Commit ed27b826 introduced patch-tool redaction corruption that:
- Replaced max_token_length=16000 with max_token_length=***
- Truncated api_key=os.getenv(...) to api_key=os.get...EY
- Truncated tokenizer_name to NousRe...1-8B
- Deleted 409 lines including _run_tests(), _eval_with_timeout(),
evaluate(), wandb_log(), and the __main__ entry point
Restores the file from pre-corruption state (ed27b826^) and re-applies
the two legitimate changes from subsequent commits:
- eval_concurrency config field (from ed27b826)
- docker_image registration in register_task_env_overrides (from ed27b826)
- ManagedServer branching for vLLM/SGLang backends (from 13f54596)
Closes#1737, #1740.
TBLite eval was bypassing ManagedServer and calling ServerManager
directly, which uses /v1/chat/completions — not available on the
atropos vllm_api_server (/generate only).
Now uses _use_managed_server() to detect vLLM/SGLang backends and
route through ManagedServer (Phase 2) with proper tool_parser and
/generate endpoint. Falls back to Phase 1 for OpenAI endpoints.
Also adds local_vllm.yaml config for running against a local vLLM
server with Docker sandboxes.
- Add eval_concurrency config field with asyncio.Semaphore
- Add local.yaml config using Docker backend (sandboxed, no cloud costs)
- Register docker_image alongside modal_image for backend flexibility
- Default: 8 parallel tasks for local runs
- Add max_concurrent_tasks config (default 8) with semaphore in TB2 eval
- Pass cwd: /app via register_task_env_overrides for TB2 tasks
- Add /home/ to host path prefixes as safety net for container backends
When all 86 TerminalBench2 tasks fire simultaneously, each creates a Modal sandbox
via asyncio.run() inside a thread pool worker. Modal's blocking calls deadlock
when too many are created at once. The semaphore ensures max 8 concurrent creations.
Co-Authored-By: hermes-agent[bot] <hermes-agent[bot]@users.noreply.github.com>
Adds eval-only benchmark for YC-Bench (collinear-ai/yc-bench), a
deterministic long-horizon benchmark where the agent acts as CEO of an
AI startup over a simulated 1-3 year run.
Key design decisions verified against the official yc-bench repo:
- Uses 'sim init' (NOT 'yc-bench run') to avoid starting a competing
built-in agent loop
- Correct DB table names: 'companies' and 'sim_events'
- Correct 4 domains: research, inference, data_environment, training
- Penalty values are preset-dependent (not hardcoded in system prompt)
- Sequential evaluation (each run is 100-500 turns)
- Follows TerminalBench2 patterns: KeyboardInterrupt handling,
cleanup_all_environments(), tqdm logging handler, streaming JSONL
yc-bench added as optional dependency: pip install hermes-agent[yc-bench]
Closes#340
Introduced a new evaluation script for the OpenThoughts-TBLite environment, enabling users to run evaluations with customizable options. The script includes logging capabilities and real-time output, enhancing the evaluation process for terminal agents. This addition complements the existing benchmarking tools and improves usability for users.
Introduced a new evaluation environment for OpenThoughts-TBLite, including the main evaluation script, configuration YAML, and README documentation. This environment provides a faster alternative to Terminal-Bench 2.0, featuring 100 difficulty-calibrated tasks for terminal agents. The setup allows for easy evaluation and configuration, enhancing the benchmarking capabilities for terminal agents.
- Updated task filter descriptions for clarity and added a new skip task feature to exclude incompatible tasks.
- Introduced a set of modal incompatible tasks to prevent execution errors in cloud environments.
- Implemented streaming JSONL logging for task results, preserving data even on interruptions.
- Refactored task evaluation logic to include skipped task reporting and improved error handling.
- Implemented a cleanup process to terminate any remaining sandboxes after evaluation, addressing issues with orphaned thread pool workers.
- Enhanced logging to inform users about the cleanup process, ensuring better resource management and user awareness.
- Added task_timeout parameter to enforce a maximum wall-clock time for each task, automatically scoring as FAIL if exceeded.
- Introduced terminal_timeout and tool_pool_size parameters to improve command execution and concurrency management.
- Updated logging to provide detailed task execution times and timeout handling, enhancing overall monitoring.
- Removed outdated evaluate_config.yaml file to streamline configuration management.
- Increased max_token_length from 16000 to 32000 to allow for longer inputs.
- Adjusted agent_temperature from 0.6 to 0.8 for more varied responses.
- Extended test_timeout from 180 to 600 seconds to accommodate longer evaluations.
- Updated data directory path for saving evaluations to ensure proper organization.
- Introduced new environments: Terminal Test Environment and SWE Environment, each with default configurations for testing and software engineering tasks.
- Added TerminalBench 2.0 evaluation environment with comprehensive setup for agentic LLMs, including task execution and verification.
- Enhanced ToolContext with methods for uploading and downloading files, ensuring binary-safe operations.
- Updated documentation across environments to reflect new features and usage instructions.
- Refactored existing environment configurations for consistency and clarity.
