/agent by Firecrawl vs A.I.G (AI Infra Guard): Features, Pricing & Which Is Better (2026)
A side-by-side comparison of /agent by Firecrawl and A.I.G (AI Infra Guard) — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
/agent by Firecrawl
Firecrawl
Web crawling, scraping, and search API delivering clean, structured web data for AI agents and builders.
Key features
- Web Crawling & Scraping API: Programmatic endpoints to crawl and scrape web pages at scale, returning extracted content for downstream use.
- Search API: Full-text search over indexed web content to retrieve relevant pages and snippets for reasoning and retrieval-augmented workflows.
- Scalable Infrastructure: Engineered to handle large-scale web coverage and high-throughput requests to deliver broad internet coverage to applications.
- Clean Structured Outputs: Normalizes and structures scraped web data so it is ready for machine consumption and reasoning without extensive preprocessing.
- Agent Integration: Designed to feed AI agents and builders with ready-to-use web knowledge for tasks like question answering, decision-making, and automation.
- Developer-Friendly Access: Exposes programmatic access and tooling (APIs and docs) to integrate web data into pipelines and agent architectures.
- Crawl and scrape web pages at scale
- Structured, cleaned outputs ready for reasoning
- Search API over crawled/indexed web content
- Credits-based consumption model (referenced)
- Enterprise and custom integrations
- API endpoints for crawling and scraping web content at scale
- Search/indexing capabilities across crawled content
- Returns clean, structured, normalized data ready for reasoning
- Designed for integration with AI agents and builder workflows
- Scalable infrastructure for large-volume web data collection
Best for
- Feeding AI Agents with Web Knowledge: Provide agents with up-to-date, structured web content to answer questions, follow news, or perform tasks requiring current information.
- Retrieval-Augmented Generation: Augment large language models with precise web documents and snippets for improved factuality and context.
- Large-Scale Research & Data Collection: Collect and normalize web content across many sites for analysis, training data, or academic research.
- Market & Competitive Intelligence: Aggregate public web signals, product pages, and news to monitor competitors and market trends at scale.
- Content Aggregation & Curation: Gather and standardize content from multiple sources for feeds, summaries, or curated knowledge bases.
- Real-Time Web Monitoring: Track changes on web pages and surface updated content to applications and workflows that require timely information.
- Feeding up-to-date web content to conversational agents
- Large-scale data extraction for ML training
- Building search experiences over live web data
- Automating monitoring and intelligence from public web sources
- Feeding up-to-date web knowledge to conversational agents and assistants
- Building search and discovery features over live web content
- Extracting structured data from websites for ML training and analytics
- Monitoring and alerting on web content changes for compliance or brand monitoring
- Augmenting retrieval-augmented generation (RAG) pipelines with fresh web sources
A.I.G (AI Infra Guard)
Tencent Zhuque Lab
Tencent's open-source AI red teaming platform for scanning agents, agent skills, MCP servers and AI infrastructure, plus LLM jailbreak evaluation.
Key features
- Agent Skills Scan: Audits agent skill packages against a nine-category risk taxonomy aligned with the public SkillTrustBench T01-T09 classification, including detection of .pyc bytecode bypasses and charset smuggling.
- MCP Server Scan: Inspects MCP servers for threats such as tool poisoning, credential exfiltration and command injection, with tool whitelisting to prevent remote code execution during dynamic scanning.
- AI Infrastructure Vulnerability Scanning: Checks deployed AI components against a library that has grown to roughly 130 components and over 2,000 CVE rules, covering frameworks such as llama.cpp.
- Jailbreak Evaluation: Runs single-turn jailbreak operators plus multi-turn attack techniques including Many-Shot, PAIR, GOAT and ActorAttack to measure a model's resistance.
- Agent Scan with OWASP Coverage: Assesses running agents using OWASP-derived skills and web exfiltration detection, with a dedicated agent red team skill for comprehensive assessment.
- Standalone Scanner CLIs: skill-scan, mcp-scan and agent-scan each install as an independent command-line tool so scans can be wired directly into enterprise CI/CD pipelines.
- Docker Deployment with Web UI: Deploy the full platform with Docker on 4GB+ RAM and reach the web interface at localhost:8088, or use a one-click install script or a source build.
- AI Security Skill Market: A catalog of official security scanning skills, with the frontend fully open-sourced and integration available from OpenClaw chat via the aig-scanner skill.
Best for
- Pre-Deployment Agent Audit: Scan an internally built agent and its skill bundle for prompt injection, exfiltration and privilege risks before it is released to staff.
- MCP Supply Chain Review: Vet third-party MCP servers for tool poisoning and credential exfiltration before connecting them to production assistants.
- CI/CD Security Gate: Run skill-scan as a standalone CLI on every pull request so risky agent skills fail the build rather than shipping.
- Model Safety Benchmarking: Measure how a deployed LLM holds up against single and multi-turn jailbreak techniques before and after guardrail changes.
- AI Infrastructure Patch Triage: Inventory AI serving components and match them against the CVE rule library to prioritise which hosts need patching.
- Security Research and Reporting: Use the open scan engines and SkillTrustBench alignment as a reproducible basis for internal or published AI security research.