Neuronum Docs | Server

Neuronum Server

About Neuronum Server

Neuronum Server is an agent-wrapper that transforms your model into an agentic backend server that can interact with "kybercell" (official Neuronum Client) or the Neuronum Client API and installed tools.

Requirements

Python >= 3.8
CUDA-compatible GPU (for Neuronum Server)
CUDA Toolkit (for Neuronum Server)

Connect To Neuronum

Installation

Create and activate a virtual environment:

Bash

python3 -m venv ~/neuronum-venv
source ~/neuronum-venv/bin/activate

Install the Neuronum SDK:

Bash

pip install neuronum==2026.01.0.dev1

Note: Always activate this virtual environment (source ~/neuronum-venv/bin/activate) before running any neuronum commands.

Create a Neuronum Cell (secure Identity)

Bash

neuronum create-cell

Connect your Cell

Bash

neuronum connect-cell

Start the Server

Bash

neuronum start-server

This command will:

Clone the neuronum-server repository (if not already present)
Create a Python virtual environment
Install all dependencies (vLLM, PyTorch, etc.)
Start the vLLM server in the background
Launch the Neuronum Server

Check Server Status

Bash

neuronum status

This will show if the Neuronum Server and vLLM Server are currently running with their PIDs.

Viewing Logs

Bash

tail -f neuronum-server/server.log
tail -f neuronum-server/vllm_server.log

Stopping the Server

Bash

neuronum stop-server

What the Server Does

Once running, the server will:

Connect to the Neuronum network using your Cell credentials
Initialize a local SQLite database for conversation memory and knowledge storage
Auto-discover and launch any MCP servers in the tools/ directory
Process messages from clients via the Neuronum network
Execute scheduled tasks defined in the tasks/ directory

Server Configuration

The server can be customized by editing the neuronum-server/server.config file. Here are the available options:

File Paths

Python

LOG_FILE = "server.log"              # Server log file location
DB_PATH = "agent_memory.db"          # SQLite database for conversations and knowledge
TASKS_DIR = "./tasks"                # Directory for scheduled tasks

Model Configuration

Python

MODEL_MAX_TOKENS = 512               # Maximum tokens in responses (higher = longer answers)
MODEL_TEMPERATURE = 0.3              # Creativity (0.0 = deterministic, 1.0 = creative)
MODEL_TOP_P = 0.85                   # Nucleus sampling (lower = more predictable)

vLLM Server

Python

VLLM_MODEL_NAME = "Qwen/Qwen2.5-3B-Instruct"  # Model to load
                                               # Examples: "Qwen/Qwen2.5-1.5B-Instruct",
                                               #           "meta-llama/Llama-3.2-3B-Instruct"
VLLM_HOST = "127.0.0.1"              # Server host (127.0.0.1 = local only)
VLLM_PORT = 8000                     # Server port
VLLM_API_BASE = "http://127.0.0.1:8000/v1"  # Full API URL

Conversation & Knowledge

Python

CONVERSATION_HISTORY_LIMIT = 10      # Recent messages to include in context
KNOWLEDGE_RETRIEVAL_LIMIT = 5        # Max knowledge chunks to retrieve
FTS5_STOPWORDS = {...}               # Words to exclude from knowledge search

After modifying the configuration, restart the server for changes to take effect:

Bash

neuronum stop-server
neuronum start-server