Conductor is a LLM agnostic framework for building sophisticated AI applications using a subagent architecture. It provides a robust platform for orchestrating multiple specialized AI agents to accomplish complex tasks, with features like LLM-based planning, memory persistence, and dynamic tool use.
It provides a robust and flexible platform for orchestrating multiple specialized AI agents to accomplish complex tasks. This project is inspired by the concepts outlined in "The Rise of Subagents" by Phil Schmid at https://www.philschmid.de/the-rise-of-subagents and it aims to provide a practical implementation of this powerful architectural pattern.
I read a paper called "The Rise of Subagents" by Phil Schmid at https://www.philschmid.de/the-rise-of-subagents and thought it was an incredibly powerful architectural pattern for running AI agents with complex tasks.
So, I decided to build a practical implementation of this system with a central Orchestrator that manages a fleet of implicit or explicit Subagents. Each subagent is a specialized, isolated AI agent designed to perform a specific subtask. More details in the repo README at https://github.com/skanga/conductor
It's hard to evaluate such a tool. I scanned my OSS MCP server for databases at https://github.com/skanga/dbchat and it found 0 vulnerabilities. Now I'm wondering if my code is perfect :-) or the tool has issues!
DBChat is a powerful MCP server that lets you have natural language conversations with your database from clients like Claude Desktop. Ask it to do complex analysis, generate beautiful visualizations, or build custom interactive dashboards based your data. Works with any JDBC-compatible database with support for most SQL DBs like PostgreSQL, MySQL, Oracle, SQL Server, SQLite, MongoDB, etc.
Hi Maxwell, I did work on some similar stuff and have some thoughts, suggestions & connections that could help. Feel free to reach out to my yc username at googles email service.
But GenAI is a major upheaval. In most those, a massive amount of initial capital is invested, but the payoff happens slowly - only over decades. But the payoffs are huge.
Think about electricity. Building out the grid was HUGELY expensive. But then benefits are derived for decades. Same for the highway system, railway system, etc.
You mentioned that spRAG uses OpenAI for embeddings, Claude 3 Haiku for AutoContext, and Cohere for reranking. Can you explain why & how did you make those choices?