- Introduced a default agent identity prompt to ensure consistent behavior across platforms.
- Added platform-specific formatting hints for CLI, WhatsApp, Telegram, and Discord to guide the agent's output style.
- Updated the AIAgent initialization to accept a platform parameter, enhancing adaptability to different interfaces.
- Appended the current local date and time to the active system prompt to provide context for the model, addressing potential misinterpretations due to training cutoffs.
- Removed the skills_categories tool from the skills toolset, streamlining the skills functionality to focus on skills_list and skill_view.
- Updated the system prompt to dynamically build a compact skills index, allowing the model to quickly reference available skills without additional tool calls.
- Cleaned up related code and documentation to reflect the removal of skills_categories, ensuring clarity and consistency across the codebase.
- Added functionality to signal and terminate long-running terminal commands when a new user message is received, allowing for immediate agent response.
- Introduced a global interrupt event in the terminal tool to facilitate early termination of subprocesses.
- Updated the AIAgent class to handle interrupts gracefully, ensuring that remaining tool calls are skipped and appropriate messages are returned to maintain valid message sequences.
- Introduced a caching strategy that reduces input token costs by ~75% on multi-turn conversations by caching the conversation prefix.
- Added functions to apply cache control markers to messages, enhancing efficiency in token usage.
- Updated AIAgent to auto-enable prompt caching for Claude models, with configurable cache TTL.
- Enhanced logging to track cache hit statistics when caching is active, improving monitoring of token usage.
- 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.
- Modified `model_tools.py` to update default model IDs and add new RL function `rl_test_inference`.
- Enhanced `README.md` with installation instructions for submodules and updated API key usage.
- Improved `rl_cli.py` to load configuration from `~/.hermes/config.yaml` and set terminal working directory for RL tools.
- Updated `run_agent.py` to handle empty string arguments as empty objects for better JSON validation.
- Refined installation scripts to ensure submodules are cloned and installed correctly, enhancing setup experience.
- Enhanced the AIAgent class to support interrupt requests, allowing for graceful interruption of ongoing tasks and processing of new messages.
- Updated the HermesCLI to manage user input in a persistent manner, enabling real-time interruption of the agent's conversation.
- Introduced a mechanism in the GatewayRunner to handle incoming messages while an agent is running, allowing for immediate response to user commands.
- Improved overall user experience by providing feedback during interruptions and ensuring that pending messages are processed correctly.
- Updated the AIAgent class to extract the first user message for trajectory formatting, improving the accuracy of user queries in the trajectory format.
- Enhanced the GatewayRunner to convert transcript history into the agent format, ensuring proper handling of message roles and content.
- Adjusted the typing indicator refresh rate to every 2 seconds for better responsiveness.
- Improved error handling in the message sending process for the Telegram adapter, implementing a fallback mechanism for Markdown parsing failures, and logging send failures for better debugging.
- Updated the `skills_categories` function to include a `verbose` parameter, allowing users to request skill counts per category.
- Modified the `handle_skills_function_call` method to pass the `verbose` argument to `skills_categories`.
- Improved error handling in the `AIAgent` class by injecting a recovery message when invalid JSON arguments are detected, guiding users on how to correct their tool calls.
- Enhanced the `GatewayRunner` to return a user-friendly error message if the agent fails to generate a final response, improving overall user experience.
- Introduced a new callback mechanism in the AIAgent class to send tool progress messages during execution, enhancing user feedback in messaging platforms.
- Updated the GatewayRunner to support tool progress notifications, allowing users to enable or disable this feature via environment variables.
- Enhanced the CLI setup wizard to prompt users for enabling tool progress messages and selecting the notification mode (all or new), improving configuration options.
- Updated relevant documentation to reflect the new features and configuration settings for tool progress notifications.
- Increased the default maximum tool-calling iterations from 20 to 60 in the CLI configuration and related files, allowing for more complex tasks.
- Updated documentation and comments to reflect the new recommended range for iterations, enhancing user guidance.
- Implemented backward compatibility for loading max iterations from the root-level configuration, ensuring a smooth transition for existing users.
- Adjusted the setup wizard to prompt for the maximum iterations setting, improving user experience during configuration.
- Added a new method `_extract_reasoning` to extract reasoning content from assistant messages, accommodating multiple formats from various providers.
- Updated message handling to ensure all assistant messages include reasoning content for API compatibility, preserving multi-turn reasoning context.
- Enhanced logging to capture reasoning details for debugging and analysis.
- Modified the TODO.md to reflect changes in planning and task management, emphasizing the need for structured task decomposition and progress tracking.
- Implemented automatic context compression to manage long conversations that approach the model's context limit.
- Configured the feature to summarize middle turns while protecting the first three and last four turns, ensuring important context is retained.
- Added configuration options in `cli-config.yaml` and environment variables for enabling/disabling compression and setting thresholds.
- Updated documentation in `README.md`, `cli.md`, and `.env.example` to explain the context compression functionality and its configuration.
- Enhanced the `cli.py` to load compression settings into environment variables, ensuring seamless integration with the CLI.
- Completed the implementation of context compression as outlined in the TODO list, marking it as a significant enhancement to conversation management.
- Implemented automatic session logging, saving conversation trajectories to the `logs/` directory in JSON format, with each session having a unique identifier.
- Updated the CLI to display the session ID in the welcome banner for easy reference.
- Introduced an interactive sudo password prompt in CLI mode, allowing users to enter their password with a 45-second timeout, enhancing user experience during command execution.
- Documented session logging and interactive sudo features in `README.md`, `cli.md`, and `cli-config.yaml.example` for better user guidance.
- Introduced a default skills guidance prompt to assist the model in checking relevant skills before technical tasks.
- Updated the logic in AIAgent to auto-include skills guidance when skills tools are available, enhancing the model's contextual understanding during API calls.
- Introduced `cli-config.yaml.example` to provide a template for configuring the CLI behavior, including model settings, terminal tool configurations, agent behavior, and toolsets.
- Created `cli.py` for an interactive terminal interface, allowing users to start the Hermes Agent with various options and toolsets.
- Added `hermes` launcher script for convenient CLI access.
- Updated `model_tools.py` to support quiet mode for suppressing output during tool initialization and execution.
- Enhanced logging in various tools to respect quiet mode, improving user experience by reducing unnecessary output.
- Added `prompt_toolkit` to `requirements.txt` for improved CLI interaction capabilities.
- Created `TODO.md` for future improvements and enhancements to the Hermes Agent framework.
- Updated `trajectory_compression.yaml` to include a new `per_trajectory_timeout` setting, allowing for a timeout of 300 seconds per trajectory. This enhancement helps prevent hanging on problematic entries during processing, improving overall reliability and efficiency in trajectory handling.
- Updated logging configuration in `run_agent.py` to suppress debug messages from additional third-party libraries, reducing noise in logs.
- Enhanced shell scripts for terminal tasks to utilize Singularity for containerized execution, including pre-build SIF image logic and improved logging.
- Refactored tool initialization in `mixture_of_agents_tool.py`, `vision_tools.py`, and `web_tools.py` to implement lazy loading of API clients, optimizing resource usage and error handling.
- Updated ephemeral system prompts in shell scripts to provide clearer guidance on task execution and resource 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 `.env.example` to include new API keys and configuration options for the mini-swe-agent backend, including support for local, Docker, and Modal environments.
- Added `.gitmodules` to include mini-swe-agent as a submodule for easier integration.
- Refactored `mini_swe_runner.py` to use the updated model format and default to OpenRouter for API calls.
- Enhanced `model_tools.py` to support the new terminal tool definitions and ensure compatibility with the mini-swe-agent backend.
- Updated `README.md` to reflect changes in setup instructions and environment variable configurations.
- Improved `terminal_tool.py` to manage execution environments and lifecycle, ensuring proper cleanup and error handling.
- Introduced `terminal_hecate.py` for executing commands on MorphCloud VMs, providing an alternative backend for terminal operations.
- 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 methods to check for meaningful content after <think> blocks and to retrieve messages up to the last complete assistant turn.
- Introduced retry logic for handling truncated responses and invalid JSON arguments in tool calls, with a maximum retry limit.
- Improved logging for invalid JSON and empty responses, ensuring better error tracking and handling.
- Updated the batch data generation script to adjust dataset file, batch size, and ephemeral system prompt for improved context management.
- 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.
