Quick Start¶

Get AgentShip running and your first agent in under 5 minutes.

Step 1: Run AgentShip¶

git clone https://github.com/Agent-Ship/agent-ship.git
cd agent-ship
make docker-setup

Setup will prompt for your API key, create .env, and start the API + PostgreSQL. When ready:

Service	URL
AgentShip Studio	http://localhost:7001/studio
API / Swagger	http://localhost:7001/swagger

Step 2: Chat with a Built-in Agent¶

Open http://localhost:7001/studio, pick any agent, and start chatting. No extra setup.

Step 3: Create Your First Agent¶

Create two files in src/all_agents/my_agent/:

main_agent.yaml

agent_name: my_agent
llm_provider_name: openai
llm_model: gpt-4o
temperature: 0.4
execution_engine: adk   # or langgraph
description: My helpful assistant
instruction_template: |
  You are a helpful assistant that answers questions clearly.

main_agent.py

from src.all_agents.base_agent import BaseAgent
from src.service.models.base_models import TextInput, TextOutput
from src.agent_framework.utils.path_utils import resolve_config_path

class MyAgent(BaseAgent):
    def __init__(self):
        super().__init__(
            config_path=resolve_config_path(relative_to=__file__),
            input_schema=TextInput,
            output_schema=TextOutput,
        )

Restart: make docker-restart. Your agent is auto-discovered — no registration.

Next Steps¶

Agent Configuration — YAML fields, engines, streaming
Agent Patterns — single agent, orchestrator, tools
MCP Integration — PostgreSQL, GitHub, and other MCP servers

Local Development (No Docker)¶

pipenv install
cp .env.example .env   # add your API key
make dev               # http://localhost:7001