Neuro SAN Annual Report Reader

Neuro SAN applied to Cognizant's 2024 Annual Report.

Analyzes a LinkedIn profile URL and delivers a personalized summary of Cognizant's 2024 Annual Report — surfacing only the content most relevant to the user's industry and seniority level. The LinkedIn profile is scraped via the Apify apimaestro/linkedin-profile-detail actor, classified into a broad interest category and seniority level (Executive, Manager, or Practitioner), and used to filter the full report in a single agent call.

For more details about Neuro SAN, please check the Neuro SAN library and Neuro SAN Studio repository.

Getting started

Installation

Clone the repo:

git clone https://github.com/shrushtiimehta/neuro-san-annual-report-reader

Go to dir:

cd neuro-san-annual-report-reader

Ensure you have a supported version of python (e.g. 3.12 or 3.13):

python --version

Create a dedicated Python virtual environment:

python -m venv venv

Source it:

• For Windows:

  .\venv\Scripts\activate.bat && set PYTHONPATH=%CD%
  

• For Mac:

  source venv/bin/activate && export PYTHONPATH=`pwd`
  

Install the requirements:

pip install -r requirements.txt

IMPORTANT: By default the server relies on OpenAI's gpt-5.2 model. Set the OpenAI API key, and add it to your shell configuration so it's available in future sessions.

You can get your OpenAI API key from https://platform.openai.com/signup. After signing up, create a new API key in the API keys section in your profile.

NOTE: Replace XXX with your actual OpenAI API key. NOTE: This is OS dependent.

• For macOS and Linux:

  export OPENAI_API_KEY="XXX" && echo 'export OPENAI_API_KEY="XXX"' >> ~/.zshrc
  

• For Windows:

  • On Command Prompt:

  set OPENAI_API_KEY=XXX
  

  • On PowerShell:

  $env:OPENAI_API_KEY="XXX"
  

IMPORTANT: This project also requires an Apify API key to scrape LinkedIn profiles. Get yours from the Apify Integrations Console and subscribe to the apimaestro/linkedin-profile-detail actor.

• For macOS and Linux:

  export APIFY_API_KEY="XXX" && echo 'export APIFY_API_KEY="XXX"' >> ~/.zshrc
  

• For Windows:

  • On Command Prompt:

  set APIFY_API_KEY=XXX
  

  • On PowerShell:

  $env:APIFY_API_KEY="XXX"
  

Other providers such as Anthropic, AzureOpenAI, Ollama and more are supported too but will require proper setup. Look at the .env.example file to set up environment variables for specific use-cases.

For testing the API keys, please refer to this documentation

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Run

Start the server and client with a single command, from the project root directory:

1. Start the server and client with a single command, from the project root directory:

   python -m run
   

2. Navigate to http://localhost:4173/ to access the UI.

3. (Optional) Check the logs:

   • For the server logs: logs/server.log
   • For the client logs: logs/nsflow.log
   • For the agents logs: logs/thinking_dir/*

Use the --help option to see the various config options for the run command:

python -m run --help

Using the agent networks

Select the annual_report_reader network and share a LinkedIn profile URL, for instance:

Analyze <linkedin-url> and summarize the annual report content most relevant to their interests or simply paste the URL and let the agent do the rest.

The agent will scrape the profile, classify the person's interest category and seniority level, and return a personalized summary of Cognizant's 2024 Annual Report filtered to what is most relevant to their role.

You can follow up with questions: What does the report say about AI agent development platforms?