v0.2.1

This is one of the most exciting releases yet. Proud to introduce the latest GPTR x LangGraph integration showcasing the power of flow engineering and multi agent collaboration! Check out the full implementation in the new directory `multi_agents`.

By using [LangGraph](https://python.langchain.com/docs/langgraph/), the research process can be significantly improved in depth and quality by leveraging multiple agents with specialized skills. Inspired by the recent [STORM](https://arxiv.org/abs/2402.14207) 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.

## The Multi Agent Team
The research team is made up of 7 AI 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.

### Architecture
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<img align="center" height="600" src="https://cowriter-images.s3.amazonaws.com/gptr-langgraph-architecture.png">
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