AI agent tools for agent evaluation
Short answer: Our top pick for agent evaluation: trulens, with the highest AgentPick Score (75/100) among tools tagged "agent-evaluation". Runners-up: future-agi (74) and ClawBench (67). Compare what each does, its license and how recently it was updated below.
| # | Tool | What it does | Score | Stars | License | Updated |
|---|---|---|---|---|---|---|
| #1 | trulens | Evaluation and Tracking for LLM Experiments and AI Agents | 75/100 | ★ 3.6k | MIT | 13 Oct 2026 |
| #2 | future-agi | Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. Tracing · Evals · Simulations · Datasets · Ga… | 74/100 | ★ 2.1k | Apache-2.0 | 14 Oct 2026 |
| #3 | ClawBench | Open-source benchmark for browser AI agents on daily tasks. | 67/100 | ★ 1k | Apache-2.0 | 13 Oct 2026 |
| #4 | agentenv-framework | Creating realistic RL environments requires collaboration between researchers, engineers, and domain experts across many dimensions: artifacts, enviro… | 64/100 | ★ 297 | Apache-2.0 | 14 Oct 2026 |
| #5 | SkillEvaluator | Multi-tier framework for evaluating AI agent skills with quality gates, semantic overlap detection, synthetic evaluation dataset generation, and live … | 62/100 | ★ 568 | Apache-2.0 | 14 Oct 2026 |
| #6 | pandaprobe | open source agent engineering platform: traces, evals, and metrics to debug and improve your AI agents. Integrates with LangGraph, CrewAI, Claude Agen… | 61/100 | ★ 790 | Apache-2.0 | 4 Oct 2026 |
| #7 | any-agent | A single interface to use and evaluate different agent frameworks | 45/100 | ★ 1.2k | Apache-2.0 | 1 Oct 2026 |
| #8 | intelligent-audit-system | AuditPilot: auditable enterprise AI agents for evidence-grounded workflows, governed tools, evaluation harnesses, human review, and remediation delive… | 25/100 | ★ 1.2k | See repo | 12 Aug 2026 |
Source: each project's public GitHub repository (description, stars, license, last push). Listed tools aren't tested or ranked by AgentPick.
How to choose
- Read the license before using it commercially.
- Check how recently it was updated and how active its issues are.
- Run it on a small, low-risk task first.
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