Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Bridge EvoAgentX (1000+ star open-source framework) with OpenClaw. Enables self-evolving agentic workflows - workflows that automatically improve over time t...
Bridge EvoAgentX (1000+ star open-source framework) with OpenClaw. Enables self-evolving agentic workflows - workflows that automatically improve over time t...
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Integrates the EvoAgentX framework with OpenClaw for self-evolving agentic workflows.
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
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
# Install EvoAgentX framework pip install evoagentx # Verify installation python3 -c "import evoagentx; print(evoagentx.__version__)"
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
from evoagentx.evolution import EvolutionEngine engine = EvolutionEngine() optimized_workflow = await engine.evolve( workflow=MyWorkflow(), iterations=10, evaluation_criteria={"accuracy": 0.95} )
Multi-agent orchestration State management Tool integration
TextGrad: Prompt optimization AFlow: Workflow structure optimization MIPRO: Multi-step reasoning optimization
Encoded success patterns containing: Task type classification Approach methodology Outcome metrics Context requirements
# Start with basic research workflow workflow = ResearchWorkflow() # Evolve for better results evolution = await workflow.evolve( dataset=research_queries, metric="comprehensiveness" )
# EvoAgentX automatically selects optimal tools workflow = AgentWorkflow( tools=["web_search", "browser", "file_io"], auto_select=True )
All evolution happens locally (no data exfiltration) Genomes contain no credentials Evaluation uses synthetic data when possible
EvoAgentX GitHub: https://github.com/EvoAgentX/EvoAgentX Documentation: https://evoagentx.github.io/EvoAgentX/ arXiv Paper: https://arxiv.org/abs/2507.03616
1.0.0 - Initial release with core EvoAgentX integration
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.