A.I.G (AI Infra Guard) vs Trulens: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of A.I.G (AI Infra Guard) and Trulens — 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.
Trulens
TruEra
Open-source toolkit to instrument, evaluate, and track LLM applications with feedback functions and dashboard-driven comparisons.
Key features
- Fine-Grained Instrumentation: Records calls across prompt, model, retriever, and knowledge-source boundaries to capture full context for each LLM interaction and enable detailed post-hoc analysis.
- Feedback Functions Framework: Pluggable evaluators (feedback functions) that run automatically alongside app executions to check for metrics like groundedness, helpfulness, and safety and flag failing responses.
- RAG-Focused Tooling: Built-in patterns and examples for Retrieval-Augmented Generation workflows (the RAG Triad) to evaluate retriever effectiveness and end-to-end grounding of responses.
- Dashboard & Leaderboards: A web UI to view runs, compare app versions, surface failure modes, and maintain leaderboards for experiments and evaluation metrics.
- Provider & Stack Agnostic Integrations: Support for multiple model providers and orchestration layers (examples and issue threads reference OpenAI, Ollama, Gemini, LangChain adapters), allowing reuse across different stacks.
- Virtual Records & Simulation: Utilities like TruVirtual and VirtualApp to create virtualized records for offline testing and deterministic evaluation of feedback functions.
- Observability & OTEL Plans: Design docs and a PRD for OpenTelemetry integration to standardize spans and make instrumentation more debuggable and extensible.
- Package Distribution & Quickstart: Installable Python package (pip install trulens) with quick usage examples to instrument a prototype and start collecting evaluations rapidly.
- Fine-grained, stack-agnostic instrumentation to capture app records and interactions with LLMs and retrievers
- Configurable feedback functions for automated evaluation (e.g., groundedness, correctness, custom metrics)
- Support for virtual apps and virtual records to simulate and evaluate pipelines
- Integrations/providers for multiple LLM endpoints (OpenAI, Azure OpenAI, LiteLLM, Ollama, Gemini, TruLlama) and retriever backends
- Dashboard/UI for visualizing runs, leaderboards, token usage and cost metrics
- Experiment tracking and run comparison across app versions and configurations
- Python package available on PyPI (pip install trulens) and hosted source/issue tracker on GitHub
- Provider-specific feedback provider classes (e.g., trulens_eval.feedback.provider.openai.AzureOpenAI)
- Support for popular stacks like LangChain and vector stores (examples include Pinecone integration)
- Extensible feedback/provider architecture to add custom evaluators and endpoints
Best for
- Instrumenting LLM Apps: Add TruLens instrumentation to a RAG or chat app to automatically record prompts, model outputs, retriever calls, and metadata for later analysis.
- Automated Feedback Evaluation: Run feedback functions on each recorded run to detect hallucinations, grounding failures, or policy/safety violations during CI or experimentation.
- Model and Prompt Comparison: Use the dashboard and leaderboards to compare different model families, prompt templates, or retriever configurations side-by-side using consistent metrics.
- Offline Testing with Virtual Records: Create VirtualApp/VirtualRecord datasets to reproduce and test failure modes offline and validate feedback function fixes before deployment.
- Observability Integration: Integrate TruLens traces with OpenTelemetry (or other observability tooling) to align LLM evaluations with standard telemetry and tracing pipelines.
- Cost & Token Monitoring: Track token usage and cost metrics across different providers and model configurations to optimize for budget and performance.
- Debugging Provider Integrations: Use recorded traces and feedback outputs to diagnose provider-specific issues (e.g., adapter errors for OpenAI, LangChain, Ollama) and iterate on provider configs.
- Instrumenting and evaluating RAG systems end-to-end during development
- Running automated feedback-based evaluations of LLM outputs (groundedness, helpfulness, safety checks)
- Tracking experiments and comparing different model/prompt/knowledge-source configurations
- Monitoring token usage and cost metrics per provider and run
- Debugging provider integrations and feedback functions during development
- Creating virtualized test runs to validate evaluation logic without live calls