• Vector Databases (FAISS/Pinecone/Weaviate): Embedding-based knowledge storage to recall user interactions, past tweets, and engagement history.

• Hybrid Retrieval System: The system employs hybrid retrieval, combining dense vector embeddings with sparse keyword indexing for high-precision memory recall. A two-stage ranking system prioritizes past interactions based on engagement weight and temporal relevance.

• Retrieval-Augmented Generation (RAG): Ensures real-time awareness of trending topics, allowing the agent to craft contextually relevant tweets.

• Auto-Prompt Optimization: Dynamically adjusts its tweet structure and tone based on real-time feedback.

• User Profiling & Personalization: Tracks engagement history of specific users to tailor interactions dynamically, ensuring long-term audience retention.

• Knowledge Graphs for Semantic Linking: Enables deeper understanding of user interactions and topic association by mapping relationships between key entities.