
neuro-san-studio
A playground for neuro-san
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Neuro SAN Studio is an open-source library for building agent networks across various industries. It simplifies the development of collaborative AI systems by enabling users to create sophisticated multi-agent applications using declarative configuration files. The tool offers features like data-driven configuration, adaptive communication protocols, safe data handling, dynamic agent network designer, flexible tool integration, robust traceability, and cloud-agnostic deployment. It has been used in various use-cases such as automated generation of multi-agent configurations, airline policy assistance, banking operations, market analysis in consumer packaged goods, insurance claims processing, intranet knowledge management, retail operations, telco network support, therapy vignette supervision, and more.
README:
A playground for Neuro SAN - this repo includes working examples to get started, explore, extend, and experiment with custom multi-agent networks!
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 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 |
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- 🗂️ 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 decison 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.
Agent Network | Use-Case | Description |
---|---|---|
🧬 Agent Network Designer | Automated generation of multi-agent HOCON configurations. | Generates complex multi-agent configurations from natural language input, simplifying the creation of intricate agent workflows. |
🛫 Airline Policy Assistance | Customer support for airline policies. | Agents interpret and explain airline policies, assisting customers with inquiries about baggage allowances, cancellations, and travel-related concerns. |
🏦 Banking Operations & Compliance | Automated financial operations and regulatory compliance. | Automates tasks such as transaction monitoring, fraud detection, and compliance reporting, ensuring adherence to regulations and efficient routine operations. |
🛍️ Consumer Packaged Goods (CPG) | Market analysis and product development in CPG. | Gathers and analyzes market trends, customer feedback, and sales data to support product development and strategic marketing. |
🛡️ Insurance Agents | Claims processing and risk assessment. | Automates claims evaluation, assesses risk factors, ensures policy compliance, and improves claim-handling efficiency and customer satisfaction. |
🏢 Intranet Agents | Internal knowledge management and employee support. | Provides employees with quick access to policies, HR, and IT support, enhancing internal communications and resource accessibility. |
🛒 Retail Operations & Customer Service | Enhancing retail customer experience and operational efficiency. | Handles customer inquiries, inventory management, and supports sales processes to optimize operations and service quality. |
📞 Telco Network Support | Technical support and network issue resolution. | Diagnoses network problems, guides troubleshooting, and escalates complex issues, reducing downtime and enhancing customer service. |
📞 Therapy Vignette Supervision | Generates treatment plan for a given therapy vignette. | A good example of using multiple different expert agents working together to come up with a single plan. |
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
There are multiple ways in which we can now use the neuro-san server with a client:
Option 1: Using nsflow
as a developer-oriented web client
If you want to use neuro-san with a FastAPI-based developer-oriented client, follow these steps:
-
Start the server and client with a single command, from project root:
python -m run
-
As a default
- Frontend will be available at:
http://127.0.0.1:4173
- The client and server logs will be saved to
logs/nsflow.log
andlogs/server.log
respectively.
- Frontend will be available at:
-
To see the various config options for this app, on terminal
python -m run --help
Screenshot:
A basic web client interface is installed by default. It's a great, simple example of how to connect to a neuro-san server and interact with it. Start the server and the client in one single command:
python -m run --use-flask-web-client
The client and server logs will show on the screen,
and will also be saved to logs/server.log
and logs/client.log
respectively.
As a default, on a web browser you can now navigate to http://127.0.0.1:5003/ to start using the application.
Notes:
- Expand the
Configuration
tab at the bottom of the interface to connect to the neuro-san server host and port - Choose an Agent Network Name, e.g. "music_nerd", click Update
This Agent Network Name should match the list of agent networks that are activated in theregistries/manifest.hocon
file. - Type your message in the chat box and press 'Send' to interact with the agent network.
- Optional: open the
Agent Network Diagram
tab to visualize the interactions between the agents. - Optional: open the
Agent Communications
tab to see the messages exchanged between the agents.
You can also use neuro-san's command line interface (CLI) to start and interact with the server.
-
Export the following environment variables:
# Point the server to the manifest file containing the agent network configurations export AGENT_MANIFEST_FILE="./registries/manifest.hocon" # Point the server to the directory containing the agent Python tools export AGENT_TOOL_PATH="./coded_tools"
-
For further instructions, refer to the client/server setup in neuro-san.
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.
- Code versus Model in Multi-Agentic Systems: dives into how to design reliable multi-agent systems by dividing responsibilities between LLM reasoning and coded tools.
- Neuro SAN Is All You Need — A Data-Driven Multi-Agent Orchestration Framework: explores Neuro SAN's architecture, configuration model, adaptive communication protocol (AAOSA), and how it enables secure, extensible agent collaboration without hardcoded logic.
For more information, check out the Cognizant AI Lab Neuro SAN landing page.
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