Synthesis
AI-Powered Codebase Intelligence & Multi-Agent Copilot
Role
Full-stack engineer: LangGraph cyclic agent flow, AST parser, FAISS vector store, Next.js UI
Primary Stack
Python · FastAPI · LangGraph · FAISS · Next.js · TypeScript
“Developers spend 40% of their time navigating complex codebases, parsing call graphs, and debugging undocumented architectural dependencies across 50,000+ lines of code.”
Must index 50K+ lines of code into a semantic vector store locally
85%+ answer relevance on code navigation and architectural questions
Multi-agent reasoning loop that validates code references before responding
Why LangGraph over raw LangChain chains?▼
LangChain chains are great for linear flows. Our pipeline needed conditional routing: if the retriever returns low-confidence chunks, route to a broader AST query before generating. LangGraph's stateful graph model made this explicit and testable.
Why FAISS over cloud vector databases?▼
Cost and control. At 50K LOC, the vector dataset runs in-process with sub-10ms latency and zero cloud API overhead. FAISS with periodic snapshot saves delivers enterprise semantic search performance without external network hops.
Distributed System Architecture
Explore the multi-tier topology below. Switch between the interactive blueprint canvas, standard Mermaid.js flowcharts, and the step-by-step request simulator.
Three-stage multi-agent code analysis pipeline: AST parsing -> dense FAISS vector search -> LangGraph cyclic verification -> grounded response generation.
Engineering Post-Mortem & Next Iteration
I would add an embedding cache layer to cut API costs by ~60% on repeated indexing runs, and separate the retriever service from the generation service for independent horizontal scaling.