Reduces token usage and latency for most tasks by defaulting to
medium reasoning effort instead of xhigh. Users can still override
via config or CLI flag. Updates code, tests, example config, and docs.
Updated the default model version from "anthropic/claude-sonnet-4-20250514" to "anthropic/claude-sonnet-4.6" across multiple files including AGENTS.md, batch_runner.py, mini_swe_runner.py, and run_agent.py for consistency and to reflect the latest model improvements.
Authored by aydnOktay. Three improvements to batch_runner fault tolerance:
1) Atomic checkpoint writes (temp file + fsync + os.replace) to prevent
corruption on crashes — same pattern as auth.py's _save_auth_store().
2) Incremental checkpoints after each batch result instead of only at end,
so interrupted runs can resume with minimal progress loss.
3) Resume loads existing checkpoint state instead of initializing empty,
preventing clobber of prior progress.
Conflict resolved: kept both the incremental checkpoint logic (PR) and
the batch worker error handling (HEAD) in the imap_unordered loop.
Add daytona_image to batch_runner per-prompt container image overrides
so batch processing works with the Daytona backend. Update inline
comments in RL environment files (agent_loop, tool_context) and
process_registry docstrings to include Daytona in backend lists.
If any worker raises inside pool.imap_unordered(), the exception
propagates through the for loop and the results list is left
incomplete. The finally block correctly restores the log level but
the error is swallowed with no diagnostic information.
Added an explicit except block that logs the full traceback via
exc_info=True before re-raising, making batch worker failures
visible in logs without changing the existing control flow.
Updated the _process_single_prompt function to accept an optional 'image' field in prompt_data, allowing for per-prompt container image overrides. Implemented checks for Docker image accessibility and added logic to register task environment overrides for Docker, Modal, and Singularity. This improves flexibility in managing containerized environments for prompt execution.
Two-part implementation:
Part A - Curated Bounded Memory:
- New memory tool (tools/memory_tool.py) with MEMORY.md + USER.md stores
- Character-limited (2200/1375 chars), § delimited entries
- Frozen snapshot injected into system prompt at session start
- Model manages pruning via replace/remove with substring matching
- Usage indicator shown in system prompt header
Part B - SQLite Session Store:
- New hermes_state.py with SessionDB class, FTS5 full-text search
- Gateway session.py rewritten to dual-write SQLite + legacy JSONL
- Compression-triggered session splitting with parent_session_id chains
- New session_search tool with Gemini Flash summarization of matched sessions
- CLI session lifecycle (create on launch, close on exit)
Also:
- System prompt now cached per session, only rebuilt on compression
(fixes prefix cache invalidation from date/time changes every turn)
- Config version bumped to 3, hermes doctor checks for new artifacts
- Disabled in batch_runner and RL environments
- Introduced a new parameter `skip_context_files` in the AIAgent class to control the inclusion of context files (SOUL.md, AGENTS.md, .cursorrules) in the system prompt.
- Updated the _process_single_prompt function to set `skip_context_files` to True, preventing pollution of trajectories during batch processing and data generation.
- Updated `ALL_POSSIBLE_TOOLS` to auto-derive from `TOOL_TO_TOOLSET_MAP` for consistent schema.
- Introduced `_extract_reasoning_stats` function to track reasoning coverage in assistant turns.
- Enhanced `_process_batch_worker` to discard prompts with no reasoning and aggregate reasoning statistics.
- Updated documentation and comments for clarity on new features and changes.
- Added `max_tokens`, `reasoning_config`, and `prefill_messages` parameters to `BatchRunner` and `AIAgent` for improved model response control.
- Updated CLI to support new options for reasoning effort and prefill messages from a JSON file.
- Modified example configuration files to reflect changes in default model and summary model.
- Improved error handling for loading prefill messages and reasoning configurations in the CLI.
- Updated documentation to include new parameters and usage examples.
- Introduced new skills tools: `skills_categories`, `skills_list`, and `skill_view` in `model_tools.py`, allowing for better organization and access to skill-related functionalities.
- Updated `toolsets.py` to include a new `skills` toolset, providing a dedicated space for skill tools.
- Enhanced `batch_runner.py` to recognize and validate skills tools during batch processing.
- Added comprehensive tool definitions for skills tools, ensuring compatibility with OpenAI's expected format.
- Created new shell script `test_skills_kimi.sh` for testing skills tool functionality with Kimi K2.5.
- Added example skill files demonstrating the structure and usage of skills within the Hermes-Agent framework, including `SKILL.md` for example and audiocraft skills.
- Improved documentation for skills tools and their integration into the existing tool framework, ensuring clarity for future development and usage.
- Introduced new browser automation tools in `browser_tool.py` for navigating, interacting with, and extracting content from web pages using the agent-browser CLI and Browserbase cloud execution.
- Updated `.env.example` to include new configuration options for Browserbase API keys and session settings.
- Enhanced `model_tools.py` and `toolsets.py` to integrate browser tools into the existing tool framework, ensuring consistent access across toolsets.
- Updated `README.md` with setup instructions for browser tools and their usage examples.
- Added new test script `test_modal_terminal.py` to validate Modal terminal backend functionality.
- Improved `run_agent.py` to support browser tool integration and logging enhancements for better tracking of API responses.
- Updated batch processing to include robust resume functionality by scanning completed prompts based on content rather than indices, improving recovery from failures.
- Implemented retry logic for image downloads with exponential backoff to handle transient failures effectively.
- Refined image generation tool to utilize the FLUX 2 Pro model, updating descriptions and parameters for clarity and consistency.
- Added new configuration scripts for GLM 4.7 and Imagen tasks, enhancing usability and logging capabilities.
- Removed outdated scripts and test files to streamline the codebase.
- Replaced tqdm with rich for enhanced visual progress tracking in batch processing.
- Adjusted logging levels in AIAgent to suppress asyncio debug messages.
- Modified datagen script to reduce number of workers for improved performance.
- Integrated tqdm for progress tracking in batch processing, replacing map with imap_unordered for improved performance.
- Added base_url attribute in AIAgent to facilitate OpenRouter detection.
- Introduced normalization functions for tool statistics and error counts to ensure consistent schema across all trajectory entries, facilitating compatibility with HuggingFace datasets.
- Updated batch processing to utilize normalized tool stats and error counts, improving data integrity.
- Refactored vision tools and mixture of agents tool to integrate with OpenRouter API, replacing Nous Research API references and updating model configurations.
- Enabled reasoning capabilities in API calls for enhanced response quality across various tools.
- Improved error handling and API key validation for OpenRouter integration.
- Added support for tracking partial results and tool error counts in batch processing.
- Implemented filtering of corrupted entries during batch file combination based on valid tool names.
- Updated terminal tool to improve command execution and error handling, including retry logic for transient failures.
- Refactored model tools to use a simple terminal tool with no session persistence.
- Improved logging and error messages for invalid API responses and tool calls.
- Introduced chunked processing for large content in web tools to manage size limitations effectively.
