- name
- evoagentx-workflow
- description
- Bridge EvoAgentX (1000+ star open-source framework) with OpenClaw. Enables self-evolving agentic workflows - workflows that automatically improve over time through evolutionary optimization. Solves the gap where no EvoAgentX integration existed for OpenClaw (only 2 minimal EvoMap skills existed). Provides workflow autoconstruction, TextGrad/AFlow/MIPRO optimization algorithms, and GEP (Genome Evolution Protocol) integration.
- metadata
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 checkInstallation
# 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 result2. 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