July 2026 monthly summary for NVIDIA-NeMo/Gym focusing on Claude Code Agent improvements and benchmarking integrity. Key deliverable: Enabled unlimited turns for Claude Code Agent by making max_turns optional and omitting the --max-turns flag when null, restoring the agent’s native unlimited behavior and ensuring fair benchmarking across evaluation suites. What changed: - Adjusted harness to honor optional max_turns and only append --max-turns when not None; updated anyterminal_claude_code.yaml to max_turns: null. - Documentation and configuration files updated to reflect unlimited default behavior and usage guidelines. Business value: - Ensures unbiased performance comparisons in Terminal-Bench and SWE-bench, reducing skew from a hardcoded turn cap. - Improves reproducibility and lowers setup friction for evaluators and downstream pipelines. Technologies/skills demonstrated: - Python CLI flag handling and optional parameters, YAML/config management, and documentation updates. - Version control discipline with a focused, single-PR change that preserves backward compatibility where feasible. Top 3-5 achievements: 1) Claude Code Agent unlimited turns enabled; harness now omits --max-turns when null. 2) Harness/config/docs updated to reflect unlimited default (null means unlimited). 3) Benchmarking fairness preserved across evaluation suites. 4) Commit dc9be355da83ec88b4c89f711a2bdc7c0f23cbda documented and prepared for review. 5) Reusable config changes reduce future setup friction for evaluators.

June 2026 focused on delivering a scalable, containerized evaluation framework for Gym agents within NVIDIA-NeMo/Gym. Delivered the anyterminal_agent feature to run and evaluate arbitrary Gym agents inside a single TerminalBench task container, enabling sequential execution and automated test verification. This work supports multiple agent implementations (e.g., Hermes, Claude_code_agent) and includes flexible configuration for agent swapping, dataset preparation, and container image management. Implemented test isolation and deterministic reward collection to improve reproducibility and security. Updated Hermes agent configuration to expose Hermes 2.1 options and adjusted sensible defaults (max_turns, terminal_timeout) to enhance stability and performance. Enabled runtime caching and flexible image handling via Harbor-based dataset prep, Apptainer/SIF options, and per-task config. Overall, the month delivered a reproducible, scalable evaluation pipeline with cross-agent support and clear business value for benchmarking and experimentation.