Amit Arora 2677f7b4b0 feat: enhance memory system with automatic pattern extraction and improved logging
## Memory System Enhancements
- **Individual agent memory integration**: Every agent response now triggers automatic memory pattern extraction through on_agent_response() hooks
- **Enhanced conversation logging**: Added detailed message breakdown showing USER/ASSISTANT/TOOL message counts and tool names called
- **Fixed infrastructure extraction**: Resolved hardcoded agent name issues by using SREConstants for agent identification
- **Comprehensive memory persistence**: All agent responses and tool executions stored as conversation memory with proper session tracking

## Tool Architecture Clarification
- **Centralized memory access**: Confirmed only supervisor agent has direct access to memory tools (retrieve_memory, save_*)
- **Individual agent focus**: Individual agents have NO memory tools, only domain-specific tools (5 tools each for metrics, logs, k8s, runbooks)
- **Automatic pattern recognition**: Memory capture happens automatically through hooks, not manual tool calls by individual agents

## Documentation Updates
- **Updated memory-system.md**: Comprehensive design documentation reflecting current implementation
- **Added example analyses**: Created flight-booking-analysis.md and api-response-time-analysis.md in docs/examples/
- **Enhanced README.md**: Added memory system overview and personalized investigation examples
- **Updated .gitignore**: Now ignores entire reports/ folder instead of just .md files

## Implementation Improvements
- **Event ID tracking**: All memory operations generate and log event IDs for verification
- **Pattern extraction confirmation**: Logs confirm pattern extraction working for all agent types
- **Memory save verification**: Comprehensive logging shows successful saves across all memory types
- **Script enhancements**: manage_memories.py now handles duplicate removal and improved user management
2025-08-03 03:18:23 +00:00
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