https://github.com/kyegomez/forest-of-thoughts

A forest of autonomous agents.
https://github.com/kyegomez/forest-of-thoughts

ai artificial-intelligence machine-learning ml multi-modal multi-modality

Last synced: about 1 month ago
JSON representation

A forest of autonomous agents.

• Host: GitHub
• URL: https://github.com/kyegomez/forest-of-thoughts
• Owner: kyegomez
• License: mit
• Created: 2024-02-03T22:17:50.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-11-04T21:16:17.000Z (about 1 month ago)
• Last Synced: 2024-11-04T23:23:06.641Z (about 1 month ago)
• Topics: ai, artificial-intelligence, machine-learning, ml, multi-modal, multi-modality
• Language: Python
• Homepage: https://discord.gg/GYbXvDGevY
• Size: 224 KB
• Stars: 18
• Watchers: 4
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

Awesome Lists containing this project

• project-awesome - kyegomez/forest-of-thoughts - A forest of autonomous agents. (Python)

README

        
[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)

# Forest of thoughts

The forest of thoughts project is building an AI system called Swarm that enables agents to communicate and collaborate. The goal is to showcase Swarm's capabilities by applying it to complex real-world problems like climate change, education, and world hunger. 

The system uses a vector database to store text embeddings generated from source materials. Each agent can access tools like starting prompts and summarization techniques. The agents communicate in a 1-on-1 fashion, distributing tasks among themselves. 

Key features of Swarm include:

- Agent IDs for identification

- Feedback loops to improve outputs 

- Message classification 

- History tracking

- Query prompts and reranking to refine responses

- Summarization techniques like selective reduction, analogical reasoning, progressive summarization, narrative synthesis, paraphrasing, and layered analysis.

The project aims to prove Swarm's collaborative approach is superior to individual systems like ChatGPT. The demo will show Swarm applied to the 3 real-world problem scenarios, with side-by-side comparisons to ChatGPT. 

The goal is to highlight Swarm's ability to synthesize complex information more efficiently through agent communication and tailored summarization techniques. This process-oriented approach draws on research in knowledge synthesis and transfer learning.

Overall, the forest of thoughts project demonstrates how an AI system like Swarm that utilizes agent collaboration, vector embeddings, and smart summarization can outperform individual systems on complex reasoning tasks.

## Install

`$ pip install forest-of-thoughts`

## Usage

```python

import os

from swarms import OpenAIChat, Mixtral

from fot.main import ForestOfAgents

from dotenv import load_dotenv

from fot.summarization_prompts import selective_reduction_type

# Load env

load_dotenv()

# OpenAI API key

api_key = os.getenv("OPENAI_API_KEY")

# create llm

openai = OpenAIChat(openai_api_base=api_key)

llm = Mixtral(max_new_tokens=3000, load_in_4bit=True)

# Create a forest of agents

forest = ForestOfAgents(

    openai,

    num_agents=5,

    max_loops=1,

    max_new_tokens=100,

    summarizer_prompt=selective_reduction_type,

)

# Distribute tasks to the agents

forest.run(

    "Solve the PNP prblem and give a detailed explanation of the"

    " solution."

)

```

# License

MIT