redel 0.0.1.dev0

A toolkit for recursive delegation of LLMs

ReDel

A framework for recursive delegation of LLMs

ReDel is a toolkit for researchers and developers to build, iterate on, and analyze recursive multi-agent systems.

Built using the kani framework, it offers best-in-class support for modern LLMs with tool usage.

Features

• Modular design - ReDel makes it easy to experiment by providing a modular interface for creating tools, different delegation methods, and logs for later analysis.
• Event-driven architecture - Granular logging and a central event system makes it easy to listen for signals from anywhere in your system. Every event is automatically logged so you can run your favorite data analysis tools.
• Bundled visualization - Multi-agent systems can be hard to reason about from a human perspective. We provide a web-based visualization that allows you to interact with a configured system directly or view replays of saved runs (e.g. your own experiments!).
• Built with open, unopinionated tech - ReDel won't force you to learn bizarre library-specific tooling and isn't built by a big tech organization with their own motives. Everything in ReDel is implemented in pure, idiomatic Python and permissively licensed.

Quickstart

Requires Python 3.10+

# install python dependencies
$ pip install -e "redel[all]"
# run web visualization of a ReDel system with web browsing
$ OPENAI_API_KEY="..." python -m redel.server

Screenshots

Usage

There are two primary ways to interact with a system: interactively, through the web interface, or programmatically. The former is particularly useful to debug your system's behaviour, iterate on prompts, or otherwise provide an interactive experience. The latter is useful for running experiments and batch queries.

See the docs for more usage information at https://redel.readthedocs.io!

Server

from kani.engines.openai import OpenAIEngine
from redel import AUTOGENERATE_TITLE, ReDel
from redel.server import VizServer
from redel.tools.browsing import Browsing

# Define the LLM engines to use for each node
engine = OpenAIEngine(model="gpt-4", temperature=0.8, top_p=0.95)

# Define the configuration for each interactive session
redel_proto = ReDel(
    root_engine=engine,
    delegate_engine=engine,
    title=AUTOGENERATE_TITLE,
    tool_configs={
        Browsing: {"always_include": True},
    },
)

# configure and start the server
server = VizServer(redel_proto)
server.serve()

Programmatic

import asyncio
from kani import ChatRole
from kani.engines.openai import OpenAIEngine
from redel import ReDel, events
from redel.tools.browsing import Browsing

# Define the LLM engines to use for each node
engine = OpenAIEngine(model="gpt-4", temperature=0.8, top_p=0.95)

# Define the configuration for the session
ai = ReDel(
    root_engine=engine,
    delegate_engine=engine,
    title="Airspeed of a swallow",
    tool_configs={
        Browsing: {"always_include": True},
    },
)


# ReDel is async, so define an async function and use asyncio.run()
async def main():
    async for event in ai.query("What is the airspeed velocity of an unladen swallow?"):
        if isinstance(event, events.RootMessage) and event.msg.role == ChatRole.ASSISTANT:
            if event.msg.text:
                print(event.msg.text)


asyncio.run(main())

EMNLP Demo Experiments

[!NOTE] This section is specific to the demo/emnlp branch of this repository. You can switch branches in the top-left of the GitHub UI or by using this link: https://github.com/zhudotexe/redel/tree/demo/emnlp

This repository includes the logs of every single experiment run included in our paper in the experiments/ directory. You can load any of these runs in the visualization to view what the ReDel system did!

The experiments directory is broken down into the following structure: experiments/BENCHMARK_NAME/BENCHMARK_SPLIT/[RUN_ID]/SYSTEM_ID/QUERY_ID, where:

• BENCHMARK_NAME is the name of the benchmark (fanoutqa, travelplanner, or webarena)
• BENCHMARK_SPLIT is the split of the benchmark we ran (usually the dev/validation split)
• RUN_ID is an internal split in the FanOutQA experiment to analyze an edge-case behaviour wrt parallel function calling and long contexts
• SYSTEM_ID is the system under test, configured as in the table below
• QUERY_ID is the benchmark-specific ID of a single run (loadable in the visualizer).

System Configurations

System ID	Root Model	Delegate Model	Root Functions?	Delegation?	Root Context	Delegate Context
full	gpt-4o	gpt-4o	no	yes	128000	128000
root-fc	gpt-4o	gpt-4o	yes	yes	128000	128000
baseline	gpt-4o	N/A	yes	no	128000	N/A
small-leaf	gpt-4o	gpt-3.5-turbo	no	yes	128000	16385
small-all	gpt-3.5-turbo	gpt-3.5-turbo	no	yes	16385	16385
small-baseline	gpt-3.5-turbo	N/A	yes	no	16385	N/A
short-context	gpt-4o	gpt-4o	no	yes	8192	8192
short-baseline	gpt-4o	N/A	yes	no	8192	N/A

Reproducing Experiments

To reproduce the experiments included in this repository, we include scripts to run each benchmark.

Follow these steps to setup the environment, then follow the instructions in each benchmark. We recommend setting up a virtual environment for this project.

1. First, you'll need to clone this repository and check out the demo/emnlp branch: git clone -b demo/emnlp https://github.com/zhudotexe/redel
2. Install the necessary dependencies: pip install -r requirements.txt

FanOutQA

output path: experiments/fanoutqa/dev/trial2/SYSTEM_ID

Run

python bench_fanoutqa.py <full|root-fc|baseline|small-leaf|small-all|small-baseline|short-context|short-baseline>

This will run the given system on the FanOutQA dev set in the Open Book setting.

Evaluate

Set the FANOUTQA_OPENAI_API_KEY environment variable to a valid OpenAI API key. You can use export FANOUTQA_OPENAI_API_KEY=$OPENAI_API_KEY to copy an existing API key from environment variables.

python score_fanoutqa.py experiments/fanoutqa/**/results.jsonl

This will output a score.json file in the output path with the final scores.

TravelPlanner

output path: experiments/travelplanner/validation/SYSTEM_ID

Setup

1. Install the TravelPlanner database:
   1. Download the database from this link
   2. Extract the zip file in redel/tools/travelplanner. This should create a directory named db.
2. In another directory, clone our fork of the TravelPlanner repository. This will be used for scoring, and includes the fixes discussed in our paper.
   1. git clone https://github.com/zhudotexe/TravelPlanner

Run

python bench_travelplanner.py <full|root-fc|baseline|small-leaf|small-all|small-baseline>

Note: This benchmark does not test the short-ctx systems since this benchmark doesn't have a long-context requirement.

Evaluate

python score_travelplanner.py experiments/travelplanner/**/results.jsonl

This script will write files in the correct format for the TravelPlanner evaluation in the output path, and print the command to run to score the results.

You should now switch to the TravelPlanner repository you cloned in the setup step and run the commands output by this script.

WebArena

output path: experiments/webarena/test/SYSTEM_ID

Setup

We reproduce some of the scripts and data contained in the WebArena repository in this repo under the terms of the Apache-2.0 license, contained in experiments/webarena/vendor/LICENSE.

First, you'll need to set up your own WebArena environment. See https://github.com/web-arena-x/webarena/blob/main/environment_docker/README.md for instructions.

Next, run the following to setup the webarena configuration:

# setup env vars (see https://github.com/web-arena-x/webarena/blob/main/environment_docker/README.md for env setup)
export SHOPPING="<your_shopping_site_domain>:7770"
export SHOPPING_ADMIN="<your_e_commerce_cms_domain>:7780/admin"
export REDDIT="<your_reddit_domain>:9999"
export GITLAB="<your_gitlab_domain>:8023"
export MAP="<your_map_domain>:3000"
export WIKIPEDIA="<your_wikipedia_domain>:8888/wikipedia_en_all_maxi_2022-05/A/User:The_other_Kiwix_guy/Landing"
export HOMEPAGE="<your_homepage_domain>:4399"
# generate config files
python experiments/webarena/generate_test_data.py

You'll also need to ensure Playwright is installed:

playwright install chromium

Run

First, make sure you have reset your WebArena environment (see https://github.com/web-arena-x/webarena/blob/main/environment_docker/README.md#environment-reset).

Then, launch the WebArena environment.

As the default WebArena script is incompatible with asyncio, ReDel launches a separate process to handle the WebArena environment, which it communicates with over a pipe. This is done automatically.

Finally, run the bench script:

python bench_webarena.py <full|root-fc|baseline|small-leaf|small-all|small-baseline|short-context|short-baseline>

License

We release ReDel under the terms of the MIT license, included in LICENSE. ReDel is intended for academic and personal use only. To use ReDel for commercial purposes, please contact us.