LeafMesh — Multi-Agent AI Orchestration Platform

YAML-native multi-agent AI platform with self-healing and evolutionary capabilities

LeafMesh transforms multi-agent AI development through declarative YAML configuration that becomes executable intelligence. Built on the MANAGED_MESH architecture with production-ready coordination and persistence.

Core Features

YAML-Native Intelligence - Zero-code agent creation with AST-parsed configuration
Built-in Coordination - Manager and Summarizer agents provide automatic oversight
MANAGED_MESH Architecture - Direct agent communication with conditional routing
Redis-Powered Persistence - Automatic session management and conversation history
Enterprise Tool Ecosystem - 15+ built-in tools with OpenAI-compatible function calling
Advanced Parallel Processing - Multi-session threading with intelligent coordination

Production Features

Self-Healing Networks - 6 autonomous healing actions with failure detection
Evolutionary Optimization - Genetic algorithms with real fitness testing
Adaptive Model Intelligence - ML-powered model selection with performance prediction

Quick Start

1. Installation

pip install leafmesh

2. Environment Setup

# Required: OpenAI API key
export OPENAI_API_KEY="your-openai-key"

# Optional: Additional providers
export ANTHROPIC_API_KEY="your-anthropic-key"
export GOOGLE_API_KEY="your-google-key"

3. Redis Setup

Local Redis:

# macOS
brew install redis && brew services start redis

# Ubuntu/Debian
sudo apt install redis-server && sudo systemctl start redis

# Docker
docker run -d -p 6379:6379 redis:alpine

4. Basic Usage

from leafmesh import LeafMesh

# Initialize from YAML configuration
sdk = LeafMesh.from_yaml("config.yaml")

# Start the mesh
await sdk.start()

# Process requests
response = await sdk.process_request(
    session_id="user_session",
    input_data={"message": "Hello, how can you help me?"}
)

print(response)

Example YAML Configuration:

name: "my_mesh"
architecture: "managed_mesh"

# Built-in coordination
manager:
  enabled: true
  model: "gpt-4o"

summarizer:
  enabled: true
  model: "gpt-4o-mini"

# User-defined agents
agents:
  conversation_agent:
    name: "conversation_agent"
    model: "gpt-4o-mini"
    prompt: "You are a helpful AI assistant."
    yields:
      response: "string"
      confidence: "number"
    tools: ["calculator", "current_time"]

Architecture Overview

LeafMesh implements a MANAGED_MESH architecture with:

LLM Agents - YAML-defined with optional Python enhancement
Manager Agent - Built-in coordination and rule enforcement
Summarizer Agent - Omnipresent monitoring and analysis
Redis Persistence - Automatic session and conversation storage
Event System - All communication flows through events
Tool System - OpenAI-compatible function calling

For detailed architecture information, see docs/ARCHITECTURE.md

Agent Enhancement

Add Python logic to YAML-defined agents:

@sdk.intelligence("conversation_agent")
async def enhance_conversation(llm_response, input_data, context):
    """Add business logic to agent responses"""

    # Access conversation history
    history = context.get("conversation_history", [])

    # Enhance the LLM response
    enhanced_response = add_context(llm_response, history)

    # Trigger other agents conditionally
    if needs_specialist(enhanced_response):
        await sdk.trigger_agents(data={"analysis": enhanced_response})

    return {
        "response": enhanced_response,
        "confidence": calculate_confidence(enhanced_response)
    }

Revolutionary Features

Self-Healing Networks

# Enable automatic failure recovery
await sdk.enable_self_healing()

# Monitor agent health
health = await sdk.get_agent_health_status()
stats = await sdk.get_healing_statistics()

Evolutionary Optimization

# Optimize mesh configuration automatically
test_scenarios = [
    {"input": "Test case 1", "agents": ["conversation_agent"]},
    {"input": "Test case 2", "agents": ["technical_agent"]}
]

best_genome = await sdk.evolve_swarm_architecture(test_scenarios)
await sdk.apply_evolved_configuration()

Adaptive Model Selection

# Automatic model selection based on request characteristics
response = await sdk.adaptive_execute(
    prompt="Analyze this complex scenario",
    preferred_models=["gpt-4o", "claude-3.5-sonnet"]
)

Documentation

Architecture Guide - Technical implementation details
Debugging Guide - Troubleshooting and monitoring
Getting Started - Run create-leafmesh my-project to scaffold a complete example project

Use Cases

LeafMesh excels at:

Customer Service Systems - Multi-tier workflows with self-healing
Data Analysis Pipelines - Collaborative analytical workflows
Content Creation - Coordinated writing and editing
Decision Support - Complex decision-making with oversight
Workflow Automation - Business process automation

Framework Comparison

Feature	LeafMesh	LangGraph	CrewAI	AutoGen
YAML Configuration	Primary	Code-based	Code-based	Code-based
Built-in Coordination	Manager/Summarizer	Manual	Manual	Manual
Auto-Persistence	Redis	Manual	Manual	Manual
Self-Healing	Production	None	None	None
Evolutionary Optimization	Genetic Algorithm	None	None	None

Licensing

LeafMesh is commercial software owned by LeafCraft.

Evaluation: 30-day free evaluation for research/development
Commercial: Requires valid commercial license for revenue-generating use
Enterprise: Custom enterprise licensing available

Licensing: info@leafcraftstudios.com

Getting Started

Install LeafMesh and set up Redis
Create your first YAML configuration with basic agents
Add Python enhancements for custom logic
Enable revolutionary features for production

LeafMesh: Production-ready multi-agent AI with YAML-driven simplicity

postgres crewai langgraph autogen a2a mcp composio zapier claude-agent openai-agents scheduler slack telegram teams discord kafka sqs mqtt imap listeners email channels knowledge-mongo knowledge-postgres knowledge-pinecone knowledge-docs knowledge connectors dev

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