Skill: kylechen26/evoagentx-workflow (version 1.0.2)

--- 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: { "openclaw": { "requires": { "bins": ["python3", "pip"] }, "install": [ { "id": "pip", "kind": "pip", "package": "evoagentx", "bins": ["python3"], "label": "Install EvoAgentX framework", }, ], }, } --- # 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: ```bash # 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 ```bash # 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: ```python 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 ```python 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 ```python # 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 ```python # 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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)