Report mAIstro

Report mAIstro is an open-source research assistant that generates comprehensive reports on any topic, following a workflow similar to Google's Gemini Deep Research. It combines planning, parallel web research, and structured writing with human oversight.

Key features:

• Uses OpenAI o-series reasoning model (default) for intelligent report planning
• Enables human review and iteration of the research plan
• Parallelizes web research across multiple report sections, using Claude-3.5-Sonnet for report writing
• Produces well-formatted markdown reports
• Supports customizable models, prompts, and report structure

Short summary:

🚀 Quickstart

Clone the repository:

git clone https://github.com/langchain-ai/report_maistro.git
cd report_maistro

Set API keys for Anthropic (default writer), OpenAI (default planner), and Tavily for free web search up to 1000 requests):

Edit the .env file with your API keys:

export TAVILY_API_KEY=<your_tavily_api_key>
export ANTHROPIC_API_KEY=<your_anthropic_api_key>
export OPENAI_API_KEY=<your_openai_api_key>

Launch the assistant with the LangGraph server, which will open in your browser:

Mac

# Install uv package manager
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install dependencies and start the LangGraph server
uvx --refresh --from "langgraph-cli[inmem]" --with-editable . --python 3.11 langgraph dev

Windows

# Install dependencies 
pip install -e .
pip install langgraph-cli[inmem]

# Start the LangGraph server
langgraph dev

Use this to open the Studio UI:

- 🚀 API: http://127.0.0.1:2024
- 🎨 Studio UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
- 📚 API Docs: http://127.0.0.1:2024/docs

(1) Provide a Topic and hit Submit:

(2) This will generate a report plan:

(3) You can review the section of the plan in Studio. If you like them, hit Continue.

(4) If you want to add feedback, add Feedback On Report Plan and Submit:

(5) If you have given feedback, continue iterating until you are happy and then select Accept Report Plan:

📖 Customizing the report

Optionally, provide a description of the report structure you want as a configuration. You can further tune this during the feedback phase. While a topic alone can generate 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.

See some example report types here!

Motivation

Automating research and report writing is a common need. Deep Research from Google is a great example of this. This open source project mirror the flow of Deep Research, but allow you to customize the models, prompts, and research report structure.

How it works

1. Plan and Execute - Report mAIstro follows a plan-and-execute workflow that separates planning from research, allowing for better resource management, human-in-the-loop approval, 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

2. 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.

3. 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.

UX

Local deployment

Follow the quickstart to run the assistant locally.

Hosted deployment

You can easily deploy to LangGraph Platform .