LangGraph x GPT Researcher

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.

Looking for the AG2 version? See multi_agents_ag2/ and the AG2 docs page.

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: Multi-agents are utilizing the same configuration of models like GPT-Researcher does. However, only the SMART_LLM is used for the time being. Please refer to the LLM config pages.

The Multi Agent Team

The research team is made up of 8 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 found in this root folder including langgraph:

   bashCopy

   pip install -r requirements.txt
   

2. Update env variables, see the GPT-Researcher docs for more details.

3. Run the application:

   bashCopy

   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.