# Agentic RAG with Regolo + Elysia: Build a "Second Brain" that reasons, not just retrieves

👉[Try Regolo on Elysia](https://dashboard.regolo.ai)

Standard RAG chatbots are dumb. They retrieve 5 chunks and summarize them, often missing the "big picture" or failing when you need aggregation (e.g., "count the invoices").
To build a true "Second Brain," you need an **agent** that decides *how* to search (keyword vs. vector), when to summarize, and when to show a table instead of text—without sending your private knowledge base to US clouds.

**Deploy Elysia (Weaviate’s decision-tree framework) powered by Regolo’s 120B models in under 10 minutes. Get secure, local-first RAG that thinks before it answers.**

## **Outcome**

- **Reasoning over Retrieval:** Elysia uses decision trees to pick tools (Query, Aggregate, Summarize). Regolo’s gpt-oss-120b or Llama-3.3 drives these decisions with high accuracy.
- **Zero-Data Retention:** Your documents stay in your local Weaviate or private cloud. Regolo only sees the transient inference context, never training on your files.
- **Dynamic UI:** The agent auto-selects the best view (Tables, Charts, Cards) based on your data, not just text walls.

## **Prerequisites (Fast)**

- **[Regolo API Key](http://dashboard.regolo.ai)**: From your dashboard.
- **Docker**: To run Weaviate locally.
- **Python 3.12+**: For the Elysia backend.​

## **Step-by-Step (Code Blocks)**

### **1) Run Weaviate Locally (Self-Hosted Memory)**

Don't use a cloud DB if you want privacy. Run Weaviate on your machine.

```
# docker-compose.yml
services:
  weaviate:
    image: cr.weaviate.io/semitechnologies/weaviate:1.33.0
    ports:
    - "8082:8080"
    - "50051:50051"
    environment:
      QUERY_DEFAULTS_LIMIT: 25
      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
      PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
      ENABLE_MODULES: 'text2vec-ollama,generative-ollama' # Modules for local vectors
    volumes:
    - weaviate_data:/var/lib/weaviateCode language: Dockerfile (dockerfile)
```

Command: **docker-compose up -d**
Expected output: Weaviate listening on **http://localhost:8082**

### **2) Install & Start Elysia**

Elysia is the "brain" and UI. Install it via pip.

```
pip install elysia
elysia startCode language: Bash (bash)
```

Expected output: Elysia UI opens in your browser (usually port 8000 or similar).

### **3) Ingest Your Docs with Regolo Embeddings**

We need to turn your PDFs/MDs into vectors. Use the specialized Regolo embedder.

```
git clone https://github.com/regolo-ai/regolo-docs-embedder
cd regolo-docs-embedder
# Follow README to point to your local docs folder
python embed.py --api_key $REGOLO_API_KEYCode language: Bash (bash)
```

Expected output: Your local Weaviate is now populated with vectors generated by Qwen3-Embedding-8B (via Regolo).

### **4) Connect Elysia to Regolo (The Brain)**

In the Elysia UI Settings, configure the "Brain" to use Regolo's inference.

- **Provider**: openai (Regolo is compatible)
- **Base URL**: https://api.regolo.ai/v1
- **API Key**: YOUR\_REGOLO\_KEY
- **Base Model**: Qwen2.5-14B-Instruct (Fast, for simple routing)
- **Complex Model**: Llama-3.3-70B-Instruct or gpt-oss-120b (Smart, for reasoning)

### **5) Chat & Reason**

Ask a complex question: *"Analyze the Q3 financial reports and summarize the risk factors in a table."* Expected output: Elysia uses the "Complex Model" to plan the task, queries Weaviate for Q3 docs, and renders a structured table in the UI—not just a markdown block.

## **Production-Ready: Agent Configuration**

To make the agent consistent, tune the system prompt in Elysia's config file (elysia\_config.yaml or UI).

```
agent:
  system_prompt: |
    You are a Senior Analyst. 
    ALWAYS cite your sources using [Doc ID].
    If the user asks for data comparison, output a JSON that the UI renders as a table.
  tools:
    - name: "weaviate_search"
      description: "Search the vector DB"
    - name: "calculator"
      description: "Perform math on retrieved numbers"Code language: Bash (bash)
```

## **Benchmarks & Costs**

| **Feature** | **Regolo + Elysia (Self-Hosted)** | **SaaS RAG (ChatPDF, etc.)** |
|---|---|---|
| **Privacy** | **High**. Docs local, inference stateless. | Low. Docs uploaded to their cloud. |
| **Intelligence** | **Agentic**. Plans multi-step answers. | Single-step retrieval only. |
| **Cost** | **Inference only** (~€5/mo for heavy use). | Subscription fees (€20+/user). |
| **UI** | **Dynamic**. Tables, charts, cards. | Text only. |

👉[Deploy your Second Brain](https://dashboard.regolo.ai)

![](https://regolo.ai/wp-content/uploads/2025/09/image-16-1024x483.png)---

## **Resources & Community**

**Official Documentation:**

- [Regolo Platform](https://regolo.ai) - European LLM provider, Zero Data-Retention and 100% Green

**Related Guides:**

- [Boost Your Workflows with Regolo AI on n8n](https://regolo.ai/boost-your-workflows-with-regolo-ai-on-n8n/)
- [Build Multi-Agent Workflows with crewAI Teams](https://regolo.ai/build-multi-agent-workflows-with-crewai-teams/)

**Join the Community:**

- [Regolo Discord](https://discord.gg/ZzZvuR2y) - Share your automation builds
- [CheshireCat GitHub](https://github.com/cheshire-cat-ai) - Contribute plugins
- Follow Us on X[ @regolo\_ai](https://x.com/regolo_ai) - Show your integrations!
- Open discussion on our[ Subreddit Community](https://www.reddit.com/r/regolo_ai/)

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## **🚀 Ready to Deploy?**

## [**Get Free Regolo Credits →**](https://dashboard.regolo.ai)

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