Agentifact assessment — independently scored, not sponsored. Last verified Mar 8, 2026.
Inspect (AISI)
Open-source LLM evaluation framework by the UK AI Security Institute. Includes 100+ pre-built evals covering coding, agentic tasks, reasoning, knowledge, behavior, and multi-modal understanding. Features chainable solver components, MCP tool support, sandboxed execution, multi-agent primitives, and a VS Code extension plus web-based log viewer for monitoring runs. Free under open-source license.
Viable option — review the tradeoffs
You need reproducible, production-grade evaluations for LLMs and agents across coding, reasoning, agentic tasks, and safety behaviors, with sandboxing and rich observability.
Excellent reproducibility and depth for serious audits; runtime overhead from sandboxing and learning curve for custom solvers, but scales well with parallelism/caching.
You want to evaluate existing agents (LangChain/AutoGen) or custom workflows without rebuilding from scratch.
Seamless integration with full Inspect benefits; quirks include async function requirements and explicit state management.
Regulatory or safety audits demand tamper-proof logs, isolated tool execution, and advanced scoring beyond exact match.
Production-ready auditability trusted by AISI/METR/DeepMind; heavier compute for sandboxes but essential for untrusted agents.
Steep learning curve for custom evals
Core concepts (Task/Solver/Scorer) and async composition require Python proficiency; not 'plug-and-play' for simple benchmarks.
Sandbox runtime overhead
Docker/K8s isolation slows runs (esp. for high-parallelism agent evals); mitigate with caching, batching, and local vLLM for non-tool tasks.
Trust Breakdown
What It Actually Does
Inspect lets you test AI language models on coding, reasoning, safety, and agent tasks using ready-made benchmarks and custom setups. It runs evaluations reproducibly with datasets, model solvers, and scorers, plus logging for analysis.[1][7][8]
Open-source LLM evaluation framework by the UK AI Security Institute. Includes 100+ pre-built evals covering coding, agentic tasks, reasoning, knowledge, behavior, and multi-modal understanding. Features chainable solver components, MCP tool support, sandboxed execution, multi-agent primitives, and a VS Code extension plus web-based log viewer for monitoring runs.
Free under open-source license.
Fit Assessment
Best for
- ✓llm-evaluation
- ✓model-testing
- ✓agent-evaluation
- ✓knowledge-retrieval
Not ideal for
- ✗rate-limit-errors-with-high-parallelism
Known Failure Modes
- rate-limit-errors-with-high-parallelism
Score Breakdown
Protocol Support
Capabilities
Governance
- sandboxed-execution
- tool-approval-policies
- custom-tool-isolation