This project provides tools to:
Analyze Python code using an LSP (Language Server Protocol).
Automatically repair or suggest fixes for code using DSPyRepair.
Evaluate code correctness and quality using custom scoring metrics and LLM based metrics.
Run tests on multiple Python code snippets to assess the repair and evaluation system.
The goal of this project is to build a plug-and-play memory management system for conversational AI agents.
Most AI applications struggle with long-term personalization and contextual memory. This system provides a centralized memory layer that AI agents can connect to via simple APIs.
It handles:
Short-term conversation memory
Long-term semantic memory
Persistent user profiles
Context retrieval for better responses
The system is designed to be agent-agnostic, meaning any conversational AI (chatbot, assistant, agent) can integrate with it.
Understanding the evolution of online communities is crucial for analyzing social dynamics on platforms during politically sensitive periods. Using Louvain, HDBSCAN, and Ward clustering, we analyze community structures on a private X dataset collected during an election period, and employ LLM-based labeling to assess semantic coherence, finding that Ward’s clusters are the most interpretable. In addition, we propose TopoTemp, a novel framework combining topological data analysis with sequential modeling to predict community evolution in temporal social networks, achieving consistent improvements over baselines. Furthermore, we experiment on both the X and Reddit Hyperlinks dataset to determine the impact of snapshot-level, as well as community-level, topological features when applied to community evolution prediction. This work highlights the importance of data granularity and feature selection in dynamic network analysis and offers a practical foundation for future research in community evolution prediction.