Here’s a compact, practical catalog of tools and libraries you can use to simulate, train, orchestrate, and evaluate AI agents. I’ve grouped them by purpose so you can pick what fits your project.

Environment / simulation platforms

• OpenAI Gym — Classic RL environments and API standard (easy to plug into many RL libraries). Good for toy problems and benchmarking.
• DeepMind Control Suite — Continuous-control tasks with high-quality physics; great for benchmarking control algorithms.
• Unity ML-Agents — Unity-based 3D environments with strong support for curriculum learning, imitation, and multi-agent setups. Good if you need custom visuals/physics.
• CARLA — High-fidelity autonomous driving simulator (sensor suites, traffic scenarios).
• Habitat (Meta) — Photorealistic 3D indoor navigation and embodied AI tasks.
• Isaac Sim / Isaac Gym (NVIDIA) — High-performance GPU-based physics for robotics and large-scale parallel sim.
• MuJoCo — Accurate continuous-control physics engine (widely used in robotics/control research).
• Webots — Open-source robot simulator for education and research.

Reinforcement learning libraries / agent training frameworks

• Stable Baselines3 — High-level, well-maintained PyTorch RL implementations (PPO, SAC, DQN, etc.). Easy-to-use for prototyping.
• RLlib (Ray) — Scalable RL training (distributed), good for large experiments and multi-agent setups.
• Dopamine — Research-oriented RL framework from Google for reproducible experiments.
• CleanRL — Minimal, readable implementations of RL algorithms (good for learning and reproducibility).

Multi-agent and population-based tools

• PettingZoo — Standardized API for multi-agent RL environments.
• MAgent — Research platform for many-agent systems and emergent behaviors.
• RLLib (again) — also supports multi-agent configurations at scale.

Agent orchestration / high-level agent frameworks (for language agents, tool use, planning)

• LangChain — Orchestration framework for building language-model-driven agents, chains, prompts, tool integrations, memory.
• LlamaIndex (now called LlamaHub/Indexing tools) — Retrieval and knowledge-augmented agent building (combines docs + LLMs).
• Haystack — Retrieval and QA pipelines, useful for tool-using agents with documents.
• BabyAGI / Auto-GPT-style projects — Open-source agent templates that chain tasks for autonomous workflows (prototype-level).
• Microsoft Bonsai (historical) / Azure ML agents — enterprise-grade agent orchestration (check current availability for cloud-managed offerings).

Evaluation, benchmarking, and safety tooling

• OpenAI Procgen / DeepMind Procgen-like environments — test generalization and robustness.
• GEM / BEHAVIOR / other benchmark suites — task-specific benchmarks for embodied agents, driving, etc.
• Safety Gym (OpenAI) — environments and metrics designed to test safe-constrained RL policies.

Simulation utilities and visualization

• TensorBoard / Weights & Biases — training metrics, visualizations, experiment tracking.
• PettingZoo/Stable-Baselines integrations — helpers to visualize episodes, replay buffers, etc.
• RViz / Gazebo — robotics visualization and integration with ROS.

Robotics stacks

• ROS (Robot Operating System) + Gazebo/Ignition — real-robot interfacing, sim-to-real workflows.
• MoveIt — motion planning stack integrated with ROS.

Language/Hybrid agents and tool-using frameworks

• OpenAI SDKs (and Agent APIs if available) — for connecting LLMs to actions/tools (check current API docs).
• LangChain (again) — connectors to tools, memory, planning, and action-execution loops.
• ReAct / Toolformer-style architectures — not a library but design patterns for combining reasoning and actions.

Supporting libraries and infrastructure

• PyTorch / TensorFlow — core DL frameworks.
• JAX / Haiku / Flax — high-performance research stacks, especially for large-scale parallelism.
• Docker / Kubernetes — containerized, scalable training and deployment.
• Ray (Tune/Serve) — distributed hyperparameter tuning, rollout storage, distributed training.

How to choose quickly

• Prototyping on standard RL tasks: Gym + Stable Baselines3.
• 3D/visual/custom worlds: Unity ML-Agents or Habitat.
• Autonomous driving: CARLA.
• Robotics with sim-to-real: Isaac Sim / MuJoCo + ROS.
• Large-scale distributed experiments: Ray RLlib + Tune.
• Language agents that call tools/actions: LangChain + LLM provider SDK.

If you want, I can:

• Recommend a specific stack given your use case (robotics, game AI, language agents, multi-agent social sims).
• Provide quick example code to run a simple RL agent in Gym or set up a LangChain agent that calls an external tool.