Your framework for creating secure agents.

Monan is currently in active development (Alpha). Some features may change. We welcome community collaboration via PRs, Issues, and feedback to help us reach version 1.0.

The Ultimate SDK for Native AI Agents on Bun. Build, orchestrate, and scale local agents with bare-metal performance, embedded vectors, and complex workflows.

Monan is a framework designed to eliminate the latency and complexity of AI orchestration. Unlike Python-based solutions, Monan leverages Bun's native architecture to offer local inference, SQLite vector storage, and workflow orchestration in a single, cohesive tool.

🚀 Installation

bun add monan-sdk

To use the CLI (required to run and test):

bun add -g monan-sdk

⚡ Quick Start

In Monan, you define your agent in code, but execution (Chat or API) is managed by the CLI. This ensures optimization and standardization.

⚠️ Local Models: All models available on Ollama can be used. Just pay attention to the string formatting: <model-name>:<parameter-amount>. Important: When using local models, you must run ollama serve in a separate terminal before executing your agent.

Cloud Models: We support OpenRouter for access to OpenAI, Anthropic, and others.

1. Create the file agent.ts inside the src folder:

import { Agent } from 'monan-sdk';

// Define and export your agent
export const assistant = new Agent({
  name: "SupportBot",
  model: 'qwen3-vl:30b', // Default local model
  description: "A helpful assistant to answer questions.",
  // Optional: Pre-define default behaviors
  config: {
    temperature: 0.2,
    maxTokens: 1000
  }
});

2. Run in Terminal (Interactive Mode): Simulates a chat for quick testing.

monan test assistant:src.agent

3. Run as API (Production Mode): Automatically spins up an optimized Elysia server.

monan run assistant:src.agent --port 3000
# Endpoint available at: http://localhost:3000
# Docs: http://localhost:3000/docs

☁️ OpenRouter & Privacy (PII Masking)

Not everyone has a GPU rig. Monan integrates natively with OpenRouter, allowing you to use external providers (OpenAI, Anthropic, Gemini) easily.

Security First: When using external providers, Monan enables PII Masking by default (maskPII: true). This automatically redacts sensitive data (emails, phone numbers, API keys) before sending the context to the cloud.

import { Agent } from 'monan-sdk';

const cloudAgent = new Agent({
  name: "CloudAssistant",
  // Use OpenRouter model naming
  model: "openai/gpt-5.2-pro",
  // Token can also be set via OPEN_ROUTER_API_KEY env var
  openRouterToken: "<your-api-token>", 
  // Privacy Settings
  maskPII: true, // Default is true for external calls. Set to false to disable.
  description: "Uses GPT-5.2 but masks sensitive user data."
});

🎯 Custom System Prompts

Define custom system prompts to guide your agent's behavior and personality. If no systemPrompt is provided, Monan automatically generates one based on the agent's name and description.

import { Agent } from 'monan-sdk';

const specialistAgent = new Agent({
  name: "DataAnalyst",
  model: "qwen3-vl:30b",
  description: "Analyzes complex datasets",
  // Custom system prompt for precise behavior
  systemPrompt: `You are a senior data analyst with 10 years of experience.
Your role is to:
1. Analyze data patterns and anomalies
2. Provide actionable insights
3. Present findings with statistical confidence levels
4. Suggest improvements based on data trends

Always be precise, use data-driven reasoning, and provide sources for your claims.`,
  config: {
    temperature: 0.2,
    maxTokens: 2048
  }
});

System Prompt Priority:

• If systemPrompt is provided, it will be used as the primary system instruction
• If not provided, Monan automatically generates: "You are {name}. {description}"
• If additional system messages are in the message history, the system prompt is prepended to them

🔧 Advanced Usage: LoRA & Adapters

Small local models often struggle with specific instructions. To use LoRA/QLoRA adapters locally, you must create a custom model in Ollama first. This ensures maximum performance by merging the adapter weights at the engine level.

Step 1: Create a Modelfile Create a file named Modelfile in your project root:

FROM gemma3:4b
# Path to your GGUF adapter
ADAPTER ./adapters/finance-v1.gguf
# Optional: Set default parameters for this specific LoRA
PARAMETER temperature 0.2

Step 2: Build the Custom Model Run this command in your terminal:

ollama create finance-expert -f Modelfile

Step 3: Use the Model in Monan Now, simply refer to your new custom model by name.

const specializedAgent = new Agent({
  name: "FinanceExpert",
  model: "finance-expert", // <--- The name you created in Step 2
  description: "A specialized agent for financial analysis using a custom LoRA."
});

🚦 Model Routing

For complex systems, you shouldn't use a heavy model for everything. The Router allows you to dynamically direct requests to the most appropriate agent based on intent or complexity.

import { Router, Agent } from 'monan-sdk';

const fastAgent = new Agent({ model: "gemma3:4b" }); // Fast, cheap
const smartAgent = new Agent({ model: "openai/gpt-5.2-pro" }); // Smart, expensive 

// The Router acts as the entry point
export const mainRouter = new Router({
  // You can use a fine-tuned router model created via Modelfile (see above)
  model: "router-custom-v2", 
  default: fastAgent, // Default to the cheap model
  routes: [
    { 
      intent: "coding_complex", 
      description: "Complex coding tasks or architecture planning",
      agent: smartAgent 
    },
    { 
      intent: "casual_chat", 
      description: "General conversation and greetings",
      agent: fastAgent 
    }
  ]
});

Run the router just like an agent:

monan run mainRouter:src.router

🔀 Workflow Orchestration

Monan features a powerful and Open Source orchestration engine. You can chain agents, run tasks in parallel, and integrate custom tools.

import { Agent, Workflow } from 'monan-sdk';
import { tool } from 'monan/tools';

// --- Tools ---
class SearchTools {
  @tool({ name: "web_search", description: "Search the web." })
  async search(query: string) { return `Results for: ${query}`; }
}

// --- Agents ---
const researcher = new Agent({ 
  name: "researcher", 
  model: 'qwen3-vl:8b', 
  tools: [new SearchTools()] 
});

const writer = new Agent({ name: "writer", model: 'qwen3-vl:30b' });

// --- Workflow ---
export const blogFlow = new Workflow()
  .add(researcher)
  .add(writer)
  .build();

To run the workflow:

monan run blogFlow:src.agent

🛠️ Advanced Tool System

Monan provides a type-safe, self-documenting tool system with automatic validation using Zod. Tools are defined as properties and automatically integrated into agent context.

Quick Start: Creating Your First Tool

The simplest way to create a tool is as a property in a class:

import { tool, extractTools } from 'monan-sdk';
import { z } from 'zod';

class WeatherTools {
  getTemperature = tool({
    description: "Get current temperature for a city",
    inputSchema: z.object({
      city: z.string()
    }),
    execute: async (input: { city: string }) => {
      // Your implementation here
      return { temperature: 22, unit: "°C", city: input.city };
    }
  });
}

// That's it! Create an instance and extract tools
const weatherTools = new WeatherTools();
const tools = extractTools(weatherTools);

Using Tools with Agents

Once extracted, tools integrate seamlessly with agents:

import { Agent } from 'monan-sdk';

const weatherAgent = new Agent({
  name: "WeatherBot",
  model: "qwen3-vl:30b",
  description: "Provides weather information and forecasts",
  tools: tools // Automatically makes tools available to the agent
});

The agent now has access to all your tools and will intelligently decide when to use them based on user queries.

Creating Tools with Type Safety

import { tool, extractTools } from 'monan-sdk';
import { z } from 'zod';

class CalculatorTools {
  add = tool({
    name: 'add',
    description: "Add two numbers and return the result",
    inputSchema: z.object({
      a: z.number().describe("First number"),
      b: z.number().describe("Second number")
    }),
    execute: async (input: { a: number; b: number }) => {
      return { result: input.a + input.b };
    }
  });

  multiply = tool({
    name: 'multiply',
    description: "Multiply two numbers",
    inputSchema: z.object({
      x: z.number(),
      y: z.number()
    }),
    execute: async (input: { x: number; y: number }) => {
      return { result: input.x * input.y };
    }
  });
}

// Extract all tools from the class
const calculatorTools = new CalculatorTools();
const tools = extractTools(calculatorTools);

// Use with an Agent
const mathAgent = new Agent({
  name: "MathExpert",
  model: "qwen3-vl:30b",
  description: "An agent specialized in mathematical calculations",
  tools: tools
});

Tool Features

✅ Type-Safe Inputs: Automatic Zod validation ensures correct input types
✅ Self-Documenting: Descriptions and schemas automatically visible to LLM
✅ Error Handling: Graceful failure with descriptive validation messages
✅ Performance: Optimized for Bun runtime with zero overhead
✅ Composable: Multiple tool classes per agent for clean organization
✅ Async Support: All tools support async/await for API calls and I/O

Tool Input Validation

All tool inputs are automatically validated against their Zod schemas before execution. Invalid inputs never reach your code:

class DataTools {
  fetchUser = tool({
    name: 'fetchUser',
    description: "Fetch user data by ID",
    inputSchema: z.object({
      userId: z.string().uuid("Must be a valid UUID"),
      includeMetadata: z.boolean().optional()
    }),
    execute: async (input: { userId: string; includeMetadata?: boolean }) => {
      // Input is GUARANTEED to be valid here
      // userId is a valid UUID, includeMetadata is boolean or undefined
      return { id: input.userId, name: "John Doe" };
    }
  });
}

Validation Example:

// ✅ Valid call - works perfectly
await userTool.execute({ userId: "550e8400-e29b-41d4-a716-446655440000" });

// ❌ Invalid call - validation error with helpful message
// Error: Tool validation failed: userId must be a valid UUID
await userTool.execute({ userId: "invalid-id" });

Real-World Example: API Integration

Here's a practical example integrating with external APIs:

import { tool, extractTools } from 'monan-sdk';
import { z } from 'zod';

class ApiTools {
  searchRepositories = tool({
    name: 'searchRepositories',
    description: "Search GitHub repositories by keyword",
    inputSchema: z.object({
      query: z.string().min(1, "Search query required"),
      limit: z.number().int().min(1).max(100).default(10)
    }),
    execute: async (input: { query: string; limit: number }) => {
      const response = await fetch(
        `https://api.github.com/search/repositories?q=${encodeURIComponent(input.query)}&per_page=${input.limit}`
      );
      const data = await response.json();
      return {
        total: data.total_count,
        repositories: data.items.map(r => ({
          name: r.name,
          url: r.html_url,
          stars: r.stargazers_count,
          description: r.description
        }))
      };
    }
  });

  getWeather = tool({
    description: "Get weather forecast for a location",
    inputSchema: z.object({
      lat: z.number().min(-90).max(90),
      lon: z.number().min(-180).max(180),
      days: z.number().int().min(1).max(7).default(1)
    }),
    execute: async (input: { lat: number; lon: number; days: number }) => {
      // Call your weather API
      return { temperature: 22, forecast: "Sunny" };
    }
  });
}

const apiTools = new ApiTools();
const tools = extractTools(apiTools);

const assistantAgent = new Agent({
  name: "Assistant",
  model: "qwen3-vl:30b",
  tools: tools
});

Advanced: Custom Validation Rules

Add custom validation logic beyond basic type checking:

import { tool } from 'monan-sdk';
import { z } from 'zod';

class AdvancedTools {
  transfer = tool({
    description: "Transfer funds between accounts",
    inputSchema: z.object({
      fromAccount: z.string(),
      toAccount: z.string(),
      amount: z.number()
        .positive("Amount must be positive")
        .max(10000, "Cannot transfer more than 10,000")
    }).refine(
      (data) => data.fromAccount !== data.toAccount,
      { message: "Cannot transfer to the same account" }
    ),
    execute: async (input) => {
      // This only runs if all validations pass
      return { success: true, transactionId: "TXN-001" };
    }
  });
}

Using Multiple Tool Classes

Organize tools by domain and combine them in a single agent:

import { tool, extractTools } from 'monan-sdk';

class DatabaseTools {
  query = tool({
    description: "Execute database query",
    inputSchema: z.object({ sql: z.string() }),
    execute: async (input) => ({ rows: [] })
  });
}

class FileTools {
  readFile = tool({
    description: "Read file contents",
    inputSchema: z.object({ path: z.string() }),
    execute: async (input) => ({ content: "" })
  });
}

class NetworkTools {
  fetchUrl = tool({
    description: "Fetch URL contents",
    inputSchema: z.object({ url: z.string().url() }),
    execute: async (input) => ({ html: "" })
  });
}

// Combine all tools
const allTools = [
  ...extractTools(new DatabaseTools()),
  ...extractTools(new FileTools()),
  ...extractTools(new NetworkTools())
];

const omniscientAgent = new Agent({
  name: "OmniscientAssistant",
  model: "qwen3-vl:30b",
  tools: allTools
});

Best Practices

1. Clear Descriptions

// ❌ Bad - Vague description
const badTool = tool({
  description: "Do something",
  inputSchema: z.object({ x: z.string() }),
  execute: async (input) => ({})
});

// ✅ Good - Clear, specific description
const goodTool = tool({
  description: "Convert email address to lowercase and validate format",
  inputSchema: z.object({ 
    email: z.string().email("Valid email required") 
  }),
  execute: async (input) => ({ normalized: input.email.toLowerCase() })
});

2. Descriptive Field Names in Schemas

// ✅ Good - LLM understands context
inputSchema: z.object({
  sourceAccountId: z.string().describe("Account ID of sender"),
  destinationAccountId: z.string().describe("Account ID of recipient"),
  amountUSD: z.number().describe("Amount in US dollars")
})

3. Proper Error Handling

fetchData = tool({
  description: "Fetch data from external API",
  inputSchema: z.object({ url: z.string().url() }),
  execute: async (input) => {
    try {
      const response = await fetch(input.url, { timeout: 5000 });
      if (!response.ok) {
        return { error: `HTTP ${response.status}: ${response.statusText}` };
      }
      return { data: await response.json() };
    } catch (error) {
      return { error: `Failed to fetch: ${error.message}` };
    }
  }
})

4. Keep Tools Focused

// ❌ Bad - Too many responsibilities
extractData = tool({
  description: "Extract, transform, validate, and persist data",
  // ...
});

// ✅ Good - Single responsibility
const tools = [
  tool({ description: "Extract data from source", /* ... */ }),
  tool({ description: "Transform extracted data", /* ... */ }),
  tool({ description: "Validate data structure", /* ... */ }),
  tool({ description: "Persist data to storage", /* ... */ })
];

🧠 Hyper Agents (Orchestration)

For highly complex tasks, use a HyperAgent. Unlike a simple Router, a Hyper Agent creates a plan, breaks down the task, and manages a team of specialized agents to execute the work, synthesizing the final result.

It implements the Plan-Delegate-Synthesize pattern automatically.

import { Agent, HyperAgent } from 'monan-sdk';
import { extractTools } from 'monan-sdk';
// Assume tools are imported...

// 1. Define Specialized Agents (The Team)
const coder = new Agent({
  name: "SeniorDev",
  model: "qwen2.5-coder:7b",
  description: "Writes, debugs and explains Python and TypeScript code."
});

const researcher = new Agent({
  name: "WebResearcher",
  model: "llama3:8b",
  tools: tools, // web search tools
  description: "Searches the internet for current events and documentation."
});

// 2. Define the Hyper Agent (The Manager)// This agent needs a smarter model to handle logic (e.g., Llama 3 70b, GPT-4)
export const techLead = new HyperAgent({
  name: "TechLead",
  model: "openai/gpt-5.2-pro", // Needs high reasoning capability
  agents: [coder, researcher], // <--- The Squad
  description: "Manages the development of software features requiring updated docs.",
});

Run it:

monan run techLead:src.agent

Scenario:

• User: "Build a Python script to fetch the latest stock price of Apple using the yahoo-finance API, but first check the API documentation for the correct endpoint."

• HyperAgent Logic:

  1. Calls WebResearcher: "Find yahoo-finance API documentation for stock prices."

  2. Receives doc info.

  3. Calls SeniorDev: "Write a Python script using this endpoint [url]..."

  4. Output: Returns the code to the user.

🧠 Embeddings & Local Memory

Monan uses bun:sqlite's native optimization alongside Hugging Face models to create instant RAG.

import { LocalEmbeddings } from 'monan/embeddings';
import { SQLiteVectorStore } from 'monan/memory';

const embedder = new LocalEmbeddings({ model: "BAAI/bge-large-en-v1.5" });

const memory = new SQLiteVectorStore({ 
  path: "./monan.db", 
  embedder 
});

export const memoryAgent = new Agent({
  name: "RecallBot",
  knowledgeBase: memory
});

💻 CLI Commands

The CLI is the heart of Monan:

• monan init <project-name>: Creates a new project with the boilerplate structure.
• monan test <agent-var-name>:<file>: Runs the agent/workflow in the terminal for debugging.
• monan run <agent-var-name>:<file>: Spins up the production server (Elysia API).
• monan save <agent-var-name>:<file>: Indexes your agent for the UI.

💎 Plans

We believe powerful tools should be accessible. The UI is now included in the Community Plan.

Feature	Community (Free)	Enterprise
Framework SDK (Open Source)	✅	✅
CLI & Local Inference	✅	✅
Monan UI (Visual Interface)	✅	✅
OpenRouter Integration	✅	✅
Workflow Orchestration	✅	✅
KPIs & Metrics Dashboard	❌	✅
Audit Logs (Compliance)	❌	✅
SSO & RBAC	❌	✅
Priority Support	❌	✅

Community Plan: Includes everything you need to build, deploy, and visualize your agents locally. Enterprise Plan: Designed for companies requiring governance, detailed metrics, and SLA support.

⭐ Support the Project

We are building the future of Local AI in JavaScript/Bun. If you enjoy Monan's performance:

1. Give this repository a Star ⭐️ (Help us reach our goal of 100 stars!).
2. Open an Issue with suggestions.
3. Comment in discussions about how you are using Monan.

---

Built with ❤️ using Bun.