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LangGraph

LangGraph is a library for building stateful, multi-actor applications with LLMs. This example uses Langgraph to automate the process of an in depth research on any given topic.

Use case

By using Langgraph, the research process can be significantly improved in depth and quality by leveraging multiple agents with specialized skills. Inspired by the recent STORM paper, this example showcases how a team of AI agents can work together to conduct research on a given topic, from planning to publication.

An average run generates a 5-6 page research report in multiple formats such as PDF, Docx and Markdown.

Please note: This example uses the OpenAI API only for optimized performance.

The Multi Agent Team

The research team is made up of 7 AI agents:

  • Human - The human in the loop that oversees the process and provides feedback to the agents.
  • Chief Editor - Oversees the research process and manages the team. This is the "master" agent that coordinates the other agents using Langgraph.
  • Researcher (gpt-researcher) - A specialized autonomous agent that conducts in depth research on a given topic.
  • Editor - Responsible for planning the research outline and structure.
  • Reviewer - Validates the correctness of the research results given a set of criteria.
  • Revisor - Revises the research results based on the feedback from the reviewer.
  • Writer - Responsible for compiling and writing the final report.
  • Publisher - Responsible for publishing the final report in various formats.

How it works

Generally, the process is based on the following stages:

  1. Planning stage
  2. Data collection and analysis
  3. Review and revision
  4. Writing and submission
  5. Publication

Architecture


Steps

More specifically (as seen in the architecture diagram) the process is as follows:

  • Browser (gpt-researcher) - Browses the internet for initial research based on the given research task.
  • Editor - Plans the report outline and structure based on the initial research.
  • For each outline topic (in parallel):
    • Researcher (gpt-researcher) - Runs an in depth research on the subtopics and writes a draft.
    • Reviewer - Validates the correctness of the draft given a set of criteria and provides feedback.
    • Revisor - Revises the draft until it is satisfactory based on the reviewer feedback.
  • Writer - Compiles and writes the final report including an introduction, conclusion and references section from the given research findings.
  • Publisher - Publishes the final report to multi formats such as PDF, Docx, Markdown, etc.

How to run

  1. Install required packages:
    pip install -r requirements.txt
  2. Update env variables
    export OPENAI_API_KEY={Your OpenAI API Key here}
    export TAVILY_API_KEY={Your Tavily API Key here}
  3. Run the application:
    python main.py

Usage

To change the research query and customize the report, edit the task.json file in the main directory.

Task.json contains the following fields:

  • query - The research query or task.
  • model - The OpenAI LLM to use for the agents.
  • max_sections - The maximum number of sections in the report. Each section is a subtopic of the research query.
  • include_human_feedback - If true, the user can provide feedback to the agents. If false, the agents will work autonomously.
  • publish_formats - The formats to publish the report in. The reports will be written in the output directory.
  • source - The location from which to conduct the research. Options: web or local. For local, please add DOC_PATH env var.
  • follow_guidelines - If true, the research report will follow the guidelines below. It will take longer to complete. If false, the report will be generated faster but may not follow the guidelines.
  • guidelines - A list of guidelines that the report must follow.
  • verbose - If true, the application will print detailed logs to the console.

For example:

{
  "query": "Is AI in a hype cycle?",
  "model": "gpt-4o",
  "max_sections": 3, 
  "publish_formats": { 
    "markdown": true,
    "pdf": true,
    "docx": true
  },
  "include_human_feedback": false,
  "source": "web",
  "follow_guidelines": true,
  "guidelines": [
    "The report MUST fully answer the original question",
    "The report MUST be written in apa format",
    "The report MUST be written in english"
  ],
  "verbose": true
}

To Deploy

pip install langgraph-cli
langgraph up

From there, see documentation here on how to use the streaming and async endpoints, as well as the playground.

NextJS Frontend App

The React app (located in frontend directory) is our Frontend 2.0 which we hope will enable us to display the robustness of the backend on the frontend, as well.

It comes with loads of added features, such as:

  • a drag-n-drop user interface for uploading and deleting files to be used as local documents by GPTResearcher.
  • a GUI for setting your GPTR environment variables.
  • the ability to trigger the multi_agents flow via the Backend Module or Langgraph Cloud Host (currently in closed beta).
  • stability fixes
  • and more coming soon!

Run the NextJS React App with Docker

Step 1 - Install Docker

Step 2 - Clone the '.env.example' file, add your API Keys to the cloned file and save the file as '.env'

Step 3 - Within the docker-compose file comment out services that you don't want to run with Docker.

$ docker-compose up --build

Step 4 - By default, if you haven't uncommented anything in your docker-compose file, this flow will start 2 processes:

  • the Python server running on localhost:8000
  • the React app running on localhost:3000

Visit localhost:3000 on any browser and enjoy researching!

Run the NextJS React App with NPM

cd frontend
nvm install 18.17.0
nvm use v18.17.0
npm install --legacy-peer-deps
npm run dev