Getting Started

Getting started

• ✨ Start from an idea → Copilot builds your multi-agent workflows
  E.g. "Build me an assistant for a food delivery company to handle delivery status and missing items. Include the necessary tools."
• 🌐 Connect MCP servers
  Add the MCP servers in Settings → import the tools into Rowboat.
• 📞 Integrate into your app using the HTTP API or Python SDK
  Grab the Project ID and generated API Key from Settings and use the API.

Powered by OpenAI's Agents SDK, Rowboat is the fastest way to build multi-agents!

Quick start

Step 1. Set your OpenAI key:

export OPENAI_API_KEY=your-openai-api-key

Step 2. Clone the repository and start Rowboat docker

git clone git@github.com:rowboatlabs/rowboat.git
cd rowboat
docker-compose up --build

Step 3. Access the app at http://localhost:3000.

Note: See the Using custom LLM providers section below for using custom providers like OpenRouter and LiteLLM.

Demo

Create a multi-agent assistant with MCP tools by chatting with Rowboat

Integrate with Rowboat agents

There are 2 ways to integrate with the agents you create in Rowboat

Option #1: HTTP API

You can use the API directly at http://localhost:3000/api/v1/. See API Docs for details.

curl --location 'http://localhost:3000/api/v1/<PROJECT_ID>/chat' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <API_KEY>' \
--data '{
    "messages": [
        {
            "role": "user",
            "content": "tell me the weather in london in metric units"
        }
    ],
    "state": null
}'

Option #2: Python SDK

You can use the included Python SDK to interact with the Agents

from rowboat import Client, StatefulChat
from rowboat.schema import UserMessage, SystemMessage

# Initialize the client
client = Client(
    host="http://localhost:3000",
    project_id="<PROJECT_ID>",
    api_key="<API_KEY>"
)

# Create a stateful chat session (recommended)
chat = StatefulChat(client)
response = chat.run("What's the weather in London?")
print(response)

# Or use the low-level client API
messages = [
    SystemMessage(role='system', content="You are a helpful assistant"),
    UserMessage(role='user', content="Hello, how are you?")
]

# Get response
response = client.chat(messages=messages)
print(response.messages[-1].content)

Using custom LLM providers

By default, Rowboat uses OpenAI LLMs (gpt-4o, gpt-4.1, etc.) for both agents and copilot, when you export your OPENAI_API_KEY.

However, you can also configure custom LLM providers (e.g. LiteLLM, OpenRouter) to use any of the hundreds of available LLMs beyond OpenAI, such as Claude, DeepSeek, Ollama LLMs and so on.

Step 1: Set up your custom LLM provider using the variables below, for example (assuming LiteLLM):

export PROVIDER_BASE_URL=http://host.docker.internal:4000/
export PROVIDER_API_KEY=sk-1234

Rowboat uses "gpt-4.1" as the default model for agents and copilot but this can be overridden as follows, for example (assuming LiteLLM):

export PROVIDER_DEFAULT_MODEL=claude-3-7-sonnet-latest
export PROVIDER_COPILOT_MODEL=gpt-4o

Notes:

• Copilot is optimized for gpt-4o/gpt-4.1. We strongly recommend using these models for best performance.
• You can specify different models for the copilot and each agent, but all of them must belong to the same provider (e.g. LiteLLM)
• The integration is provider-agnostic and should work with any service that implements the OpenAI messages format.
• OpenAI-specific tools (e.g., web_search) will not work with non-OpenAI providers. If you get an error, remove these tools.

Step 2 (No change): Clone the repository and start Rowboat docker

git clone git@github.com:rowboatlabs/rowboat.git
cd rowboat
docker-compose up --build

Step 3 (No change): Access the app at http://localhost:3000.