I built an AI Agent that builds n8n workflows

Gama — Thu, 05 Mar 2026 00:41:04 +0000

I built an AI agent that builds n8n workflows

I’ve been working on a project called Flow Agent, and I’d love for the community to check it out.

The core idea was to put my skills to the test as I continue to sharpen my AI agent development,

What is it?

Flow Agent is an AI-powered companion for n8n developers.
Instead of dragging and dropping every node manually, you can just chat with it.

Create: Describe a process, and it generates the workflow.
Refine: Ask it to add logic or change a node.
Update: Modify existing workflows via simple conversation.

Check it out on GitHub: https://github.com/gama2219/flowagent

[Boost]

Gama — Mon, 25 Aug 2025 19:44:36 +0000

DumbDEVMemeBot

Aug 25 '25

Credit: @ivis1

Comments 1

1 min read

Optimizing the LangGraph Supervisor: A Simple Handoff Tool Modifications 🚀.

Gama — Thu, 21 Aug 2025 14:22:08 +0000

LangGraph's create_supervisor function is a powerful way to manage communication between multiple agents. However, its performance can be improved. A key optimization lies in how agents hand off tasks to one another. I've found that by modifying the create_handoff_tool, you can significantly reduce data transfer and improve overall performance.
The Problem
The default create_handoff_tool passes the entire conversation history to the new agent when a handoff occurs. While this ensures context, it is inefficient. For long conversations, the message list can become huge, leading to:

Slower Execution: Large data payloads take time to serialize and send.
Wasted Resources: The new agent has to process a lot of unnecessary information.
Increased Errors: A large, messy context can sometimes confuse the new agent, causing it to fail on simple tasks. The Solution: A Smarter Handoff The fix is to make the handoff tool smarter. Instead of passing the entire state, we can modify the tool to explicitly require and pass only a specific query. This forces the LLM to summarize the handoff request into a concise query string. Here's the code for the modified create_handoff_tool:

By adding query: Annotated[str, ...], we are guiding the LLM to extract the most critical piece of information. The handoff_message sent to the new agent will now be short and precise.
The Impact
This small change has a big impact on the overall performance of your multi-agent supervisor system: