Neuro SAN Studio: Build Multi-Agent Systems with Declarative Config

What does this agent do, and when should you use it?

Neuro SAN Studio is an open-source developer platform from Cognizant AI Lab that serves as a playground for the Neuro SAN multi-agent orchestration framework. It provides ready-to-run examples, tutorials, and tools to design, test, and deploy sophisticated agent networks using declarative HOCON configuration files. The studio includes a command-line tool `ns`, a web UI called nsflow, and a meta-agent called Agent Network Designer that can generate entire agent networks from natural language descriptions. It supports flexible integration with various LLM providers (OpenAI, Anthropic, Google Gemini) and external tools/frameworks. Installation uses the `uv` package manager, and initialization is done via `ns init`. The platform targets researchers, developers, and domain experts, emphasizing data-driven configuration and adaptive communication (AAOSA protocol).

Neuro SAN Studio enables users to build and run multi-agent systems. Specifically, it scaffolds a project structure via ns init, which creates folders like config, mcp, registries, coded_tools, and middleware. Users can import predefined agent networks (e.g., Agent Network Designer and industry examples) with ns import. Running ns run starts the Neuro SAN server (default localhost:8080) and the nsflow UI (localhost:4173). The Agent Network Designer takes natural language input and generates agent network configurations, including agent roles, connections, instructions, and sample queries. The tool also supports ns chat to converse directly with an agent network, ns check-llm-keys to validate API keys, and ns check-config to validate HOCON configurations. These operations ultimately produce interactive multi-agent applications for scenarios like customer support, financial compliance, insurance claims, and more.

1. Researchers exploring adaptive multi-agent systems can experiment with prebuilt examples or custom networks.
2. Developers can rapidly prototype production solutions, generating configurations from natural language and integrating external APIs.
3. Domain experts (e.g., policy admins) can design agent interactions without coding to handle customer inquiries.
4. Enterprises can deploy automated customer support, such as airline policy assistance or banking compliance monitoring.
5. IT teams can implement internal knowledge management, letting employees query HR and IT policies via agents.
6. Developers can use Agent Network Designer to create tailored multi-agent networks for specific industry problems.

What are this agent's strengths and limitations?

How do you install or deploy this agent?

Requires Python and uv (Linux/macOS). In a project folder run:

mkdir my_project && cd my_project
uv init
uv venv
source .venv/bin/activate
uv add neuro-san-studio

Initialize the project:

ns init

Choose an LLM provider (OpenAI, Anthropic, or Google Gemini) when prompted. Set your API key:

export OPENAI_API_KEY="XXX"  # or other provider key, or create .env

Validate setup:

ns check-llm-keys
ns check-config

How do you use this agent?

Start the server and UI with ns run, then open http://localhost:4173/ in your browser. Click the NEW button to launch the Agent Network Designer, enter a natural-language description, and it will generate an agent network. Click Launch to test it. Alternatively, import prebuilt networks with ns import basic or ns import hello_world. Use ns chat <agent-name> for direct conversation, and ns export to bundle a network as a .hocon or .zip file. See ns --help for full command details.

FAQ