Second Brain Multi-Agent AI System
A personal knowledge retrieval and cognitive assistance platform powered by a coordinated multi-agent architecture. The system turns personal markdown notes, captured snippets, and reference materials into an interactive, context-aware digital memory.
Architecture & Agent Roles
- Search & Retrieval Agent: Performs hybrid retrieval combining lexical keyword indexing with dense semantic vector search.
- Memory & Context Agent: Maintains structured short-term conversational context alongside long-term associative memory.
- Synthesis Agent: Cross-references retrieved facts, verifies source citations, and synthesizes structured answers.
- Refinement & Linking Agent: Discovers latent semantic connections between isolated notes and proposes new associations.
Key Highlights
- Dual Memory Layer: Combines vector databases for embedding similarity with structured metadata filtering for precise temporal and topical scoping.
- Orchestrated Workflows: Implemented with LangGraph to model resilient conversational loops, backtracking, and goal verification.
Technology Stack
- Orchestration: Python, LangGraph
- AI & Retrieval: Retrieval-Augmented Generation (RAG), Dense Vector Search, Embedding Pipelines
- Data Stores: Vector Databases, Local Markdown Storage