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.
Good question. I’ll publish benchmarks soon, but the core difference is that Fahmatrix is fully Java, no JNI, and minimalistic — ideal for small projects or environments like Android. Tablesaw and Arrow are more powerful, but heavier. Fahmatrix aims to be the “just enough” middle ground.
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.
Sidebar: interesting how you typed out the email address (I’ve also seen variations like me at gmail dot com). This worked for spam and phishing crawlers that just did a brute force search across texts. But now that we have LLMs, I’m wondering if this will be effective anymore, since it gets the context as well and is not just a “dumb” regexp search?
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.
Not sure public infrastructure is a good comparison, because that kind of stuff benefits the public.
GenAI is definitely a major upheaval, and the payoffs will definitely be huge, but those payoffs will go to those who fund it at the detriment of the working class who haven’t been sharing in the productivity gains made by capital.
I just don’t see the public benefit that we see from public infrastructure. If anything GenAI will exacerbate and accelerate all the structural issues we have in our economy and society.
https://github.com/skanga/Conductor
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.