Below is a practical, category‑organized list of the best kinds of tools and specific projects you can use to evaluate AI agents (reasoning, planning, tool use, and environment interaction). I focus on well‑known, maintained tools and on the kinds of evaluation you’ll want to run.

Summary (quick):

• For automated model/agent scoring: OpenAI Evals, EleutherAI / LM-eval-harness, Hugging Face Evaluate.
• For agent benchmarks: HumanEval, MBPP, BIG-bench, MT-Bench / AGIEval / other agent‑focused suites.
• For simulation/environments: OpenAI Gym, MiniGrid/MiniHack, Procgen, Habitat, DeepMind Lab.
• For behavior / tool use / web automation testing: Playwright / Selenium (web), Dockerized simulators, Task environments like ALFWorld or BabyAI.
• For human evaluation & labeling: Scale, Mechanical Turk, Labelbox, Toloka.
• For observability & experiment tracking: Weights & Biases, MLflow, Neptune, Prometheus + Grafana.
• For adversarial / red‑teaming testing: automated prompt fuzzers, adversarial example libraries, and custom scenario generators.
• For reproducibility & CI: GitHub Actions, reproducible containers, tests that run evaluation suites automatically.

1. Automated evaluation frameworks (fast way to compare many models/agents)

• OpenAI Evals — open-source evaluation harness for creating tasks, checking outputs, and running automated / human evals. Good for scoring agent responses and building custom tests.
• LM-eval-harness (EleutherAI) — classic toolkit for running many language model benchmarks (HumanEval, GSM8K, etc.) against models.
• Hugging Face Evaluate (and Datasets) — reusable metrics (BLEU, ROUGE, EM, accuracy, etc.) and dataset integration for batch evaluation. Why use: fast, scriptable, integrates with many models and metrics.

2. Benchmarks and standardized test suites (for capability comparisons)

• HumanEval / MBPP / CodeEval — for code‑writing agents.
• BIG-bench (and BIG-bench Lite) — diverse tasks for reasoning and general capabilities.
• MT-Bench, AGIEval, other agent/assistant specific benchmark collections — focus on instruction following, multi‑turn, tool use. Why use: standardized comparisons, known tasks that others report on.

3. Agent‑specific evaluation & task suites (planning, tool use, multi‑step)

• OpenAI / third‑party agent benchmarks (look for “agent” or “assistant” test suites like MT-Bench, AgentBench variants).
• Simulated task environments: ALFWorld, BabyAI, MiniHack — evaluate agents that interact with simulated worlds and require multi‑step planning. Why use: tests for long‑horizon planning, action sequencing, tool chaining.

4. Simulation & environment libraries (for embodied or interactive agents)

• OpenAI Gym — classic reinforcement learning environments.
• MiniGrid / MiniHack — lightweight gridworld tasks for planning and perception.
• Procgen / DeepMind Lab / Habitat — more complex visuals/embodiment.
• Web automation environments: use Playwright or Selenium to create browser‑based task environments for web‑capable agents. Why use: controlled, reproducible environments to test interaction and robustness.

5. Human evaluation & crowdsourcing (gold standard for many agent behaviors)

• Amazon Mechanical Turk, Scale AI, Toloka, Labelbox — collect human judgments on helpfulness, safety, faithfulness, preference, and naturalness.
• Pairwise A/B comparison setups and rubric‑based scoring (consistency, correctness, harm, hallucination). Why use: measure subjective qualities and nuanced failure modes that automated metrics miss.

6. Observability, metrics & experiment tracking (for continuous evaluation)

• Weights & Biases, MLflow, Neptune — track runs, metrics, and outputs; store sampled agent trajectories.
• Prometheus + Grafana — monitor production agents (latency, error rate, traffic, resource usage).
• Sentry / Honeycomb — track runtime errors, exceptions from agent tool calls. Why use: understand performance drift, regressions, and production issues.

7. Robustness, safety, and adversarial testing

• Prompt fuzzers / adversarial example generators — automatically create edge prompts to induce hallucination or failure.
• Red‑teaming frameworks / manual red team playbooks — structured tests for harmful outputs, jailbreaks, misinformation.
• Stress tests: long‑conversation drift, instruction ambiguity, compositional tasks, corrupted inputs, truncated tool outputs. Why use: identify catastrophic failure modes and improve guardrails.

8. Metrics and what to measure (practical checklist)

• Task success / accuracy / pass@k (for code), exact match (EM), F1 for extraction tasks.
• Latency, throughput, token cost (compute/cost tradeoffs).
• Hallucination rate (factually incorrect assertions per 100 responses).
• Instruction compliance / helpfulness / user satisfaction (via human rating).
• Safety metrics: rate of unsafe outputs, policy violations, vulnerability to jailbreaks.
• Robustness metrics: performance under noisy inputs, paraphrases, or adversarial prompts.

9. Production integration & CI testing

• Unit tests for agent tool wrappers, synthetic scenario tests (e.g., “agent should call search tool when question contains certain intents”).
• End‑to‑end regression tests that run a small set of benchmark tasks on every model update via GitHub Actions or similar. Why use: prevent regressions, ensure tool interfaces remain compatible.

10. Practical workflow / recommended stack (small teams)

• Rapid prototyping + automated scoring: OpenAI Evals or LM-eval-harness + Hugging Face datasets.
• Human validation: small MTurk/Toloka studies for subjective metrics.
• Monitoring in production: Weights & Biases for experiments + Prometheus/Grafana for runtime.
• Red‑teaming and safety: manual playbooks + automated fuzzers.
• Reproducibility: Docker images, pinned dataset versions, CI integration.

11. Tips for meaningful evaluation

• Combine automated metrics with human ratings — each catches different failures.
• Design scenario‑based tests that reflect your real user tasks (not only academic benchmarks).
• Measure cost and latency along with capability — different tradeoffs matter for agents that call tools.
• Track a small set of "canaries" (representative tasks) for daily CI checks, and run full suites less frequently.
• Log full agent traces (observations, tool calls, tool outputs, final responses) so you can debug failures later.

If you want, I can:

• Recommend a concrete starter stack (specific repo links, Docker examples, CI steps) tailored to your language/model/framework choices (e.g., LLM provider, whether agent uses tool calls or a browser).
• Draft a minimal evaluation checklist and CI pipeline you can drop into a repo.

Which would you prefer?