConfig["configurable"]["arcjet_correlation_id"]` (then its `["metadata"]`) → ambient context var → none. A checkpoint outside any sequence still evaluates policy; it just produces decisions that join no Sequence. `arcjet_sequence()` called with no ID generates a sortable one, which is the right answer only for a genuinely new entrypoint — derive it from a session the caller already has when you can, or you build a Sequence nobody goes looking for. Inside an agent, pass the ID through the config instead. Both the middleware and any guarded tool below it read the same key, so one run produces one Sequence rather than splitting between them: ```py await agent.ainvoke( {"messages": [...]}, config={"configurable": {"arcjet_correlation_id": session.id}}, ) ``` The config wins over an enclosing `arcjet_sequence`, and `configurable` is checked before `metadata`. LangChain's own `run_id` is deliberately not used: a Sequence should be joinable from the session a human would go looking for, not from an ID the framework mints per run. **Propagation across boundaries** — Context variables do not cross process or executor boundaries. To carry a correlation ID: | Boundary | Propagates? | Solution | | --- | --- | --- | | `asyncio.create_task()` | Yes | No action needed | | `asyncio.to_thread()` | Yes | No action needed | | `ThreadPoolExecutor.submit()` | **No** | Use `copy_context()` | | `loop.run_in_executor()` | **No** | Use `copy_context()` | | `ProcessPoolExecutor` | **Not reliably** | A `fork` start method happens to inherit; `spawn` and `forkserver` do not. Never depend on it — serialize the ID as data | | Generators, async generators | **Only while consumed inside** | A generator does not carry the context it was created in, so one consumed after the block exits sees nothing. Read the ID before yielding | | Celery / RQ / Dramatiq | **No** | Carry the ID in the job payload | For `ThreadPoolExecutor` and `loop.run_in_executor`, use the `copy_context()` idiom: ```py from concurrent.futures import ThreadPoolExecutor from contextvars import copy_context # Inside a sequence context ctx = copy_context() with ThreadPoolExecutor(max_workers=1) as executor: # The worker runs with the same correlation context result = executor.submit(ctx.run, worker_function).result() ``` Note: A single `Context` object cannot be entered twice concurrently. Create a fresh `copy_context()` for each submission. **Redaction is a named future phase** — Sensitive information currently **denies** rather than redacting. No SDK surface mutates content; a value that fails the sensitive-info check is not rewritten and passed through. Redaction would require an explicit carve-out from the checkpoint ADR's no-mutation rule. Do not assume redaction is coming for free. ### Remotely configured policy inputs Map application values explicitly as server-visible or local. Server inputs are evaluated and retained as policy evidence. Local strings remain in SDK memory; only a correlation digest and local-rule attestation are sent. ```py from arcjet.guard import local_input, server_input decision = await aj.guard( label="email.sent", actor=user_id, inputs={ "recipient": server_input.string(to), "subject": local_input.string(subject), "content": server_input.string(body), }, ) print(decision.policy_evaluation, decision.policy_results) ``` Each entry in `decision.policy_results` is a `PolicyRuleResult` whose `result` uses the same `RuleResult*` types as positional results. A policy rule that runs a detector on Arcjet reports `execution == "SERVER"`: sensitive information as `RuleResultSensitiveInfo` (with `billing`), and destination IP threat as `RuleResultIpThreat` (`detected`, `risk_level`, `reputation`, `activities`, `host`, `ip`; reason `"IP_THREAT"`). Server results never appear in `LocalDetectSensitiveInfo.results(decision)`. Rules are optional. Passing no rules (or ``rules=[]``) still calls Guard and sends the label, actor, and policy inputs, so a remotely configured policy can protect the action. An empty rules list does not mean “allow without checking.” When a local input triggers an enforced remote-policy rule, the SDK omits raw and server-exposed policy inputs and sends only privacy-safe local evidence to Guard. Guard records and returns the final decision and decision ID. ### LangChain tool checkpoints Install the optional integration with `pip install "arcjet[langchain]"`, then wrap a tool immediately before execution: ```py from arcjet.guard import local_input, server_input from arcjet.guard.langchain import guard_tool guarded_send_email = guard_tool( guard=aj, tool=send_email_tool, action="email.sent", on_guard_error="deny", # default — blocks if Guard is unavailable actor=lambda config: config["configurable"]["user_id"], inputs=lambda arguments, _config: { "recipient": server_input.string(arguments["to"]), "subject": local_input.string(arguments["subject"]), "content": server_input.string(arguments["body"]), }, ) ``` The guarded tool advertises the same schema as the tool it wraps and delegates to it, so the model is told what it would have been told without the guard. Every way of calling the tool is a checkpoint — `invoke()`, `ainvoke()`, `run()`, `arun()`, streaming and batching, and the tool's own `func` — and each call is evaluated exactly once. LangChain normalizes a call before the checkpoint reads it, so a policy resolver sees the config the tool runs with, including one a chain passed down without the caller re-threading it. The guarded tool is an instance of the wrapped tool's class, so application code and LangChain itself keep taking the same branch when they check a tool's concrete type. It is a generated subclass, created once per tool class and kept for the life of the process: a tool class that hooks `__init_subclass__` to register or validate its subclasses sees that subclass once, at the first `guard_tool()` call for it, and a registry keyed by class name gains an entry for it. A trace still shows the tool's own `name`; it is `repr()` and the class name that show `ArcjetGuarded<ClassName>`. A tool that keeps its own state under one of the names the guard uses for its own — `_arcjet_state` — is refused by `guard_tool()`, because a guarded tool is an instance of the tool's class and there is nowhere else for either to live. Rename the tool's attribute. A blocked call is reported to the tool's callbacks the way LangChain reports the outcome: a denial the tool's `handle_tool_error` converts is a run that ends with the handled content, and anything else is a start followed by an error. So a blocked call appears on a trace rather than leaving a gap, and a handled denial is not counted as a failure. What the handler returns is formatted by LangChain's own code, so a denial reaches the model shaped exactly as the tool's own error would have been. `rules` is a list, or a callable that returns one for each call. It takes the same `(arguments, config)` as `inputs`, so a rule can be bound from the call's arguments — a token bucket that charges each recipient for the segments the call sends, or a local sensitive-information check over the text it sends: ```py from arcjet.guard import LocalDetectSensitiveInfo, TokenBucket credits = TokenBucket(refill_rate=100, interval_seconds=60, max_tokens=1000) no_emails = LocalDetectSensitiveInfo(deny=["EMAIL"]) guarded_send_sms = guard_tool( guard=aj, tool=send_sms_tool, action="sms.sent", rules=lambda arguments, config: [ credits(key=arguments["to"], requested=arguments["segments"]), no_emails(arguments["body"]), ], ) ``` On `ainvoke()` / `arun()` it may be an async function; on the blocking entrypoints it must not be. If a resolver fails, Guard still sees the call — the decision is made without that input rather than not made at all — and `on_guard_error` decides whether the call may run. A rules resolver that raises, or returns anything but a list of bound rules, fails the same way: Guard is called with no local rules. Configure the tool before you guard it. The guarded tool carries a copy of the tool's state, but the wrapped tool is what executes, so anything you change on the guarded tool afterwards does not reach the call — `callbacks`, `tags`, `handle_tool_error`, `args_schema`, `response_format`, and any other field. A blocked call is reported from the wrapped tool too, so allowed and blocked calls agree with each other. The same applies to a method: if the tool's class has a helper that configures it — `bind_user()`, say — calling that on the guarded tool sets the value on the guarded tool, and the wrapped tool still runs without it. Call it before guarding, or call it on the tool you still hold. `response_format` is worth calling out because ignoring it does more than nothing: a tool that returns `(content, artifact)` runs with the wrapped tool's format, so the tuple is JSON-encoded into the message content and the artifact is dropped. `args_schema` follows that same rule, which matters if you want to hide an argument from the model. Narrow the tool **before** you guard it, so the wrapped tool parses against the narrow schema and the hidden argument is discarded rather than reaching the tool body. Narrowing the guarded tool instead changes nothing at all, because every schema question is answered by the tool it wraps. ```py class PublicEmailArgs(BaseModel): to: str # `internal_note` is deliberately absent send_email_tool.args_schema = PublicEmailArgs # narrow first, then guard guarded_send_email = guard_tool(guard=aj, tool=send_email_tool, action="email.sent") ``` Note that "discarded" is not "rejected": pydantic ignores an unknown field by default, so a caller that sends `internal_note` anyway gets a successful call with the field dropped, not an error. To reject it, give the narrow schema `model_config = ConfigDict(extra="forbid")`, or bind a rule to the argument. Narrow before guarding for the same reason you configure before guarding: changing the wrapped tool's schema afterwards leaves the two disagreeing about what to advertise, for a tool built with `Tool(...)` and no schema of its own. > [!WARNING] > Pickle executes arbitrary code as it loads. Load a pickled tool only from a > source you control — your own worker queue or process pool — and never from a > user, a network peer, or shared storage anyone else can write to. A guarded > tool is not a safe transport format for untrusted input, and the checkpoint > does not protect the load: the code runs before any rule does. A guarded tool can be pickled if the tool it wraps can and its resolvers can, which is what sending tools to a worker process needs. Fields set on the guarded tool survive the round trip. Resolvers are pickled as you gave them, so a lambda or a closure makes the tool unpicklable — use a module-level function if the tool has to cross a process boundary. The client is not pickled with it — it cannot cross a process boundary, and pickling it would write your site key into whatever the pickle is stored in — so call `register_arcjet()` in the receiving process before loading the tool. The core `guard()` API and the LangChain helper have intentionally different defaults when evaluation is unavailable: | API | Default when Guard is unavailable | How to change it | | --- | --- | --- | | `guard()` (core) | Allow (fail open), with `has_failed_open()` returning `True` | Gate manually on `has_failed_open()` | | `guard_tool()` | Block (fail closed) | Set `on_guard_error="allow"` | For `guard_tool()`, unavailable means either that the pre-execution checkpoint raised while resolving the actor or policy inputs, or calling Guard, or that Guard returned an `ALLOW` decision whose `has_failed_open()` is `True`. The latter can result from a deadline, response parse failure, local rule failure, missing decision, or server-returned rule error—not only an Arcjet Cloud outage. With the default `on_guard_error="deny"`, the wrapped tool does not execute and `ArcjetToolUnavailableError` is raised. This is distinct from `ArcjetToolDeniedError`, which represents a real `DENY` decision and carries that decision. Handle an unavailable evaluation as an operational failure that may warrant alerting or retrying; do not treat it as a policy denial. Set `on_guard_error="allow"` only at call sites where availability matters more than enforcement, such as a read-only lookup. ### Guarding an agent's tool calls When the model chooses the tool, guard the agent instead of each tool. Install `arcjet[langchain-agents]`, then give `create_agent` the middleware: ```py from langchain.agents import create_agent from arcjet.guard.langchain import ArcjetMiddleware, ToolPolicy agent = create_agent( model="openai:gpt-4o", tools=[send_email, search_orders], middleware=[ ArcjetMiddleware( policies={"send_email": ToolPolicy(action="email.sent")}, tools=[send_email, search_orders], ) ], ) ``` Tools without a policy pass through unguarded, so you protect the ones that do something consequential and leave the rest alone. A `ToolPolicy`'s `actor`, `inputs` and `rules` may each be a callable of the tool call's parsed arguments, so a rule can be bound from them: `rules=lambda arguments: [credits(key="u_1", requested=arguments["count"])]`. Under `awrap_tool_call` the callable may be async. A rules callable that fails is handled like a failed `inputs` callable: Guard is called with no local rules and `on_guard_error` decides. Pass `tools=` the same sequence you gave `create_agent`. A policy is matched by tool name, so without it a typo — or a renamed `@tool` function — leaves that tool unguarded and looks exactly like a healthy allow. Given the tools, a key naming none of them is refused where you wrote it. The client is optional. Without `guard=`, the checkpoint uses whatever you registered with `register_arcjet()`, which is what you want in an application that registers once at startup. ### CrewAI tool hooks `arcjet.guard.crewai` needs the `crewai` you already have — install it yourself with `pip install "crewai>=1.15.3,<2"` (Python `>=3.10,<3.14`). There is no Arcjet extra for it, for the ChromaDB reason [above](#guard-surfaces-extras-and-propagation). Nothing here imports `arcjet.guard.langchain`, and importing `arcjet.guard` never imports CrewAI. See the [`examples/fastapi-crewai-guard`](./examples/fastapi-crewai-guard) example for end-to-end FastAPI wiring. > [!NOTE] > CrewAI installs `chromadb`, which has an unpatched critical RCE > ([CVE-2026-45829](https://nvd.nist.gov/vuln/detail/CVE-2026-45829)). The > exposed surface is ChromaDB's **Python FastAPI server**, which loads a > client-supplied HuggingFace model before it authenticates the request. > CrewAI's own memory uses an embedded client and starts no server, so a > default CrewAI app is not reachable this way. If you do run a ChromaDB > server, use the Rust path (`chroma run` / the Docker images), which is > unaffected, and keep the port off untrusted networks. CrewAI's first-class gate is `PRE_TOOL_CALL`. Register it once; every tool a crew, LiteAgent, MCP adapter, or crew-injected tool list executes hits the hook. A deny raises `HookAborted(reason, source="arcjet")` so the tool does not run. CrewAI swallows every other exception — raising `ArcjetDeniedError` or `ArcjetUnavailableError` from the hook would *run* the tool. ```py from arcjet.guard import launch_arcjet_sync, server_input from arcjet.guard.crewai import ToolPolicy, register_arcjet_hooks aj = launch_arcjet_sync(key=arcjet_key) register_arcjet_hooks( guard=aj, # Default label for tools without a policy. Per-tool overrides below. action=lambda ctx: f"{ctx.tool_name}.invoked", on_guard_error="deny", # default — HookAborted if Guard is unavailable correlation_id=session_id, # caller-owned; or open arcjet_sequence() policies={ "send_email": ToolPolicy( action="email.sent", inputs=lambda arguments, _ctx: { "recipient": server_input.string(arguments["to"]), }, ), }, ) # Screen inbound user text yourself. There is no guard_inbound helper. decision = aj.guard( label="chat.inbound", inputs={"content": server_input.string(user_text)}, ) if decision.conclusion == "DENY" or decision.has_failed_open(): raise RuntimeError("refusing to start the crew") crew.kickoff(inputs={"topic": user_text}) ``` `policies=` keys and `tools=` filters are sanitized the way CrewAI sanitizes tool names (`Send Email` and `send_email` match) — by CrewAI's own function, so matching cannot drift from it. Crew, task, and agent *names* are attached as metadata. Their ids are never read and never used as a correlation id — pass `correlation_id` or open `arcjet_sequence()`. A resolver receives the tool's arguments as the model sent them, unfiltered, because a policy reading `arguments["user_id"]` is reading the tool's own argument. When you want to offer only free text to a scanning rule, apply `free_text_arguments()`, which drops id-shaped keys (`tool_call_id`, `trace_id`, `*_id`): ```py from arcjet.guard.crewai import free_text_arguments ``` Register once per process. CrewAI's hook registry is global and appends, so a second `register_arcjet_hooks()` would evaluate every tool call twice and double-charge a rate limit; it raises `ArcjetMisconfiguration` instead. Call `unregister()` on the returned handle to replace a registration. The hook path is synchronous, so the client must have a blocking `guard()` (`launch_arcjet_sync`). An async client is refused at registration rather than per call — a hook cannot report a wiring mistake, so under `on_guard_error="allow"` it would silently allow every tool call. `HookAborted.reason` is telemetry only; the agent always sees `Tool execution blocked by hook. Tool: {name}`. Only `PRE_TOOL_CALL` is registered. `POST_TOOL_CALL` is not a policy surface — it fires on blocked calls too — so this integration does not register it at all and cannot deny or rewrite a result there. The capture records the *decision*: a hook cannot observe the tool body, because CrewAI turns a failing tool into a result string. `human_input` / `request_human_input` is HITL, not a deny path. `BaseTool.run` does not dispatch `PRE_TOOL_CALL`. If you call a CrewAI tool yourself, wrap it: ```py from arcjet.guard.crewai import guard_tool guarded_send = guard_tool( guard=aj, tool=send_email_tool, action="email.sent", on_guard_error="deny", ) guarded_send.run(to="a@example.com", body="…") ``` `rules` may be a callable of the call's arguments, like `actor` and `inputs`, so a rule can be bound from them: `rules=lambda arguments: [no_emails(arguments["body"])]`. It is called synchronously on `arun()` too. A rules callable that fails is handled like a failed `inputs` callable: Guard is called with no local rules and `on_guard_error` decides. A wrapped tool raises `ArcjetDeniedError` / `ArcjetUnavailableError` — the only CrewAI surface that uses those types. `guard_tool` returns a *copy* carrying the checkpoint, and one call evaluates once however deep the tool's own entrypoints delegate. Give the crew the copy: the hook skips it, so a wrapped tool a crew executes is evaluated once. The original is deliberately left unwrapped, so handing *it* to a crew is still covered by the hook rather than silently unguarded. A tool the crew reaches without CrewAI recording an `_original_tool` is evaluated by both surfaces, which is fail-closed but charges twice — wrap or hook such a tool, not both. ### OpenAI Agents function tools `arcjet.guard.openai_agents` needs `pip install "arcjet[openai-agents]"` (`openai-agents>=0.19.0,<1`). Nothing here imports `arcjet.guard.langchain` or `arcjet.guard.crewai`, and importing `arcjet.guard` never imports OpenAI Agents. The Python page is [`/guards/openai-agents-py/`](https://docs.arcjet.com/guards/openai-agents-py/); it does not replace the JS page [`/guards/openai-agents/`](https://docs.arcjet.com/guards/openai-agents/). See the [`examples/fastapi-openai-agents-guard`](./examples/fastapi-openai-agents-guard) example for end-to-end FastAPI wiring (an offline stub-model harness lives at [`examples/openai-agents-guard-verify`](./examples/openai-agents-guard-verify)). ```py from arcjet.guard import launch_arcjet, server_input from arcjet.guard.openai_agents import guard_tool, openai_agents_context from agents import Agent, Runner, function_tool aj = launch_arcjet(key=arcjet_key) @function_tool def send_email(to: str, body: str) -> str: """Send an email.""" return "sent" guarded_send = guard_tool( guard=aj, tool=send_email, action="email.sent", on_guard_error="deny", # default ) agent = Agent(name="mailer", tools=[guarded_send]) app_context = {"session_id": session_id} # Screen inbound user text yourself. There is no guard_inbound helper. ctx = openai_agents_context(app_context) decision = await aj.guard( label="chat.inbound", inputs={"content": server_input.string(user_text)}, correlation_id=ctx.correlation_id, metadata=ctx.metadata, ) if decision.conclusion == "DENY" or decision.has_failed_open(): raise RuntimeError("refusing to start the run") await Runner.run(agent, user_text, context=app_context) ``` #### Screen inbound before `Runner.run` SDK `input_guardrails` on the `Agent` are not Arcjet. Screen user text with core `guard()` / `guard_sync()` before `Runner.run`. `protect()` and the request path are fail-open — the caller must check `has_failed_open()`. A checkpoint on a tool still defaults to `on_guard_error="deny"`. #### `needs_approval` is not a policy gate `needs_approval` / `require_approval` / `on_approval` is human-in-the-loop. This helper does not wrap it and does not mutate `RunConfig`. Input tool guardrails normally run after approval, immediately before invoke. Set `RunConfig.tool_execution.pre_approval_tool_input_guardrails=True` yourself if you want the check before the HITL pause. That flag is an application opt-in only; Arcjet does not set it. #### Deny with a `ToolInputGuardrail` on `FunctionTool` (`reject_content`) `guard_tool` returns a copy whose `tool_input_guardrails` start with an Arcjet guardrail. Official: those run before `on_invoke_tool` and can skip the call. A deny is `ToolGuardrailFunctionOutput.reject_content` of a JSON `{ arcjetDenied, reason, message, retryable, retryAfterSeconds? }`. It does not `raise_exception()` (that halts the run) and it does not raise an Arcjet error from `on_invoke_tool` (the SDK `default_tool_error_function` would swallow it into a generic string). Already-branded tools are skipped. Hosted tools, MCP, Computer / Shell / ApplyPatch, handoffs, and `Agent.as_tool()` are not on that authored `FunctionTool` path. `openai_agents_context` reads a caller-owned `correlation_id` / `session_id` / `conversation_id` / `group_id`, then the enclosing `arcjet_sequence()`. It never mints. It never reads `trace_id`. It never constructs `OpenAIConversationsSession()` just to get an id. ### Claude Agent SDK tools and hooks `arcjet.guard.claude_agent_sdk` needs `pip install "arcjet[claude-agent-sdk]"` (`claude-agent-sdk>=0.2.127,<1`). Nothing here imports `arcjet.guard.langchain`, `arcjet.guard.crewai`, or `arcjet.guard.openai_agents`, and importing `arcjet.guard` never imports the Claude Agent SDK. The Python page is [`/guards/claude-agent-sdk-py/`](https://docs.arcjet.com/guards/claude-agent-sdk-py/); it does not replace the JS page [`/guards/claude-agent-sdk/`](https://docs.arcjet.com/guards/claude-agent-sdk/). ```py from arcjet.guard import DetectPromptInjection, TokenBucket, launch_arcjet from arcjet.guard.claude_agent_sdk import ( claude_agent_context, guard_hooks, guard_tool, ) from claude_agent_sdk import tool aj = launch_arcjet(key=arcjet_key) session_id = conversation_id # a UUID the app already minted mcp_limit = TokenBucket(refill_rate=20, interval_seconds=60, max_tokens=20) @tool("send_email", "Send an email", {"to": str, "body": str}) async def send_email(args): return {"content": [{"type": "text", "text": "sent"}]} guarded_send = guard_tool( guard=aj, tool=send_email, action="email.sent", session_id=session_id, # authored handler has no extra.session_id on_guard_error="deny", # default ) hooks = guard_hooks( guard=aj, session_id=session_id, action=lambda hook: f"{hook['tool_name']}.invoked", rules=lambda call: [mcp_limit(key=call["tool_name"], requested=1)], exclude=[{"server": "mail", "name": "send_email"}], inbound={ "action": "message.received", "rules": lambda arguments: [DetectPromptInjection()(arguments["prompt"])], }, ) ``` On DENY the authored handler returns exactly `{"content": [{"type": "text", "text": <json.dumps(ArcjetDenialResult)>}], "is_error": True}`. It does not throw (the SDK swallows into `str(e)`) and it does not set `structuredContent` (`create_sdk_mcp_server` drops it). Already-branded tools are skipped. #### Screen inbound on `UserPromptSubmit` There is no `guard_inbound` helper. Put prompt-injection and other inbound rules on `guard_hooks(..., inbound=...)`. A deny is `{"decision": "block"}`. `protect()` and the request path are fail-open — the caller must check `has_failed_open()`. These helpers default to `on_guard_error="deny"`. #### `can_use_tool` is not a policy gate `can_use_tool` is ask-only HITL. `allowed_tools`, allow rules, and `bypassPermissions` / `acceptEdits` skip it. There is no `guard_can_use_tool`. `permissionDecision: "ask"` is HITL, not deny. Use `guard_tool` for authored tools or `PreToolUse` for unwrapped ones. #### Deny unwrapped tools on `PreToolUse` Built-ins (Bash, Write, …) and MCP tools not passed through `guard_tool` are gated here. `PreToolUse` returns `permissionDecision: "deny"`. `rules` / `actor` / `inputs` / `metadata` callables receive `tool_input` plus `tool_name` so a local rate limit can key on the name. List every `guard_tool` wrapper in `exclude` as `{"server": ..., "name": ...}` so the reported `mcp__{server}__{name}` name is not double-gated. A bare authored name does not match every server's tool of that name. `PostToolUse` is capture-only (`claude.phase: after`) and is not skipped by `exclude`. `actor=` / `inputs=` next to `inbound=` do not install a tool gate; pass `tools=True` for the default `{tool_name}.invoked` gate with empty rules. `claude_agent_context` reads hook `session_id` first, then the caller-owned `session_id=` fallback, then `arcjet_sequence()`. It never mints. It never reads `trace_id`. Wrap-time `session_id=` / `correlation_id=` (including `inbound["session_id"]`) must be a UUID the app already minted — the same value you pass to `query()`. ### Claude Managed Agents (hosted REST+SSE) `arcjet.guard.claude_managed_agents` needs `pip install "arcjet[claude-managed-agents]"` (`anthropic>=0.92.0,<2`). This is **not** `arcjet.guard.claude_agent_sdk`. There is no PreToolUse, no `guard_tool`, and no `guard_inbound`. Anthropic runs the tool loop (beta `managed-agents-2026-04-01`). Shared JS+Python docs: [`/guards/claude-managed-agents/`](https://docs.arcjet.com/guards/claude-managed-agents/). ```py from arcjet.guard import DetectPromptInjection, launch_arcjet from arcjet.guard.claude_managed_agents import ( claude_managed_agents_context, guard_custom_tool, guard_events, ) aj = launch_arcjet(key=arcjet_key) ctx = claude_managed_agents_context({"session_id": app_session_id}) send = guard_events( guard=aj, send=client.beta.sessions.events.send, action="message.received", rules=lambda arguments: [DetectPromptInjection()(arguments["prompt"])], correlation_id=ctx.correlation_id, on_guard_error="deny", # default ) handle = guard_custom_tool( guard=aj, run=send_email, action="email.sent", correlation_id=ctx.correlation_id, on_guard_error="deny", # default ) await send( session.id, events=[{"type": "user.message", "content": [{"type": "text", "text": user_text}]}], ) # On agent.custom_tool_use: # wrap-time session_id= is caller-owned correlation; anthropic_session_id # is Anthropic's ses_... and is only used to post the result. await handle(event, send=send, anthropic_session_id=session.id) ``` On DENY the custom-tool helper does not run the tool. It sends a real `user.custom_tool_result` (`custom_tool_use_id`, `content`, `is_error`) with JSON `{ arcjetDenied, reason, message, retryable, retryAfterSeconds? }`. `guard_events` does not call `sessions.events.send` when inbound `user.message` / `initial_events` is denied. For a self-hosted `EnvironmentWorker`, wrap the custom `@beta_tool` / `@beta_async_tool` with the same helper (`tool=`) — that replaces `.call`, which is what `SessionToolRunner` invokes — and put it in the worker's `tools` factory. On DENY the wrapper raises `ToolError` so the runner posts `is_error=True`. The CLI worker cannot register custom tools. #### What this extra cannot gate Default `permission_policy: always_allow` **cannot** be gated — Anthropic-cloud bash / read / write run before any customer hook. `web_search` / `web_fetch` always run on Anthropic. MCP: Anthropic is the client; customer-side Guard is on custom tools and MCP servers **they** host. HITL `always_ask` / `user.tool_confirmation` is not policy. `protect()` and the request path are fail-open — the caller must check `has_failed_open()`. These helpers default to `on_guard_error="deny"`. `claude_managed_agents_context` reads a caller-owned `correlation_id` / `session_id`, then `arcjet_sequence()`. It never mints. It never reads `trace_id`. It never treats Anthropic session / event ids as if we created them. Wrap-time `session_id=` is that caller-owned id. `handle(..., anthropic_session_id=)` is Anthropic's `ses_...`, used only to post `user.custom_tool_result`. `guard_events` always returns an async callable — `await` it even when wrapping the sync client. ### Strands Agents tools and hooks `arcjet.guard.strands_agents` needs `pip install "arcjet[strands-agents]"` (`strands-agents>=1.11.0,<2`). Nothing here imports `arcjet.guard.langchain`, `arcjet.guard.crewai`, `arcjet.guard.openai_agents`, `arcjet.guard.claude_agent_sdk`, or `arcjet.guard.claude_managed_agents`, and importing `arcjet.guard` never imports Strands. The Python page is [`/guards/strands-agents-py/`](https://docs.arcjet.com/guards/strands-agents-py/); it does not replace the JS page [`/guards/strands-agents/`](https://docs.arcjet.com/guards/strands-agents/). ```py from arcjet.guard import TokenBucket, launch_arcjet, server_input from arcjet.guard.strands_agents import ( guard_hooks, guard_tool, strands_agent_context, ) from strands import Agent, tool aj = launch_arcjet(key=arcjet_key) session_id = user_id # a caller-owned id the app already minted mcp_limit = TokenBucket(refill_rate=20, interval_seconds=60, max_tokens=20) @tool def send_email(recipient: str, body: str) -> str: """Send an email.""" return "sent" guarded_send = guard_tool( guard=aj, tool=send_email, action="email.sent", session_id=session_id, # wrap-time fallback; invocation_state is preferred on_guard_error="deny", # default ) hooks = guard_hooks( guard=aj, action=lambda call: f"{call['tool_name']}.invoked", rules=lambda call: [mcp_limit(key=call["tool_name"], requested=1)], session_id=session_id, ) agent = Agent( tools=[guarded_send], hooks=[hooks], ) # Screen inbound user text yourself. There is no guard_inbound helper. invocation_state = {"sessionId": session_id} ctx = strands_agent_context(invocation_state) decision = await aj.guard( label="chat.inbound", inputs={"content": server_input.string(user_text)}, correlation_id=ctx.correlation_id, metadata=ctx.metadata, ) if decision.conclusion == "DENY" or decision.has_failed_open(): raise RuntimeError("refusing to start the run") agent(user_text, invocation_state=invocation_state) ``` On DENY `guard_tool` yields an error-status tool result whose text is the JSON `{ arcjetDenied, reason, message, retryable, retryAfterSeconds? }` envelope. It does not throw (the SDK swallows into `Error: {Type} - {message}`). It wraps `stream()` so caller-owned `invocation_state` is visible; tool-handler kwargs are never a correlation source. Already-branded tools are skipped on the before hook path. #### Screen inbound before `agent()` There is no `guard_inbound` helper and no dedicated prompt hook on the Strands hooks bus (`MessageAddedEvent` is observe-only; `BeforeInvocationEvent.cancel` aborts the whole run). Screen user text with core `guard()` / `guard_sync()` before `agent()`. `protect()` and the request path are fail-open — the caller must check `has_failed_open()`. These helpers default to `on_guard_error="deny"`. #### `event.interrupt()` is not a policy gate `BeforeToolCallEvent.interrupt()` is human-in-the-loop. This helper does not call it. Use `guard_tool` for authored tools or `cancel_tool` for unwrapped ones. #### Deny unwrapped tools on `BeforeToolCallEvent.cancel_tool` MCP, vended, and built-in tools not passed through `guard_tool` are gated here. `cancel_tool` set to a string (the JSON `ArcjetDenialResult`) or `True` skips the handler. `rules` / `actor` / `inputs` / `metadata` callables receive `tool_use.input` plus `tool_name`. Wrapped tools are skipped by the `_arcjet_guarded` brand so the two surfaces do not double-call Guard. `AfterToolCallEvent` is capture-only (`strands.phase: after`) and is not skipped by the brand. A cancel or tool exception is not recorded as `success`. `BeforeToolsEvent.cancel` is not set. Official Strands docs: a batch cancel produces an error result for every tool and skips execution entirely, so no per-tool `BeforeToolCallEvent` fires. The JS adapter avoided `BeforeToolsEvent.cancel` for that reason; Python is the same, so the primary gate is per-tool `cancel_tool`. `strands_agent_context` reads `invocation_state.correlationId`, then `sessionId`, then `requestId` (and the snake_case aliases). It never mints. It never reads `trace_id`. It never reads `agent.id` or SessionManager auto-ids. ### Google ADK tools and plugin `arcjet.guard.google_adk` needs `pip install "arcjet[google-adk]"` (`google-adk>=2.0.0,<3`). Nothing here imports `arcjet.guard.langchain`, `arcjet.guard.crewai`, `arcjet.guard.openai_agents`, `arcjet.guard.claude_agent_sdk`, `arcjet.guard.claude_managed_agents`, or `arcjet.guard.strands_agents`, and importing `arcjet.guard` never imports Google ADK. The Python page is [`/guards/google-adk-py/`](https://docs.arcjet.com/guards/google-adk-py/); it does not replace the JS page [`/guards/google-adk/`](https://docs.arcjet.com/guards/google-adk/). This is not JS `@arcjet/guard/google-adk/v2` (plugin only, no `guardTool`). ```py from arcjet.guard import launch_arcjet, server_input from arcjet.guard.google_adk import ( google_adk_context, guard_plugin, guard_tool, ) from google.adk.agents import LlmAgent from google.adk.runners import Runner aj = launch_arcjet(key=arcjet_key) session_id = user_id # a caller-owned id the app already minted def send_email(to: str, body: str) -> dict[str, str]: """Send an email.""" return {"status": "sent"} # Pick one gate per tool — do not stack both. before_tool = guard_tool( guard=aj, action="email.sent", actor=session_id, inputs=lambda call: { "recipient": server_input.string(str(call.get("to", ""))), "body": server_input.string(str(call.get("body", ""))), }, session_id=session_id, on_guard_error="deny", # default ) agent = LlmAgent( name="email_agent", model="gemini-2.0-flash", instruction="Send email with send_email.", tools=[send_email], before_tool_callback=before_tool, ) # Runner-wide alternative for unwrapped / MCP tools. Put Arcjet first. # plugin = guard_plugin(guard=aj, session_id=session_id) # runner = Runner(..., plugins=[plugin]) app_context = {"sessionId": session_id} ctx = google_adk_context(app_context) decision = await aj.guard( label="chat.inbound", inputs={"content": server_input.string(user_text)}, actor=session_id, correlation_id=ctx.correlation_id, metadata=ctx.metadata, ) if decision.conclusion == "DENY" or decision.has_failed_open(): raise RuntimeError("refusing to start the run") ``` On DENY `guard_tool` / `guard_plugin` return a skip dict `{ arcjetDenied, reason, message, retryable, retryAfterSeconds? }`. `None` allows the tool. They never return `{}` (falsy in ADK's callback chain, so the tool would run) and never throw. `request_confirmation` / `require_confirmation` are HITL, not a policy gate. #### Screen inbound before `runner.run_async` There is no `guard_inbound` helper. Screen user text with core `guard()` / `guard_sync()` before `runner.run_async`. `protect()` and the request path are fail-open — the caller must check `has_failed_open()`. These helpers default to `on_guard_error="deny"`. #### Human approval is not a policy gate `request_confirmation` / `require_confirmation` are human-in-the-loop. This helper does not call them. `SecurityPlugin` is not the Arcjet gate. `google_adk_context` reads `correlationId`, then `sessionId`, then `conversationId` (and the snake_case aliases). It never mints. It never reads `trace_id`. It never reads an ADK-generated `invocation_id`. It never reads `toolContext.sessionId` or `session.id`. ### Sync usage For Flask, Django, or other sync frameworks, use `launch_arcjet_sync`: ```py 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") if decision.has_failed_open(): raise RuntimeError("Guard unavailable; refusing to run the tool") ``` ### 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 ```py 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 ```py 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 ```py 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 ```py 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") result = prompt_scan.result(decision) if result and result.billing: print(result.billing.unit, result.billing.count) ``` Guard billing is optional. Prompt injection usage is reported in `tokens`, while content moderation usage is reported in `text_units` (text chunks), so always inspect `billing.unit` rather than assuming the unit. ### Content moderation ```py from arcjet.guard import ModerateContent moderate = ModerateContent() decision = await aj.guard( label="tools.chat", rules=[moderate(user_message)], ) if decision.conclusion == "DENY": print("Harmful content detected") result = moderate.result(decision) if result: print(result.detected) if result.billing: print(result.billing.unit, result.billing.count) ``` `experimental_ModerateContent` remains as a deprecated alias for `ModerateContent`. The result reports `detected` and optional `billing` only — not per-category scores. ### 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`. ```py 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`: ```py 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): ```py 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: ```py 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 ```py decision = await aj.guard(label="tools.weather", rules=[...]) # Layer 1: conclusion and reason decision.conclusion # "ALLOW" or "DENY" decision.reason # "RATE_LIMIT", "PROMPT_INJECTION", "MODERATE_CONTENT", "SENSITIVE_INFO", "IP_THREAT", "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 SDK rule inputs (optional; defaults to empty for policy-only calls) | | `label` | `str` | Label identifying this guard call (required) | | `actor` | `str \| None` | Actor used by remote policy selection/evaluation | | `inputs` | `PolicyInputMap \| None` | Typed server-visible or local remote-policy inputs | | `metadata` | `Metadata \| None` | Structured metadata — see [Metadata](#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. ```py 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: ```py user_limit = TokenBucket( refill_rate=10, interval_seconds=60, max_tokens=100, mode="DRY_RUN", ) ``` ### Recording what happened with `capture()` `guard()` decides whether something is allowed. `capture()` records that it happened. Use it for the actions you want to see in a security trace but do not want to gate — a refund issued, a document exported, a tool call completed. ```py decision = await aj.guard(label="refund", rules=[inp]) if decision.conclusion == "ALLOW": refund_id = issue_refund(...) aj.capture( action="refund.issued", correlation_id=workflow_id, # ties this to other calls in the workflow decision_id=decision.id, # ties it to the decision above metadata={"amount_cents": 4999, "invoice": {"id": "inv_123"}}, ) ``` `capture()` returns immediately and is not awaited, even on the async client. Events are queued and sent in the background, batched together. It is **best-effort and never affects a decision**: - It never raises. A bad field is dropped and the rest of the event is sent; an event with no usable `action` is dropped entirely. - Under sustained load or a failing backend, events are dropped rather than slowing your request down. A failed send is never retried. - Nothing is dropped silently. Drops are reported through the `arcjet` logger with a stable code — `AJ3001` (queue full), `AJ3002` (send failed), `AJ3003` (flush deadline). The `arcjet` logger is already at `WARNING`, so you only need to attach a handler to see them. Repeats of the same code are coalesced for a minute and the suppressed count is reported with the next line for that code, or by the next `flush()`. A burst that ends without either will under-report its total — the figure is a count of events seen, not a guaranteed total. Pass your own logger to receive **every** diagnostic uncoalesced, which is what you need to keep a metric of dropped events: ```py aj = launch_arcjet_sync(key=arcjet_key, logger=my_logger) ``` Each record carries `code` and `count` attributes alongside the message, so a handler can route or count on them without parsing text. Do not put secrets or PII in `metadata`; it is stored as untrusted data. #### Delivering events before shutdown Delivery is asynchronous, so events queued as your process exits may never be sent — the sync worker is a daemon thread and will not hold the interpreter open. Call `flush()` at a shutdown point: ```py # Async (FastAPI lifespan, or any async teardown) await aj.flush() # Sync (Flask teardown, atexit, or the end of a script) arcjet_sync_guard.flush() ``` `flush()` waits up to `timeout_ms` (default 1000) for the events outstanding when you called it. On expiry, queued events are dropped and a request already on the wire is abandoned — not cancelled, so it may still arrive, and nothing will tell you either way. Both are counted in the `AJ3003` report. Events captured *while* a flush is waiting are not its responsibility and survive its deadline, so calling `flush()` per request in a concurrent server cannot discard another request's telemetry. There is no `close()`: a client holds no connection of its own to release, so flushing is the only shutdown step that changes what gets delivered. ### Registering a client (optional) Passing the client explicitly is the recommended path, and everything above does exactly that. Registration is a shortcut for the case it cannot cover: code too deep in an application to be handed a client, where `capture()` is often most useful. `launch_arcjet()` never touches global state. Registering is always a separate, explicit call: ```python # wherever your application starts up import os from arcjet.guard import launch_arcjet, register_arcjet register_arcjet(launch_arcjet(key=os.environ["ARCJET_KEY"])) ``` `capture()` is then importable on its own and reaches the registered client: ```python # deep in application code — nothing was passed down here from arcjet.guard import capture async def refund(invoice_id: str) -> None: await issue_refund(invoice_id) capture(action="refund.issued", metadata={"invoice": invoice_id}) ``` #### Sync and async `capture()` is one function for both client flavors, because it queues and returns on each of them. `guard()` and `flush()` cannot be, so they come in pairs matching `launch_arcjet` / `launch_arcjet_sync`: | Registered client | Guard | Flush | | --- | --- | --- | | `launch_arcjet()` | `await guard(...)` | `await flush()` | | `launch_arcjet_sync()` | `guard_sync(...)` | `flush_sync()` | Registration accepts either and does not record which, so calling the wrong one is not something a type checker can catch. It fails open and reports `AJ3007` on the registered client's logger. #### What happens with nothing registered `guard()` and `guard_sync()` return a fail-open `ALLOW` carrying an error result, so a caller that inspects the decision can see that no policy ran. They do not raise. ```python decision = await guard([limit(key=user_id)], label="refund") if decision.has_failed_open(): # No rule was evaluated. Treat this as "policy did not run", not as a pass. ... ``` `capture()` drops the event silently, and the `flush()` variants return. Nothing is logged: the client that would have carried a logger is the thing that is missing, so the only available sink would be an unconfigurable warning on a request path. #### Registering twice, and unregistering Registration is guarded. A second client does not displace the first — the attempt is reported as `AJ3004` on the **incumbent's** logger, so a library or a stray second `launch_arcjet()` cannot quietly redirect an application's telemetry to a different key. Registering the client that is already registered is a silent no-op. `unregister_arcjet()` takes no argument and clears whatever is there. The cost is that anything calling it clears the application's client and every free call afterwards fails open, so **libraries should not call it** — they take a client explicitly. That is a convention, not something the SDK enforces. The registration is a module-level global, so it is visible from every thread and every event loop. It is deliberately *not* a `contextvars.ContextVar`: a context variable set at startup is invisible inside worker threads, which is exactly how Flask, Django and other WSGI servers run request handlers, so registration would appear to work in development and silently do nothing in production. ### Testing `arcjet.guard.testing` registers an in-memory client that records calls and talks to nothing: ```python from arcjet.guard import capture from arcjet.guard.testing import register_test_client async def test_refund_captures_an_event(): with register_test_client() as arcjet: await refund("inv_1") assert arcjet.captures[0].action == "refund.issued" ``` It drives every guard surface — the free calls, `guard_tool`, the middleware and the capture handlers alike — because each identifies a client by the shape of its `guard()` rather than by its class: ```python from arcjet.guard.langchain import guard_tool from arcjet.guard.testing import register_test_client def test_sending_an_email_is_guarded(): with register_test_client() as arcjet: guarded = guard_tool( guard=arcjet, tool=send_email, action="email.sent", on_guard_error="allow", ) guarded.invoke({"to": "a@b.c", "subject": "hi"}) assert [call.label for call in arcjet.guards] == ["email.sent"] ``` Pass `on_guard_error="allow"` unless the test is asserting a denial: the recorder answers a fail-open decision, which the default treats as an unevaluated policy and refuses. The `with` block unregisters the client on the way out, including when the test fails part-way through. Note the `await`: the capture happens wherever the code under test reaches it, so a test that forgets to await an async function asserts before the event exists. Usually this belongs in a fixture: ```python import pytest from arcjet.guard.testing import register_test_client @pytest.fixture def arcjet(): with register_test_client() as client: yield client ``` `register_test_client()` raises if a client is already registered, which surfaces a leak from an earlier test rather than letting this one assert against the wrong recorder. `unregister()` is also available for teardown that cannot use `with`, and only clears the registration if it is still this client. Each recorded capture goes through the same validation and metadata encoding as a real `capture()`, so a call the real client would drop is not recorded here either, and anything the SDK rewrote is on `capture.warnings`. Recording itself is synchronous — once the code under test reaches `capture()`, the event is there with no flushing or waiting. The test client answers both `guard()` and `guard_sync()`, so it does not care which flavor your application uses. It records the call and returns a fail-open `ALLOW`, because no rule actually ran. It is not a mock server and does not let you stub per-rule verdicts. `guard_tool()` accepts it too — it identifies a client by the shape of its `guard()`, not by its class. Because the recorder answers a fail-open decision, pass `on_guard_error="allow"` unless the test is asserting the denial. ## Best practices ### Single-instance pattern Create one Arcjet client at startup and reuse it across all requests: ```py # 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: ```py 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`: ```py 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: ```sh 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: ```py 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](#metadata) | | `correlation_id` | `str` | Opaque id correlating this call with other `protect()`/`guard()` calls | | `ip_src` | `str` | Manual IP override (advanced) | ### Decision response ```py 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: ```py 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](https://docs.arcjet.com/support). ## Security This repository follows the [Arcjet Security Policy](https://docs.arcjet.com/security). ## Compatibility Packages maintained in this repository support CPython 3.10+ on macOS, Windows, and glibc Linux (Debian, Ubuntu, RHEL, and `*-slim` / manylinux container images). Two runtime dependencies ship native code: - [`wasmtime`](https://pypi.org/project/wasmtime/) — local WASM rule evaluation - [`pyqwest`](https://pypi.org/project/pyqwest/) — HTTP client used by `connect-python` Those packages publish `musllinux` wheels today, so `pip install arcjet` often succeeds on Alpine without a compiler. Alpine/musl is still **not a supported target**. A future wheel gap forces a source build (Rust plus a C toolchain), which is what broke `python3.10-alpine` images after local analysis landed. Prefer a glibc image such as `python:3.10-slim` or `astral/uv:python3.10-trixie-slim`. ## License Licensed under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0).