Your launchpad for building intelligent multi-agent systems. Neuro SAN Studio is a hands-on playground for the Neuro SAN framework, featuring ready-to-run examples, tutorials, and tools that let you design, test, and deploy sophisticated agent networks in minutes—not months. Whether you're a researcher exploring adaptive AI systems, a developer prototyping production solutions, or a domain expert configuring agents without code, this studio handles the orchestration complexity so you can focus on solving real problems.

Neuro SAN is the open-source library powering the Cognizant Neuro® AI Multi-Agent Accelerator, allowing domain experts, researchers and developers to immediately start prototyping and building agent networks across any industry vertical.

Neuro SAN library

Neuro AI system of agent networks (Neuro SAN) is an open-source, data-driven multi-agent orchestration framework designed to simplify and accelerate the development of collaborative AI systems. It allows users—from machine learning engineers to business domain experts—to quickly build sophisticated multi-agent applications without extensive coding, using declarative configuration files (in HOCON format).

Neuro SAN enables multiple large language model (LLM)-powered agents to collaboratively solve complex tasks, dynamically delegating subtasks through adaptive inter-agent communication protocols. This approach addresses the limitations inherent to single-agent systems, where no single model has all the expertise or context necessary for multifaceted problems.

Build a multi-agent network in minutes	Neuro SAN overview	Quick start

• 🗂️ Data-Driven Configuration: Entire agent networks are defined declaratively via simple HOCON files, empowering technical and non-technical stakeholders to design agent interactions intuitively.
• 🔀 Adaptive Communication (AAOSA Protocol): Agents autonomously determine how to delegate tasks, making interactions fluid and dynamic with decentralized decision-making.
• 🔒 Sly-Data: Sly Data facilitates safe handling and transfer of sensitive data between agents without exposing it directly to any language models.
• 🧩 Dynamic Agent Network Designer: Includes a meta-agent called the Agent Network Designer – essentially, an agent that creates other agent networks. Provided as an example with Neuro SAN, it can take a high-level description of a use-case as input and generate a new custom agent network for it.
• 🛠️ Flexible Tool Integration: Integrate custom Python-based "coded tools," APIs, databases, and even external agent ecosystems (Agentforce, Agentspace, CrewAI, MCP, A2A agents, LangChain tools and more) seamlessly into your agent workflows.
• 📈 Robust Traceability: Detailed logging, tracing, and session-level metrics enhance transparency, debugging, and operational monitoring.
• 🌐 Extensible and Cloud-Agnostic: Compatible with a wide variety of LLM providers (OpenAI, Anthropic, Azure, Ollama, etc.) and deployable in diverse environments (local machines, containers, or cloud infrastructures).

Here are a few examples of use-cases that have been implemented with Neuro SAN. For more examples, check out docs/examples.md.

And many more: check out docs/examples.md.

To dive into Neuro SAN and start building your own multi-agent networks, this repository contains a collection of demos for the neuro-san library.

You'll find comprehensive documentation, example agent networks, and tutorials to guide you through your first steps.

Clone the repo:

git clone https://github.com/cognizant-ai-lab/neuro-san-studio

Go to dir:

cd neuro-san-studio

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-4o 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"

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

Neuro SAN Studio provides a user-friendly environment to interact with agent networks.

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

Screenshot:

Ready to dive in? Check out the user guide for a detailed overview of the neuro-san library and its features.

For a detailed tutorial, refer to docs/tutorial.md.

For examples of agent networks, check out docs/examples.md.

For the development guide, check out docs/dev_guide.md.

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• Vibe coding project evaluator: a scalable framework that evaluates vibe-coded projects on different criteria.

• Neuro SAN Web Client: a basic Flask web client interface for Neuro SAN.
• Neuro SAN Slack app a Slack integration that lets you interact with Neuro SAN directly from your workspace.

• Website: Cognizant AI Lab
• YouTube: Decision AI
• X: @cognizantailab
• LinkedIn: Cognizant AI Lab

For more information, check out the Cognizant AI Lab Neuro SAN landing page.