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# RowBoat Labs
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Please visit https://www.rowboatlabs.com to learn more about RowBoat Labs
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# RowBoat Labs
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🌐 Visit [RowBoat Labs](https://www.rowboatlabs.com) to learn more!
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# Agents
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---
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## Overview
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- RowBoat Agents is a multi-agent framework which powers conversations based on agentic workflows.
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- The Rowboat Agents framework has been built upon [OpenAI Swarm](https://github.com/openai/swarm), with modifications and improvements. Please see the `NOTICE.md` file in this directory, for attribution notes and more details. OpenAI Swarm is available under the MIT license as of the time of this writing.
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# 🤖 Agents
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## Graph-based framework
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- Multi-agent systems are typically implemented as graphs, where each agent is a node in the graph.
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- RowBoat Agents is a stateless implementation of such a graph-based system (specifically, a DAG or directed acyclic graph).
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- At every turn of conversation, the graph is traversed based on `messages`, `state` and `workflow` (which defines the agents, tools and connections between them)
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- `Workflows` can be configured in the no-code RowBoat Studio (UI) with the assistance of an AI copilot. Instructions to set up the Studio can be found in the [main README](https://github.com/rowboatlabs/rowboat/tree/dev).
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- At each turn of conversation, the agent graph object is created from scratch. The graph is then run, which produces the next set of `messages` and `state`. The `messages` will be shown to the user by the upstream service. Additionally, if the `messages` contain tool calls, then the upstream service must invoke the necessary tools and send the results back to the framework as the next turn.
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## 📝 Overview
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- ⚙️ RowBoat Agents is a multi-agent framework that powers conversations using agentic workflows.
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- 🚀 Built on top of [OpenAI Swarm](https://github.com/openai/swarm) with custom enhancements and improvements. Check the `NOTICE.md` for attribution and licensing details (MIT license).
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## Key request and response fields
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---
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### Request
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- `messages`: List of messages from the user
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- `state`: Represents the currently active agent and agent-level histories
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- `workflow`: Represents the graph of agents, tools and connections between them
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## 🕸️ Graph-based Framework
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- 🔄 Multi-agent systems are represented as graphs, where each agent is a node in the graph.
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- 📊 RowBoat Agents uses a stateless Directed Acyclic Graph (DAG).
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- 🗨️ At each conversation turn:
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- The graph is traversed based on `messages`, `state`, and `workflow` (which defines agents, tools, and their connections).
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- 🛠️ Configure Workflows using the RowBoat Studio (UI) with the help of an AI copilot. Setup instructions can be found in the [main README](https://github.com/rowboatlabs/rowboat/tree/dev).
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- 💡 Each turn starts with a fresh graph, generating the next `messages` and `state`, which the upstream service displays to the user.
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- If `messages` contain tool calls, the upstream service invokes the necessary tools and sends the result back to continue the interaction.
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Note: See `tests/sample_requests/default_example.json` for an example of a complete request JSON
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---
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### Response
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- `messages`: List of response messages (which might have tool calls to be invoked)
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- `state`: New state of the conversation which is to be passed back during invocation of the next turn, since the framework itself is stateless
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## 🗂️ Key Request and Response Fields
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Note: See `tests/sample_responses/default_example.json` for an example of a complete response JSON
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### 📤 Request
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- 📝 `messages`: List of user messages
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- 🔄 `state`: Active agent state and histories
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- 🛠️ `workflow`: Graph of agents, tools, and connections
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# Using the framework
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📁 Example JSON: `tests/sample_requests/default_example.json`
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## Set up conda env
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Standard conda env setup process:
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- `conda create -n myenv python=3.12`
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- `conda activate myenv`
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- Note: python>=3.10
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---
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## Install dependencies
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Install either using poetry or using pip
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### 📥 Response
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- 📝 `messages`: List of response messages (may contain tool calls)
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- 🔄 `state`: Updated state to pass in the next request (since the framework is stateless)
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📁 Example JSON: `tests/sample_responses/default_example.json`
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---
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# 🛠️ Using the Framework
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## ⚙️ Set Up Conda Environment
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- `conda create -n myenv python=3.12`
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- `conda activate myenv`
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- ⚠️ Note: Python >= 3.10 required
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---
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## 📦 Install Dependencies
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### If using poetry
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- `pip install poetry`
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### If using pip
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`pip install -r requirements.txt`
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## Set up .env file
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## 🔑 Set up .env file
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Copy `.env.example` to `.env` and add your API keys
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## Run interactive test
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## 🧪 Run interactive test
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`python -m tests.interactive --config default_config.json --sample_request default_example.json --load_messages`
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- `--config`: Config json filename, under `configs` folder
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- `--sample_request`: Path to the sample request file, under `tests/sample_requests` folder
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- `--load_messages`: If set, it will additionally load the initial set of messages from the sample request file. Else, user input will be required starting from the first message.
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## Set up app server
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## 🌐 Set up app server
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- For local testing: `flask --app src.app.main run --port=4040`
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- To set up the server on remote: `gunicorn -b 0.0.0.0:4040 src.app.main:app`
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## Run test client
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## 🖥️ Run test client
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`python -m tests.app_client --sample_request default_example.json`
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- `--sample_request`: Path to the sample request file, under `tests/sample_requests` folder
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# More details
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# 📖 More details
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## Specifics
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- **Format**: Uses OpenAI's messages format when passing messages.
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- **LLMs**: Currently, only OpenAI LLMs (e.g. gpt-4o, gpt-4o-mini) are supported. Easy to expand to other LLMs like Claude, Gemini or self-hosted models.
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- **Responses**: Here are some examples of responses that the framework can return:
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## 🔍 Specifics
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- ⚙️ **Format**: Uses OpenAI's messages format when passing messages.
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- 🤖 **LLMs**: Currently, only OpenAI LLMs (e.g. gpt-4o, gpt-4o-mini) are supported. Easy to expand to other LLMs like Claude, Gemini or self-hosted models.
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- 📤 **Responses**: Here are some examples of responses that the framework can return:
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- A list of one user-facing message
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- A list of one or more tool calls
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- A list of one user-facing message and one or more tool calls
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- **Errors**: Errors are thrown as a tool call `raise_error` with the error message as the argument. Real-time error handling will be managed by the upstream service.
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- ⚠️ **Errors**: Errors are thrown as a tool call `raise_error` with the error message as the argument. Real-time error handling will be managed by the upstream service.
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## Important directories and files
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## 🗂️ Important directories and files
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- `src/`: Contains all source code for the agents app
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- `src/app/`: Contains Flask app which exposes the framework as a service
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- `src/graph/`: Contains logic to run every turn of the conversation
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- `configs/`: Contains graph configurations (changed infrequently)
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- `tests/sample_requests/`: Contains sample request files for the agents app
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## High-level flow
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## 🔄 High-level flow
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- `app/main.py` receives the request JSON from an upstream service, parses it and sends it to `src/graph/core.py`
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- `src/graph/core.py` creates the agent graph object from scratch and uses `src/swarm/core.py` to run the turn
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- `src/swarm/core.py` runs the turn by performing actual LLM calls and internal tool invocations to transitiion between agents
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- `src/graph/core.py` returns the response messages and the new state to `app/main.py`, which relays it back to the upstream service
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- The upstream services appends any new user messages to the history of messages and sends the messages back along with the new state to `app/main.py` as part of the next request. The process repeats until the upstream service completes its conversation with the user.
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## Limitations
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## 🚫 Limitations
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- Does not support streaming currently.
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- Cannot respond with multiple user-facing messages in the same turn.
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