Report mAIstro

Report mAIstro creates easily customizable reports on any user-supplied topic.

See report examples here!

Quickstart

Populate the .env file:

$ cp .env.example .env

Load this folder in LangGraph Studio

Optionally, provide a description of the report structure you want as a configuration.

See some example report types here!

Provide a topic and run the graph to produce a report

Motivation

RAG systems are a powerful tool for interacting with LLMs, enabling quick and accurate question-answering capabilities. However, individual questions often serve a larger purpose: informing decisions and strategies. While reports are essential decision-making tools, creating comprehensive reports remains a time-consuming challenge. Report mAIstro transforms this process by leveraging LLMs to generate detailed reports through natural language, making report creation more accessible and efficient!

Overview

Phase	Objective	Report mAIstro Implementation
Structure	How is the report organized?	Uses LLM to convert natural language instructions into structured section objects
Research	What are the information sources?	Web search via Tavily API
Orchestration	How is report generation managed?	Three-phase LangGraph workflow: 1) Planning to define sections, 2) Parallel research for content, 3) Generation of introduction/conclusion using gathered context
Reporting	How is the report presented to the user?	Generates structured markdown files
UX	What is the user interaction pattern?	Synchronous workflow where user provides inputs and waits for the report to be generated

Natural Language Report Creation - Report mAIstro requires just two inputs from users:
- A topic for the report
- An optional structure in natural language
While a topic alone can generate basic reports, we found that providing a structure significantly improves quality. For example, business strategy reports might need case studies, while comparative analyses benefit from structured comparison tables. The natural language structure acts as a flexible template, guiding the AI to create more focused and relevant reports.
Plan and Execute - Report mAIstro follows a plan-and-execute workflow that separates planning from research, allowing for better resource management and significantly reducing overall report creation time:
- Planning Phase: An LLM analyzes the user's topic and structure using a planning prompt to create the report sections first.
- Research Phase: The system parallelizes web research across all sections requiring external data:
  - Uses Tavily API for targeted web searches
  - Processes multiple sections simultaneously for faster report generation
  - Synthesizes gathered information into coherent section content
Sequential Writing - The report generation follows a logical sequence:
- First, completes all research-dependent sections in parallel
- Then generates connecting sections like introductions and conclusions
- Uses insights from research sections to create cohesive narratives
- Maintains contextual awareness across all sections
While this sequence can be customized via the structure, the default flow ensures that conclusions meaningfully incorporate research findings.
Managing different types - Report mAIstro is built on LangGraph, which has native support for configuration management using assistants. The report structure is a field in the graph configuration, which allows users to create different assistants for different types of reports.

See the provided notebook or example reports for examples! These include:

Market Analysis: Compare products, services, or companies across key metrics

Industry Research: Analyze market trends, innovations, and future outlook

Strategic Case Studies: Extract actionable insights from business histories

Technical Guides: Step-by-step implementation instructions

Custom Templates: Design your own report structure in natural language

Testing in notebook

Create a virtual environment and install dependencies:

$ python3 -m venv report_maistro
$ source report_maistro/bin/activate
$ pip install -r requirements.txt

Supply your OpenAI API key:

$ cp .env.example .env

Run the notebook:

$ cd ntbk
$ jupyter notebook report_maistro.ipynb

Deploying