Technical Architecture

YoYo is built on an AI Agent architecture + RAG (Retrieval-Augmented Generation) and the LangChain framework, enabling human-like perception → reasoning → action.

Key Modules :

1. Perception : Processes user inputs (text/voice).

2. Reasoning : Leverages LLMs to analyze queries, decide actions (e.g., external retrieval), or fetch answers from internal knowledge.

3. Action : Executes tasks (RAG retrieval, database/API calls, real-time crypto pricing).

4. Memory : Stores conversation history, task status, tone, and user feedback.

RAG Workflow :

1. Retrieval : Searches external sources for relevant info.

2. Augmentation : Contextualizes retrieved data for LLMs.

3. Generation : Produces responses aligned with YoYo’s personality and tone.

Advanced Architecture :

• LangGraph-based agents outperform traditional prompt/workflow systems by enabling "cyclic thinking" and self-iteration through human feedback.

• Multi-AI Scoring : Inspired by DeepSeek’s R1 model, outputs are filtered by scoring agents for professionalism and tone consistency.

Future Upgrade : Cross-platform long-term memory will sync data (Twitter, Telegram) to maintain consistent user relationships.