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AutonomousAgent inherits all execution methods from Agent, including synchronous, asynchronous, and streaming options.

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Synchronous Execution

The simplest way to run an autonomous agent: 
from upsonic import AutonomousAgent, Task

agent = AutonomousAgent(
    model="openai/gpt-4o",
    workspace="/path/to/project"
)

# Execute and print result
task = Task("Read README.md and summarize it")
result = agent.print_do(task)
print(result)

# Or execute without printing
task = Task("Create a new file called hello.py with a hello world program")
result = agent.print_do(task)
print(result)

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Asynchronous Execution

For async applications: 
from upsonic import AutonomousAgent, Task
import asyncio

async def main():
    agent = AutonomousAgent(
        model="openai/gpt-4o",
        workspace="/path/to/project"
    )
    
    # Execute asynchronously
    task = Task("Search for TODO comments in all Python files")
    result = await agent.do_async(task)
    print(result)

asyncio.run(main())

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Streaming Output

Stream responses as they’re generated: 
from upsonic import AutonomousAgent, Task
import asyncio

async def main():
    agent = AutonomousAgent(
        model="openai/gpt-4o",
        workspace="/path/to/project"
    )
    
    # Stream the response
    task = Task("Analyze the project structure and suggest improvements")
    async for chunk in agent.astream(task):
        print(chunk, end='', flush=True)
    print()

asyncio.run(main())

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Long Tasks

The agent handles complex long tasks automatically: 
from upsonic import AutonomousAgent, Task

agent = AutonomousAgent(
    model="openai/gpt-4o",
    workspace="/path/to/project"
)

task = Task("""
1. Create a new directory called 'src'
2. Create a Python file 'src/main.py' with a simple Flask app
3. Create a requirements.txt with Flask as a dependency
4. List all created files
""")


# The agent will use multiple tools to complete this task
result = agent.print_do(task)

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Working with Files

Example of reading and modifying files: 
from upsonic import AutonomousAgent, Task

agent = AutonomousAgent(
    model="openai/gpt-4o",
    workspace="/path/to/project"
)

# Read a file
task = Task("Read the config.py file and explain its settings")
agent.print_do(task)

# Edit a file (agent will read first, then edit)
task = Task("Update the DEBUG setting to False in config.py")
agent.print_do(task)

# Search files
task = Task("Find all files that import the 'requests' library")
agent.print_do(task)

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Running Shell Commands

Execute terminal commands safely: 
from upsonic import AutonomousAgent, Task

agent = AutonomousAgent(
    model="openai/gpt-4o",
    workspace="/path/to/project",
    shell_timeout=10  # 60 second timeout
)

# Run commands
task = Task("run a shell command that waits 120 seconds")
agent.print_do(task)

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Accessing Results

Get detailed information about the execution: 
from upsonic import AutonomousAgent, Task

agent = AutonomousAgent(
    model="openai/gpt-4o",
    workspace="/path/to/project"
)

task = Task("Count lines of code in all Python files")
result = agent.print_do(task)

# Get the run output with metadata
run_output = agent.get_run_output()
if run_output:
    print(f"Output: {run_output.output}")
    print(f"Tool calls: {run_output.tool_call_count}")
    print(f"Status: {run_output.status}")

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Session Persistence

Session memory is enabled by default, so the agent remembers context between tasks: 
from upsonic import AutonomousAgent, Task

agent = AutonomousAgent(
    model="openai/gpt-4o",
    workspace="/path/to/project"
    # full_session_memory=True is the default
)

# First task
task = Task("Read the user model in models/user.py")
agent.print_do(task)

# Second task - agent remembers the previous context
task = Task("Add an 'email' field to that user model")
agent.print_do(task)

# Get session usage
usage = agent.get_session_usage()
print(f"Total tokens used: {usage.total_tokens}")