Skip to content

Latest commit

 

History

History
198 lines (142 loc) · 4.63 KB

5-minute-quickstart.md

File metadata and controls

198 lines (142 loc) · 4.63 KB

5-Minute Quickstart

From zero to governed agent in 5 minutes.

TL;DR

pip install agent-os-kernel
from agent_os import KernelSpace

kernel = KernelSpace(policy="strict")

@kernel.register
async def my_agent(task: str):
    return f"Processed: {task}"

# Run with kernel governance
import asyncio
result = asyncio.run(kernel.execute(my_agent, "Hello, Agent OS!"))
print(result)

That's it. Your agent now runs with kernel-level policy enforcement.

Step 1: Install

pip install agent-os-kernel

Optional extras:

pip install agent-os-kernel[cmvk]           # Verification
pip install agent-os-kernel[observability]  # Prometheus/OpenTelemetry
pip install agent-os-kernel[full]           # Everything

Step 2: Create Your First Agent

Create a file called my_agent.py:

from agent_os import KernelSpace

# Initialize the kernel with strict policy
kernel = KernelSpace(policy="strict")

@kernel.register
async def analyze_data(task: str):
    """Your agent logic goes here."""
    # This could be any LLM call, data processing, etc.
    return f"Analysis complete: {task}"

# Execute with governance
if __name__ == "__main__":
    import asyncio
    
    result = asyncio.run(
        kernel.execute(analyze_data, "Summarize Q4 sales data")
    )
    print(result)

Step 3: Run It

python my_agent.py

Output:

Analysis complete: Summarize Q4 sales data

What Just Happened?

┌─────────────────────────────────────────────────────────┐
│              USER SPACE (Your Code)                     │
│   analyze_data() runs here                              │
├─────────────────────────────────────────────────────────┤
│              KERNEL SPACE (Agent OS)                    │
│   Every action checked against policies                 │
│   Violations → SIGKILL (non-catchable)                  │
└─────────────────────────────────────────────────────────┘
  1. @kernel.register wraps your function with kernel governance
  2. kernel.execute() runs your agent through the policy engine
  3. If policy violated → automatic SIGKILL before execution

Add an LLM (Optional)

from agent_os import KernelSpace
from openai import OpenAI

kernel = KernelSpace(policy="strict")
client = OpenAI()

@kernel.register
async def smart_agent(task: str):
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": task}]
    )
    return response.choices[0].message.content

if __name__ == "__main__":
    import asyncio
    result = asyncio.run(kernel.execute(smart_agent, "What is 2+2?"))
    print(result)

Try Policy Enforcement

See what happens when your agent tries something blocked:

@kernel.register
async def dangerous_agent(task: str):
    import os
    os.remove("/etc/passwd")  # ← This will be blocked!
    return "Done"

Output:

⚠️  POLICY VIOLATION DETECTED
⚠️  Signal: SIGKILL
⚠️  Action: file_write
⚠️  Status: TERMINATED

The kernel blocked the action before it executed.

Next Steps

Time Tutorial What You'll Learn
10 min 30-Minute Deep Dive Policies, signals, VFS
15 min Episodic Memory Agent memory that persists
15 min Verification Detect hallucinations
20 min Time-Travel Debugging Replay and debug decisions

Common Patterns

Wrap Existing LangChain Agents

from agent_os.integrations import LangChainKernel
from langchain.agents import AgentExecutor

kernel = LangChainKernel()
governed_agent = kernel.wrap(my_langchain_agent)

Wrap OpenAI Assistants

from agent_os.integrations import OpenAIKernel

kernel = OpenAIKernel()
governed = kernel.wrap_assistant(assistant, client)

Wrap CrewAI

from agent_os.integrations import CrewAIKernel

kernel = CrewAIKernel()
governed = kernel.wrap(my_crew)

Get Help