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-projectto 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
Copyright 2025 LeafCraft. All rights reserved.
1 maintainer
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