When Honcho is active:
- System prompt uses Honcho prefetch instead of USER.md
- memory tool target=user add routes to Honcho
- MEMORY.md untouched in all cases
When disabled, everything works as before.
Also wires up contextTokens config to cap prefetch size.
Opt-in persistent cross-session user modeling via Honcho. Reads
~/.honcho/config.json as single source of truth (shared with
Claude Code, Cursor, and other Honcho-enabled tools). Zero impact
when disabled or unconfigured.
- honcho_integration/ package (client, session manager, peer resolution)
- Host-based config resolution matching claude-honcho/cursor-honcho pattern
- Prefetch user context into system prompt per conversation turn
- Sync user/assistant messages to Honcho after each exchange
- query_user_context tool for mid-conversation dialectic reasoning
- Gated activation: requires ~/.honcho/config.json with enabled=true
The `hermes` CLI entry point (hermes_cli/main.py) and the agent runner
(run_agent.py) only loaded .env from the project installation directory.
After the standard installer, code lives at ~/.hermes/hermes-agent/ but
config lives at ~/.hermes/ — so the .env was never found.
Aligns these entry points with the pattern already used by gateway/run.py
and rl_cli.py: load ~/.hermes/.env first, fall back to project root .env
for dev-mode compatibility.
Also fixes:
- status.py checking .env existence and API keys at PROJECT_ROOT
- doctor.py KeyError on tool availability (missing_vars vs env_vars)
- doctor.py checking logs/ and Skills Hub at PROJECT_ROOT instead of HERMES_HOME
- doctor.py redundant logs/ check (already covered by subdirectory loop)
- mini-swe-agent loading config from platformdirs default instead of ~/.hermes/
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Simplified the logic for determining support for reasoning based on the base URL by introducing clearer variable names.
- Added product attribution for the Nous Portal to the extra body of requests when applicable, enhancing tagging for better tracking.
- Introduced a new static method `_clean_session_content` in the `AIAgent` class to convert REASONING_SCRATCHPAD tags to <think> blocks and clean up whitespace in session logs.
- Updated the `_save_session_log` method to utilize the cleaned content for assistant messages, ensuring consistency in session logs.
- Changed the default output directory for TTS audio files from `~/voice-memos` to `~/.hermes/audio_cache`, reflecting a more appropriate storage location.
- Updated the `clear_interrupt` method to also reset the global tool interrupt signal, improving the clarity of interrupt management within the agent.
- This change ensures that all interrupt states are properly cleared, enhancing the reliability of the agent's operation.
- Introduced a new configuration option for reasoning effort in the CLI, allowing users to specify the level of reasoning the agent should perform before responding.
- Updated the CLI and agent initialization to incorporate the reasoning configuration, enhancing the agent's responsiveness and adaptability.
- Implemented logic to load reasoning effort from environment variables and configuration files, providing flexibility in agent behavior.
- Enhanced the documentation in the example configuration file to clarify the new reasoning effort options available.
- Implemented functionality to load ephemeral prefill messages from a JSON file, enhancing few-shot priming capabilities for the agent.
- Introduced a mechanism to load an ephemeral system prompt from environment variables or configuration files, ensuring dynamic prompt adjustments at API-call time.
- Updated the CLI and agent initialization to utilize the new prefill messages and system prompt, improving the overall interaction experience.
- Enhanced configuration options with new environment variables for prefill messages and system prompts, allowing for greater customization without persistence.
- Removed static methods for converting and checking <REASONING_SCRATCHPAD> tags, simplifying the codebase.
- Replaced calls to the removed methods with direct function calls for better clarity and maintainability.
- Updated trajectory saving logic to utilize a dedicated function for improved organization and readability.
- Introduced a shared interrupt signaling mechanism to allow tools to check for user interrupts during long-running operations.
- Updated the AIAgent to handle interrupts more effectively, ensuring in-progress tool calls are canceled and multiple interrupt messages are combined into one prompt.
- Enhanced the CLI configuration to include container resource limits (CPU, memory, disk) and persistence options for Docker, Singularity, and Modal environments.
- Improved documentation to clarify interrupt behaviors and container resource settings, providing users with better guidance on configuration and usage.
- Introduced a method to strip <think> blocks from content, improving text visibility.
- Implemented counters to reset nudge intervals when memory and skill tools are used, enhancing user guidance.
- Captured content from turns with tool calls to provide fallback responses, ensuring continuity in conversation.
- Updated nudge logic to remind users about saving memories and creating skills based on interaction patterns.
- Added skills configuration options in cli-config.yaml.example, including a nudge interval for skill creation reminders.
- Implemented skills guidance in AIAgent to prompt users to save reusable workflows after complex tasks.
- Enhanced skills indexing in the prompt builder to include descriptions from SKILL.md files for better context.
- Updated the agent's behavior to periodically remind users about potential skills during tool-calling iterations.
- Added configuration options for memory nudge interval and flush minimum turns in cli-config.yaml.example.
- Implemented memory flushing before conversation reset, clearing, and exit in the CLI to ensure memories are saved.
- Introduced a flush_memories method in AIAgent to handle memory persistence before context loss.
- Added periodic nudges to remind the agent to consider saving memories based on user interactions.
- Introduced MEMORY_GUIDANCE and SESSION_SEARCH_GUIDANCE to improve agent's contextual awareness and proactive assistance.
- Updated AIAgent to conditionally include tool-aware guidance in prompts based on available tools.
- Enhanced descriptions in memory and session search schemas for clearer user instructions on when to utilize these features.
- Eliminated the `compression_model` variable from the AIAgent class, as it was not being utilized.
- Cleaned up the context compressor initialization for improved clarity and maintainability.
- Relocated functions related to model metadata, including fetch_model_metadata, get_model_context_length, estimate_tokens_rough, and estimate_messages_tokens_rough, to agent/model_metadata.py for better organization and maintainability.
- Updated imports in run_agent.py to reflect the new location of these functions.
- Added functionality to suppress logging noise from specific modules when in quiet mode, improving user experience in CLI.
- Updated terminal_tool.py to change the log level for fallback directory usage from warning to debug, providing clearer context without cluttering logs.
- Added methods for handling sudo password and dangerous command approval prompts using a callback mechanism in cli.py.
- Integrated these prompts with the prompt_toolkit UI for improved user experience.
- Updated terminal_tool.py to support callback registration for interactive prompts, enhancing the CLI's interactivity.
- Introduced a background thread for API calls in run_agent.py to allow for interrupt handling during long-running operations.
- Enhanced error handling for interrupted API calls, ensuring graceful degradation of user experience.
- Introduced new methods in run_agent.py for building API keyword arguments and normalizing assistant messages from API responses.
- Added functionality for compressing conversation context and managing session state in SQLite.
- Improved tool call execution handling, including enhanced logging and error management.
- Updated path handling in multiple platform files to utilize pathlib for better compatibility and readability.
- Updated various modules including cli.py, run_agent.py, gateway, and tools to replace silent exception handling with structured logging.
- Improved error messages to provide more context, aiding in debugging and monitoring.
- Ensured consistent logging practices throughout the codebase, enhancing traceability and maintainability.
- Introduced logging functionality in cli.py, run_agent.py, scheduler.py, and various tool modules to replace print statements with structured logging.
- Enhanced error handling and informational messages to improve debugging and monitoring capabilities.
- Ensured consistent logging practices across the codebase, facilitating better traceability and maintenance.
- Eliminated the `_log_api_payload` method used for temporary debugging, streamlining the codebase.
- Updated the `_save_session_log` method to save the full raw session, including all messages and metadata, improving the clarity and completeness of session logs.
- Adjusted session log entry to include additional context such as `base_url` and `platform` for better tracking.
- Changed the session logging directory from `~/.hermes-agent/logs/` to `~/.hermes/sessions/` for consistency.
- Updated the `run_agent.py` to reflect the new logging path, ensuring session logs are stored correctly alongside gateway sessions.
- Incremented schema version to 2 and added a new column `finish_reason` to the `messages` table.
- Implemented a method to flush un-logged messages to the session database, ensuring data integrity during conversation interruptions.
- Enhanced error handling to persist messages in various early-return scenarios, preventing data loss.
- Implemented a multi-provider authentication system for the Hermes Agent, supporting OAuth for Nous Portal and traditional API key methods for OpenRouter and custom endpoints.
- Enhanced CLI with commands for logging in and out of providers, allowing users to authenticate and manage their credentials easily.
- Updated configuration options to select inference providers, with detailed documentation on usage and setup.
- Improved status reporting to include authentication status and provider details, enhancing user awareness of their current configuration.
- Added new files for authentication handling and updated existing components to integrate the new provider system.
- Added a spinner to visually indicate task delegation progress in quiet mode, improving user experience during batch processing.
- Implemented a method to update spinner text dynamically based on remaining tasks, providing real-time feedback.
- Enhanced the `delegate_task` function to include per-task completion messages, ensuring clarity on task status during execution.
- Updated the KawaiiSpinner class to allow message updates while running, facilitating better interaction during long-running tasks.
- Introduced the `delegate_task` tool, allowing the main agent to spawn child AIAgent instances with isolated context for complex tasks.
- Supported both single-task and batch processing (up to 3 concurrent tasks) to enhance task management capabilities.
- Updated configuration options for delegation, including maximum iterations and default toolsets for subagents.
- Enhanced documentation to provide clear guidance on using the delegation feature and its configuration.
- Added comprehensive tests to ensure the functionality and reliability of the delegation logic.
- Updated the tool name from "search" to "search_files" across multiple files to better reflect its functionality.
- Adjusted related documentation and descriptions to ensure clarity in usage and expected behavior.
- Enhanced the toolset definitions and mappings to incorporate the new naming convention, improving overall consistency in the codebase.
- Introduced a new `execute_code` tool that allows the agent to run Python scripts that call Hermes tools via RPC, reducing the number of round trips required for tool interactions.
- Added configuration options for timeout and maximum tool calls in the sandbox environment.
- Updated the toolset definitions to include the new code execution capabilities, ensuring integration across platforms.
- Implemented comprehensive tests for the code execution sandbox, covering various scenarios including tool call limits and error handling.
- Enhanced the CLI and documentation to reflect the new functionality, providing users with clear guidance on using the code execution tool.
- Added a new `clarify_tool` to enable the agent to ask structured multiple-choice or open-ended questions to users.
- Implemented callback functionality for user interaction, allowing the platform to handle UI presentation.
- Updated the CLI and agent to support clarify questions, including timeout handling and response management.
- Enhanced toolset definitions and requirements to include the clarify tool, ensuring availability across platforms.
- Added a new `skill_manager_tool` to enable agents to create, update, and delete their own skills, enhancing procedural memory capabilities.
- Updated the skills directory structure to support user-created skills in `~/.hermes/skills/`, allowing for better organization and management.
- Enhanced the CLI and documentation to reflect the new skill management functionalities, including detailed instructions on creating and modifying skills.
- Implemented a manifest-based syncing mechanism for bundled skills to ensure user modifications are preserved during updates.
- Updated the logic for stopping the thinking spinner to improve clarity in tool execution messages.
- Removed unnecessary checks for tool calls, simplifying the spinner's stop behavior while maintaining informative output for users.
- Eliminated the 'read' action from the memory tool and related logging in the agent, streamlining the available actions to 'add', 'replace', and 'remove'.
- Updated error messages and documentation to reflect the removal of the 'read' action, ensuring clarity in the API's usage.
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 SlashCommandCompleter for command autocompletion, enhancing user experience by suggesting commands as users type.
- Enabled multiline input with Shift+Enter, allowing users to enter longer messages more conveniently.
- Implemented paste detection to handle large text inputs, saving them to temporary files and replacing them with compact references in the input area.
- Updated input area styling and hint display to improve usability and feedback during agent operation.
- Removed ANSI escape codes for color in tool activity messages to simplify output.
- Updated the _get_cute_tool_message method to provide a cleaner, more consistent format for various tool activities.
- Enhanced readability by aligning messages and removing unnecessary complexity, ensuring a more straightforward user experience.
- Introduced ANSI escape codes for color-coded CLI messages to enhance readability.
- Updated the _get_cute_tool_message method to generate clean, aligned activity lines for various tools, replacing kawaii ASCII art with a more structured format.
- Simplified message construction for web tools, terminal commands, and process management, ensuring consistent and scannable output.
- Updated the _build_tool_preview function to include detailed previews for new tools: 'todo', 'send_message', and various 'rl_' tools, improving user feedback during task execution.
- Added emoji representations for tools in GatewayRunner, including 'process', 'todo', and 'send_message', to enhance visual clarity in progress messages.
- Improved handling of task management and messaging outputs, ensuring more informative and user-friendly interactions.
Single `todo` tool that reads (no params) or writes (provide todos array
with merge flag). In-memory TodoStore on AIAgent, no system prompt
mutation, behavioral guidance in tool description only. State re-injected
after context compression events. Gateway sessions hydrate from
conversation history. Added to all platform toolsets.
Also wired into RL agent_loop.py with per-run TodoStore and fixed
browser_snapshot user_task passthrough from first user message.
- Enhanced the _build_tool_preview function to include specific formatting for the 'process' tool, displaying action, session_id, data, and timeout when applicable.
- This update improves the clarity of tool previews, particularly for actions that require session tracking and timeout management.
- 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.
- 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.
