Morgana A magical witch assistant equipped with an enchanted grimoire — powered by AI, yet shaped by you: your agents, your tools, woven into its spells.

Overview

Morgana is a conversational AI framework designed to handle complex scenarios through a sophisticated multi-agent intent-driven architecture. Built on cutting-edge .NET 10 and leveraging the actor model via Akka.NET, Morgana orchestrates specialized AI agents that collaborate to understand, classify and resolve customer inquiries with precision and context awareness.

The system is powered by Microsoft.Agents.AI, enabling seamless integration with Large Language Models (LLMs) while maintaining strict governance through guard rails and policy enforcement.

Morgana will kindly assist you with her magical AI potions just the way you need, just the way you want.

Core Philosophy

Traditional chatbot systems struggle with complexity—they either become monolithic and unmaintainable, or lack the contextual awareness needed for sophisticated interactions.

Morgana reimagines conversational AI through 4 foundational pillars that work in harmony to deliver an orchestration framework that is powerful yet remarkably simple to configure.

🎭 Actor System | 🤖 Agent System | 📝 Prompting System | 💾 Context System

🎭 Morgana Actor System

Resilient orchestration through Akka.NET message-driven architecture

Morgana leverages the actor model to create a fault-tolerant, scalable orchestration layer. Each conversation is managed by a hierarchy of specialized actors that collaborate through asynchronous message passing:

• ConversationManager: Stable entry point owning the lifecycle of a single user session
• ConversationSupervisor: Orchestrates the entire conversation flow and coordinates child actors
• Guard: Validates every interaction against business policies and brand guidelines
• Classifier: Analyzes user intent through LLM-powered classification
• Router: Dynamically routes requests to appropriate agents and serves as message bus for P2P context synchronization

This architecture ensures that failures are isolated, system state remains consistent, and conversations can scale horizontally without bottlenecks.

Actors Conversation Flow

graph LR
  %% Frontend
  U@{shape: circle, label: "👤 User"} --> CLD@{shape: rounded, label: "🌐 Cauldron"}

  %% Backend boundary
  subgraph Morgana["Morgana"]
    CM@{shape: rounded, label: "Manager"}
    SV@{shape: rounded, label: "Supervisor"}

    G@{shape: rounded, label: "Guard"}
    C@{shape: rounded, label: "Classifier"}
    R@{shape: rounded, label: "Router"}
    MA@{shape: rounded, label: "Agent"}
  end

  %% FE → BE
  CLD --> CM
  CM -- 1. Creates conversation and activates actor --> SV

  %% Internal BE flow
  SV -- 2. Asks for language compliance --> G
  SV -- 4. Asks for intent classification --> C
  SV -- 6. Asks for agent routing --> R
  R -- 7. Activates agent for intent handling --> MA

  %% External systems
  G -. 3 Prompts for language compliance .-> LLM@{shape: braces, label: "LLM (Azure OpenAI, Anthropic)"}
  C -. 5 Prompts for intent classification .-> LLM
  MA -. 8 MCP tool discovery .-> MCP@{shape: das, label: "MCP Server"}
  MA -. 9 Intent handling .-> LLM

Loading

🤖 Morgana Agent System

Declarative specialization with automatic discovery and dynamic capabilities

Agents in Morgana are domain specialists that self-register through declarative attributes, eliminating manual configuration and enabling true plugin-based extensibility. Each agent inherits from MorganaAgent and declares its responsibilities through simple annotations:

[HandlesIntent("billing")]
public class BillingAgent : MorganaAgent { }

At startup, Morgana automatically discovers all agents across configured assemblies and validates bidirectional consistency between declared intents and classifier configuration—fail-fast guarantees ensure errors are caught before reaching production.

Agents express their capabilities through tools, which can be native implementations (inherited from MorganaTool) or dynamically acquired from external MCP servers:

[ProvidesToolForIntent("billing")]
public class BillingTool : MorganaTool 
{
    public async Task<string> GetInvoices(string userId, int count) { }
}

[HandlesIntent("monkeys")]
[UsesMCPServers("MonkeyMCP")]
public class MonkeyAgent : MorganaAgent { }  // Acquires tools at runtime!

The MCP integration is particularly powerful: agents can extend their capabilities by consuming Model Context Protocol servers, where external tools become indistinguishable from native implementations. This enables rapid prototyping, microservice integration, and ecosystem-driven feature development—all without writing a single line of tool implementation code.

The framework provides adapters (MorganaAgentAdapter, MorganaToolAdapter) that bridge the declarative configuration with runtime activation, handling validation, dependency injection, and lifecycle management transparently.

Agents Conversation Flow

graph LR
  %% Frontend
  U@{shape: circle, label: "👤 User"} --> CLD@{shape: rounded, label: "🌐 Cauldron"}

  %% Backend boundary
  subgraph Morgana["Morgana"]
    CM@{shape: rounded, label: "Manager"}
    SV@{shape: rounded, label: "Supervisor"}

    G@{shape: rounded, label: "Guard"}
    MA@{shape: rounded, label: "Agent"}
  end

  %% FE → BE
  CLD --> CM
  CM -- 1. Continues conversation and engages actor --> SV

  %% Internal BE flow
  SV -- 2. Asks for language compliance --> G
  SV -- 4. Engages agent for intent handling --> MA

  %% External systems
  G -. 3 Prompts for language compliance .-> LLM@{shape: braces, label: "LLM (Azure OpenAI, Anthropic)"}
  MA -. 5 Intent handling .-> LLM

Loading

📝 Morgana Prompting System

First-class artifacts with layered personality architecture

Prompts are not hardcoded strings in Morgana—they are versioned, maintainable project artifacts managed through the IPromptResolverService. This separation of concerns enables prompt engineering teams to iterate independently from application logic, supporting A/B testing, localization, and behavioral evolution without redeployment.

The system distinguishes between two prompt categories:

• System prompts (morgana.json): Define actor behaviors, global policies, and orchestration rules
• Domain prompts (agents.json): Define agent personalities, instructions, and tool configurations

A unique characteristic of Morgana is its Layered Personality System. Every interaction maintains a consistent global personality (Morgana's core character) while allowing agents to express domain-appropriate specializations:

• Global Layer: Defines Morgana's fundamental character, tone, and values
• Agent Layer: Adds contextual traits that complement (never contradict) the global personality

For example, BillingAgent might be "a pragmatic and concrete witch" while ContractAgent is "a patient and empathetic witch"—both remain recognizably "Morgana" while adapting to domain-specific user needs. This creates vertical consistency across conversations with horizontal variation per expertise area, delivering a unified brand experience that feels naturally specialized.

Prompts also define Global Policies (critical rules like context handling, interaction tokens) that are automatically composed into agent instructions, ensuring system-wide behavioral consistency without repetition.

💾 Morgana Context System

Distributed memory with encrypted persistence and P2P synchronization

Morgana extends Microsoft.Agents.AI framework with a sophisticated context management layer that balances isolation, sharing, and persistence. Each agent maintains its own isolated context through MorganaAIContextProvider, a custom implementation that manages both conversation history and stateful variables.

Context isolation ensures agents operate independently—their memories, variables, and state remain private by default. However, the system enables selective P2P synchronization for shared variables declared in configuration:

{
  "Name": "userId",
  "Scope": "context",
  "Shared": true
}

When one agent collects shared information (e.g., customer ID), the RouterActor broadcasts updates to all other agents through a publish/subscribe mechanism. This eliminates redundant user interactions—information provided once becomes available everywhere, creating a seamless multi-agent experience.

Conversation persistence is handled through the IConversationPersistenceService abstraction, with a default SQLite implementation providing enterprise-grade security. Each agent's context and message history are encrypted using industry-standard algorithms and stored per-conversation, enabling sessions to resume across application restarts without losing context.

The persistence layer supports multi-agent history reconciliation: while agents maintain isolated threads, Morgana reconstructs a unified timeline for UI presentation, giving users an uninterrupted conversational experience regardless of which agents participated behind the scenes.

This architecture delivers 3 critical benefits—all configurable through declarative JSON rather than imperative code:

• data security through encryption and isolation
• intelligent context sharing via P2P sync
• resilient conversations that survive system restarts

---

The Morgana Difference: These 4 pillars work together to create an orchestration framework where adding a new domain agent requires only configuration and attribute decoration—no changes to the core framework, no manual registration, no brittle coupling. It's conversational AI architecture designed for domain-driven evolution at scale.

---

🚀 Quick Start (Docker Hub)

To use latest images from Docker Hub (example showcase for what Morgana looks like and how it works):

# 📋 Copy the production template
cp production.env.template .env

# ✏️ Configure your secrets
nano .env  # or use your favorite editor

# 🐳 Start the containers
docker compose up

# ✅ Open your browser at http://localhost:5002

# 🛑 Stop the containers
docker compose down

🛠️ Development (Build from source)

For developers who want to build from source code (recommended, since you will give Morgana your domain knowledge in terms of intents, agents and tools):

# 📋 Copy the development template
cp development.env.template .env

# ✏️ Configure your secrets
nano .env

# 🔨 Build .NET projects (from project root)
dotnet build ./Morgana
dotnet build ./Cauldron

# 🐳 Build Docker images
docker compose --env-file .env --env-file .env.versions build

# 🚀 Start the containers
docker compose --env-file .env --env-file .env.versions up

# ✅ Open your browser at http://localhost:5002

# 🛑 Stop the containers
docker compose --env-file .env --env-file .env.versions down

🧩 Build your Morgana Agents --> coming soon in v0.18

Morgana.Framework is now available as NuGet package, so you can build your specialized agents without touching Morgana's source code.

Just reference the package in any .NET project, model your agents along with their tools and prompts, and build: the resulting assembly is a ready-to-use Morgana plugin. Drop it into the plugins/ folder of your Morgana instance and it will be automatically discovered and executed at startup.

A concrete example is already in front of you: Morgana.Examples is exactly such a plugin: it references Morgana.Framework via NuGet, defines a set of specialized agents, and is the one powering the production (showcase) Docker distribution out of the box.

This is the foundation of the Morgana Agent Ecosystem: independent, shareable, domain-specific plugin agents that can be developed, distributed, and adopted across any Morgana-powered application.