Quickstart

Get a working autonomous agent in under 5 minutes.

Install

pip install gantrygraph

For desktop, browser, or cloud features install the relevant extras:

pip install 'gantrygraph[desktop]'   # screenshot + mouse/keyboard
pip install 'gantrygraph[browser]'   # Playwright web automation
pip install 'gantrygraph[cloud]'     # REST API server

Step 1 — A minimal agent

The simplest agent: an LLM that can reason and respond.

from gantrygraph import GantryEngine
from langchain_anthropic import ChatAnthropic

agent = GantryEngine(
    llm=ChatAnthropic(model="claude-sonnet-4-6"),
    max_steps=5,
)

result = agent.run("What is 17 × 23? Show your working.")
print(result)

GantryEngine manages the observe → think → act → review loop. max_steps prevents infinite loops. run() is synchronous — use arun() in async code.

Step 2 — Give it a tool

Turn any function into an agent tool with @gantry_tool. The LLM reads the docstring to understand what the tool does.

from gantrygraph import GantryEngine, gantry_tool
from langchain_anthropic import ChatAnthropic

@gantry_tool
async def get_weather(city: str) -> str:
    """Return the current weather for a city."""
    return f"Sunny, 22°C in {city}"   # replace with a real API call

agent = GantryEngine(
    llm=ChatAnthropic(model="claude-sonnet-4-6"),
    tools=[get_weather],
    max_steps=5,
)

print(agent.run("What's the weather in Milan and Tokyo?"))

Sync or async — both work

@gantry_tool wraps both def and async def functions. Prefer async def for I/O-heavy tools.

Step 3 — Read and write files safely

FileSystemTools sandboxes the agent inside a directory. It can never touch files outside workspace, even if the LLM tries.

from gantrygraph import GantryEngine
from gantrygraph.actions import FileSystemTools, ShellTool
from langchain_anthropic import ChatAnthropic

agent = GantryEngine(
    llm=ChatAnthropic(model="claude-sonnet-4-6"),
    tools=[
        FileSystemTools(workspace="/my/project"),
        ShellTool(
            workspace="/my/project",
            allowed_commands=["python", "pytest", "git"],
        ),
    ],
    max_steps=20,
)

result = agent.run(
    "Run the test suite. If any tests fail, read the relevant source files "
    "and fix the failures. Then run the tests again to confirm."
)
print(result)

What's next

Pick the guide that matches what you want to build:

I want to…	Guide
Control the mouse and keyboard	Automate the desktop
Scrape or interact with websites	Automate a browser
Connect GitHub, Notion, Postgres	Connect MCP tools
Require a human to approve actions	Add human approval
Run tasks in parallel	Multi-agent swarm
Expose the agent as a REST API	Deploy as an API

Or read The agent loop to understand how everything fits together.