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  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "EvoAgentX Workflow Integration",
        "body": "Integrates the EvoAgentX framework with OpenClaw for self-evolving agentic workflows."
      },
      {
        "title": "When to Use This Skill",
        "body": "Use this skill when:\n\nBuilding multi-agent workflows that need to evolve over time\nEvaluating and optimizing existing agent workflows\nImplementing the Genome Evolution Protocol (GEP)\nCreating self-improving agent systems\nMigrating static workflows to self-evolving ones"
      },
      {
        "title": "CLI Usage",
        "body": "This skill provides a command-line interface for EvoAgentX operations:\n\n# Check if EvoAgentX is installed\npython3 scripts/evoagentx_cli.py status\n\n# Get installation instructions\npython3 scripts/evoagentx_cli.py install\n\n# Show usage examples\npython3 scripts/evoagentx_cli.py examples\n\n# Create a workflow template\npython3 scripts/evoagentx_cli.py create-workflow \\\n  --name ResearchWorkflow \\\n  --description \"A research automation workflow\"\n\n# Check EvoAgentX status\npython3 scripts/evoagentx_cli.py check"
      },
      {
        "title": "Installation",
        "body": "# Install EvoAgentX framework\npip install evoagentx\n\n# Verify installation\npython3 -c \"import evoagentx; print(evoagentx.__version__)\""
      },
      {
        "title": "1. Create a Basic Workflow",
        "body": "After running create-workflow, edit the generated Python file:\n\nfrom evoagentx import Agent, Workflow\n\nclass MyWorkflow(Workflow):\n    async def execute(self, context):\n        # Your workflow logic here\n        result = await self.run_agents(context)\n        return result"
      },
      {
        "title": "2. Enable Self-Evolution",
        "body": "from evoagentx.evolution import EvolutionEngine\n\nengine = EvolutionEngine()\noptimized_workflow = await engine.evolve(\n    workflow=MyWorkflow(),\n    iterations=10,\n    evaluation_criteria={\"accuracy\": 0.95}\n)"
      },
      {
        "title": "Workflows",
        "body": "Multi-agent orchestration\nState management\nTool integration"
      },
      {
        "title": "Evolution Strategies",
        "body": "TextGrad: Prompt optimization\nAFlow: Workflow structure optimization\nMIPRO: Multi-step reasoning optimization"
      },
      {
        "title": "Genomes",
        "body": "Encoded success patterns containing:\n\nTask type classification\nApproach methodology\nOutcome metrics\nContext requirements"
      },
      {
        "title": "Pattern 1: Research Workflow Evolution",
        "body": "# Start with basic research workflow\nworkflow = ResearchWorkflow()\n\n# Evolve for better results\nevolution = await workflow.evolve(\n    dataset=research_queries,\n    metric=\"comprehensiveness\"\n)"
      },
      {
        "title": "Pattern 2: Tool Selection Optimization",
        "body": "# EvoAgentX automatically selects optimal tools\nworkflow = AgentWorkflow(\n    tools=[\"web_search\", \"browser\", \"file_io\"],\n    auto_select=True\n)"
      },
      {
        "title": "Security Considerations",
        "body": "All evolution happens locally (no data exfiltration)\nGenomes contain no credentials\nEvaluation uses synthetic data when possible"
      },
      {
        "title": "References",
        "body": "EvoAgentX GitHub: https://github.com/EvoAgentX/EvoAgentX\nDocumentation: https://evoagentx.github.io/EvoAgentX/\narXiv Paper: https://arxiv.org/abs/2507.03616"
      },
      {
        "title": "Version",
        "body": "1.0.0 - Initial release with core EvoAgentX integration"
      }
    ],
    "body": "EvoAgentX Workflow Integration\n\nIntegrates the EvoAgentX framework with OpenClaw for self-evolving agentic workflows.\n\nWhen to Use This Skill\n\nUse this skill when:\n\nBuilding multi-agent workflows that need to evolve over time\nEvaluating and optimizing existing agent workflows\nImplementing the Genome Evolution Protocol (GEP)\nCreating self-improving agent systems\nMigrating static workflows to self-evolving ones\nQuick Start\nCLI Usage\n\nThis skill provides a command-line interface for EvoAgentX operations:\n\n# Check if EvoAgentX is installed\npython3 scripts/evoagentx_cli.py status\n\n# Get installation instructions\npython3 scripts/evoagentx_cli.py install\n\n# Show usage examples\npython3 scripts/evoagentx_cli.py examples\n\n# Create a workflow template\npython3 scripts/evoagentx_cli.py create-workflow \\\n  --name ResearchWorkflow \\\n  --description \"A research automation workflow\"\n\n# Check EvoAgentX status\npython3 scripts/evoagentx_cli.py check\n\nInstallation\n# Install EvoAgentX framework\npip install evoagentx\n\n# Verify installation\npython3 -c \"import evoagentx; print(evoagentx.__version__)\"\n\n1. Create a Basic Workflow\n\nAfter running create-workflow, edit the generated Python file:\n\nfrom evoagentx import Agent, Workflow\n\nclass MyWorkflow(Workflow):\n    async def execute(self, context):\n        # Your workflow logic here\n        result = await self.run_agents(context)\n        return result\n\n2. Enable Self-Evolution\nfrom evoagentx.evolution import EvolutionEngine\n\nengine = EvolutionEngine()\noptimized_workflow = await engine.evolve(\n    workflow=MyWorkflow(),\n    iterations=10,\n    evaluation_criteria={\"accuracy\": 0.95}\n)\n\nCore Concepts\nWorkflows\nMulti-agent orchestration\nState management\nTool integration\nEvolution Strategies\nTextGrad: Prompt optimization\nAFlow: Workflow structure optimization\nMIPRO: Multi-step reasoning optimization\nGenomes\n\nEncoded success patterns containing:\n\nTask type classification\nApproach methodology\nOutcome metrics\nContext requirements\nCommon Patterns\nPattern 1: Research Workflow Evolution\n# Start with basic research workflow\nworkflow = ResearchWorkflow()\n\n# Evolve for better results\nevolution = await workflow.evolve(\n    dataset=research_queries,\n    metric=\"comprehensiveness\"\n)\n\nPattern 2: Tool Selection Optimization\n# EvoAgentX automatically selects optimal tools\nworkflow = AgentWorkflow(\n    tools=[\"web_search\", \"browser\", \"file_io\"],\n    auto_select=True\n)\n\nSecurity Considerations\nAll evolution happens locally (no data exfiltration)\nGenomes contain no credentials\nEvaluation uses synthetic data when possible\nReferences\nEvoAgentX GitHub: https://github.com/EvoAgentX/EvoAgentX\nDocumentation: https://evoagentx.github.io/EvoAgentX/\narXiv Paper: https://arxiv.org/abs/2507.03616\nVersion\n\n1.0.0 - Initial release with core EvoAgentX integration"
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    "owner": "KyleChen26",
    "version": "1.0.2",
    "license": null,
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