Snak
About
Toolkit for creating blockchain agents that interact with the Starknet network, enabling wallet management, DeFi operations, and smart contract interactions through modular plugins
Details
- License
- MIT
Explore
- Supports multiple AI providers (OpenAI, Anthropic, Google Gemini, Ollama).
- Customizable agent configuration with lore, objectives, knowledge, and plugins.
- Two operating modes: interactive and autonomous.
- Built-in short‑term memory and optional LangSmith tracing.
- Integrates with MCP servers for extended functionality.
- Available as both CLI/server and programmable NPM package.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
SnakCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
- Starknet wallet (recommended: Argent X)
- AI provider API key (Anthropic/OpenAI/Google Gemini/Ollama)
- Docker and Docker compose installed
- Node.js and pnpm installed
STARKNET_PUBLIC_ADDRESS="YOUR_STARKNET_PUBLIC_ADDRESS"
STARKNET_PRIVATE_KEY="YOUR_STARKNET_PRIVATE_KEY"
STARKNET_RPC_URL="YOUR_STARKNET_RPC_URL"
SERVER_API_KEY="YOUR_SERVER_API_KEY" # A secret key for your agent server API
SERVER_PORT="3001"
POSTGRES_USER=admin
POSTGRES_HOST=localhost
POSTGRES_DB=postgres
POSTGRES_PASSWORD=admin
POSTGRES_PORT=5432
NODE_ENV="development" # "development" or "production"
2. Configure AI Models (Optional):
The config/models/default.models.json file defines the default AI models used for different tasks (fast, smart, cheap). You can customize this file or create new model configurations (e.g., my_models.json) and specify them when running the agent. See config/models/example.models.json for the structure.
The agent uses the provider field in the model configuration to determine which API key to load from the .env file (e.g., if provider is openai, it loads OPENAI_API_KEY).
3. Create your agent configuration file (e.g., default.agent.json or my_agent.json) in the config/agents/ directory:
json{
"name": "Your Agent name",
"group": "Your Agent group",
"description": "Your AI Agent Description",
"lore": ["Some lore of your AI Agent 1", "Some lore of your AI Agent 1"],
"objectives": [
"first objective that your AI Agent need to follow",
"second objective that your AI Agent need to follow"
],
"knowledge": [
"first knowledge of your AI Agent",
"second knowledge of your AI Agent"
],
"interval": "Your agent interval beetween each transaction of the Agent in ms,",
"chatId": "Your Agent Chat-id for isolating memory",
"max_iterations": "The number of iterations your agent will execute before stopping",
"mode": "The mode of your agent, can be interactive, autonomous or hybrid",
"memory": {
"enabled": "true or false to enable or disable memory",
"shortTermMemorySize": "The number of messages your agent will remember"
},
"plugins": ["Your first plugin", "Your second plugin"],
"mcp_servers": {
"nxp_server_example": {
"command": "npx",
"args": ["-y", "@npm_package_example/npx_server_example"],
"env": {
"API_KEY": "YOUR_API_KEY"
}
},
"local_server_example": {
"command": "node",
"args": ["node /path/to/local_server/dist/index.js"]
}
}
}
You can simply create your own agent configuration using our tool on snakagent
pnpm run start --agent="name_of_your_config.json" --models="name_of_your_config.json"
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"snak": {
"snak": {
"command": "node",
"args": [
"/absolute/path/to/snak/mcp_server/snak/dist/index.js",
"plugin_name_1",
"plugin_name_2",
"plugin_name_3"
]
}
}
}
}
McpServers
{
"snak": {
"command": "node",
"args": [
"/absolute/path/to/snak/mcp_server/snak/dist/index.js",
"plugin_name_1",
"plugin_name_2",
"plugin_name_3"
]
}
}
<div align="center">
<picture>
<!-- For users in dark mode, load a white logo -->
<source media="(prefers-color-scheme: dark)" srcset="https://github.com/KasarLabs/brand/blob/main/projects/snak/snak-full-white-alpha.png?raw=true">
<!-- Default image for light mode -->

</picture>
<p>
<a href="https://www.npmjs.com/package/starknet-agent-kit">
</a>
<a href="https://github.com/kasarlabs/snak/blob/main/LICENSE">
</a>
<a href="https://github.com/kasarlabs/snak/stargazers">
</a>
<a href="https://nodejs.org">
</a>
</p>
</div>
A Agent Engine for creating powerful and secure AI Agents powered by Starknet. Available as both an NPM package and a ready-to-use backend.
Quick Start
Prerequisites
- Starknet wallet (recommended: Argent X)
- AI provider API key (Anthropic/OpenAI/Google Gemini/Ollama)
- Docker and Docker compose installed
- Node.js and pnpm installed
Installation
git clone https://github.com/kasarlabs/snak.git
cd snak
pnpm install
Configuration
1. Create a .env file by copying .env.example:
cp .env.example .env
Then, fill in the necessary values in your .env file:
# --- Starknet configuration (mandatory) ---
STARKNET_PUBLIC_ADDRESS="YOUR_STARKNET_PUBLIC_ADDRESS"
STARKNET_PRIVATE_KEY="YOUR_STARKNET_PRIVATE_KEY"
STARKNET_RPC_URL="YOUR_STARKNET_RPC_URL"
--- AI Model API Keys (mandatory) ---
Add the API keys for the specific AI providers you use in config/models/default.models.json
The agent will automatically load the correct key based on the provider name.
Example for OpenAI:
OPENAI_API_KEY="YOUR_OPENAI_API_KEY" # (e.g., sk-...)
Example for Anthropic:
ANTHROPIC_API_KEY="YOUR_ANTHROPIC_API_KEY" # (e.g., sk-ant-...)
Example for Google Gemini:
GEMINI_API_KEY="YOUR_GEMINI_API_KEY"
Example for DeepSeek:
DEEPSEEK_API_KEY="YOUR_DEEPSEEK_API_KEY"
Note: You do not need an API key if using a local Ollama model.
--- General Agent Configuration (mandatory) ---
SERVER_API_KEY="YOUR_SERVER_API_KEY" # A secret key for your agent server API
SERVER_PORT="3001"
--- PostgreSQL Database Configuration (mandatory) ---
POSTGRES_USER=admin
POSTGRES_HOST=localhost
POSTGRES_DB=postgres
POSTGRES_PASSWORD=admin
POSTGRES_PORT=5432
--- LangSmith Tracing (Optional) ---
Set LANGSMITH_TRACING=true to enable tracing
LANGSMITH_TRACING=false
LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
LANGSMITH_API_KEY="YOUR_LANGSMITH_API_KEY" # (Only needed if LANGSMITH_TRACING=true)
LANGSMITH_PROJECT="Snak" # (Optional project name for LangSmith)
--- Node Environment ---
NODE_ENV="development" # "development" or "production"
2. Configure AI Models (Optional):
The config/models/default.models.json file defines the default AI models used for different tasks (fast, smart, cheap). You can customize this file or create new model configurations (e.g., my_models.json) and specify them when running the agent. See config/models/example.models.json for the structure.
The agent uses the provider field in the model configuration to determine which API key to load from the .env file (e.g., if provider is openai, it loads OPENAI_API_KEY).
3. Create your agent configuration file (e.g., default.agent.json or my_agent.json) in the config/agents/ directory:
{
"name": "Your Agent name",
"group": "Your Agent group",
"description": "Your AI Agent Description",
"lore": ["Some lore of your AI Agent 1", "Some lore of your AI Agent 1"],
"objectives": [
"first objective that your AI Agent need to follow",
"second objective that your AI Agent need to follow"
],
"knowledge": [
"first knowledge of your AI Agent",
"second knowledge of your AI Agent"
],
"interval": "Your agent interval beetween each transaction of the Agent in ms,",
"chatId": "Your Agent Chat-id for isolating memory",
"max_iterations": "The number of iterations your agent will execute before stopping",
"mode": "The mode of your agent, can be interactive, autonomous or hybrid",
"memory": {
"enabled": "true or false to enable or disable memory",
"shortTermMemorySize": "The number of messages your agent will remember"
},
"plugins": ["Your first plugin", "Your second plugin"],
"mcp_servers": {
"nxp_server_example": {
"command": "npx",
"args": ["-y", "@npm_package_example/npx_server_example"],
"env": {
"API_KEY": "YOUR_API_KEY"
}
},
"local_server_example": {
"command": "node",
"args": ["node /path/to/local_server/dist/index.js"]
}
}
}
You can simply create your own agent configuration using our tool on snakagent
Usage
Prompt Mode
Run the promt:
# start with the default.agent.json
pnpm run start
start with your custom configuration
pnpm run start --agent="name_of_your_config.json" --models="name_of_your_config.json"
Server Mode
Run the server :
# start with the default.agent.json
pnpm run start:server
start with your custom configuration
pnpm run start:server --agent="name_of_your_config.json" --models="name_of_your_config.json"
Available Modes
| | Interactive Mode | Autonomous Mode |
| ----------- | ---------------- | --------------- |
| Prompt Mode | ✅ | ✅ |
| Server Mode | ✅ | ✅ |
Implement Snak in your project
1. Install snak package
#using npm
npm install @snakagent
using pnpm
pnpm add @snakagent
2. Create your agent instance
import { SnakAgent } from 'starknet-agent-kit';
const agent = new SnakAgent({
provider: new RpcProvider({ nodeUrl: process.env.STARKNET_RPC_URL }),
accountPrivateKey: process.env.STARKNET_PRIVATE_KEY,
accountPublicKey: process.env.STARKNET_PUBLIC_ADDRESS,
aiModel: process.env.AI_MODEL,
aiProvider: process.env.AI_PROVIDER,
aiProviderApiKey: process.env.AI_PROVIDER_API_KEY,
signature: 'key',
agentMode: 'interactive',
agentconfig: y,
});
const response = await agent.execute("What's my ETH balance?");
Actions
To learn more about actions you can read this doc section.
A comprehensive interface in the Kit will provide an easy-to-navigate catalog of all available plugins and their actions, making discovery and usage simpler.
To add actions to your agent you can easily follow the step-by-steps guide here
Contributing
Contributions are welcome! Feel free to submit a Pull Request.
License
MIT License - see the LICENSE file for details.
---
For detailed documentation visit docs.kasar.io
Sign in to leave a review
Use Google, GitHub, or an email account so ratings stay tied to real people.
No reviews posted yet.



