automagik-hive 0.1.1b2

Automagik Hive

Automagik Hive

Vibe your AI agents into existence - The only framework where natural language creates production-ready multi-agent systems. Describe what you want, get coordinated AI teams that actually work together.

⚡ Built on Agno's blazing-fast core (3μs agent instantiation)
🤖 Currently supports vibe coding via Claude Code (more AI assistants coming soon)

Get Started • Watch Demo • Examples • Join Community

See Vibe Coding in Action

[Placeholder: 30-second demo video showing natural language → working agent]

Watch how describing "I need a customer support team" becomes a fully deployed multi-agent system with intelligent routing, shared context, and production-ready APIs.

Get Started in Minutes

Option 1: Vibe Coding (Claude Code)

# One-line install
curl -sSL https://raw.githubusercontent.com/namastexlabs/automagik-hive/main/install.sh | bash

# Launch in Claude Code
automagik-hive genie

# Vibe your first agent into existence
> I need a customer support agent that handles billing questions

Currently available in Claude Code. Support for Cursor, Codex, and other AI assistants coming soon.

Option 2: YAML Configuration (Available Now for All)

# Create agents with simple YAML - no AI assistant required
automagik-hive --init my-workspace

# Edit ai/agents/*/config.yaml
automagik-hive --dev

Ready to build your second agent? → View Examples

The Multi-Agent Reality Check

What Others Call "Multi-Agent"

❌ Simple agents chained with unreliable tool calling
❌ No shared state between agents
❌ Message passing that breaks under load
❌ "No-code" that hits walls immediately

What We Built

✅ True coordination with shared memory and context
✅ Deterministic workflows that validate and retry
✅ Vibe coding that extends to real Python
✅ Production systems running hundreds of agents

How We Compare

Feature	Automagik Hive	LangChain	AutoGen	CrewAI
Vibe Coding	✅ Natural language	❌ Code only	❌ Code only	❌ YAML only
True Multi-Agent	✅ Shared state & memory	⚠️ Tool calling	⚠️ Message passing	✅ Coordination
Time to First Agent	Minutes	30+ minutes	20+ minutes	10+ minutes
Agent Performance	3μs instantiation	Varies	Varies	Varies
Production Ready	✅ Built-in	⚠️ DIY	⚠️ DIY	✅ Yes

What People Are Building

🎯 Personal Assistant - Daily Task Management

Challenge: Keeping track of emails, calendar, and tasks across platforms
Solution: Personal coordination agent that analyzes and prioritizes

Vibe: "I need an assistant that reads my emails, identifies action items, 
and creates calendar reminders for important deadlines"

Result: Never miss deadlines, automated task extraction

🏢 Customer Support Team - Intelligent Routing

Challenge: Handle diverse customer queries without hiring multiple specialists
Solution: Multi-agent team with automatic routing

team:
  mode: route  # Automatic intelligent routing
  name: "Customer Service AI"
  members:
    - billing-specialist
    - technical-support
    - account-manager

Result: 24/7 support, 70% query resolution without human intervention

💼 Enterprise Workflow - Order Processing

Challenge: Complex order fulfillment with multiple validation steps
Solution: Deterministic workflow with parallel processing

workflow:
  name: "Order Fulfillment"
  steps:
    - validate_input      # Ensure data is correct
    - parallel:           # Speed up independent tasks
        - check_inventory
        - verify_payment
        - calculate_shipping
    - process_order
    - send_notification

Result: 3x faster processing, automatic fraud detection

📊 SaaS Lead Qualification

Prototype: Natural language lead scoring rules
Production: Python integration with CRM and enrichment APIs

# Started with vibe coding, extended with Python
def enhance_lead_scorer():
    agent = Agent.from_yaml("lead-scorer/config.yaml")
    agent.add_tool(enrich_company_data)  # Custom API integration
    agent.add_tool(calculate_lead_score)  # Proprietary scoring
    return agent

Result: Sales team iterates on scoring rules without dev help, 40% better conversion

Found your use case? Start building → Get Started

Why It's Fast

Built on Agno's blazing-fast core:

• 3 microseconds agent instantiation (spawn 1000s of agents instantly)
• 6.5KB memory per agent (run entire teams on minimal infrastructure)
• Model agnostic - Use any LLM without vendor lock-in
• True multi-agent coordination - Shared context and memory (not just tool calling)

Unlike other "multi-agent" frameworks that just chain simple agents with unreliable tool calling, Automagik Hive provides genuine team coordination with shared state and deterministic workflows.

Your Journey

Discover (1 min) → First Agent (5 min) → Understanding (30 min) → Production (Your pace)

No overwhelming documentation. Learn as you build.

Phase 1: Rapid Prototyping

• Vibe agents into existence with natural language (Claude Code)
• Or define agents in YAML - no AI assistant needed
• Test behaviors instantly via REST API
• Iterate without complex code

Phase 2: Enhancement (When Needed)

• Add custom tools and integrations
• Implement business logic in Python
• Connect to your databases
• Extend with pure Python

Phase 3: Production Deployment

• Use automagik-hive --serve for Docker deployment
• PostgreSQL + pgvector included
• Authentication built-in
• Scale horizontally

No rewrites. No migrations. No platform lock-in.

Architecture That Scales

graph LR
    Person[Anyone] -->|Vibe Coding| Proto[Working Agent]
    Proto -->|Works Great| Use[Daily Use]
    Proto -->|Needs More| Dev[Developer]
    Dev -->|Python| Prod[Production Scale]
    
    style Proto fill:#90EE90
    style Prod fill:#87CEEB

Start Simple with YAML

# ai/agents/support-agent/config.yaml
agent:
  name: "Customer Support"
  agent_id: "support-agent"
  
instructions: |
  You help customers with billing and account issues.
  You have access to the knowledge base.
  
knowledge_filter:
  business_unit_filter: "customer_support"

Extend with Python When Needed

# ai/agents/support-agent/agent.py
from agno.agent import Agent

def get_support_agent() -> Agent:
    # Start with YAML config
    agent = Agent.from_yaml("config.yaml")
    
    # Add custom logic when needed
    agent.add_tool(check_billing_system)
    agent.add_tool(create_support_ticket)
    
    # Full Agno API available - no restrictions!
    return agent

See the power? Install now → Installation Guide

Deterministic Workflows

While vibe coding handles 80% of use cases, sometimes you need guarantees:

Teams vs Workflows

Use Teams for intelligent routing and collaboration
Use Workflows for deterministic, validated execution

# Chain agents like LEGO blocks with guarantees
workflow:
  name: "Order Processing"
  steps:
    - validate_input      # Ensure data is correct
    - process_payment     # With retry logic
    - parallel:           # Speed up independent tasks
        - check_inventory
        - calculate_shipping
        - verify_fraud
    - validate_output     # Confirm results
    - retry_if_failed     # Automatic recovery

Why Workflows Matter

• Deterministic Results: Same input → same output, every time
• Input/Output Validation: Catch errors before they propagate
• Automatic Retry: Built-in recovery from transient failures
• Parallel Performance: Run independent steps simultaneously
• Audit Trail: Track every step for compliance

Built by Practitioners, for Practitioners

We created Automagik Hive to solve our own frustrations at Namastex Labs. After building multi-agent systems for clients ranging from startups to Fortune 500 companies, we were tired of:

• Rewriting the same boilerplate for every project
• "Multi-agent" tools that were just simple agents with flaky tool calling
• Prototypes that needed complete rewrites for production

So we built the framework we wished existed. Now we're sharing it with you.

In Production: Powering hundreds of agents for real businesses
Battle-Tested: From startup MVPs to enterprise-scale deployments
Continuously Improved: We use this daily, so we keep it working

Who's Using Automagik Hive?

🎯 Individual Developers

Tired of: Writing 1000 lines of boilerplate for simple agents
With Hive: Vibe your idea → Working agent in minutes
Result: Ship AI features 10x faster

🏢 Product Teams

Tired of: Waiting weeks for dev resources to prototype
With Hive: Natural language → Testable agents immediately
Result: Validate ideas before writing code

🚀 Enterprises

Tired of: Prototypes that can't scale to production
With Hive: Same YAML from prototype to millions of requests
Result: Innovation at startup speed, enterprise reliability

GENIE: Your Vibe Coding Companion

GENIE transforms natural language into working AI systems through vibe coding.

# Launch GENIE in Claude Code
automagik-hive genie

# Vibe your agents into existence
> I need a data pipeline agent that processes CSV files and sends summaries to Slack

# GENIE creates:
# - YAML configuration
# - Python extension points
# - REST API endpoints
# - Documentation

Currently Available: Claude Code users can vibe agents today
Coming Soon: Cursor, Codex, Gemini CLI, Open Coder

Even without GENIE, you can:

• Build with YAML configuration (no AI assistant needed)
• Use templates to get started quickly
• Extend with Python when ready

GENIE accelerates development but isn't required - the framework stands on its own.

Installation

Quick Install (Recommended)

# One-line installation
curl -sSL https://raw.githubusercontent.com/namastexlabs/automagik-hive/main/install.sh | bash

# Initialize your workspace
automagik-hive --init my-ai-workspace

# Start building
automagik-hive --dev

Manual Installation

# Clone the repository
git clone https://github.com/namastexlabs/automagik-hive.git
cd automagik-hive

# Install with UV (recommended)
uv sync

# Or with pip
pip install -e .

# Set up environment
cp .env.example .env
# Edit .env with your configuration

# Start development server
uv run automagik-hive --dev

Docker Deployment

# Production deployment with Docker
automagik-hive --serve

# Your agents are now live!
# API: http://localhost:8886
# Database: PostgreSQL with pgvector
# Auth: Built-in API key authentication

Project Structure

my-ai-workspace/
├── ai/
│   ├── agents/           # Your AI agents
│   │   └── my-agent/
│   │       ├── config.yaml    # Agent definition
│   │       └── agent.py       # Optional Python extensions
│   ├── teams/            # Multi-agent teams
│   │   └── my-team/
│   │       └── config.yaml    # Team routing logic
│   └── workflows/        # Business workflows
│       └── my-workflow/
│           └── config.yaml    # Step definitions
├── knowledge/            # RAG knowledge base
│   └── data.csv         # Your knowledge data
└── .env                 # Configuration

API Endpoints

Every agent, team, and workflow automatically gets REST API endpoints:

# Single agent
POST /v1/agents/{agent_id}/run
GET  /v1/agents/{agent_id}/sessions

# Team routing
POST /v1/teams/{team_id}/run

# Workflow execution
POST /v1/workflows/{workflow_id}/run

# Knowledge management
POST /v1/knowledge/upsert
GET  /v1/knowledge/search

Advanced Features

Knowledge RAG System

# Automatic vector search from CSV data
agent.knowledge_filter = {
    "business_unit_filter": "customer_support",
    "product_filter": "billing"
}

Session Management

# Maintain conversation context
session = agent.create_session(user_id="customer-123")
response = session.run("What's my billing status?")

Custom Tools

# Add any Python function as a tool
@agent.tool
def check_inventory(product_id: str) -> dict:
    """Check product availability"""
    return {"available": True, "quantity": 42}

Model Flexibility

# Use any LLM provider
agent:
  model: "gpt-4"  # or claude-3, gemini-pro, llama-3, etc.
  provider: "openai"  # 23+ providers supported via Agno

Roadmap

Available Now

• ✅ YAML-first agent configuration
• ✅ Python extensibility
• ✅ Auto-generated REST APIs
• ✅ PostgreSQL + pgvector integration
• ✅ Docker deployment
• ✅ GENIE with Claude Code (vibe coding support)

Coming Soon

• 🔄 GENIE for Cursor
• 🔄 GENIE for Codex
• 🔄 GENIE for Gemini CLI
• 🔄 GENIE for Open Coder
• 🔄 Visual workflow designer
• 🔄 Agent marketplace
• 🔄 One-click cloud deployment

Under Consideration

• 💭 WebSocket streaming improvements
• 💭 GraphQL API generation
• 💭 Kubernetes operators
• 💭 Monitoring dashboard

Join the Movement

We're preparing for public launch. Be among the first:

Early Access Benefits

• 🎯 Priority Support - Direct access to the team building this
• 🚀 Shape the Framework - Your feedback drives our roadmap
• 📚 Exclusive Resources - Early access to templates and guides
• 🤝 Founding Member Status - Recognition in the community

Get Involved

• ⭐ Star this repo to show support
• 👀 Watch for updates and releases
• 🐛 Report issues to help us improve
• 🔀 Submit PRs - we welcome contributions!

Contributing

We welcome contributions! Whether it's bug fixes, new features, or documentation improvements, we appreciate your help.

Development Setup

# Clone and install dev dependencies
git clone https://github.com/namastexlabs/automagik-hive.git
cd automagik-hive
uv sync

# Run tests
uv run pytest

# Run linting
uv run ruff check --fix

# Run type checking
uv run mypy .

Contribution Guidelines

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

Automagik Hive is MIT licensed. See LICENSE file for details.

Support

• 📖 Documentation
• 💬 Discord Community
• 🐛 Issue Tracker
• 💬 Discussions

Acknowledgments

Built with ❤️ by Namastex Labs using:

• Agno - The blazing-fast multi-agent framework
• PostgreSQL - The world's most advanced open source database
• pgvector - Vector similarity search for PostgreSQL
• FastAPI - Modern, fast web framework for building APIs

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Ready to vibe your first agent into existence?

🚀 Get Started Now | 📺 Watch Demo | ⭐ Star on GitHub

The first framework bringing vibe coding to true multi-agent systems. Natural language creates production-ready AI teams with genuine coordination.

Made with ❤️ by the Automagik Team