Inspect AI
Open-source framework for large language model evaluation developed by the UK AI Safety Institute
2.5K+
GitHub Stars
50+
Built-in Benchmarks
UK AISI
Government-Backed
Overview
Inspect AI is an open-source framework for evaluating large language models, developed and maintained by the UK AI Safety Institute (AISI). It provides a comprehensive Python-based toolkit for creating, running, and analyzing evaluations with built-in support for popular benchmarks like MMLU, GSM8K, HellaSwag, and ARC. The framework emphasizes reproducibility, extensibility, and safety-focused evaluation with features like sandboxed code execution, multi-turn agent tasks, and detailed scoring mechanisms. Inspect supports all major model providers including OpenAI, Anthropic, Google, Mistral, and local models via Ollama or HuggingFace.
The Verdict
Who Should Use Inspect AI?
Best For
- AI safety researchers and red teamers
- Teams needing reproducible evaluations
- Organizations building custom benchmarks
- Multi-model comparison studies
- Government and compliance-focused teams
Not Ideal For
- Simple prompt testing (use Promptfoo)
- RAG-specific evals (try RAGAS)
- Non-Python teams (Python-only)
- CI/CD-first workflows (less integrated)
What's Great
- Government-backed with strong safety focus
- 50+ built-in benchmark implementations
- Sandboxed code execution for agent evals
- Multi-turn conversation support
- Extensible solver and scorer architecture
- Provider-agnostic model support
- Detailed logging and analysis tools
Watch Out For
- Python-only (no JavaScript/TypeScript SDK)
- Steeper learning curve than simpler tools
- Less CI/CD integration out of the box
- Smaller community than commercial tools
- Documentation still maturing
Pricing
Open Source
$0
MIT License, full features, unlimited use
Community
Free
GitHub support, documentation
View all features & details
Core Features
- Declarative task definitions
- Built-in benchmark suite (MMLU, GSM8K, etc.)
- Multi-turn agent evaluations
- Sandboxed code execution
- Custom solver chains
- Model-graded scoring
- Detailed logging and replay
- Parallel evaluation runs
Model Providers
- OpenAI (GPT-4, GPT-4o)
- Anthropic (Claude 3.5, Claude 4)
- Google (Gemini Pro, Ultra)
- Mistral AI
- Azure OpenAI
- AWS Bedrock
- Ollama (local models)
- HuggingFace Transformers
Built-in Benchmarks
- MMLU (Massive Multitask)
- GSM8K (Math reasoning)
- HellaSwag (Commonsense)
- ARC (Reasoning)
- TruthfulQA
- HumanEval (Code)
- GPQA (Graduate-level QA)
- SWE-bench (Software engineering)
Platforms
- Python 3.10+
- pip / conda install
- Docker support
- VS Code extension
- CLI interface
- Jupyter notebooks
How It Compares
| Feature | Inspect AI | Promptfoo | DeepEval | OpenAI Evals |
|---|---|---|---|---|
| Open Source | Yes (MIT) | Yes (MIT) | Yes | Yes |
| Language | Python | JS/TS | Python | Python |
| Built-in Benchmarks | 50+ | 10+ | 15+ | 20+ |
| Agent Evals | Multi-turn + sandbox | Basic | Basic | Limited |
| Safety Focus | UK AISI backed | General | General | OpenAI focus |
| CI/CD Integration | Manual | Native | Pytest | Basic |
| Model Providers | All major | All major | All major | OpenAI-first |
| Best For | Safety research | CI/CD testing | Unit tests | OpenAI apps |
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