Shedding Old Baggage, I Designed My Own Agent Framework

Tim Wood — Mon, 15 Jun 2026 08:53:25 +0000

Hello everyone, before I begin, let me ask a quick question: how did you all learn about Agents?

I initially started with deep learning papers, but their mathematical principles completely overwhelmed me. Later, I found a tutorial on GitHub, but I still only had a superficial understanding; each concept was fragmented and couldn't be systematically pieced together. Then I wondered if I wasn't suited for AI and even considered giving up… Finally, I came up with an idea: I would create a demo of an agent, compare it with mainstream agents, and incorporate some of my own ideas. Would that work? And so, this project came about.

Great, let's get started. But here's the question: what is an agent? What problem does it solve? I eagerly researched various reports and even asked about GPT and Gemini. Oh, so it's driven by LLM to complete a series of tasks (I have to complain, there are so many ridiculous terms in this field).

So I'm wondering, what can the agent do? What problems can it solve? How is it different from what I've encountered in the GPT Q&A section on the website?

Then I remembered the local knowledge bases that were so popular last year, so why don't I build one? I started writing scripts using Python and a vector database.

After that, with the help of AI, I started my first agent-like project: a local knowledge base. After finishing the coding, when I first executed python main.py, I saw that the local notes were first split, then embedded, and then stored in the database. Then I tried to write my first search command. I asked the local ollama 3 hey, can you tell me what's in the local knowledge base? It quickly gave me a reply... Not bad, at least it's a good start.

The Excitement Didn’t Last Long
At first, I was thrilled. It actually worked! But as I tried to make it more powerful — adding tool calling, multi-step reasoning, memory across sessions, and letting multiple agents collaborate — things quickly fell apart.
Python was slow when handling concurrent agents. Memory management became messy. The workflow logic turned into a spaghetti of callbacks and state machines. Every time I wanted to change the flow (add a new step, add failover, or support human-in-the-loop), I had to rewrite half the code. Debugging long-running agent sessions felt like chasing ghosts.
I kept thinking: “There must be a better way.”
Why I Chose Go
I’ve always loved Go for its simplicity, performance, and great concurrency support. So I asked myself — what if I rebuild this agent system in Go from the ground up?
That decision became the starting point of GoAgentX.
How GoAgentX Was Born
I started small, just like before — implementing basic LLM calling and simple RAG. But this time everything felt different:

Goroutines made concurrent agents naturally fast and lightweight.
Strong typing and clean interfaces forced me to design clearer abstractions.
Channels and context made workflow orchestration and cancellation much more reliable.

As the project grew, I gradually added the features I wished I had in my Python experiments:

Dynamic DAG Workflows — Agents’ execution flow can be built and modified at runtime. No more hardcoded sequences.
Memory Distillation — Long-term memory is automatically summarized and compressed so agents don’t drown in context.
AHP (Agent-to-Agent Hierarchical Protocol) — A clean way for agents to communicate, delegate, and collaborate.
Leader + Sub-Agent architecture with Failover — If the leader fails, another agent can seamlessly take over.
Pluggable vector stores (PostgreSQL pgvector, Qdrant, etc.) and high-performance benchmarks (many operations under 1µs with zero allocation hot paths).

From a simple local knowledge base demo, it evolved into a production-ready multi-agent framework.
What GoAgentX Is Today
GoAgentX is no longer just my personal learning project. It’s a general-purpose, high-performance Multi-Agent framework written in Go that focuses on reliability, observability, and scalability.
Whether you want to build:

Complex RAG systems
Autonomous research agents
Multi-agent collaboration workflows

repo: https://github.com/Timwood0x10/GoAgentX

Next, I will start a series of articles to gradually break down the design concept of this project, helping everyone to better understand my design approach. Discussions and comments are welcome.

Of course, if you don't like this narrative style, you can tell me, and I'll try to be more serious.

DEV Community: Tim Wood

Shedding Old Baggage, I Designed My Own Agent Framework