Very cool. I only started learning graphs a few days ago. I’ll never do normal RAG again without them. I wonder how far one can push it. More on reddit.com

Being a programmer, it is better than autogen and CrewAI as it is way more flexible. But if you're not into programming, you will find it complex and not that easy to use. But trust me, it is very powerful compared to its counterparts and is highly underrated More on reddit.com

Consider taking a look at Eidolon (full disclosure, I am a contributor to this project) The idea behind the project is to make it easy to define and connect agents. I am calling it an "Agent Service Framework" DM me (or ask for help on our discord ) if you would like to chat. We are super new and 100% focussed on empowering devs. That means we can spend time right now pairing to get your project up and running. Below is generic advice about how I would think about implementing this, regardless of whether I am using langchain, autogen, custom code, etc. Copilot agent just like you mention. Easy conversational memory Since you want the sql generation to be based off an explicit user request, I would recommend having an explicit "action" where explicitly ask the copilot agent to generate an explicit description of natural language description of the data needed, and then ship that off to your sql generation agent. This is different than relying on it to call a tool (which you will get suggestions to do), since it removes one thing that the agent has to "get right" for your product to work. If you expect the user to explicitly provide all the information needed (and the sql generation agent will not need any additional information in the context), you can go directly to the sql generation agent here. If you do decide to go through the copilot, note that the entire request/response does not need to live in the main conversation memory. That costs tokens, so consider splitting this into a separate memory "thread". Similarly, you you do not need historical information for each instance of sql generation (although you do need instance memory so per sql generation agent so that the sql can be regenerated in case of error) Then you send the table to the user (without going through the chatbot! that is wasted tokens and room for hallucinations) and also to the summarization agent. When they finish you continue streaming the summary response back to the user as well. More on reddit.com

Yep, search documentation on this sub and you'll see a new post about it ever day. Thankfully, there's a youtube video for most topics More on reddit.com