arc-agi-benchmarking

VitaBench evaluates agents in real-world service scenarios such as delivery, in-store operations, and OTA, using databases, API tools, and 100 tasks per domain. Configure the models, run the vita command, save simulations under data/simulations, and re-evaluate existing runs when needed. The current release updates datasets, tools, evaluator models, and metrics, so scores should be interpreted alongside the chosen configuration.

AgencyBench V2 covers six capabilities across game, frontend, backend, code, research, and MCP work, with 32 real-world long-horizon scenarios and 138 tasks. A user-simulation agent supplies iterative feedback while a Docker sandbox runs functional and visual checks using rule-based, vision-based, and LLM judges. Scenarios require model and evaluator configuration, and game or frontend cases also need the remote Docker sandbox.

skill-optimizer provides both an agent skill for authoring eval suites and a local CLI for executing them in Docker against OpenRouter models. Cases and suites can be repeated across trials to benchmark skill behavior, debug failures, and compare reliability across models. The workbench can also expose hidden services such as MCP servers to the agent during a controlled test.

TRAIL is a benchmark dataset with 148 annotated agent execution traces and 841 errors across reasoning, execution, and planning. The traces come from software-engineering and information-retrieval tasks, and the evaluation scripts run a LiteLLM-compatible model on GAIA or SWE Bench splits before calculating scores. It is intended for studying error localization in complex agent workflows.