By Zpankz
Self-referential self-improving agent framework for Claude Code. Inspired by Meta's HyperAgents paper — evolutionary optimization of skills, agents, hooks, and code via LLM-driven mutation and fitness-based selection.
View, query, and manage the HyperAgents evolutionary archive. Shows generation history, fitness scores, lineage trees, and best performers. Use: /hyperagents:archive [show|best|lineage|prune|export]
Evaluate a specific generation or the current codebase against fitness criteria. Supports staged (quick) and full evaluation modes. Use: /hyperagents:evaluate [--genid <id>] [--domain <domain>] [--staged]
Start or resume a HyperAgents evolutionary improvement loop. Iteratively mutates code/skills/agents, evaluates fitness, and selects parents for the next generation. Use: /hyperagents:evolve [--domain <domain>] [--generations <n>] [--resume]
Select the next parent generation for mutation using configurable selection strategies. Use: /hyperagents:select-parent [--method <method>]
Show the current state of the HyperAgents evolution process — active generation, archive health, fitness trajectory, and next steps. Use: /hyperagents:status
Ensemble agent that combines predictions from multiple archive generations to produce superior results. Selects the best-performing generation for each task based on archive fitness data, or uses majority voting across top-K generations.
Fitness evaluation agent that scores a generation's output against domain-specific criteria. Runs the evaluation harness, generates reports, and computes fitness scores. Used by the evolve command to assess each generation.
Self-referential meta-agent that generates code improvements. Given a target codebase and previous evaluation results, it produces diffs that improve fitness scores. Can modify any part of the code, including agent definitions, skills, and its own selection/evaluation logic. Dispatch this agent in a git worktree for sandboxed mutation.
Task execution agent that solves domain-specific tasks. This is the agent that gets evolved — its code and prompts are modified by the meta-agent across generations. Evaluates tasks and returns structured predictions.
Create and configure domain-specific evaluation harnesses for the HyperAgents evolution loop. Defines how tasks are loaded, agents are invoked, predictions are collected, and scores are computed. Triggers when setting up evaluation domains or creating custom fitness functions.
Manage the HyperAgents evolutionary archive — an append-only log of all code generations with fitness scores, lineage tracking, and diff storage. Triggers when working with .hyperagents/ directory, archive.jsonl files, or generation metadata.
Domain-agnostic fitness evaluation for evolved code generations. Defines evaluation harness interfaces, scoring contracts, and multi-domain aggregation. Triggers when evaluating code quality, running benchmarks, or scoring agent outputs.
Evolutionary parent selection algorithms for choosing which generation to mutate next. Implements random, best, score-proportional, and novelty-aware selection. Triggers when selecting parents, managing exploration/exploitation tradeoffs, or configuring evolution strategy.
Apply HyperAgents' self-referential improvement pattern to any code artifact. Triggers when Claude is asked to 'improve', 'optimize', 'evolve', or 'self-improve' code, agents, skills, or prompts. Also triggers on repeated failures as an automatic recovery strategy.
Modifies files
Hook triggers on file write and edit operations
Uses power tools
Uses Bash, Write, or Edit tools
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A Claude Code plugin that brings self-referential self-improving agents to your development workflow. Inspired by Meta's HyperAgents paper — evolutionary optimization of skills, agents, hooks, and code via LLM-driven mutation and fitness-based selection.
HyperAgents runs an evolutionary loop over your code:
SELECT PARENT → MUTATE (meta-agent) → EVALUATE (fitness) → ARCHIVE → repeat
Each generation, a meta-agent modifies target code in a sandboxed git worktree. The changes are scored by a pluggable fitness function. The best mutations survive and become parents for the next generation. The system can even improve its own selection algorithms and evaluation criteria.
# Clone the plugin
git clone https://github.com/Zpankz/hyperagents-plugin.git ~/.claude/plugins/hyperagents-plugin
# Or symlink from a local clone
ln -s /path/to/hyperagents-plugin ~/.claude/plugins/hyperagents-plugin
| Command | Description |
|---|---|
/hyperagents:evolve | Start or resume an evolutionary improvement loop |
/hyperagents:evaluate | Evaluate a generation against fitness criteria |
/hyperagents:archive | View, query, and manage the evolutionary archive |
/hyperagents:select-parent | Select next parent with configurable strategy |
/hyperagents:status | Show evolution progress dashboard |
| Agent | Model | Purpose |
|---|---|---|
meta-agent | Opus | Self-referential code mutator — can modify any file including itself |
task-agent | Sonnet | Domain task executor — the agent that gets evolved |
evaluator | Haiku | Fitness evaluation runner |
ensemble-agent | Haiku | Combines predictions from multiple archive generations |
# Archive management
bash scripts/archive-manager.sh show|best|lineage|fitness|validate|export
# Fitness scoring
bash scripts/fitness-scorer.sh compute|compare|rank|trend
Unlike traditional agent frameworks where the orchestrator is fixed, HyperAgents allows the meta-agent to modify any part of the codebase — including:
All generations are stored in an append-only .hyperagents/archive.jsonl. Each generation records its parent, fitness scores, code diff, and metadata. This enables:
All mutations happen in isolated git worktrees. Failed mutations never touch the main branch. Diffs are captured and stored for deterministic replay.
A two-phase evaluation strategy: quick check on 10% of samples first, full evaluation only if the quick check passes. Saves 75%+ compute by rejecting obviously broken mutations early.
hyperagents-plugin/
├── .claude-plugin/plugin.json # Plugin manifest
├── commands/ # 5 slash commands
├── agents/ # 4 specialized subagents
├── skills/ # 6 auto-activating skills
├── hooks/ # 3 event-driven hooks
└── scripts/ # 2 CLI utilities
HyperAgents: Self-Referential Self-Improving Agents by Jenny Zhang, Bingchen Zhao, Wannan Yang, Jakob Foerster, Jeff Clune, Minqi Jiang, Sam Devlin, Tatiana Shavrina (Meta / FAIR, 2026).
CC-BY-NC-SA-4.0 (matching the original HyperAgents repository)
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