惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Hacker News - Newest:
Hacker News - Newest: "LLM"
S
SegmentFault 最新的问题
The Cloudflare Blog
T
The Exploit Database - CXSecurity.com
C
CXSECURITY Database RSS Feed - CXSecurity.com
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
P
Privacy & Cybersecurity Law Blog
有赞技术团队
有赞技术团队
www.infosecurity-magazine.com
www.infosecurity-magazine.com
雷峰网
雷峰网
Google DeepMind News
Google DeepMind News
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
T
Threatpost
The Hacker News
The Hacker News
人人都是产品经理
人人都是产品经理
阮一峰的网络日志
阮一峰的网络日志
大猫的无限游戏
大猫的无限游戏
L
Lohrmann on Cybersecurity
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
W
WeLiveSecurity
T
Threat Research - Cisco Blogs
博客园 - 叶小钗
量子位
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
Simon Willison's Weblog
Simon Willison's Weblog
I
Intezer
S
Secure Thoughts
V
V2EX
Apple Machine Learning Research
Apple Machine Learning Research
小众软件
小众软件
酷 壳 – CoolShell
酷 壳 – CoolShell
Microsoft Azure Blog
Microsoft Azure Blog
博客园_首页
Help Net Security
Help Net Security
爱范儿
爱范儿
C
Cybersecurity and Infrastructure Security Agency CISA
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
腾讯CDC
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
C
Check Point Blog
WordPress大学
WordPress大学
Last Week in AI
Last Week in AI
T
Tor Project blog
NISL@THU
NISL@THU
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
I
InfoQ
The Register - Security
The Register - Security
Recent Announcements
Recent Announcements
N
News and Events Feed by Topic
B
Blog RSS Feed

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant Common SOC 2 Failures (Real World) Stop Vibe-Checking Your AI App: A Practical Guide to Evals How to Use SonarQube and SonarScanner Locally to Level Up Your Code Quality Your Next To-Do App Is Dead — I Replaced Mine with an OpenClaw AI Sign a Nostr event in 60 lines of Python using coincurve — no nostr-sdk, no nbxplorer, no rust toolchain ITGC Audit Explained Like You’re in Big 4 Patch Tuesday abril 2026: Microsoft parcha 163 vulnerabilidades y un zero-day en SharePoint Stop scraping everything: a better way to track competitor price changes Listing on MCPize + the Official MCP Registry while routing payments OUTSIDE the marketplace — how I kept 100% of my x402 revenue Building an AI-Powered Risk Intelligence System Using Serverless Architecture Why We Ripped Function Overloading Out of Our AI Toolchain Testing AI-Generated Code: How to Actually Know If It Works SaaS Churn Is Killing Your Business. Here Is What to Do About It (Without a Support Team) The Speed of AI Is No Longer Linear - And Self-Improving Models Are Why How to Implement RBAC for MCP Tools: A Practical Guide for Engineering Teams From Standard Quote to Persuasive Proposal: AI Automation for Arborists I built a CLI that scaffolds complete multi-tenant SaaS apps Axios CVE-2025–62718: The Silent SSRF Bug That Could Be Hiding in Your Node.js App Right Now The dashboard that ended our friendship Data Pipelines Explained Simply (and How to Build Them with Python) The Hidden Cost of AI Systems Nobody Talks About. undefined vs undeclared, and how typeof behaves Switching from file-based jobs to NATS/Kafka in Rust without changing code io_uring Adventures: Rust Servers That Love Syscalls Why Agentic AI is Killing the Traditional Database The POUR principles of web accessibility for developers and designers Quantum Neural Network 3D — A Deep Dive into Interactive WebGL Visualization How To Install Caveman In Codex On macOS And Windows Automation Pipeline Reliability: Why Your Workflow Breaks When Nobody Is Watching I Built an 'Open World' AI Coding Agent — It Works From ANY Folder From Freelancing to Product: A Tech Service Company's SaaS Transformation China's AI Giants: Adding Tencent Hunyuan & ByteDance Doubao to AI University (74 Providers) On the Vibe Coders and Their Lies clerk: Auto-Summarize Your Claude Code Sessions AI Weekly — 2026/04/10–04/17 | The Model Lockdown Is Here, but the Toolchain Is the Real Battleground AI 週報 — 2026/04/10–2026/04/17 模型封鎖潮來了,但工具鏈才是真戰場 Maybe this is how Open-Source apps are born... 🚀 Fine-Tune LLMs with LoRA and QLoRA: 2026 Guide tRPC v11 + Next.js App Router: End-to-End Type Safety Without the Boilerplate ShadCN UI in 2026: Why I Stopped Installing Component Libraries and Started Owning My Components SaaS Billing in React Server Components: Stripe + Supabase Without a Single `useEffect` Join our DEV Weekend Challenge — $1,000 in Prizes Across TEN winners! Submissions Due April 20 at 6:59 AM UTC. Implementing FSRS Spaced Repetition in Flutter + Supabase — Adding Memory Science to an AI Learning App "I Texted My Localhost From the Train — Claude Code Fixed the Bug Before I Got Home" I Built a Sales Prep AI and It Went Deeper Than Expected Design to Code #2: One JSON, Eleven Outputs Solving the 100M-Row Problem: A Summary Table Pattern for High-Volume Push Notification Logs Flutter Web With Wasm: What Actually Changes For Developers I Built 50 Royalty-Free Soundtracks for My Side Project in a Weekend Using AI Music Generation The Vibe Coding Security Checklist: 7 Things to Check Before You Ship Stop Letting Googlebot Guess Fix Your React App's SEO Right Desconstruindo o Streaming do LinkedIn: Como Criar um Engine de Extração de Vídeo de Alta Performance com HLS e FFmpeg (EDA Part-1) EDA (Exploratory Data Analysis) Explained With Real Life — Why Looking at Your Data Is the Most Important Step in Machine Learning Brand Relationship Management at Scale: Our 4-Touch Outreach System for 200+ Brands Why String.fromEnvironment() Might Return an Empty String in Dart JGuardrails 1.0.0 — Hardening Java LLM Apps Against Jailbreaks, Toxicity, and Prompt Injection Plan and Schedule a Full Week of Threads Content From One Claude Conversation Coding Cat Oran Ep3, Five Tables Changed Everything Updated: BFF Pattern I'm done watching freelancers get buried by 200 proposals. So I'm building the alternative. This is my first post BFS Algorithm in Java Step by Step Tutorial with Examples Tracking LLM Pricing Monthly: An Open Dataset for 22 AI Models How We Measure Content ROI on a Comparison Site: Revenue Attribution Without Perfect Data Introducing Nova AI Ops: The AI-Native Operating System for SRE Teams I built a free desktop video downloader for Windows — Grabbit How Talkie OCR Helps Vision-Impaired & Dyslexic Users Read the World Around Them VRCFaceTracking安装和iPhone面捕配置教程,有bug Even CrowdStrike Can't See Your Agents The Automation Gold Rush: What n8n Workflows and Claude Are Opening Up for Developers Right Now
Put a hard stop in front of your CrewAI crew's tool calls
Brian Hall · 2026-06-22 · via DEV Community

CrewAI makes it easy to stand up a crew. You give a few agents roles, hand them tools, let them delegate work to each other, and the thing mostly runs itself. That autonomy is the appeal. It's also the problem. Once a crew is moving, every agent in it can reach for a tool, and there's nothing between the model deciding to call something and the call actually happening.

The usual fix is a careful prompt and crossed fingers. Or a second LLM that "reviews" the action, which is hoping with extra latency. I wanted a check that doesn't depend on a model being in a good mood: something deterministic that runs before the tool call fires and gives a real answer. Allow it, hold it for a human, or block it.

That's what Faramesh does. It's open source, it works with CrewAI through a one-line wrapper, and this is the actual end-to-end setup, every command and every policy snippet pulled straight from how the tool really works.

The idea

A tool call is the moment an agent stops reasoning and starts doing. Reading a doc is one thing. Spending money, sending mail to a customer, or hitting a production API is another. Those are the moments worth putting a rule in front of.

Faramesh runs as a local daemon. Your whole policy lives in one file, governance.fms, and the daemon checks every tool call against it before the call runs. No LLM sits in that decision path, so the same action under the same policy always gets the same verdict. You get one of three:

  • permit the call runs
  • defer the call pauses and waits for a human to approve or reject
  • deny the call is blocked before it happens

The point is that it's deterministic. You can read the policy, reason about it, and know what it'll do. That's the whole difference from asking a second model to babysit the first one.

Install

Install the CLI:

curl -fsSL https://raw.githubusercontent.com/faramesh/faramesh-core/main/install.sh | bash
faramesh --version

Then add the SDK to your CrewAI project:

pip install faramesh-sdk crewai

Generate the policy

From the root of your project:

faramesh init

Faramesh inspects the repo, finds your framework, discovers your tools, and writes a starter governance.fms. The important part of the default: every discovered tool starts at defer. Nothing runs until you've reviewed it. That's the safe direction to fail.

Wire the crew

This is the only step that's CrewAI-specific, and it's small. You wrap each agent's tools in a GovernedToolSet and give that set an identity. Here's a crew before:

from crewai import Agent, Crew, Task
from crewai_tools import SerperDevTool, BraveSearchTool

researcher = Agent(
    role="researcher",
    tools=[SerperDevTool(), BraveSearchTool()],
)
writer = Agent(role="writer", tools=[])

crew = Crew(agents=[researcher, writer], tasks=[...])

And after:

from faramesh import GovernedToolSet
from crewai import Agent, Crew, Task

researcher_tools = GovernedToolSet(
    [SerperDevTool(), BraveSearchTool()],
    agent_id="research-crew/researcher",
)

researcher = Agent(role="researcher", tools=researcher_tools)
writer     = Agent(role="writer",     tools=[])

crew = Crew(agents=[researcher, writer], tasks=[...])

That's the whole integration. Use one GovernedToolSet per agent so each crew member gets its own identity in the policy. That's what lets you give the researcher and the writer different rules, which matters more than it sounds like, since in a crew the agents have genuinely different jobs and should have genuinely different permissions.

Write the rules, per role

Open governance.fms. Because each agent has its own id, you write a policy block per role. Here the researcher can search but nothing else, and the writer can't touch tools at all:

import "github.com/faramesh/faramesh-registry/frameworks/crewai@1.0.0"

agent "research-crew/researcher" {
  default deny

  rules {
    permit serper_search
    permit brave_search
  }

  rate_limit "*_search": 30 per minute

  budget daily {
    max       $20
    on_exceed defer
  }
}

agent "research-crew/writer" {
  default deny
  rules { }
}

A few things worth reading off that:

default deny means anything not explicitly allowed gets blocked. You opt tools in, you don't opt them out. Rules are checked top to bottom and the first match wins.

The rate_limit line caps both search tools at 30 calls a minute, so a confused agent can't hammer an API in a loop. The budget block puts a daily ceiling on spend and, when it's hit, defers instead of denying, the work pauses for a human rather than just dying. The writer's empty rule block plus default deny means it has no tool access at all, which is exactly right for an agent whose job is to write, not act.

Validate before you ship anything:

faramesh check
faramesh plan

check parses and type-checks the file. plan prints the exact decision diff, so you can see what changes before it's live.

Apply and run

Turn on enforcement and run the crew normally:

faramesh apply
python my_crew.py

A permit returns the tool result like nothing's there. A defer returns a structured response telling the agent its action is pending approval, the crew doesn't crash, the call just doesn't go through yet. You watch and clear the queue from another terminal:

faramesh approvals list
faramesh approvals approve apr-9001

Once approved, the agent's next attempt goes through. Or, if you've decided it should always be allowed, promote the rule to permit in the file and run faramesh apply again. One thing to know: apply is the only way to change the running policy. There's no quiet hot-reload where editing a file changes what your crew can do mid-run. You edit, you apply. It's deliberate on purpose.

Crews delegate, so the policy understands delegation

The thing that makes CrewAI CrewAI is agents handing work to each other. Faramesh models that directly. If your researcher delegates to your writer, you can bound what that delegation is allowed to carry:

agent "research-crew/researcher" {
  delegate {
    target_agent = "research-crew/writer"
    scope        = "read-only"
    ttl          = "5m"
  }
}

The daemon validates delegation against the crew's actual structure at runtime, so one agent can't quietly hand another a capability it wasn't granted. That's a failure mode specific to multi-agent setups, and it's nice to have it covered in the same file as everything else.

Why bother

Every decision the daemon makes also lands in a tamper-evident log you can verify offline with faramesh audit verify. That matters the day someone asks what your crew actually did and "I think the prompt told it not to" isn't a good enough answer.

None of this makes your agents smarter. It means the moments that carry real risk go through a deterministic rule you wrote and can read, instead of through luck. For a single agent that's useful. For a crew, where several agents are acting and delegating at once, it's the difference between a demo and something you'd leave running.

Faramesh is open source. The repo is at github.com/faramesh/faramesh-core if you want to poke around or break it. If you wire it into a crew and something's off, tell me. That's all super helpful feedback at this point.