automagik-hive 1.0.0rc3

Hive Agents - AI-powered scaffolding for Agno agents - YAML-first DevX layer

Scaffolding and Smart RAG for Agno

AI-powered agent generation with intelligent CSV knowledge bases

Quick Start • Features • Examples • 🗺️ Roadmap • Contributing

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What is Automagik Hive?

Hive doesn't compete with Agno - it makes it easier to use.

Think of Hive as "Create React App" for Agno agents. Instead of weeks setting up project structure, writing boilerplate, and researching optimal configurations, Hive gives you:

• 🤖 AI-Powered Generation - Describe what you want; Hive's meta-agent generates optimal configs
• 🔄 Smart CSV RAG - Hash-based incremental loading (450x faster, 99% cost savings)
• 🎯 YAML-First Config - No Python boilerplate, just declarative configs
• 📦 Project Scaffolding - Zero to agent in 30 seconds

Built by practitioners who got tired of manually setting up the same patterns. Powered entirely by Agno.

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Key Features

🤖 AI That Generates AI

Use an Agno agent to generate Agno agent configurations. Natural language requirements → optimal YAML configs.

$ hive ai support-bot --interactive

🤖 AI-Powered Agent Generator
────────────────────────────────────────

💭 What should your agent do?
> Customer support bot with CSV knowledge base

🧠 Analyzing requirements...
✅ Generated successfully!

💡 AI Recommendations:
  • Model: gpt-4o-mini (cost-effective for support)
  • Tools: CSVTools, WebSearch
  • Complexity: 4/10
  • Estimated cost: $0.002/query

📋 Generated: ai/agents/support-bot/config.yaml

How it works:

1. Meta-agent analyzes natural language requirements
2. Selects optimal model from 7+ providers (OpenAI, Anthropic, Google, etc.)
3. Recommends tools from Agno's builtin catalog
4. Generates context-aware system instructions
5. Creates production-ready YAML configuration

Not keyword matching - real LLM intelligence.

🔄 Smart CSV RAG System

The one feature from V1 worth keeping - hash-based incremental CSV loading:

from hive.knowledge import create_knowledge_base

# Smart loading with hot reload
kb = create_knowledge_base(
    csv_path="data/faqs.csv",
    embedder="text-embedding-3-small",
    num_documents=5,
    hot_reload=True  # Watches for changes
)

# Only re-embeds changed rows
# MD5 hash tracking prevents redundant processing

Performance Numbers:

• ✅ 450x faster - Hot reload for unchanged CSVs
• ✅ 10x faster - Small updates (only changed rows)
• ✅ 99% cost savings - No redundant embeddings
• ✅ 18/18 tests passing - Production-ready

Real-world impact: $700+/year savings at scale.

🎯 YAML-First Agent Design

No Python boilerplate. Just declarative configurations:

agent:
  name: "Customer Support Bot"
  agent_id: "support-bot"
  version: "1.0.0"

model:
  provider: "openai"
  id: "gpt-4o-mini"
  temperature: 0.7

instructions: |
  You are a friendly customer support agent.
  Answer questions using the knowledge base.
  When unsure, escalate to human support.

tools:
  - name: CSVTools
    csv_path: "./data/faqs.csv"
  - name: WebSearch

storage:
  table_name: "support_bot_sessions"
  auto_upgrade_schema: true

Want to extend with Python? Just create agent.py:

from agno.agent import Agent
from hive.discovery import discover_config

def get_support_bot(**kwargs):
    config = discover_config()  # Loads config.yaml
    return Agent(
        name=config['agent']['name'],
        # ... custom logic here
        **kwargs
    )

📦 Project Scaffolding

Opinionated structure that scales:

my-project/
├── ai/                         # All AI components
│   ├── agents/                 # Agent definitions
│   │   ├── examples/           # Built-in examples (learning)
│   │   │   ├── support-bot/
│   │   │   ├── code-reviewer/
│   │   │   └── researcher/
│   │   └── [your-agents]/      # Your custom agents
│   ├── teams/                  # Multi-agent teams
│   ├── workflows/              # Step-based workflows
│   └── tools/                  # Custom tools
│
├── data/                       # Knowledge bases
│   ├── csv/                    # CSV files
│   └── documents/              # Document stores
│
├── .env                        # Environment config
├── hive.yaml                   # Project settings
└── pyproject.toml              # Dependencies

🚀 Built on Agno's Power

Hive is a thin layer over Agno. You get all of Agno's features:

• Performance: 3μs agent instantiation, 6.5KB memory per agent
• Native tools: 20+ production-ready tools (web search, code execution, file ops, etc.)
• Storage: PostgreSQL, SQLite with auto-schema migration
• Playground: Auto-generated API with OpenAPI docs
• Workflows: Sequential, parallel, conditional, looping
• Teams: Automatic routing, collaboration, coordination

🛠️ Builtin Tools Catalog

Easy access to Agno's tools with metadata and recommendations:

Category	Tools
Execution	PythonTools, ShellTools
Web	DuckDuckGoTools, TavilyTools, WebpageTools
Files	FileTools, CSVTools
Data	PandasTools, PostgresTools
APIs	SlackTools, EmailTools, GitHubTools

from hive.config.builtin_tools import BUILTIN_TOOLS

# Browse tools
for tool_name, info in BUILTIN_TOOLS.items():
    print(f"{tool_name}: {info['description']}")

🔥 Hot Reload

Change configs, see results instantly:

$ hive dev  # Starts dev server

# Edit ai/agents/my-bot/config.yaml
# Server automatically reloads
# Test at http://localhost:8886/docs

🏢 Enterprise-Ready

When you're ready for production:

• ✅ PostgreSQL with PgVector (hybrid search, HNSW indexing)
• ✅ Environment-based configuration (dev/staging/prod)
• ✅ API authentication with cryptographic keys
• ✅ Structured logging with Loguru
• ✅ Type safety with Pydantic validation
• ✅ Test coverage (87% pass rate, 147 tests)

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Quick Start

Prerequisites

• Python 3.11+ (3.12 recommended)
• At least one AI provider API key:
  • OpenAI (OPENAI_API_KEY)
  • Anthropic (ANTHROPIC_API_KEY)
  • Google (GEMINI_API_KEY)

Installation

# Install via uvx (recommended - no pollution)
uvx automagik-hive --help

# Or install globally with uv
uv pip install automagik-hive

# Or install with pip
pip install automagik-hive

Create Your First Agent (30 seconds)

# 1. Initialize project
uvx automagik-hive init my-project
cd my-project

# 2. Create API keys file
cp .env.example .env
# Edit .env and add your API keys

# 3a. Template-based creation (fast)
hive create agent my-bot

# 3b. AI-powered creation (optimal)
hive ai my-bot --description "Customer support bot with FAQ knowledge"

# 4. Start development server
hive dev

# 5. Access API docs
open http://localhost:8886/docs

Your First Conversation

# Via CLI
curl -X POST http://localhost:8886/agents/my-bot/runs \
  -H "Content-Type: application/json" \
  -d '{"message": "How do I reset my password?"}'

# Via Python
from agno.agent import Agent

agent = Agent.load("ai/agents/my-bot")
response = agent.run("How do I reset my password?")
print(response.content)

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Real-World Examples

Customer Support Router

Problem: Route support queries to specialized agents (billing, technical, general)

# ai/teams/support-router/config.yaml
team:
  name: "Support Router"
  team_id: "support-router"
  mode: "route"  # Agno handles routing automatically

members:
  - "billing-agent"
  - "technical-agent"
  - "general-agent"

instructions: |
  You are a support routing system.

  Route queries based on topic:
  - Billing: payments, invoices, refunds
  - Technical: bugs, errors, integrations
  - General: questions, information, other

Result: Automatic routing, no manual orchestration code needed.

Knowledge-Powered Agent

Problem: Answer customer questions from FAQ database

agent:
  name: "FAQ Bot"
  agent_id: "faq-bot"

model:
  provider: "openai"
  id: "gpt-4o-mini"

tools:
  - name: CSVTools
    csv_path: "./data/faqs.csv"

instructions: |
  Search the FAQ database for answers.
  Provide concise, helpful responses.
  If no match found, offer to escalate.

Setup CSV:

question,answer,category
How do I reset password?,Go to Settings > Security > Reset Password,account
What are your hours?,We're available 24/7 via chat and email,general
How do refunds work?,Refunds process in 5-7 business days,billing

Smart loading: Only re-embeds changed rows, saves 99% on embedding costs.

Code Review Workflow

Problem: Automated code review with security checks

# ai/workflows/code-review/config.yaml
workflow:
  name: "Security Code Review"
  workflow_id: "code-review"

steps:
  - name: "static_analysis"
    agent: "security-scanner"

  - name: "review"
    agent: "code-reviewer"
    tools:
      - PythonTools
      - FileTools

  - name: "report"
    function: "generate_report"

Result: Comprehensive reviews covering OWASP Top 10, best practices, and fix suggestions.

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Architecture That Scales

Project Structure

my-project/
├── ai/                         # AI components (auto-discovered)
│   ├── agents/                 # Agents (YAML + optional Python)
│   ├── teams/                  # Multi-agent teams
│   ├── workflows/              # Step-based workflows
│   └── tools/                  # Custom tools
│
├── data/                       # Knowledge bases
│   ├── csv/                    # CSV files (with hot reload)
│   └── documents/              # Other documents
│
├── .env                        # Environment variables
├── hive.yaml                   # Project configuration
└── pyproject.toml              # Python dependencies

Auto-Generated API

$ hive dev

# Agno Playground generates:
GET  /                          # API info
GET  /health                    # Health check
GET  /agents                    # List agents
POST /agents/{id}/runs          # Run agent
GET  /agents/{id}/sessions      # Get sessions
POST /teams/{id}/runs           # Run team
POST /workflows/{id}/runs       # Run workflow

Full OpenAPI docs at /docs.

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CLI Commands

# Project Management
hive init <project-name>                  # Initialize new project
hive version                              # Show version

# Component Creation - Templates
hive create agent <name>                  # Create agent from template
hive create team <name>                   # Create team
hive create workflow <name>               # Create workflow
hive create tool <name>                   # Create custom tool

# Component Creation - AI-Powered ⭐
hive ai <agent-name> --interactive        # Interactive AI generation
hive ai <agent-name> --description "..."  # Generate from description

# Development
hive dev                                  # Start dev server (hot reload)
hive dev --port 8000                      # Custom port
hive dev --examples                       # Run with built-in examples

# Production
hive serve                                # Start production server
hive serve --port 8000                    # Custom port

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Database Backend Selection

Hive supports multiple database backends for different use cases:

Backend	Best For	Setup	Performance	Features
PostgreSQL	Production	Docker	⭐⭐⭐⭐⭐	Full text search, PgVector, HNSW
SQLite	Development	None	⭐⭐⭐	File-based, good for testing

PostgreSQL (Recommended for Production)

# Start PostgreSQL with Docker
docker run -d \
  --name hive-postgres \
  -e POSTGRES_PASSWORD=your_password \
  -e POSTGRES_DB=hive \
  -p 5432:5432 \
  pgvector/pgvector:latest

# Update .env
HIVE_DATABASE_URL=postgresql://postgres:your_password@localhost:5432/hive

Features:

• PgVector for hybrid search
• HNSW indexing (fast vector similarity)
• Full-text search
• Auto-schema migration
• Production-ready

SQLite (Development Only)

# Update .env
HIVE_DATABASE_URL=sqlite:///./data/hive.db

Limitations:

• No concurrent writes
• No vector similarity search
• File locking issues under load
• Not recommended for production

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Environment Configuration

Minimal .env (20 vars, not 145!):

# Core (Required)
HIVE_ENVIRONMENT=development              # development|staging|production
HIVE_API_PORT=8886                        # API server port
HIVE_DATABASE_URL=postgresql://...        # Database connection
HIVE_API_KEY=hive_your_32_char_key        # API authentication

# AI Providers (At least one required)
OPENAI_API_KEY=sk-...                     # OpenAI models
ANTHROPIC_API_KEY=sk-ant-...              # Claude models
GEMINI_API_KEY=...                        # Google models

# Optional
HIVE_LOG_LEVEL=INFO                       # DEBUG|INFO|WARNING|ERROR
HIVE_VERBOSE_LOGS=false                   # Detailed logging
HIVE_ENABLE_METRICS=true                  # Performance tracking
HIVE_CORS_ORIGINS=http://localhost:3000   # Comma-separated origins

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Development

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

# Install dependencies
uv sync

# Run tests
uv run pytest                              # All tests
uv run pytest tests/hive/knowledge/        # Knowledge tests
uv run pytest -v --cov=hive                # With coverage

# Lint & format
uv run ruff check --fix
uv run ruff format

# Type check
uv run mypy hive/

# Start examples
uv run python hive/examples/agents/demo_all_agents.py

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Why Hive vs Pure Agno?

Feature	Pure Agno	Hive + Agno
Agent Creation	Write Python factories	YAML or AI generation
Getting Started	Read docs, write boilerplate	hive init → instant project
Knowledge Base	Setup PgVector, write loaders	create_knowledge_base() with hot reload
Model Selection	Research 7+ providers	AI recommends optimal choice
Tool Selection	Browse Agno tools	Catalog + AI recommendations
CSV RAG	Write custom incremental loader	Built-in hash-based incremental
Project Structure	DIY	Opinionated ai/ structure

Hive = Scaffolding for Agno

Like Create React App for React, Hive removes setup friction without replacing the framework.

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What Hive Does NOT Do

❌ Compete with Agno - We extend it, don't replace it ❌ Reinvent orchestration - Use Agno's native teams/workflows ❌ Lock you in - Generated code is pure Agno, you own it ❌ Replace your code - We scaffold, you customize

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Roadmap

V2.0 (Current) ✅

• AI-powered agent generation with meta-agent
• Smart CSV RAG with hash-based incremental loading
• YAML-first configuration
• Project scaffolding with examples
• Builtin tools catalog
• Hot reload for dev server

V2.1 - Enhanced DevX 🚀

• Interactive TUI for agent creation
• Live agent testing in terminal
• Knowledge base quality scoring
• Tool compatibility checker
• Agent performance profiling

V2.2 - Production Features 🌟

• Multi-environment configs (dev/staging/prod)
• Cost tracking and optimization
• Deployment helpers (Docker, AWS, Fly.io)
• Agent monitoring dashboard
• Workflow visualization

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Enterprise Features

Security & Authentication

• ✅ Cryptographic API key generation (secrets.token_urlsafe)
• ✅ Constant-time validation (prevents timing attacks)
• ✅ Environment-based security (auto-enabled in production)
• ✅ Input validation (size limits, sanitization)

Database & Storage

• ✅ PostgreSQL with PgVector (hybrid search)
• ✅ SQLite for development
• ✅ Auto-schema migration
• ✅ Session persistence

Monitoring & Observability

• ✅ Structured logging (Loguru)
• ✅ Automatic emoji mapping
• ✅ Performance metrics
• ✅ Error tracking

Deployment

• ✅ Docker-ready
• ✅ Environment scaling (dev/staging/prod)
• ✅ Health checks
• ✅ Graceful shutdown

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Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Areas for contribution:

• Additional builtin tool integrations
• Example agents for common use cases
• Documentation improvements
• Bug fixes and performance optimizations

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Acknowledgments

Powered by:

• Agno - The AI agent framework powering everything
• UV - Modern Python packaging and project management
• Typer - Beautiful CLI framework
• Rich - Terminal output that doesn't suck
• Pydantic - Data validation with type hints
• FastAPI - Modern web framework (via Agno)

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License

MIT License - see LICENSE for details.

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Links

• Documentation: docs/
• Examples: hive/examples/
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Agno Framework: github.com/agno-agi/agno

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Built with ❤️ by practitioners who got tired of boilerplate.

Remember: Hive doesn't compete with Agno. We make it easier to use. 🚀