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How to Build a Local RAG System with OpenClaw and Ollama

A complete step-by-step tutorial on building a 100% offline, privacy-preserving Retrieval-Augmented Generation (RAG) system using OpenClaw and Ollama.
CN

Matteo Giardino

•

Jun 17, 2026

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openclaw
llm
tutorial
How to Build a Local RAG System with OpenClaw and Ollama

Running AI locally is no longer just for tinkerers; it's a hard requirement for companies dealing with sensitive, proprietary data. Cloud-based Retrieval-Augmented Generation (RAG) solutions are powerful, but they force you to send your internal documents to third-party APIs. In this guide, I'll show you how to build a 100% offline, privacy-first RAG system using OpenClaw and Ollama.

The Privacy Problem with Cloud RAG

Most enterprise AI tutorials assume you're comfortable uploading PDFs, company wikis, and financial reports to OpenAI or Anthropic. For many CTOs and developers, this is a non-starter due to compliance and data governance.

By moving the entire RAG pipeline locally, you get:

  • Zero data leakage: Your documents never leave your machine.
  • No recurring costs: Once your hardware is set up, inference is free.
  • Air-gapped operation: It works perfectly without an internet connection.

Ollama makes running the models trivial, and OpenClaw provides the perfect agentic framework to orchestrate the retrieval and generation phases.

The Local RAG Stack

Our offline architecture relies on three main components:

  1. Ollama: Acts as our local LLM provider (running a model like llama3) and our embedding engine (using nomic-embed-text).
  2. Local Vector Store: We'll use ChromaDB for its simplicity and pure-Python, zero-setup local execution.
  3. OpenClaw: The orchestrator that takes user queries, searches the vector database, and synthesizes the final answer.

Step 1: Installing Ollama and OpenClaw

First, make sure you have both Ollama and OpenClaw installed on your system.

Pull the necessary models using the Ollama CLI:

# Pull the model for generation
ollama pull llama3

# Pull the model for creating vector embeddings
ollama pull nomic-embed-text

Step 2: Setting up the Vector Database

Next, we need a script to ingest our documents, convert them into vector embeddings, and store them locally.

import chromadb
import requests
import json

client = chromadb.PersistentClient(path="./local_rag_db")
collection = client.get_or_create_collection(name="internal_docs")

# Function to get embeddings from Ollama
def get_embedding(text):
    response = requests.post('http://localhost:11434/api/embeddings',
                             json={'model': 'nomic-embed-text', 'prompt': text})
    return response.json()['embedding']

# Example ingestion
documents = [
    "OpenClaw is a modular AI agent framework.",
    "Ollama allows you to run large language models locally."
]

for i, doc in enumerate(documents):
    embedding = get_embedding(doc)
    collection.add(
        embeddings=[embedding],
        documents=[doc],
        ids=[f"doc_{i}"]
    )
print("Ingestion complete!")

Step 3: Configuring the OpenClaw Agent

Now, we create an OpenClaw agent and give it a tool to query our local ChromaDB.

import { Agent, Tool } from 'openclaw';
import { ChromaClient } from 'chromadb';

const db = new ChromaClient({ path: "./local_rag_db" });

const searchDocsTool = new Tool({
  name: "search_internal_docs",
  description: "Search the local vector database for internal documents.",
  execute: async (query) => {
    // In a real app, you'd convert the query to an embedding via Ollama first
    const collection = await db.getCollection("internal_docs");
    const results = await collection.query({
        queryTexts: [query],
        nResults: 2
    });
    return results.documents[0].join("\n");
  }
});

const ragAgent = new Agent({
  model: 'ollama/llama3',
  tools: [searchDocsTool],
  systemPrompt: "You are a helpful assistant. Always use the search_internal_docs tool to answer questions based on the local knowledge base."
});

Conclusion

By combining OpenClaw's flexible agent architecture with Ollama's frictionless local models, building a robust, privacy-first RAG system is achievable in under an hour. This setup scales beautifully for internal knowledge bases and offline deployments.

If you hit any roadblocks during setup, check out the OpenClaw documentation for more advanced tool configurations.

CN
Matteo Giardino