Sourcify
Effortlessly find and manage open-source dependencies for your projects.

The open-source framework for rigorous large language model evaluation and safety testing.

Inspect is a state-of-the-art open-source evaluation framework developed by the UK AI Safety Institute to standardize the measurement of large language model (LLM) capabilities and safety profiles. Built on a modular Python architecture, Inspect allows researchers and AI architects to define 'Tasks' comprising three core components: Solvers (the logic driving the model), Scorers (the metrics for success), and Datasets (the evaluation samples). Its technical architecture is specifically designed to handle complex, multi-turn agentic workflows where models must use tools, interact with sandboxed environments, and solve multi-step problems. By 2026, Inspect has transitioned from a government research tool to the industry standard for enterprise LLM validation, bridging the gap between raw model performance and production-ready safety requirements. It provides native support for virtually all major model providers, including OpenAI, Anthropic, Google, and local vLLM/Ollama deployments, ensuring a unified interface for cross-model benchmarking. The framework's ability to generate high-fidelity 'Inspect Logs' enables deep forensic analysis of model reasoning paths, which is critical for compliance with emerging global AI regulations like the EU AI Act.
Inspect is a state-of-the-art open-source evaluation framework developed by the UK AI Safety Institute to standardize the measurement of large language model (LLM) capabilities and safety profiles.
Explore all tools that specialize in red teaming. This domain focus ensures Inspect delivers optimized results for this specific requirement.
Executes model-generated code in isolated Docker containers to safely test agentic capabilities without risking host system integrity.
Allows for complex evaluation pipelines where one model critiques another, or where multiple scoring metrics are aggregated into a weighted safety score.
A built-in web-based GUI for deep-diving into individual evaluation trials, showing full prompt/response history and internal solver state.
A middleware-like architecture where developers can chain multiple solvers (e.g., Chain-of-Thought, Self-Correction) before reaching the scorer.
Pre-configured tasks for evaluating common safety risks like cyber-offense, chemical/biological weapon knowledge, and persuasion.
Asynchronous execution of evaluation trials across multiple model instances to maximize throughput and minimize wall-clock time.
Logs are stored in a standardized JSON format that can be easily ingested by downstream observability platforms like Arize or LangSmith.
Install the framework via 'pip install inspect-ai'.
Configure environment variables for model provider API keys (e.g., OPENAI_API_KEY).
Define a evaluation dataset in JSON, CSV, or S3/HuggingFace formats.
Implement a custom 'Solver' to define the model's prompting strategy or tool-use logic.
Create a 'Scorer' to evaluate model outputs using exact match, regex, or LLM-as-a-judge.
Define a 'Task' function decorated with @task to bundle the dataset, solver, and scorer.
Execute the evaluation via the CLI: 'inspect eval task.py --model openai/gpt-4o'.
Launch the Inspect Log Viewer using 'inspect view' to visualize trial results.
Iterate on prompt templates and model parameters based on scoring metrics.
Integrate the task into a CI/CD pipeline for automated regression testing.
All Set
Ready to go
Verified feedback from other users.
"Highly praised by AI researchers for its 'clean' API and the quality of the Log Viewer. Viewed as more rigorous than simple prompt-management tools."
Post questions, share tips, and help other users.
Effortlessly find and manage open-source dependencies for your projects.

End-to-end typesafe APIs made easy.

Page speed monitoring with Lighthouse, focusing on user experience metrics and data visualization.

Topcoder is a pioneer in crowdsourcing, connecting businesses with a global talent network to solve technical challenges.

Explore millions of Discord Bots and Discord Apps.

Build internal tools 10x faster with an open-source low-code platform.

Open-source RAG evaluation tool for assessing accuracy, context quality, and latency of RAG systems.

AI-powered synthetic data generation for software and AI development, ensuring compliance and accelerating engineering velocity.