mirror of
https://github.com/block/goose.git
synced 2026-07-17 12:56:20 +02:00
Add blog post: How I Used RPI to Build an OpenClaw Alternative (#7051)
This commit is contained in:
Rizel Scarlett
Binary file not shown.
|
After Width: | Height: | Size: 1.3 MiB |
Binary file not shown.
|
After Width: | Height: | Size: 115 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 389 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 525 KiB |
@@ -0,0 +1,137 @@
|
||||
---
|
||||
title: "How I Used RPI to Build an OpenClaw Alternative"
|
||||
description: "Learn how I built a minimal, personal AI agent using goose and the RPI method."
|
||||
authors:
|
||||
- rizel
|
||||
---
|
||||
|
||||
|
||||
|
||||
Everyone on Tech Twitter has been buying Mac Minis, so they could run a local agentic tool called [OpenClaw](https://openclaw.ai/). OpenClaw is a messaging-based AI assistant that connects to platforms such as Discord and Telegram allowing you to interact with an AI agent through DMs or @mentions. Under the hood, it uses an agent called Pi to execute tasks, browse the web, write code, and more.
|
||||
|
||||
Seeing the hype made me want to get my hands dirty. I wanted to see if I could build a lite version for myself. I wanted something minimal that used [goose](https://github.com/block/goose) as the engine instead of Pi. I tentatively dubbed it AltOpenClaw.
|
||||
|
||||
<!-- truncate -->
|
||||
|
||||
## Choosing RPI
|
||||
|
||||
My usual move is to just jump in, start breaking things, and refactor as I go. I actually prefer the back and forth conversation with an agent because it helps me learn how the project works in real time. But when I tried that here, I hit a wall fast. goose did not naturally know what OpenClaw was, and it kept hallucinating how to use its own backend. It would forget context mid-conversation or suggest API calls that simply did not exist.
|
||||
|
||||
I realized I needed to change my approach. While I love the iterative learning process, I needed a way to give the agent a better foundation so our pair programming sessions actually made progress. I decided to try the [RPI method (Research, Plan, Implement)](/docs/tutorials/rpi). This is a framework introduced by [HumanLayer](https://humanlayer.dev/) that trades raw speed for predictability. It is built into goose as a series of recipes. Since I did not fully understand the technical landscape myself, this investment in structure felt like the right move to help us both get on the same page.
|
||||
|
||||
---
|
||||
|
||||
### Research
|
||||
|
||||
First, I needed goose to understand what I was building and whether it was even possible. I kicked things off with a detailed research prompt:
|
||||
|
||||
```
|
||||
/research_codebase topic="learn what openclaw is, how people use it,
|
||||
and how it works. learn if goose can actually be used as a backend
|
||||
or if that's not yet possible; understand the port issues especially
|
||||
if you have an instance of goose that's running to help you build
|
||||
an agent that uses goose as a backend. learn if there will be any
|
||||
auth issues"
|
||||
```
|
||||
|
||||
goose spawned multiple parallel subagents to investigate.
|
||||
|
||||
**Key findings from the research:**
|
||||
|
||||
* **OpenClaw uses its own embedded agent runtime (Pi)**, not goose. This meant there was no existing integration to copy.
|
||||
* **goose CAN be used as a backend!** The `goosed` server exposes a full HTTP API.
|
||||
* **Port conflicts are manageable.** We just needed to run on a different port with `GOOSE_PORT=3001`.
|
||||
* **Authentication is simple.** We could pass a secret key in the `X-Secret-Key` header.
|
||||
|
||||
The research also mapped out all the relevant API endpoints, such as `POST /sessions` to create a new session and `POST /sessions/{id}/reply` to handle the actual messaging.
|
||||
|
||||
---
|
||||
|
||||
### Plan
|
||||
|
||||
With the research complete, I asked goose to create an implementation plan. This is where we defined the personality and security of the bot:
|
||||
|
||||
```
|
||||
/create_plan ticket-or-context="I want to build a Discord MCP server
|
||||
for goose that replicates the popular features of OpenClaw but with
|
||||
better security. Core Features: Users can DM the bot or @ it in a
|
||||
channel to give goose tasks. goose responds in Discord with results.
|
||||
Security requirements: Allowlist (only specific Discord user IDs can
|
||||
interact), Approval flow (before goose executes any tool/action, the
|
||||
bot posts what it wants to do and waits for user approval),
|
||||
Non-allowlisted users get a polite 'you don't have access'"
|
||||
```
|
||||
|
||||
goose analyzed the requirements and produced a detailed plan with four phases:
|
||||
|
||||
1. Phase 1: Project Setup (Discord.js skeleton and allowlist)
|
||||
2. Phase 2: goose HTTP Client (Connecting to the API and handling SSE streaming)
|
||||
3. Phase 3: Tool Approval Flow (The UI for ✅/❌ reactions)
|
||||
4. Phase 4: Polish & Error Handling (Slash commands and session management)
|
||||
|
||||
I liked this phased approach because it gave us less to debug at each step. We could handle features in chunks rather than trying to fix everything at once.
|
||||
|
||||
---
|
||||
|
||||
### Implement
|
||||
|
||||
With the plan in place, I gave the signal to start building:
|
||||
|
||||
```
|
||||
/implement_plan start building
|
||||
```
|
||||
|
||||
The first two phases were surprisingly smooth. Within an hour, the bot was online and I could actually DM it. Seeing a Discord message trigger a goose session for the first time was a massive win.
|
||||
|
||||
First, we tested if AltOpenClaw could respond to me with a joke!
|
||||
|
||||
|
||||
|
||||
However, as every developer knows, it was not all perfect. We still ran into some classic real-world hurdles during implementation:
|
||||
|
||||
* The SSE (Server-Sent Events) format was different than we expected. We spent a good chunk of time debugging why the messages were not appearing until we realized the event structure was nested deeper than anticipated.
|
||||
* My local path did not have npm properly mapped, which led to a brief detour.
|
||||
* Discord has a strict limit on message length. If goose wrote a long script, the bot would just crash. We had to implement a chunking system on the fly.
|
||||
|
||||
Currently, the tool approval feature is still a work in progress. I actually got so excited that the core part of the project was working that I sat down to write this post before finishing the UI for the reactions.
|
||||
|
||||
|
||||
## The Takeaway
|
||||
|
||||
The RPI method felt like a superpower, even if it didn't magically delete every bug from the project. There is a big difference between fighting a hallucination and fighting a real technical challenge.
|
||||
|
||||
When I didn't use RPI, goose hallucinated non-existent endpoints and tried to build a complex MCP server when a simple HTTP API was all we needed. Those are the kinds of bugs that waste hours because you are chasing ghosts.
|
||||
|
||||
|
||||
|
||||
Instead, RPI helped us clear the conceptual fog so we could focus on real implementation details like SSE parsing and character limits.
|
||||
|
||||
By forcing the agent to research first, it built up the context it was missing. It is a bit slower at the start (which I barely have patience for), but it turns the agent into a much more capable partner for that back and forth learning process I enjoy.
|
||||
|
||||
I even had AltOpenClaw push its own [repository](https://github.com/blackgirlbytes/discord-goose-bot) to GitHub.
|
||||
|
||||
|
||||
|
||||
## Try It Out
|
||||
|
||||
If you want more reliability from your agent, give the [RPI recipes](/docs/tutorials/rpi) in goose a shot:
|
||||
|
||||
* `/research_codebase`
|
||||
* `/create_plan`
|
||||
* `/implement_plan`
|
||||
* `/iterate_plan`
|
||||
|
||||
Happy hacking!
|
||||
|
||||
<head>
|
||||
<meta property="og:title" content="How I Used RPI to Build an OpenClaw Alternative" />
|
||||
<meta property="og:type" content="article" />
|
||||
<meta property="og:url" content="https://block.github.io/goose/blog/2026/02/06/rpi-openclaw-alternative" />
|
||||
<meta property="og:description" content="Learn how I built a minimal, personal AI agent using goose and the RPI method." />
|
||||
<meta property="og:image" content="https://block.github.io/goose/assets/images/blogbanner-7c71d1a80441079767f7fd25b9e27385.png" />
|
||||
<meta name="twitter:card" content="summary_large_image" />
|
||||
<meta property="twitter:domain" content="block.github.io/goose" />
|
||||
<meta name="twitter:title" content="How I Used RPI to Build an OpenClaw Alternative" />
|
||||
<meta name="twitter:description" content="Learn how I built a minimal, personal AI agent using goose and the RPI method." />
|
||||
<meta name="twitter:image" content="https://block.github.io/goose/assets/images/blogbanner-7c71d1a80441079767f7fd25b9e27385.png" />
|
||||
</head>
|
||||
Reference in New Issue
Block a user