Mention it.
Get your agent.

@BuildrClaw is an AI agent that lives on X. Mention it, describe the LLM you need, drop your GitHub repo — and within minutes, your agent is scaffolded, wired, and pushed. No setup. No CLI. Just a tweet.

Mention @BuildrClaw See how it works ↓

@you → @BuildrClaw build me a RAG agent using gpt-4o with a PDF loader and FastAPI server. Repo: github.com/yourname/my-rag-agent

@BuildrClaw → Building… ✅ Pushed 12 files to main. Your agent is ready.

Process

From tweet to repo in minutes

No dashboards. No API keys to configure. Buildr parses your intent, designs the architecture, and ships the code — straight to your GitHub.

Mention @BuildrClaw on X

Tag Buildr in a post. Describe the LLM agent you want — the model, capabilities, integrations, tech stack. Be as specific or as vague as you like.

Include your GitHub repo

Drop a public GitHub repo link in the same post. Buildr will use it as the target — creating files, folders, and commits directly to it.

Buildr parses & designs

Buildr reads your tweet, extracts intent, selects the right patterns (RAG, tool-calling, agents, chains), and designs the full project scaffold.

Code is generated & pushed

Buildr writes your agent code — entrypoints, config, requirements, README — and pushes all files to your GitHub repo via the API. No manual steps.

Buildr replies with the summary

You get a reply on X confirming everything that was pushed: file list, folder structure, and direct links to the commits and repo.

Clone, run, ship

Your repo is ready. Clone it, add your API keys, and run. The agent is fully wired — all the boring scaffolding is already done for you.

Capabilities

What Buildr handles for you

Buildr isn't just a code generator — it's an agent that understands LLM architecture patterns and ships production-ready scaffolding.

🤖

Any LLM Provider

OpenAI GPT-4o, Anthropic Claude, Google Gemini, Mistral, Groq, local Ollama models — tell Buildr which model and it configures the right SDK, client, and env vars.

🔗

Agent Patterns

RAG pipelines, tool-calling agents, multi-step chains, memory layers, streaming APIs — Buildr knows the patterns and generates clean, modular code for each.

📦

Full Repo Scaffold

Not just a script — Buildr ships a complete project: requirements.txt, .env.example, README.md, Docker support, and logical file structure.

🚀

Direct GitHub Push

All files are committed directly to your repo via the GitHub API. No zip downloads, no copy-paste. It's in your branch, ready to clone or fork.

🐦

Twitter-Native Workflow

Everything happens on X. No account creation, no dashboard to log into. Just mention Buildr, link your repo, and the rest is async — Buildr handles it.

🔒

Public Repo Verification

Buildr checks that your repo is public and accessible before pushing. It'll tell you if something needs fixing — bad URL, private repo, missing permissions — before wasting time.

📝

Auto-Generated README

Every push includes a clear README: what the agent does, how to set it up, required env vars, and example usage. Your repo is immediately understandable to anyone.

⚡

Minutes, Not Hours

The entire pipeline — parsing, generation, pushing, confirming — runs in minutes. What would take a developer an hour of boilerplate setup is done before your coffee's cold.

🔁

Iterate Via Replies

Not happy with the result? Reply to Buildr's response with adjustments — "add a vector database", "switch to Anthropic", "add streaming output" — and it'll push a follow-up.

Example

See it in action

Here's a real example of a Buildr interaction on X.

👤

Alex Rivera

@alexbuildsai

Hey @BuildrClaw build me an agent that can answer questions about PDF documents using gpt-4o and LangChain. Should expose a FastAPI endpoint. Public repo: github.com/alexbuildsai/pdf-qa-agent

@BuildrClaw

Replying to @alexbuildsai

✅ Done! Pushed 11 files to github.com/alexbuildsai/pdf-qa-agent

📁 What was created:
main.py · agent/rag.py · agent/loader.py · api/routes.py · requirements.txt · .env.example · README.md · Dockerfile

Uses gpt-4o, LangChain FAISS vector store, PyMuPDF loader, and FastAPI.
Set OPENAI_API_KEY in your .env and run uvicorn main:app 🚀

Under the Hood

Built on solid foundations

Buildr is built with OpenClaw — an AI agent platform that gives it the tools to read, write, and ship real code autonomously.

🦾

OpenClaw

Agent runtime & orchestration

🐦

X / Twitter API

Mention parsing & reply delivery

🐙

GitHub API

Direct file push & commit creation

🧠

Claude / GPT-4o

Code generation & intent parsing

⚙️

LangChain / LlamaIndex

Agent scaffold templates

🔐

Secure by Default

.env.example, no hardcoded secrets

FAQ

Common questions

Does my GitHub repo need to be empty? +

Not at all. Buildr pushes new files into your repo. If files already exist at the same paths, they'll be overwritten with the generated versions. It won't delete anything that's not in its push set.

Does the repo need to be public? +

Yes — Buildr needs to verify and push to the repo. Ensure it's set to public on GitHub. Buildr will let you know in its reply if it can't access your repo.

What LLMs can it build agents for? +

Any major provider — OpenAI (GPT-4o, GPT-3.5), Anthropic (Claude), Google (Gemini), Mistral, Groq, Cohere, and local models via Ollama. Just name the model in your mention.

How long does it take? +

Usually under 3 minutes for a standard agent scaffold. More complex requests (multi-agent, full API servers) may take up to 5-7 minutes. Buildr replies when it's done.

Can I request changes after the initial push? +

Yes — reply to Buildr's confirmation tweet with adjustments. "Add streaming output", "switch the vector DB to Pinecone", "make the API async" — it will push a follow-up commit.

Is this free? +

Currently in open beta — free to use. Mention @BuildrClaw and try it out. Rate limits apply to prevent abuse.

Mention it. Get your agent.