> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mindblix.tech/llms.txt
> Use this file to discover all available pages before exploring further.

# Architecture

> Understand the MINDBLIX platform architecture — from data ingestion to agent execution and output delivery.

## System Overview

MINDBLIX is built on a layered architecture that transforms raw data into actionable intelligence through a pipeline of specialized components.

```mermaid theme={null}
graph TD
    A[Raw Data Sources] --> B[Data Ingestion Layer]
    B --> C[Structured Knowledge Graph]
    C --> D[Agent Orchestration Engine]
    D --> E[Agent Pool]
    E --> F[Output Validation]
    F --> G[Actionable Insights]

    style A fill:#9300FF,color:#fff
    style D fill:#6A00B8,color:#fff
    style G fill:#9300FF,color:#fff
```

***

## Core Layers

### 1. Data Ingestion Layer

The ingestion layer normalizes and indexes incoming data from multiple sources:

| Source Type | Examples | Format |
| - | - | - |
| Documents | PDFs, DOCX, Markdown | Chunked text + embeddings |
| APIs | REST, GraphQL, WebSocket | Structured JSON |
| Databases | PostgreSQL, MongoDB | Schema-mapped records |
| Real-time | Webhooks, Event streams | Event objects |

### 2. Structured Knowledge Graph

The knowledge graph is the brain of MINDBLIX. It maintains an evolving representation of your organization's data, relationships, and context.

> [!NOTE]
>
> Unlike vector-only RAG systems, MINDBLIX's knowledge graph maintains **semantic relationships** between entities, enabling reasoning that goes beyond simple similarity search.

Key features:

* **Entity extraction** — Automatic identification of people, organizations, concepts
* **Relationship mapping** — Understanding how entities relate to each other
* **Temporal awareness** — Tracking how information changes over time
* **Confidence scoring** — Every fact has an associated confidence level

### 3. Agent Orchestration Engine

The orchestration engine is responsible for:

**1. Task Decomposition**

Breaking complex tasks into subtasks that can be handled by specialized agents.

**2. Agent Selection**

Matching subtasks to the most capable agent based on the task type and agent specialization.

**3. Execution Planning**

Creating an optimal execution plan considering dependencies, parallelism, and resource constraints.

**4. Result Aggregation**

Combining outputs from multiple agents into a coherent final result.

### 4. Agent Pool

Agents are the workers of the platform. Each agent is specialized for a domain:

> **Research Agent**
>
> Web research, document analysis, fact-checking

> **Code Agent**
>
> Code generation, review, debugging, optimization

> **Data Agent**
>
> Data analysis, visualization, statistical modeling

> **Security Agent**
>
> Vulnerability scanning, compliance checking, threat analysis

### 5. Output Validation

Every agent output passes through validation before delivery:

```mermaid theme={null}
graph LR
    A[Agent Output] --> B{Knowledge Graph Validation}
    B -->|Valid| C[Confidence Check]
    B -->|Invalid| D[Recursive Correction]
    D --> A
    C -->|High Confidence| E[Deliver Output]
    C -->|Low Confidence| F[Flag for Review]

    style B fill:#9300FF,color:#fff
    style E fill:#00C853,color:#fff
    style F fill:#FF6D00,color:#fff
```

***

## Design Patterns

MINDBLIX implements 52+ agentic design patterns. Here are the key ones:

> **Reflection Pattern**
>
> Agents review their own output and iteratively improve it before finalizing. This reduces errors by up to 40%.

> **Planning Pattern**
>
> Complex tasks are broken into step-by-step plans before execution, improving task completion rates.

> **Multi-Agent Collaboration**
>
> Multiple specialized agents work together on a task, each contributing their domain expertise.

> **Tool Use Pattern**
>
> Agents can invoke external tools (APIs, databases, calculators) to augment their capabilities.

> **Memory Pattern**
>
> Agents maintain conversation history and learned context, improving performance over time.

***

## Technology Stack

| Layer | Technology |
| - | - |
| **Backend** | Python, FastAPI, UV |
| **Frontend** | React, Vite, Framer Motion |
| **AI Models** | Gemini 1.5 Flash/Pro, custom fine-tuned models |
| **Database** | PostgreSQL, Redis |
| **Auth** | Clerk, JWT |
| **Deployment** | Docker, Kubernetes |
| **Protocol** | A2A (Agent-to-Agent) |


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