Editorial illustration for Terminal-Bench 2.0 Unveils Harbor: Universal Container Agent Testing Platform
Harbor: Universal Container Agent Testing Breakthrough
Terminal-Bench 2.0 launches with Harbor, testing any container-installable agent
Container testing just got a serious upgrade. Terminal-Bench, a platform known for pushing software development boundaries, has dropped its 2.0 version with a powerful new tool called Harbor.
The startup is targeting a persistent challenge in AI and software development: how to fullly test container-based agents across different architectural frameworks. Harbor represents a strategic solution for developers wrestling with complex testing environments.
What makes Harbor intriguing isn't just its technical capabilities, but its promise of flexibility. Instead of forcing developers into rigid testing protocols, the platform offers a more adaptable approach to evaluating container-installable agents.
Early indications suggest Harbor could dramatically simplify what's traditionally been a complex, time-consuming process. By providing scalable testing pipelines and custom benchmark creation, Terminal-Bench is neededly giving developers a Swiss Army knife for agent evaluation.
The platform's internal development hints at serious engineering muscle behind the release. But the real test will be how quickly developers adopt this new approach to container agent testing.
Designed to generalize across agent architectures, Harbor supports: Evaluation of any container-installable agent Scalable supervised fine-tuning (SFT) and reinforcement learning (RL) pipelines Custom benchmark creation and deployment Full integration with Terminal-Bench 2. Harbor was used internally to run tens of thousands of rollouts during the creation of the new benchmark. It is now publicly available via harborframework.com, with documentation for testing and submitting agents to the public leaderboard.
Early Results: GPT-5 Leads in Task Success Initial results from the Terminal-Bench 2.0 leaderboard show OpenAI's Codex CLI (command line interface), a GPT-5 powered variant, in the lead, with a 49.6% success rate -- the highest among all agents tested so far. Close behind are other GPT-5 variants and Claude Sonnet 4.5-based agents. Top 5 Agent Results (Terminal-Bench 2.0): Codex CLI (GPT-5) -- 49.6% Codex CLI (GPT-5-Codex) -- 44.3% OpenHands (GPT-5) -- 43.8% Terminus 2 (GPT-5-Codex) -- 43.4% Terminus 2 (Claude Sonnet 4.5) -- 42.8% The close clustering among top models indicates active competition across platforms, with no single agent solving more than half the tasks.
Submission and Use To test or submit an agent, users install Harbor and run the benchmark using simple CLI commands. Submissions to the leaderboard require five benchmark runs, and results can be emailed to the developers along with job directories for validation. harbor run -d terminal-bench@2.0 -m "<model>" -a "<agent>" --n-attempts 5 --jobs-dir <path/to/output> Terminal-Bench 2.0 is already being integrated into research workflows focused on agentic reasoning, code generation, and tool use.
According to co-creator Mike Merrill, a postdoctoral researcher at Stanford, a detailed preprint is in progress covering the verification process and design methodology behind the benchmark. Aiming for Standardization The combined release of Terminal-Bench 2.0 and Harbor marks a step toward more consistent and scalable agent evaluation infrastructure.
Terminal-Bench's Harbor represents a significant step forward in container agent testing. The platform offers unusual flexibility by supporting evaluation of any container-installable agent across different architectures.
Researchers and developers now have a strong tool for creating custom benchmarks and running complex testing pipelines. Its support for supervised fine-tuning and reinforcement learning methods suggests a versatile approach to agent development.
The framework's internal track record is promising. Terminal-Bench used Harbor to execute tens of thousands of rollouts during benchmark creation, indicating real-world stress testing capabilities.
Public availability through harborframework.com means the tech community can now access this sophisticated testing platform. Developers can potentially simplify agent evaluation processes that were previously fragmented or complex.
Still, questions remain about how different agent types will perform under Harbor's generalized testing environment. The platform's true potential will emerge as more teams experiment and submit their container-based agents.
For now, Harbor offers an intriguing solution to a persistent challenge in AI agent development: creating standardized, scalable testing frameworks that work across diverse computational architectures.
Further Reading
- Demystifying evals for AI agents - Anthropic
- Artificial Analysis: Independent LLM Evals as a Service - Latent Space
- Blogmarks that use markdown - Simon Willison
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Common Questions Answered
How does Harbor support testing container-based AI agents across different architectures?
Harbor provides a universal testing platform that can evaluate any container-installable agent, regardless of its underlying architectural framework. The tool supports scalable supervised fine-tuning (SFT) and reinforcement learning (RL) pipelines, enabling comprehensive and flexible agent testing across diverse environments.
What key capabilities does Harbor offer for researchers and developers?
Harbor enables custom benchmark creation and deployment, allowing developers to design specialized testing environments for container agents. The platform was internally used to run tens of thousands of rollouts during benchmark development, demonstrating its robust testing capabilities and versatility.
Where can developers access the Harbor testing framework?
The Harbor framework is publicly available via harborframework.com, which provides comprehensive documentation for testing and submitting agents to the public platform. Developers can explore the framework's features and integration methods through the official website.