run_mindx_coordinator.py) - Production CandidateThis script provides a Command Line Interface (CLI) for interacting with the CoordinatorAgent of the MindX system (Augmentic Project). It serves as the primary user-facing tool for issuing commands, requesting system analyses, triggering component improvements, managing the improvement backlog, and observing the system's behavior.
sys.path to correctly locate the mindx package modules.
Initializes the global Config object, which loads settings from .env files and environment variables. This configuration is then passed to the CoordinatorAgent.
Asynchronously obtains an instance of the CoordinatorAgent using the get_coordinator_agent_mindx_async factory function. This ensures all necessary sub-components of the Coordinator (like monitors, LLM handlers) are also initialized.
Handles potential errors during Coordinator initialization.
main_cli_loop):MindX CLI > prompt for user input.
Uses asyncio.to_thread(input, ...) to handle the blocking input() call in an asynchronous environment without freezing the event loop.
Command Parsing:
Parses simple commands like query, analyze_system, improve, backlog, process_backlog, approve, reject, help, quit, exit.
For commands like improve and analyze_system, it extracts arguments (e.g., target_component, analysis_context) from the input string.
For collab (if it were fully implemented), it would parse a prompt and a JSON metadata string. (Currently, collab is not a primary command in this version).
Interaction Creation: Based on the parsed command, it determines the appropriate InteractionType and constructs metadata for the CoordinatorAgent.handle_user_input method.
Output Display: Receives a JSON dictionary response from the CoordinatorAgent and pretty-prints it to the console. Uses default=str in json.dumps to handle non-serializable objects like Enums in the response.
Loop Control: Continues until "quit" or "exit" is typed, or Ctrl+C/Ctrl+D is pressed.
query <your question>: Sends the question as a general query to the CoordinatorAgent, which typically routes it to its configured LLM.
analyze_system [optional context for analysis focus]: Triggers a system-wide analysis by the CoordinatorAgent. The Coordinator will use its LLM, codebase scan, and monitor data to generate improvement suggestions, which are added to its internal backlog.
improve <component_id> [optional_context_for_improvement]: Requests the CoordinatorAgent to initiate an improvement task for a specific component.
- <component_id>: Can be a full Python module path (e.g., mindx.core.belief_system) or a special registered agent ID like self_improve_agent_cli_mindx (for SIA to improve itself).
- [optional_context_for_improvement]: Textual guidance for the improvement.
- This command results in the Coordinator invoking the SelfImprovementAgent CLI.
backlog: Displays the current list of improvement suggestions stored in the CoordinatorAgent's backlog, showing their ID (first 8 chars), priority, target, status, and a snippet of the suggestion.
process_backlog: Manually tells the CoordinatorAgent to attempt to process the highest-priority actionable (pending and approved, if critical) item from its improvement backlog. This is useful for testing the autonomous loop's decision-making or for stepping through improvements.
approve <backlog_item_id>: Approves a specific improvement item in the backlog that is currently in pending_approval status. This allows the autonomous loop to proceed with it if it's a critical target.
reject <backlog_item_id>: Rejects a specific pending_approval item, marking it as rejected_manual.
help: Displays a list of available commands and their usage.
quit / exit: Gracefully shuts down the CoordinatorAgent and exits the CLI.
main_entry's finally block):coordinator.shutdown() is called when the CLI exits, allowing the CoordinatorAgent and its managed components (like monitors and the autonomous loop task) to clean up resources and persist final states (e.g., saving the backlog).
CoordinatorAgent are built using asyncio.pathlib.Path and sys.path.insert to ensure the mindx package is discoverable. Relies on PROJECT_ROOT from mindx.utils.config.readline is imported for an improved CLI experience (history, line editing) on systems where it's available. asyncio.to_thread prevents input() from blocking.try-except blocks for user interruption (KeyboardInterrupt, EOFError) and general exceptions within the main loop and during Coordinator initialization/shutdown.pyproject.toml (or requirements.txt) are installed..env: Create a .env file in the augmentic_mindx/ project root with necessary configurations, especially MINDX_LLM__... API keys and model preferences. cd /path/to/augmentic_mindx/
python scripts/run_mindx_coordinator.py
MindX CLI > prompt appears, type commands as described above.This CLI script is the primary way for a developer or another system to interact with the MindX self-improvement capabilities at a high level.