apunkt/rowboat
1
0
Fork 0
mirror of https://github.com/rowboatlabs/rowboat.git synced 2026-04-28 01:46:23 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Update docs and readme

Browse source
This commit is contained in:
Ramnique Singh 2025-04-03 19:58:39 +05:30 • committed by GitHub
parent 304ee9c2bf
commit 66449caa8d
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
7 changed files with 174 additions and 142 deletions
Download patch file Download diff file Expand all files Collapse all files

8
apps/docs/docs/agents.md
View file

@ -3,7 +3,7 @@
## Overview
- Agents carry out a specific part of the conversation and / or perform tasks like orchestrating between other agents, triggering internal processes and fetching information.
- Agents carry out tasks through tools provided to them.
- Agents are connected to each other in a Directed Acyclic Graph (DAG). Hence, every agent has a parent agent and children agents, to which they can pass control of the conversation to.
- Agents can be connected to other agents through a mention in the agent's instruction.
## Agent Configurations
@ -11,10 +11,10 @@
The description conveys the agent's role in the multi-agent system. Writing a good description is important for other agents to know when to pass control of the conversation to an agent.
### Instructions
Agent instructions are the backbone of an agent, defining its behavior. RowBoat Studio's copilot produces a good framework for agent instructions, involving Role, Steps to Follow, Scope and Guidelines. Since agents are powered by LLMs, general best practices while writing prompts apply.
Agent instructions are the backbone of an agent, defining its behavior. RowBoat Studio's copilot produces a good framework for agent instructions, involving Role, Steps to Follow, Scope and Guidelines. Since agents are powered by LLMs, general best practices while writing prompts apply.
### Examples
The agent uses examples as a reference for behavior in different scenarios. While there are no prescribed formats to provide examples in, examples should include what the user might say, what the agent should respond with as well as indications of any tool calls to be made.
The agent uses examples as a reference for behavior in different scenarios. While there are no prescribed formats to provide examples in, examples should include what the user might say, what the agent should respond with as well as indications of any tool calls to be made.
### Prompts
Prompts attached to an agent will be used by the agent in addition to instructions.
@ -23,7 +23,7 @@ Prompts attached to an agent will be used by the agent in addition to instructio
Data sources added to an agent will be used as knowledge, retrieved using embedding match in a typical RAG fashion. Advanced configurations allow for setting number of matches, etc. RAG is currently implemented as a predefined tool call which the agent will use when it determines that it needs to retrieve knowledge. This behavior can be further fine-tuned by specifying corresponding instructions or prompts.
### Tools
Tools attached to an agent will be put out as tool calls. The behavior of when to invoke tools can be fine-tuned by specifying corresponding instructions or prompts. Adding examples to agents can also be useful in controlling tool call behavior.
Tools attached to an agent will be put out as tool calls. The behavior of when to invoke tools can be fine-tuned by specifying corresponding instructions or prompts. Adding examples to agents can also be useful in controlling tool call behavior.
### Connected Agents
In the agent graph, connected agents refer to children of an agent. An agent can choose to transfer control of the conversation to one of its children, by using internal tool calls (need not be configured separately). Similar to tools, the behavior of when to transfer the chat to a child agent can be fine-tuned by specifying corresponding instructions, examples and prompts.

12
apps/docs/docs/index.md
View file

@ -1,6 +1,6 @@
# Welcome to Rowboat
Rowboat is an open-source project that helps you build, test, and deploy multi-agent AI customer support assistants.
Rowboat is an open-source Cursor like IDE that helps you build, test, and deploy multi-agent AI systems.
**Note:** These docs are intended for both developers who would like to self-host our [open-source code](https://github.com/rowboatlabs/rowboat/) as well as users of our [hosted (managed) app](https://app.rowboatlabs.com/).
@ -12,18 +12,18 @@ Rowboat is an open-source project that helps you build, test, and deploy multi-a
## What is RowBoat?
**RowBoat is a state-of-art platform to build multi-agent AI systems in a visual interface, with the help of a copilot.**
RowBoat enables you to build, manage and deploy user-facing assistants. An assistant is made up of multiple agents, each having access to a set of tools and working together to interact with the user as a single assistant.
RowBoat enables you to build, manage and deploy user-facing assistants. An assistant is made up of multiple agents, each having access to a set of tools and working together to interact with the user as a single assistant.
For example, you can build a *credit card assistant*, where each agent handles a workflow such as *outstanding payments*, *balance inquiries* and *transaction disputes*. You can equip agents with tools to carry out tasks such as *fetching payment options*, *checking outstanding balance* and *updating user information*. The assistant would help your end-users their credit card-related needs without having to talk to a human agent on your end.
## How RowBoat works
### RowBoat Studio
RowBoat Studio lets you create human-quality customer support assistants in minutes, using a visual interface and plain language. Here are key components that you will work with:
RowBoat Studio lets you create AI agents in minutes, using a visual interface and plain language. Here are key components that you will work with:
| Component | Description | Highlights |
|------------|-------------|------------|
| Agent | Handles a specific part of the conversation and<br>performs tasks using tools, based on instructions |• Configurable using plain language instructions<br>• Orchestrate between agents connected as a graph (DAG)<br>• Can access tools and knowledge sources (RAG)|
| Agent | Handles a specific part of the conversation and<br>performs tasks using tools, based on instructions |• Configurable using plain language instructions<br>• Orchestrate between agents connected as a graph<br>• Can access tools and knowledge sources (RAG)|
| Playground | Interactive environment to test assistants<br>conversationally as you build them |• Real-time testing and debugging<br>• Inspect parameters and results of tool calls in-line<br>• Converse with individual agents or the entire assistant|
| Copilot | AI-powered concierge that creates and<br>updates agents and tools on your behalf |• Context-aware of all components including playground<br>• Improves agents based on conversations and feedback <br>• Understands your requests in plain language|
| Simulator | Simulates real-world user interactions<br>with your assistant |• Maintain and run a test-bench of different scenarios<br>• Mock tool responses for quick testing<br>• Reproduce your end-user's experience comprehensively|
@ -48,10 +48,10 @@ RowBoat Studio lets you create human-quality customer support assistants in minu
2. Alternatively, **export** your assistant as a JSON artifact from RowBoat Studio and use it to power your custom implementations.
## Why RowBoat?
Increase CSAT. Reduce costs. Improve CX.
Accelerate your path to production-ready multi-agent systems.
1. **Build** complex assistants using plain language and a visual interface
2. **Simplify** collaboration across teams, to build powerful customer experiences
2. **Integrate** tools and MCP servers in minutes
3. **Expedite** your multi-agent AI roadmap using battle-tested tooling
## Getting started

9
apps/docs/docs/oss_installation.md
View file

@ -2,7 +2,7 @@
- This is the developers guide to self-hosting the open-source version of RowBoat. To get started with the hosted app, please see [Using the Hosted App](/hosted_setup)
- Please see our [Introduction](/) page before referring to this guide.
- For direct installation steps, please head to the README of RowBoat's Github repo: [@rowboatlabs/rowboat](https://github.com/rowboatlabs/rowboat/). This page provides more context about the installation process and the different components involved.
- For direct installation steps, please head to the README of RowBoat's Github repo: [@rowboatlabs/rowboat](https://github.com/rowboatlabs/rowboat/). This page provides more context about the installation process and the different components involved.
## Overview
@ -14,20 +14,19 @@ RowBoat's codebase has three main components:
| **Copilot** | Python framework powering the copilot in RowBoat Studio |
| **RowBoat** | Frontend and backend services to power RowBoat Studio and Chat APIs |
These components are structured as separate services, each containerized with Docker. Running `docker-compose up --build` enables you to use the Studio in your browser, as well as stands up the APIs and SDK.
These components are structured as separate services, each containerized with Docker. Running `docker-compose up --build` enables you to use the Studio in your browser, as well as stands up the APIs and SDK.
## Prerequisites
All of these prerequistes have open-source or free versions.
All of these prerequisites have open-source or free versions.
| Prerequisite | Description |
|--------------|---------------|
| **Docker** | Bundles and builds all services |
| **OpenAI API Key** | Agents and Copilot services are powered by OpenAI LLMs |
| **MongoDB** | Stores workflow versions, chats and RAG embeddings |
| **Auth0 Account** | Handles user authentication and identity management for Studio |
Refer to our [Github Readme for Prerequisites](https://github.com/rowboatlabs/rowboat/?tab=readme-ov-file#prerequisites) to set up prerequisites.
## Setting up
Refer to our [Github Readme for Local Development](https://github.com/rowboatlabs/rowboat/?tab=readme-ov-file#local-development-setup) to set up Studio, Chat API and SDK via `docker-compose`.
Refer to our [Github Readme for Local Development](https://github.com/rowboatlabs/rowboat/?tab=readme-ov-file#local-development-setup) to set up Studio, Chat API and SDK via `docker-compose`.