cave-agent

CaveAgent enables stateful runtime management for LLM agents, allowing injection, manipulation, and retrieval of Python objects across turns. Operations teams can use it to build persistent, stateful workflows with Claude agents, integrating with Python-based tools and workflows.

[{"step":"Initialize CaveAgent in your Python environment. Ensure you have the required dependencies installed (e.g., `pip install cave-agent`).","tip":"Run `import cave_agent; agent = cave_agent.CaveAgent()` to start a new agent session with stateful capabilities."},{"step":"Inject a Python object into the agent's state using `agent.inject_object(OBJECT_NAME, OBJECT_STATE)`. Choose a descriptive name for the object (e.g., `user_session`, `config_manager`).","tip":"Use dictionaries or custom classes for complex state management. Avoid injecting large objects (>10MB) to prevent performance issues."},{"step":"Retrieve the object in subsequent turns with `agent.get_object(OBJECT_NAME)` to inspect or modify its state.","tip":"Always retrieve the object before modifying it to ensure you're working with the latest state. Use deep copies if you need to manipulate the state without affecting the original."},{"step":"Update the object's state using `agent.update_object(OBJECT_NAME, UPDATED_STATE)` after making changes.","tip":"Validate the updated state before calling `update_object` to avoid corrupting the persistent state. Log changes for auditing purposes."},{"step":"Chain operations across multiple agent turns by referencing the same object name. The state will persist until the agent session ends or the object is explicitly removed.","tip":"Use `agent.remove_object(OBJECT_NAME)` to clean up objects when they're no longer needed, especially in long-running workflows."}]