EvoAgentX Workflow Integration

Integrates the EvoAgentX framework with OpenClaw for self-evolving agentic workflows.

When to Use This Skill

Use this skill when:

Building multi-agent workflows that need to evolve over time
Evaluating and optimizing existing agent workflows
Implementing the Genome Evolution Protocol (GEP)
Creating self-improving agent systems
Migrating static workflows to self-evolving ones

Quick Start

CLI Usage

This skill provides a command-line interface for EvoAgentX operations:

# Check if EvoAgentX is installed
python3 scripts/evoagentx_cli.py status

# Get installation instructions
python3 scripts/evoagentx_cli.py install

# Show usage examples
python3 scripts/evoagentx_cli.py examples

# Create a workflow template
python3 scripts/evoagentx_cli.py create-workflow \
  --name ResearchWorkflow \
  --description "A research automation workflow"

# Check EvoAgentX status
python3 scripts/evoagentx_cli.py check

Installation

# Install EvoAgentX framework
pip install evoagentx

# Verify installation
python3 -c "import evoagentx; print(evoagentx.__version__)"

1. Create a Basic Workflow

After running create-workflow, edit the generated Python file:

from evoagentx import Agent, Workflow

class MyWorkflow(Workflow):
    async def execute(self, context):
        # Your workflow logic here
        result = await self.run_agents(context)
        return result

2. Enable Self-Evolution

from evoagentx.evolution import EvolutionEngine

engine = EvolutionEngine()
optimized_workflow = await engine.evolve(
    workflow=MyWorkflow(),
    iterations=10,
    evaluation_criteria={"accuracy": 0.95}
)

Core Concepts

Workflows

Multi-agent orchestration
State management
Tool integration

Evolution Strategies

TextGrad: Prompt optimization
AFlow: Workflow structure optimization
MIPRO: Multi-step reasoning optimization

Genomes

Encoded success patterns containing:

Task type classification
Approach methodology
Outcome metrics
Context requirements

Common Patterns

Pattern 1: Research Workflow Evolution

# Start with basic research workflow
workflow = ResearchWorkflow()

# Evolve for better results
evolution = await workflow.evolve(
    dataset=research_queries,
    metric="comprehensiveness"
)

Pattern 2: Tool Selection Optimization

# EvoAgentX automatically selects optimal tools
workflow = AgentWorkflow(
    tools=["web_search", "browser", "file_io"],
    auto_select=True
)

Security Considerations

All evolution happens locally (no data exfiltration)
Genomes contain no credentials
Evaluation uses synthetic data when possible

References

EvoAgentX GitHub: https://github.com/EvoAgentX/EvoAgentX
Documentation: https://evoagentx.github.io/EvoAgentX/
arXiv Paper: https://arxiv.org/abs/2507.03616

Version

1.0.0 - Initial release with core EvoAgentX integration

evoagentx-workflow

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