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Open-source AI coworker, with memory
https://www.rowboatlabs.com
agentsagents-sdkaiai-agentsai-agents-automationchatgptclaude-codeclaude-coworkgenerative-aillmmultiagentopeaniopen-sourceorchestrationproductivity
* added an example on using composio to copilot * temp save * simple example * updated the prompt * added "searching for tools..." banner * Merge branch 'dev' of github.com:rowboatlabs/rowboat into copilot-composio-prompting * changed tools default when added from "mockTool: true" to false (so they are enabled by default) * added pipelines to copilot * fixed bug that made copilot agents default to conversation type * Merge branch 'dev' of github.com:rowboatlabs/rowboat into copilot-composio-prompting * Refactor agent configuration in workflow editor to enforce controlType and outputVisibility rules. Added logic to fix existing agents with incorrect settings on mount. Updated task agent creation to ensure proper defaults for pipeline and conversational agents. Increased maxSteps for text streaming in copilot. * Merge branch 'dev' of github.com:rowboatlabs/rowboat into copilot-composio-prompting * added example * Add automated meeting preparation pipeline example with multi-step workflow for researching participants, compiling summaries, and posting to Slack. * Update example agents to use placeholders for user email and Slack channel in instructions and examples, enhancing privacy and flexibility in the meeting preparation pipeline. * Refactor sections in copilot_multi_agent.ts for clarity and consistency, including renaming section headers and improving instructions. Update query handling in copilot.ts to streamline event processing. * made the tool card block params genereic and linked to the searchRelevantTools so that it relies on that instead of examples * revert unrequired changes * Refactor agent configuration handling in workflow editor to streamline agent creation and ensure correct default values for controlType and outputVisibility. Removed redundant code for fixing existing agents and improved clarity in agent initialization logic. --------- Co-authored-by: Ramnique Singh <30795890+ramnique@users.noreply.github.com> |
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Let AI build multi-agent workflows for you in minutes
- ✨ 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
-
Set your OpenAI key
export OPENAI_API_KEY=your-openai-api-key -
Clone the repository and start Rowboat
git clone git@github.com:rowboatlabs/rowboat.git cd rowboat ./start.sh -
Access the app at http://localhost:3000.
Note: We have added native RAG support including file-uploads and URL scraping. See the RAG section of our docs for this.
Note: See the Using custom LLM providers section of our docs 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
-
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 }' -
Python SDK You can use the included Python SDK to interact with the Agents
pip install rowboatSee SDK Docs for details. Here is a quick example:
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)
Refer to Docs to learn how to start building agents with Rowboat.
