Evolving Ai Agents
Skill Verified ActiveProvides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.
To automate the improvement of AI agents by leveraging LLM-driven evolution, making agents smarter and more performant over time.
Features
- LLM-driven evolution of agent prompts, skills, and memory
- File-system based workspace contract managed via Git
- Iterative solve-observe-evolve cycles against benchmarks
- Pluggable interfaces for agents, benchmarks, and engines
- Built-in seed agents and benchmarks for common domains
Use Cases
- Optimizing agent prompts and skills against measurable benchmarks
- Building self-improving agents with automated gating and rollback
- Evolving domain-specific tool usage and procedures
- Implementing automated agent evaluation loops
Non-Goals
- Building multi-agent orchestration from scratch
- One-shot agent tasks with no iteration needed
- RAG pipeline optimization
- Prompt-only optimization without skill/memory evolution
Installation
npx skills add Orchestra-Research/AI-Research-SKILLsRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
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