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.
git clone https://github.com/acodercat/cave-agent.gitCaveAgent 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.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/acodercat/cave-agentCopy the install command above and run it in your terminal.
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I'm building a cave-agent to manage stateful interactions for my LLM agent. Here's my setup: [PYTHON_OBJECTS], [STATEFUL_LOGIC]. Can you help me design a robust state management system that can handle [NUMBER_OF_TURNS] turns while maintaining data integrity? I need to ensure [SPECIFIC_REQUIREMENTS] across the entire conversation.
# Cave-Agent State Management Plan
## Python Object Structure
```python
class ConversationState:
def __init__(self):
self.user_preferences = {}
self.context_history = []
self.session_metadata = {}
```
## Stateful Logic Implementation
- **Turn 1**: Initialize ConversationState object
- **Turn 2-5**: Append user inputs to context_history
- **Turn 6**: Analyze context_history for patterns
- **Turn 7+**: Update user_preferences based on analysis
## Data Integrity Measures
- Implement JSON serialization for cross-turn persistence
- Add validation checks for all state updates
- Set maximum size limits for context_history
## Special Requirements
- Maintain separate state for each user session
- Implement automatic cleanup after 24 hours of inactivityCloud ETL platform for non-technical data integration
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