标签：#crew

A competitor intelligence agent enables real-time pricing visibility, automated positioning tracking, and structured competitor briefs for brands without manual research. At the center of these workflows are a researcher agent responsible for gathering data and an analysis agent responsible for generating a summary and a downstream report. However, one of the things that makes the researcher agent's output trustworthy is its retrieval layer, since a poor retrieval-layer output can make the analysis agent's recommendation questionable. If the researcher agent searches the webpage and retrieves a challenge page, an empty response, or blocked content, every downstream conclusion becomes less trustworthy. With a 99.93% success rate on protected websites, ZenRows provides the reliable retrieval layer that makes these workflows practical in production. This tutorial shows why a CrewAI researcher agent can fail on protected competitor pages. It starts with a custom ZenRows-based tool setup, then later shows the MCP server as an alternative approach. Prerequisites This tutorial works best with Python 3.10 or newer. If you are on an older Python version, create a dedicated virtual environment with a current Python installation to avoid dependency conflicts. Python 3.10 to 3.13. CrewAI requires this range. ZenRows API key. Create an account at zenrows.com and copy your key from the dashboard. This key authenticates every scrape the researcher agent runs for data extraction. Anthropic API key. The crew uses Claude to drive both agents. Generate a key in the Anthropic Console. Install dependencies and the required packages using pip install "crewai[anthropic]" crewai-tools zenrows python-dotenv . Create a .env file and save your API keys there. Why the built-in ScrapeWebsiteTool fails on competitor pages The problem, as established earlier, starts before the analysis. A CrewAI workflow depends primarily on the information collected, because a competitor intelligence agent relie

"It's very clear that in the United States there is a big need for an additional crew vehicle."

The company just raised $7 million in seed funding, and is launching its app for iPhone and Android on Tuesday.

Starliner's certification may be delayed to 2027, 10 years later than Boeing's original schedule.

What are MCP Servers? The Model Context Protocol (MCP) is an open standard that lets AI agents use external tools through a unified interface. Think of it as USB-C for AI — one protocol connects any AI client (Claude Desktop, Cursor, VS Code with Cline) to any tool or data source. I built three production-ready MCP servers and published them to PyPI and GitHub. Here's what they do and how to use them. 1. Web Search MCP Server uvx crewai-web-search-mcp Two tools: web_search(query) — Searches Google/SerpAPI and returns ranked results with snippets extract_content(url) — Fetches and extracts readable content from any web page Use cases: Ask your AI about current events, research competitors, pull documentation, verify facts in real time. { "mcpServers" : { "web-search" : { "command" : "uvx" , "args" : [ "crewai-web-search-mcp" ] } } } 2. Code Review Automation MCP uvx code-review-automation Three tools: review_code(diff) — Analyzes code changes for bugs, security issues, anti-patterns, style violations check_quality(path) — Runs static analysis and returns a quality report analyze_pr(diff) — Produces a structured review: what changed, what's risky, suggestions Use cases: Paste a PR diff and get an instant review. Catch issues before they reach production. 3. Document Intelligence Server uvx document-intelligence-server Three tools: extract_document(path) — OCR and text extraction from PDFs, scanned docs, images classify_document(path) — Identifies document type (invoice, report, contract, article) summarize_document(path) — Generates a structured summary from extracted content Use cases: Process uploaded PDFs, extract data from scanned forms, summarize long reports. Pricing All three servers use a shared credit system: Tier Price Credits Free $0 50 calls/day Starter $20 2,000 calls Pro $100 12,000 calls Buy credits once, use them across any server. Credits never expire. How it works: Install with uvx crewai-web-search-mcp Use 50 free calls per day — no key needed For u

Hermes-Crew Hybrid: A Hybrid Architecture for Secure Multi-Agent AI Workflows I built a hybrid system that combines a central orchestrator (Hermes) with temporary CrewAI micro-crews, protected by 3 layers of security. Here's what it does and why it matters. The Problem Multi-agent AI systems are powerful but dangerous. When you chain multiple agents together, a single compromised agent can poison the entire workflow. Existing solutions are either too heavy (enterprise PKI infrastructure) or too light (basic regex filters). The Solution: 3-Layer Security Layer 1 — Pre-execution (MCP Tool Auditor): Before any agent can register a tool, it's audited for malicious instructions. Layer 2 — Runtime (Agent Fixer Stage): Every output from every agent passes through a 3-stage pipeline (normalization → pattern matching → embeddings) in under 1ms. Layer 3 — Pre-commit (Code Safety Hook): Before any git commit lands, the diff is analyzed by CrewAI + Ollama local. Malicious code gets rejected automatically. Architecture Hermes (Director) │ ├── MCP Tool Auditor → verifies tools before registration │ ├── Execution: venv (fast) / Docker (isolated) / auto (smart) │ ├── Agent 1: Researcher │ ├── Agent 2: Analyst │ └── Agent 3: Writer │ ├── Security Gateway (Agent Fixer Stage) → filters output (<1ms) │ └── Consolidator → parses output + generates Obsidian notes What Makes It Different 1. Portable by design. Zero hardcoded paths. Every user configures their own .env . 2. Multi-model via LiteLLM. Works with Ollama local, OpenAI, Anthropic, Gemini, Groq, OpenRouter — any provider. 3. Local-first. Everything runs on the user's machine. No cloud dependencies required. 4. Obsidian integration. Every analysis generates a structured note with YAML frontmatter. Code Safety Hook in Action When you run git commit with malicious code: ❌ [ COMMIT RECHAZADO] Code Safety detected risks: → CrewAI detected vulnerabilities: VERDICT: FAIL → Agent Fixer Stage detected anomalies: High threat score: 1.05 Fo

"Artemis III will be an extraordinary demonstration of what is possible."

The space agency said Roscosmos discovered new leaks in the Russian service module.

If confirmed, it would be the fly's first breach of the US-Mexico border.