Quick Start

This guide will get you running with gMAS in 5 minutes.

Step 1: Create Agents

from gmas.core import AgentProfile

agents = [
    AgentProfile(
        agent_id="researcher",
        display_name="Researcher",
        description="Searches for and collects information",
    ),
    AgentProfile(
        agent_id="analyst",
        display_name="Analyst",
        description="Analyzes data and forms insights",
    ),
    AgentProfile(
        agent_id="writer",
        display_name="Writer",
        description="Writes the final response",
    ),
]

Step 2: Build Graph

from gmas.builder import build_property_graph

graph = build_property_graph(
    agents,
    workflow_edges=[
        ("researcher", "analyst"),
        ("analyst", "writer"),
    ],
    query="What will AI be like in 2026?",
)

Step 3: Configure LLM

import os

from openai import OpenAI

client = OpenAI(
    api_key=os.environ["LLM_API_KEY"],
    base_url=os.environ.get("LLM_BASE_URL"),
)

def llm_caller(prompt: str) -> str:
    response = client.responses.create(
        model=os.environ["LLM_MODEL"],
        input=prompt,
    )
    return response.output_text

Step 4: Execute

from gmas.execution import MACPRunner

runner = MACPRunner(llm_caller=llm_caller)
result = runner.run_round(graph)

print(f"Execution order: {result.execution_order}")
print(f"Time: {result.total_time:.2f}s")
print(f"Answer: {result.final_answer}")

Step 5: Stream (Optional)

from gmas.execution import StreamEventType

for event in runner.stream(graph):
    if event.event_type == StreamEventType.AGENT_START:
        print(f"\n{event.agent_name} started...")
    elif event.event_type == StreamEventType.AGENT_OUTPUT:
        print(f"{event.agent_name}: {event.content[:100]}...")

Next Steps

• Key Concepts
• API Reference