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+---
+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.
+
+
+
+## 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!
+
+
+
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+