A.I.G (AI Infra Guard) vs RAGFlow: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of A.I.G (AI Infra Guard) and RAGFlow — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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.
RAGFlow
InfiniFlow
Open-source Retrieval-Augmented Generation engine combining RAG and agent capabilities to provide a richer context layer for LLMs.
Key features
- Retrieval-Augmented Pipeline: Implements end-to-end RAG flows that retrieve relevant document segments and augment LLM prompts with high-quality contextual information to improve response accuracy.
- Agent Integration: Provides mechanisms to orchestrate agent workflows that consume retrieved context for multi-step reasoning, tool invocation, and dynamic decision-making.
- Deep Document Understanding: Parses and encodes documents into semantic chunks to enable precise retrieval and reduce hallucination by supplying targeted context to models.
- Dockerized Deployment & Dev Tools: Includes Dockerfiles, docker-compose configurations, and helper scripts (e.g., download_deps.py) to simplify local setup, testing, and production deployment.
- Open-Source and Extensible: Released under Apache-2.0, with source code and docs available on GitHub for contribution, customization, and on-premise hosting.
- Documentation Sync & Website: Maintains a separate docs repository (ragflow-docs) and a synced documentation site (ragflow.io) for user guides and reference material.
- Retrieval-Augmented Generation engine combining retrieval with generation to ground LLM outputs
- Agent-style capabilities to enable multi-step or tool-augmented workflows
- Deep document understanding and processing for improved retrieval relevance
- Docker-based build and deployment (Dockerfiles and docker-compose examples, including macOS compose file)
- Repository-provided scripts for dependency/download automation (e.g., download_deps.py)
- Documentation site repository (ragflow-docs) synced with main project for usage and deployment guidance
- Apache-2.0 open-source licensing for self-hosting and modification
Best for
- Contextual Customer Support: Powering knowledge-base Q&A systems by retrieving relevant product docs and augmenting LLM responses with exact excerpts.
- LLM-Powered Assistants: Enhancing virtual assistants with up-to-date enterprise documentation and multi-step agent workflows to perform actions and fetch evidence.
- Document-Centric Automation: Automating processes that require reading, summarizing, and acting on large collections of documents using agents that leverage retrieved context.
- Research & Local Evaluation: Running self-hosted RAG experiments and evaluations with Docker-based setups for reproducible research and debugging.
- Safe Upgrades & Maintenance: Managing upgrades and deployments (via repo workflows and docker setups) while preserving indexed data and configuration during updates.
- Building LLM-powered chatbots and assistants with grounded knowledge from document stores
- Document question-answering and knowledge retrieval pipelines
- Enterprise knowledge management and searchable knowledge bases
- Augmenting LLM prompts with relevant context for improved accuracy
- Research and prototyping of RAG and agent-based LLM workflows