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Introducing evolverl on PyPI

evolveRL

Contract Address on SOL:

A revolutionary platform enabling AI agents to self-improve through evolutionary and adversarial mechanisms, bridging the gap between theoretical autonomy and actual self-reliance.

Start building truly autonomous AI agents

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from evolverl.evolution import Evolution
from evolverl.agent import Agent
from evolverl.judge import Judge, JudgingCriteria

# Initialize components
evolution = Evolution(
    population_size=10,
    generations=5,
    mutation_rate=0.2
)

agent = Agent(
    model="gpt-4o-mini",
    config={"temperature": 0.7}
)

judge = Judge(
    model="gpt-4o-mini",
    criteria=JudgingCriteria(
        correctness=1.0,
        clarity=0.7
    )
)

# Start evolution
evolved_agent = evolution.train(
    agent=agent,
    task="your_task",
    judge=judge,
    tester=tester
)
evolveRL is crucial for enabling truly autonomous AI agents by eliminating dependence on human intervention though adversarial evolutionary reinforcement.

Use Cases

Secure TEE Environment Setup
Isolated Data Processing
Confidential AI Training

Secure Environment

Create AI agents in a trusted execution environment with hardware-level security.

[2023-12-15 14:23:45] INFO

TEE environment initialized. Starting agent deployment.

[2023-12-15 14:23:47] ACTION

Agent evolution cycle started...

[2023-12-15 14:23:50] DECISION

New capability learned. Fitness score: 85%

[2023-12-15 14:23:52] WARNING

Resource optimization in progress.

[2023-12-15 14:23:55] ERROR

Evolution checkpoint saved successfully.

Evolution Monitoring

Track your AI agents as they learn and evolve with detailed performance metrics.

Continuous Evolution

Watch your AI agents adapt and improve through secure evolutionary processes.

Features

Evolutionary Optimization

Automatically generate, test, and refine prompts via evolutionary mechanisms.

Adversarial Testing

Specialized adversarial models craft tricky scenarios to expose and fix weaknesses.

Self-Improvement Loop

Continuous improvement without human intervention through evolutionary processes.

Multi-Agent Systems

Enable complex agent collaboration in decentralized and enterprise contexts.

Flexible Integration

Connect your preferred LLM and deploy on-chain or off-chain with ease.

Performance Metrics

Empirically validate improvements through comprehensive scoring mechanisms.

Community

We're grateful for the amazing open-source community that helps make our project better every day.

Ready to build truly autonomous AI agents?

Join the evolution of AI agents that improve themselves through adversarial learning.