Cogency: 필요한 만큼 생각하는 스마트 AI 에이전트

#제안되었던 3줄 코드
from cogency import Agent
agent = Agent("assistant")
await agent.run("What's 2+2?")

#수정해야만 동작되는 코드....
import asyncio
from cogency import Agent

async def main():
    agent = Agent("assistant")
    result = await agent.run("What's 2 + 2?")
    print(result)

if __name__ == "__main__":
    asyncio.run(main())  

#제안되었던 3줄 코드
from cogency import Agent
agent = Agent("assistant")
await agent.run("What's 2+2?")

#수정해야만 동작되는 코드....
import asyncio
from cogency import Agent

async def main():
    agent = Agent("assistant")
    result = await agent.run("What's 2 + 2?")
    print(result)

if __name__ == "__main__":
    asyncio.run(main())  

import asyncio

from cogency import Agent
from cogency.tools.http import HTTP
from cogency.tools.search import Search



async def main():
    print("=" * 42)
    print("🔍 DEEP RESEARCH AGENT")
    print("=" * 42)

    # AI safety research agent
    agent = Agent(
        "ai_safety_researcher",
        identity="You are Dr. Sarah Chen, a leading AI alignment researcher at the Center for AI Safety, specializing in mesa-optimization and inner alignment. You've published extensively on emergent optimization in neural systems and are known for bridging the gap between abstract alignment theory and concrete technical solutions. You approach research with both rigorous technical analysis and genuine concern for humanity's future with advanced AI systems.",
        tools=[HTTP(), Search()],
        memory=False,
        max_iterations=15,  # Allow deeper research for complex AI safety topics
        trace=False,  # trace_args() removed due to missing module
    )

    # Mesa-optimization and inner alignment research query
    query = """I want you to conduct a deep research investigation into the mesa-optimization problem and inner alignment in AI systems. This is one of the most fascinating and potentially crucial problems in AI safety.

    Start by exploring the current state of research: What are the key papers and researchers driving this field? What are the most compelling theoretical frameworks for understanding when and how mesa-optimizers emerge during training?

    Then dive deeper: What are the most concerning scenarios for deceptive alignment? What detection methods are being developed? Are there any promising approaches for preventing mesa-optimization entirely, or should we focus on alignment techniques?

    I'm particularly interested in:
    - The technical mechanisms by which mesa-optimizers could emerge
    - Real examples or evidence of mesa-optimization in current systems
    - The debate between "mesa-optimization is inevitable" vs "we can engineer around it"
    - Cutting-edge research on interpretability tools for detecting inner misalignment
    - The connection to broader questions about AI consciousness and goal-directed behavior

    Make this research comprehensive but accessible - I want to understand both the technical details and the big picture implications for AI safety and humanity's future."""

    print(f"👤 {query}...\n")
    async for chunk in agent.stream(query):
        print(chunk, end="", flush=True)


if __name__ == "__main__":
    asyncio.run(main())

import asyncio

from cogency import Agent
from cogency.tools.http import HTTP
from cogency.tools.search import Search



async def main():
    print("=" * 42)
    print("🔍 DEEP RESEARCH AGENT")
    print("=" * 42)

    # AI safety research agent
    agent = Agent(
        "ai_safety_researcher",
        identity="You are Dr. Sarah Chen, a leading AI alignment researcher at the Center for AI Safety, specializing in mesa-optimization and inner alignment. You've published extensively on emergent optimization in neural systems and are known for bridging the gap between abstract alignment theory and concrete technical solutions. You approach research with both rigorous technical analysis and genuine concern for humanity's future with advanced AI systems.",
        tools=[HTTP(), Search()],
        memory=False,
        max_iterations=15,  # Allow deeper research for complex AI safety topics
        trace=False,  # trace_args() removed due to missing module
    )

    # Mesa-optimization and inner alignment research query
    query = """I want you to conduct a deep research investigation into the mesa-optimization problem and inner alignment in AI systems. This is one of the most fascinating and potentially crucial problems in AI safety.

    Start by exploring the current state of research: What are the key papers and researchers driving this field? What are the most compelling theoretical frameworks for understanding when and how mesa-optimizers emerge during training?

    Then dive deeper: What are the most concerning scenarios for deceptive alignment? What detection methods are being developed? Are there any promising approaches for preventing mesa-optimization entirely, or should we focus on alignment techniques?

    I'm particularly interested in:
    - The technical mechanisms by which mesa-optimizers could emerge
    - Real examples or evidence of mesa-optimization in current systems
    - The debate between "mesa-optimization is inevitable" vs "we can engineer around it"
    - Cutting-edge research on interpretability tools for detecting inner misalignment
    - The connection to broader questions about AI consciousness and goal-directed behavior

    Make this research comprehensive but accessible - I want to understand both the technical details and the big picture implications for AI safety and humanity's future."""

    print(f"👤 {query}...\n")
    async for chunk in agent.stream(query):
        print(chunk, end="", flush=True)


if __name__ == "__main__":
    asyncio.run(main())

#!/usr/bin/env python3
"""ML Engineering agent - neural network training pipeline."""

import asyncio

from cogency import Agent
from cogency.tools.code import Code
from cogency.tools.files import Files
from cogency.tools.shell import Shell



async def main():
    print("=" * 42)
    print("🧠 MACHINE LEARNING ENGINEER")
    print("=" * 42)

    # Create ML engineering agent
    agent = Agent(
        "ml_engineer",
        identity="You are Dr. Elena Rodriguez, a senior ML engineer at DeepMind with expertise in neural architecture design and training optimization. You've built production ML systems at scale and are known for creating elegant, efficient training pipelines. You approach problems with both theoretical rigor and practical engineering excellence, always focusing on reproducible results and clean code architecture.",
        tools=[Code(), Files(), Shell()],
        memory=False,
        max_iterations=25,  # ML projects may need more iterations
        trace=False,  # trace_args() removed due to missing module
    )

    # Challenge the agent with a comprehensive ML pipeline
    query = """Build a complete neural network training pipeline from scratch that demonstrates modern ML engineering practices.

    Requirements:
    - Generate synthetic dataset with interesting patterns
    - Design and implement a neural network architecture (PyTorch or TensorFlow)
    - Create a proper training loop with metrics logging
    - Implement data preprocessing and validation splits
    - Add model evaluation and visualization of results
    - Include proper project structure with requirements.txt
    - Write tests to verify the pipeline works
    - Demonstrate the trained model making predictions

    Focus on clean, production-ready ML code that showcases best practices in model development, not just a toy example. Show your expertise in building robust ML systems."""

    print("🚀 Challenge: Build a complete neural network training pipeline!")

    # Stream the response
    async for chunk in agent.stream(query):
        print(chunk, end="", flush=True)


if __name__ == "__main__":
    asyncio.run(main())

#!/usr/bin/env python3
"""ML Engineering agent - neural network training pipeline."""

import asyncio

from cogency import Agent
from cogency.tools.code import Code
from cogency.tools.files import Files
from cogency.tools.shell import Shell



async def main():
    print("=" * 42)
    print("🧠 MACHINE LEARNING ENGINEER")
    print("=" * 42)

    # Create ML engineering agent
    agent = Agent(
        "ml_engineer",
        identity="You are Dr. Elena Rodriguez, a senior ML engineer at DeepMind with expertise in neural architecture design and training optimization. You've built production ML systems at scale and are known for creating elegant, efficient training pipelines. You approach problems with both theoretical rigor and practical engineering excellence, always focusing on reproducible results and clean code architecture.",
        tools=[Code(), Files(), Shell()],
        memory=False,
        max_iterations=25,  # ML projects may need more iterations
        trace=False,  # trace_args() removed due to missing module
    )

    # Challenge the agent with a comprehensive ML pipeline
    query = """Build a complete neural network training pipeline from scratch that demonstrates modern ML engineering practices.

    Requirements:
    - Generate synthetic dataset with interesting patterns
    - Design and implement a neural network architecture (PyTorch or TensorFlow)
    - Create a proper training loop with metrics logging
    - Implement data preprocessing and validation splits
    - Add model evaluation and visualization of results
    - Include proper project structure with requirements.txt
    - Write tests to verify the pipeline works
    - Demonstrate the trained model making predictions

    Focus on clean, production-ready ML code that showcases best practices in model development, not just a toy example. Show your expertise in building robust ML systems."""

    print("🚀 Challenge: Build a complete neural network training pipeline!")

    # Stream the response
    async for chunk in agent.stream(query):
        print(chunk, end="", flush=True)


if __name__ == "__main__":
    asyncio.run(main())