Skip to content

Lab · SemanticStudio

A reference build for agent platforms.

A production-ready multi-agent chat platform with 28 domain agents, 4-tier memory, GraphRAG-lite, self-learning ETL, and observability.

  • 28Domain agents
  • 5Chat modes
  • 4-tierMemory
  • MITLicense

Operating thesis

An open-source, data-first multi-agent chat platform with 28 configurable domain agents, 4 fully-customizable chat modes, 4-tier memory with Context Graph, GraphRAG-lite, self-learning ETL, and comprehensive observability.

Production tour

What the build proves

Problem

Enterprise AI chat systems struggle with data grounding, context management, and scalability. Most solutions are either too simple (single-agent, no memory) or too complex (require massive infrastructure). Organizations need a middle path that balances capability with operational simplicity.

System

Built SemanticStudio as a practical implementation of AI-native architecture principles. Features 28 specialized domain agents (enable/disable as needed), a 4-tier memory system inspired by MemGPT (with Context Graph as Tier 4), GraphRAG-lite for relationship discovery, self-learning ETL with Plan-Act-Reflect loops that can create new agents, and production-grade quality evaluation. Supports multiple LLM providers with full model configuration.

Impact

Open-sourced to demonstrate that production-grade multi-agent systems are achievable without massive infrastructure. Serves as a reference architecture for enterprises building private AI assistants grounded in their own data.

Run locally

Clone

git clone https://github.com/Brianletort/SemanticStudio.git

Install

cd SemanticStudio && npm install

Configure

cp .env.example .env && npm run dev

Useful navigation

Architecture record

An eight-part architecture record covering orchestration, memory, retrieval, learning, and production quality.