apunkt/rowboat
1
0
Fork 0
mirror of https://github.com/rowboatlabs/rowboat.git synced 2026-04-25 00:16:29 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Reorganize readme

Browse source
This commit is contained in:
akhisud3195 2025-01-14 17:02:57 +05:30
parent 2df6ce471a
commit 7cec090ace
1 changed files with 33 additions and 31 deletions
Download patch file Download diff file Expand all files Collapse all files

64
apps/agents/README.md
View file

@ -11,36 +11,6 @@ Please visit https://www.rowboatlabs.com to learn more about RowBoat Labs
- The `workflow` is a representation of the DAG containing agents, with each agent having a set of tools and connected children agents. See `tests/sample_requests/default_example.json` for an example of a complete request JSON from an upstream service.
- 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.
## Specifics
- **Format**: Uses OpenAI's messages format when passing messages.
- **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.
- **Responses**: Here are some examples of responses that the framework can return:
- A list of one user-facing message
- A list of one or more tool calls
- A list of one user-facing message and one or more tool calls
- **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.
## Limitations
- Does not support streaming currently.
- Cannot respond with multiple user-facing messages in the same turn.
# Important directories and files
- `src/`: Contains all source code for the agents app
- `src/app/`: Contains Flask app which exposes the framework as a service
- `src/graph/`: Contains logic to run every turn of the conversation
- `src/graph/core.py`: Core graph implementation which parses the workflow config, creates agents from it and runs the turn of conversation (through the `run_turn` function)
- `src/swarm/`: RowBoat's custom implementation of OpenAI Swarm, which is used by `src/graph/core.py`
- `tests/`: Contains sample requests, an interactive client and a test client which mocks an upstream service
- `configs/`: Contains graph configurations (changed infrequently)
- `tests/sample_requests/`: Contains sample request files for the agents app
# High-level flow
- `app/main.py` receives the request JSON from an upstream service, parses it and sends it to `src/graph/core.py`
- `src/graph/core.py` creates the agent graph object from scratch and uses `src/swarm/core.py` to run the turn
- `src/swarm/core.py` runs the turn by performing actual LLM calls and internal tool invocations to transitiion between agents
- `src/graph/core.py` returns the response messages and the new state to `app/main.py`, which relays it back to the upstream service
- 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.
# Using the framework
## Set up conda env
@ -75,4 +45,36 @@ Copy `.env.example` to `.env` and add your API keys
## Run test client
`python -m tests.app_client --sample_request default_example.json`
- `--sample_request`: Path to the sample request file, under `tests/sample_requests` folder
- `--sample_request`: Path to the sample request file, under `tests/sample_requests` folder
# More details
## Specifics
- **Format**: Uses OpenAI's messages format when passing messages.
- **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.
- **Responses**: Here are some examples of responses that the framework can return:
- A list of one user-facing message
- A list of one or more tool calls
- A list of one user-facing message and one or more tool calls
- **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.
## Limitations
- Does not support streaming currently.
- Cannot respond with multiple user-facing messages in the same turn.
## Important directories and files
- `src/`: Contains all source code for the agents app
- `src/app/`: Contains Flask app which exposes the framework as a service
- `src/graph/`: Contains logic to run every turn of the conversation
- `src/graph/core.py`: Core graph implementation which parses the workflow config, creates agents from it and runs the turn of conversation (through the `run_turn` function)
- `src/swarm/`: RowBoat's custom implementation of OpenAI Swarm, which is used by `src/graph/core.py`
- `tests/`: Contains sample requests, an interactive client and a test client which mocks an upstream service
- `configs/`: Contains graph configurations (changed infrequently)
- `tests/sample_requests/`: Contains sample request files for the agents app
## High-level flow
- `app/main.py` receives the request JSON from an upstream service, parses it and sends it to `src/graph/core.py`
- `src/graph/core.py` creates the agent graph object from scratch and uses `src/swarm/core.py` to run the turn
- `src/swarm/core.py` runs the turn by performing actual LLM calls and internal tool invocations to transitiion between agents
- `src/graph/core.py` returns the response messages and the new state to `app/main.py`, which relays it back to the upstream service
- 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.