Framework for evaluating and improving agents
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Updated
Jul 12, 2026 - Python
Framework for evaluating and improving agents
A curated, non-BS library of the best resources for building and evaluating AI agents — papers, blogs, talks, tools, benchmarks. Maintained by BenchFlow.
A Universal Platform for Training and Evaluation of Mobile Interaction
A graphical interface for reinforcement learning and gym-based environments.
Interoperating between (Deep) Reiforcement Learning libraries
Gymnasium-style API standard for RL environment creation in JAX
Create new gridworld gym environments easily
Workspace manager for coding agents. Interactively solve and develop Harbor tasks.
Comprehensive AI agent evaluation platform — searchable benchmark catalog, comparison matrices, automated scanner, interactive dashboards, and community-curated best practices for LLM evaluation.
A lightweight, open-source framework that turns historical GitHub pull requests into reproducible, verifiable software-engineering tasks for training and evaluating coding agents.
Foundry Lite: a public runnable sample of Veyl’s local environment harness for software-engineering agent evals.
Outcome-verified agent trajectories, benchmarks, and RL environments — with a live leaderboard and a CI gate for your agents. Offline-first, MIT.
Surge AI — large-scale human-labeled data for LLM training
Open-source SDK (Apache-2.0): RL environments, conformal calibration, a TRL-compatible reward function, the Lean 4 formal track, and the verifiable/vlabs CLI.
Turn any real software into a replayable RL environment for training AI agents — deterministic replay, verifiable rewards, TRL & verifiers adapters.
Pure Go implementation of the Gymnasium RL environment API. 3–349× faster than Python.
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