[{"_1":2,"_509":-5,"_510":-5},"loaderData",{"_3":4,"_22":23,"_320":321},"root",{"_5":6,"_7":8},"toasts",[],"visitor",{"_9":10,"_11":12},"viewportWidth",1280,"visitorSession",{"_13":14,"_16":17,"_18":-5,"_19":20,"_21":-5},"attributes",[15],"bot","id",16246667,"membership","referenceId","01996c17-eef2-7208-aa1b-bc546c003f59","userAccount","routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_24":25,"_79":-5,"_80":38,"_27":81,"_237":238,"_319":220},"availableLocales",[26,38,45,51,58,65,72],{"_27":28,"_31":35,"_36":37},"mcpServer",{"_29":30,"_31":32,"_33":34},"description","Erstellen Sie leistungsstarke und sichere KI-Agenten mit Starknet-Unterstützung.","name","Schlange","readme","Eine Agent Engine zum Erstellen leistungsstarker und sicherer KI-Agenten auf Basis von Starknet. Verfügbar als NPM-Paket und einsatzbereites Backend.\n\n## Schnellstart\n\n### Voraussetzungen\n\n* Starknet-Wallet (empfohlen: [Argent X](https://www.argent.xyz/argent-x) )\n* API-Schlüssel des KI-Anbieters (Anthropic/OpenAI/Google Gemini/Ollama)\n* Node.js und pnpm installiert\n\n### Installation\n\n```bash\ngit clone https://github.com/kasarlabs/snak.git\ncd snak\npnpm install\n```\n\n### Konfiguration\n\n1. Erstellen Sie eine `.env` Datei, indem Sie `.env.example` kopieren:\n\n```bash\ncp .env.example .env\n```\n\nGeben Sie dann die erforderlichen Werte in Ihre `.env` Datei ein:\n\n```env\n# --- Starknet configuration (mandatory) ---\nSTARKNET_PUBLIC_ADDRESS=\"YOUR_STARKNET_PUBLIC_ADDRESS\"\nSTARKNET_PRIVATE_KEY=\"YOUR_STARKNET_PRIVATE_KEY\"\nSTARKNET_RPC_URL=\"YOUR_STARKNET_RPC_URL\"\n\n# --- AI Model API Keys (mandatory) ---\n# Add the API keys for the specific AI providers you use in config/models/default.models.json\n# The agent will automatically load the correct key based on the provider name.\n\n# Example for OpenAI:\nOPENAI_API_KEY=\"YOUR_OPENAI_API_KEY\" # (e.g., sk-...)\n\n# Example for Anthropic:\nANTHROPIC_API_KEY=\"YOUR_ANTHROPIC_API_KEY\" # (e.g., sk-ant-...)\n\n# Example for Google Gemini:\nGEMINI_API_KEY=\"YOUR_GEMINI_API_KEY\"\n\n# Example for DeepSeek:\nDEEPSEEK_API_KEY=\"YOUR_DEEPSEEK_API_KEY\"\n\n# Note: You do not need an API key if using a local Ollama model.\n\n# --- General Agent Configuration (mandatory) ---\nSERVER_API_KEY=\"YOUR_SERVER_API_KEY\" # A secret key for your agent server API\nSERVER_PORT=\"3001\"\n\n# --- PostgreSQL Database Configuration (mandatory) ---\nPOSTGRES_USER=\"admin\"\nPOSTGRES_PASSWORD=\"admin\"\nPOSTGRES_ROOT_DB=\"postgres\" # Database used to create/manage the application database\nPOSTGRES_HOST=\"localhost\"\nPOSTGRES_PORT=\"5454\"\n\n# --- LangSmith Tracing (Optional) ---\n# Set LANGSMITH_TRACING=true to enable tracing\nLANGSMITH_TRACING=false\nLANGSMITH_ENDPOINT=\"https://api.smith.langchain.com\"\nLANGSMITH_API_KEY=\"YOUR_LANGSMITH_API_KEY\" # (Only needed if LANGSMITH_TRACING=true)\nLANGSMITH_PROJECT=\"Snak\" # (Optional project name for LangSmith)\n\n# --- Node Environment ---\nNODE_ENV=\"development\" # \"development\" or \"production\"\n```\n\n2. KI-Modelle konfigurieren (optional): Die Datei `config/models/default.models.json` definiert die Standard-KI-Modelle für verschiedene Aufgaben ( `fast` , `smart` , `cheap` ). Sie können diese Datei anpassen oder neue Modellkonfigurationen erstellen (z. B. `my_models.json` ) und diese beim Ausführen des Agenten angeben. Die Struktur finden Sie unter `config/models/example.models.json` .\n\n Der Agent verwendet das `provider` in der Modellkonfiguration, um zu bestimmen, welcher API-Schlüssel aus der `.env` Datei geladen werden soll (wenn `provider` beispielsweise `openai` ist, wird `OPENAI_API_KEY` geladen).\n\n3. Erstellen Sie Ihre Agenten-Konfigurationsdatei (z. B. `default.agent.json` oder `my_agent.json` ) im Verzeichnis `config/agents/` :\n\n```json\n{\n \"name\": \"Your Agent name\",\n \"group\": \"Your Agent group\",\n \"description\": \"Your AI Agent Description\",\n \"lore\": [\"Some lore of your AI Agent 1\", \"Some lore of your AI Agent 1\"],\n \"objectives\": [\n \"first objective that your AI Agent need to follow\",\n \"second objective that your AI Agent need to follow\"\n ],\n \"knowledge\": [\n \"first knowledge of your AI Agent\",\n \"second knowledge of your AI Agent\"\n ],\n \"interval\": \"Your agent interval beetween each transaction of the Agent in ms,\",\n \"chatId\": \"Your Agent Chat-id for isolating memory\",\n \"maxIterations\": \"The number of iterations your agent will execute before stopping\",\n \"mode\": \"The mode of your agent, can be interactive, autonomous or hybrid\",\n \"memory\": {\n \"enabled\": \"true or false to enable or disable memory\",\n \"shortTermMemorySize\": \"The number of messages your agent will remember\"\n },\n \"plugins\": [\"Your first plugin\", \"Your second plugin\"],\n \"mcpServers\": {\n \"nxp_server_example\": {\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@npm_package_example/npx_server_example\"],\n \"env\": {\n \"API_KEY\": \"YOUR_API_KEY\"\n }\n },\n \"local_server_example\": {\n \"command\": \"node\",\n \"args\": [\"node /path/to/local_server/dist/index.js\"]\n }\n }\n}\n```\n\nSie können Ihre eigene Agentenkonfiguration einfach mit unserem Tool auf [SnakeAgent](https://www.snakagent.com/create-agent) erstellen.\n\n## Verwendung\n\n### Eingabeaufforderungsmodus\n\nFühren Sie die Eingabeaufforderung aus:\n\n```bash\n# start with the default.agent.json\npnpm run start\n\n# start with your custom configuration\npnpm run start --agent=\"name_of_your_config.json\" --models=\"name_of_your_config.json\"\n```\n\n### Servermodus\n\nFühren Sie den Server aus:\n\n```bash\n# start with the default.agent.json\npnpm run start:server\n\n# start with your custom configuration\npnpm run start:server --agent=\"name_of_your_config.json\" --models=\"name_of_your_config.json\"\n```\n\n#### Verfügbare Modi\n\n|Interaktiver Modus|Autonomer Modus|\n|---|---|---|\n|Eingabeaufforderungsmodus|✅|✅|\n|Servermodus|✅|✅|\n\n### Implementieren Sie Snak in Ihrem Projekt\n\n1. Installieren Sie das Snake-Paket\n\n```bash\n#using npm\nnpm install @snakagent\n\n# using pnpm\npnpm add @snakagent\n```\n\n2. Erstellen Sie Ihre Agenteninstanz\n\n```typescript\nimport { SnakAgent } from 'starknet-agent-kit';\n\nconst agent = new SnakAgent({\n provider: new RpcProvider({ nodeUrl: process.env.STARKNET_RPC_URL }),\n accountPrivateKey: process.env.STARKNET_PRIVATE_KEY,\n accountPublicKey: process.env.STARKNET_PUBLIC_ADDRESS,\n aiModel: process.env.AI_MODEL,\n aiProvider: process.env.AI_PROVIDER,\n aiProviderApiKey: process.env.AI_PROVIDER_API_KEY,\n signature: 'key',\n agentMode: 'interactive',\n agentconfig: y,\n});\n\nconst response = await agent.execute(\"What's my ETH balance?\");\n```\n\n## Aktionen\n\nWeitere Informationen zu Aktionen finden Sie [in diesem Abschnitt der Dokumentation](https://docs.kasar.io/agent-actions) . Eine umfassende Benutzeroberfläche im Kit bietet einen leicht navigierbaren Katalog aller verfügbaren Plugins und ihrer Aktionen und vereinfacht so die Suche und Nutzung.\n\nUm Ihrem Agenten Aktionen hinzuzufügen, können Sie einfach der Schritt-für-Schritt-Anleitung [hier](https://docs.kasar.io/add-agent-actions) folgen.\n\n## Beitragen\n\nBeiträge sind willkommen! Senden Sie gerne einen Pull Request.\n\n## Lizenz\n\nMIT-Lizenz – Einzelheiten finden Sie in der Datei LICENSE.\n\n***\n\nAusführliche Dokumentation finden Sie unter [docs.kasar.io](https://docs.kasar.io)","German","tag","de-DE",{"_27":39,"_31":43,"_36":44},{"_29":40,"_31":41,"_33":42},"Build powerful and secure AI agents powered by Starknet.","Snak","

\n\n

\n\n $\"NPM$ \n\n\n $\"License\"$ \n\n\n $\"GitHub$ \n\n\n $\"Node$ \n\n

\n\nA Agent Engine for creating powerful and secure AI Agents powered by Starknet. Available as both an NPM package and a ready-to-use backend.\n\n## Quick Start\n\n### Prerequisites\n\n- Starknet wallet (recommended: [Argent X](https://www.argent.xyz/argent-x))\n- AI provider API key (Anthropic/OpenAI/Google Gemini/Ollama)\n- Node.js and pnpm installed\n\n### Installation\n\n```bash\ngit clone https://github.com/kasarlabs/snak.git\ncd snak\npnpm install\n```\n\n### Configuration\n\n1. Create a `.env` file by copying `.env.example`:\n\n```bash\ncp .env.example .env\n```\n\nThen, fill in the necessary values in your `.env` file:\n\n```env\n# --- Starknet configuration (mandatory) ---\nSTARKNET_PUBLIC_ADDRESS=\"YOUR_STARKNET_PUBLIC_ADDRESS\"\nSTARKNET_PRIVATE_KEY=\"YOUR_STARKNET_PRIVATE_KEY\"\nSTARKNET_RPC_URL=\"YOUR_STARKNET_RPC_URL\"\n\n# --- AI Model API Keys (mandatory) ---\n# Add the API keys for the specific AI providers you use in config/models/default.models.json\n# The agent will automatically load the correct key based on the provider name.\n\n# Example for OpenAI:\nOPENAI_API_KEY=\"YOUR_OPENAI_API_KEY\" # (e.g., sk-...)\n\n# Example for Anthropic:\nANTHROPIC_API_KEY=\"YOUR_ANTHROPIC_API_KEY\" # (e.g., sk-ant-...)\n\n# Example for Google Gemini:\nGEMINI_API_KEY=\"YOUR_GEMINI_API_KEY\"\n\n# Example for DeepSeek:\nDEEPSEEK_API_KEY=\"YOUR_DEEPSEEK_API_KEY\"\n\n# Note: You do not need an API key if using a local Ollama model.\n\n# --- General Agent Configuration (mandatory) ---\nSERVER_API_KEY=\"YOUR_SERVER_API_KEY\" # A secret key for your agent server API\nSERVER_PORT=\"3001\"\n\n# --- PostgreSQL Database Configuration (mandatory) ---\nPOSTGRES_USER=\"admin\"\nPOSTGRES_PASSWORD=\"admin\"\nPOSTGRES_ROOT_DB=\"postgres\" # Database used to create/manage the application database\nPOSTGRES_HOST=\"localhost\"\nPOSTGRES_PORT=\"5454\"\n\n# --- LangSmith Tracing (Optional) ---\n# Set LANGSMITH_TRACING=true to enable tracing\nLANGSMITH_TRACING=false\nLANGSMITH_ENDPOINT=\"https://api.smith.langchain.com\"\nLANGSMITH_API_KEY=\"YOUR_LANGSMITH_API_KEY\" # (Only needed if LANGSMITH_TRACING=true)\nLANGSMITH_PROJECT=\"Snak\" # (Optional project name for LangSmith)\n\n# --- Node Environment ---\nNODE_ENV=\"development\" # \"development\" or \"production\"\n```\n\n2. Configure AI Models (Optional):\n 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.\n\n 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`).\n\n3. Create your agent configuration file (e.g., `default.agent.json` or `my_agent.json`) in the `config/agents/` directory:\n\n```json\n{\n \"name\": \"Your Agent name\",\n \"group\": \"Your Agent group\",\n \"description\": \"Your AI Agent Description\",\n \"lore\": [\"Some lore of your AI Agent 1\", \"Some lore of your AI Agent 1\"],\n \"objectives\": [\n \"first objective that your AI Agent need to follow\",\n \"second objective that your AI Agent need to follow\"\n ],\n \"knowledge\": [\n \"first knowledge of your AI Agent\",\n \"second knowledge of your AI Agent\"\n ],\n \"interval\": \"Your agent interval beetween each transaction of the Agent in ms,\",\n \"chatId\": \"Your Agent Chat-id for isolating memory\",\n \"maxIterations\": \"The number of iterations your agent will execute before stopping\",\n \"mode\": \"The mode of your agent, can be interactive, autonomous or hybrid\",\n \"memory\": {\n \"enabled\": \"true or false to enable or disable memory\",\n \"shortTermMemorySize\": \"The number of messages your agent will remember\"\n },\n \"plugins\": [\"Your first plugin\", \"Your second plugin\"],\n \"mcpServers\": {\n \"nxp_server_example\": {\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@npm_package_example/npx_server_example\"],\n \"env\": {\n \"API_KEY\": \"YOUR_API_KEY\"\n }\n },\n \"local_server_example\": {\n \"command\": \"node\",\n \"args\": [\"node /path/to/local_server/dist/index.js\"]\n }\n }\n}\n```\n\nYou can simply create your own agent configuration using our tool on [snakagent](https://www.snakagent.com/create-agent)\n\n## Usage\n\n### Prompt Mode\n\nRun the promt:\n\n```bash\n# start with the default.agent.json\npnpm run start\n\n# start with your custom configuration\npnpm run start --agent=\"name_of_your_config.json\" --models=\"name_of_your_config.json\"\n```\n\n### Server Mode\n\nRun the server :\n\n```bash\n# start with the default.agent.json\npnpm run start:server\n\n# start with your custom configuration\npnpm run start:server --agent=\"name_of_your_config.json\" --models=\"name_of_your_config.json\"\n```\n\n#### Available Modes\n\n| | Interactive Mode | Autonomous Mode |\n| ----------- | ---------------- | --------------- |\n| Prompt Mode | ✅ | ✅ |\n| Server Mode | ✅ | ✅ |\n\n### Implement Snak in your project\n\n1. Install snak package\n\n```bash\n#using npm\nnpm install @snakagent\n\n# using pnpm\npnpm add @snakagent\n```\n\n2. Create your agent instance\n\n```typescript\nimport { SnakAgent } from 'starknet-agent-kit';\n\nconst agent = new SnakAgent({\n provider: new RpcProvider({ nodeUrl: process.env.STARKNET_RPC_URL }),\n accountPrivateKey: process.env.STARKNET_PRIVATE_KEY,\n accountPublicKey: process.env.STARKNET_PUBLIC_ADDRESS,\n aiModel: process.env.AI_MODEL,\n aiProvider: process.env.AI_PROVIDER,\n aiProviderApiKey: process.env.AI_PROVIDER_API_KEY,\n signature: 'key',\n agentMode: 'interactive',\n agentconfig: y,\n});\n\nconst response = await agent.execute(\"What's my ETH balance?\");\n```\n\n## Actions\n\nTo learn more about actions you can read [this doc section](https://docs.kasar.io/agent-actions).\nA comprehensive interface in the Kit will provide an easy-to-navigate catalog of all available plugins and their actions, making discovery and usage simpler.\n\nTo add actions to your agent you can easily follow the step-by-steps guide [here](https://docs.kasar.io/add-agent-actions)\n\n## Contributing\n\nContributions are welcome! Feel free to submit a Pull Request.\n\n## License\n\nMIT License - see the LICENSE file for details.\n\n---\n\nFor detailed documentation visit [docs.kasar.io](https://docs.kasar.io)\n","English","en-US",{"_27":46,"_31":49,"_36":50},{"_29":47,"_31":41,"_33":48},"Cree agentes de IA potentes y seguros impulsados por Starknet.","Un motor de agentes para crear agentes de IA potentes y seguros, impulsado por Starknet. Disponible como paquete NPM y como backend listo para usar.\n\n## Inicio rápido\n\n### Prerrequisitos\n\n* Cartera Starknet (recomendada: [Argent X](https://www.argent.xyz/argent-x) )\n* Clave API del proveedor de IA (Anthropic/OpenAI/Google Gemini/Ollama)\n* Node.js y pnpm instalados\n\n### Instalación\n\n```bash\ngit clone https://github.com/kasarlabs/snak.git\ncd snak\npnpm install\n```\n\n### Configuración\n\n1. Cree un archivo `.env` copiando `.env.example` :\n\n```bash\ncp .env.example .env\n```\n\nLuego, completa los valores necesarios en tu archivo `.env` :\n\n```env\n# --- Starknet configuration (mandatory) ---\nSTARKNET_PUBLIC_ADDRESS=\"YOUR_STARKNET_PUBLIC_ADDRESS\"\nSTARKNET_PRIVATE_KEY=\"YOUR_STARKNET_PRIVATE_KEY\"\nSTARKNET_RPC_URL=\"YOUR_STARKNET_RPC_URL\"\n\n# --- AI Model API Keys (mandatory) ---\n# Add the API keys for the specific AI providers you use in config/models/default.models.json\n# The agent will automatically load the correct key based on the provider name.\n\n# Example for OpenAI:\nOPENAI_API_KEY=\"YOUR_OPENAI_API_KEY\" # (e.g., sk-...)\n\n# Example for Anthropic:\nANTHROPIC_API_KEY=\"YOUR_ANTHROPIC_API_KEY\" # (e.g., sk-ant-...)\n\n# Example for Google Gemini:\nGEMINI_API_KEY=\"YOUR_GEMINI_API_KEY\"\n\n# Example for DeepSeek:\nDEEPSEEK_API_KEY=\"YOUR_DEEPSEEK_API_KEY\"\n\n# Note: You do not need an API key if using a local Ollama model.\n\n# --- General Agent Configuration (mandatory) ---\nSERVER_API_KEY=\"YOUR_SERVER_API_KEY\" # A secret key for your agent server API\nSERVER_PORT=\"3001\"\n\n# --- PostgreSQL Database Configuration (mandatory) ---\nPOSTGRES_USER=\"admin\"\nPOSTGRES_PASSWORD=\"admin\"\nPOSTGRES_ROOT_DB=\"postgres\" # Database used to create/manage the application database\nPOSTGRES_HOST=\"localhost\"\nPOSTGRES_PORT=\"5454\"\n\n# --- LangSmith Tracing (Optional) ---\n# Set LANGSMITH_TRACING=true to enable tracing\nLANGSMITH_TRACING=false\nLANGSMITH_ENDPOINT=\"https://api.smith.langchain.com\"\nLANGSMITH_API_KEY=\"YOUR_LANGSMITH_API_KEY\" # (Only needed if LANGSMITH_TRACING=true)\nLANGSMITH_PROJECT=\"Snak\" # (Optional project name for LangSmith)\n\n# --- Node Environment ---\nNODE_ENV=\"development\" # \"development\" or \"production\"\n```\n\n2. Configurar modelos de IA (opcional): El archivo `config/models/default.models.json` define los modelos de IA predeterminados que se utilizan para diferentes tareas ( `fast` , `smart` , `cheap` ). Puede personalizar este archivo o crear nuevas configuraciones de modelo (p. ej., `my_models.json` ) y especificarlas al ejecutar el agente. Consulte `config/models/example.models.json` para ver la estructura.\n\n El agente utiliza el campo `provider` en la configuración del modelo para determinar qué clave API cargar desde el archivo `.env` (por ejemplo, si `provider` es `openai` , carga `OPENAI_API_KEY` ).\n\n3. Cree su archivo de configuración del agente (por ejemplo, `default.agent.json` o `my_agent.json` ) en el directorio `config/agents/` :\n\n```json\n{\n \"name\": \"Your Agent name\",\n \"group\": \"Your Agent group\",\n \"description\": \"Your AI Agent Description\",\n \"lore\": [\"Some lore of your AI Agent 1\", \"Some lore of your AI Agent 1\"],\n \"objectives\": [\n \"first objective that your AI Agent need to follow\",\n \"second objective that your AI Agent need to follow\"\n ],\n \"knowledge\": [\n \"first knowledge of your AI Agent\",\n \"second knowledge of your AI Agent\"\n ],\n \"interval\": \"Your agent interval beetween each transaction of the Agent in ms,\",\n \"chatId\": \"Your Agent Chat-id for isolating memory\",\n \"maxIterations\": \"The number of iterations your agent will execute before stopping\",\n \"mode\": \"The mode of your agent, can be interactive, autonomous or hybrid\",\n \"memory\": {\n \"enabled\": \"true or false to enable or disable memory\",\n \"shortTermMemorySize\": \"The number of messages your agent will remember\"\n },\n \"plugins\": [\"Your first plugin\", \"Your second plugin\"],\n \"mcpServers\": {\n \"nxp_server_example\": {\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@npm_package_example/npx_server_example\"],\n \"env\": {\n \"API_KEY\": \"YOUR_API_KEY\"\n }\n },\n \"local_server_example\": {\n \"command\": \"node\",\n \"args\": [\"node /path/to/local_server/dist/index.js\"]\n }\n }\n}\n```\n\nPuede crear simplemente su propia configuración de agente utilizando nuestra herramienta en [snakagent](https://www.snakagent.com/create-agent)\n\n## Uso\n\n### Modo de aviso\n\nEjecute el mensaje:\n\n```bash\n# start with the default.agent.json\npnpm run start\n\n# start with your custom configuration\npnpm run start --agent=\"name_of_your_config.json\" --models=\"name_of_your_config.json\"\n```\n\n### Modo servidor\n\nEjecutar el servidor:\n\n```bash\n# start with the default.agent.json\npnpm run start:server\n\n# start with your custom configuration\npnpm run start:server --agent=\"name_of_your_config.json\" --models=\"name_of_your_config.json\"\n```\n\n#### Modos disponibles\n\n|Modo interactivo|Modo Autónomo|\n|---|---|---|\n|Modo de aviso|✅|✅|\n|Modo servidor|✅|✅|\n\n### Implementa Snak en tu proyecto\n\n1. Instalar el paquete snak\n\n```bash\n#using npm\nnpm install @snakagent\n\n# using pnpm\npnpm add @snakagent\n```\n\n2. Crea tu instancia de agente\n\n```typescript\nimport { SnakAgent } from 'starknet-agent-kit';\n\nconst agent = new SnakAgent({\n provider: new RpcProvider({ nodeUrl: process.env.STARKNET_RPC_URL }),\n accountPrivateKey: process.env.STARKNET_PRIVATE_KEY,\n accountPublicKey: process.env.STARKNET_PUBLIC_ADDRESS,\n aiModel: process.env.AI_MODEL,\n aiProvider: process.env.AI_PROVIDER,\n aiProviderApiKey: process.env.AI_PROVIDER_API_KEY,\n signature: 'key',\n agentMode: 'interactive',\n agentconfig: y,\n});\n\nconst response = await agent.execute(\"What's my ETH balance?\");\n```\n\n## Comportamiento\n\nPara obtener más información sobre las acciones, puede consultar [esta sección de documentación](https://docs.kasar.io/agent-actions) . La interfaz completa del Kit ofrece un catálogo fácil de navegar con todos los complementos disponibles y sus acciones, lo que simplifica su descubrimiento y uso.\n\nPara agregar acciones a su agente, puede seguir fácilmente la guía paso a paso [aquí](https://docs.kasar.io/add-agent-actions)\n\n## Contribuyendo\n\n¡Agradecemos sus contribuciones! No dude en enviar una solicitud de incorporación de cambios.\n\n## Licencia\n\nLicencia MIT: consulte el archivo LICENCIA para obtener más detalles.\n\n***\n\nPara obtener documentación detallada, visite [docs.kasar.io](https://docs.kasar.io)","Spanish","es-ES",{"_27":52,"_31":56,"_36":57},{"_29":53,"_31":54,"_33":55},"Starknet を活用した強力で安全な AI エージェントを構築します。","ヘビ","Starknet を搭載した強力で安全な AI エージェントを作成するためのエージェントエンジン。NPM パッケージとすぐに使えるバックエンドの両方でご利用いただけます。\n\n## クイックスタート\n\n### 前提条件\n\n* Starknetウォレット（推奨： [Argent X](https://www.argent.xyz/argent-x) ）\n* AI プロバイダー API キー (Anthropic/OpenAI/Google Gemini/Ollama)\n* Node.jsとpnpmがインストールされている\n\n### インストール\n\n```bash\ngit clone https://github.com/kasarlabs/snak.git\ncd snak\npnpm install\n```\n\n### 構成\n\n1. `.env.example`をコピーして`.env`ファイルを作成します。\n\n```bash\ncp .env.example .env\n```\n\n次に、 `.env`ファイルに必要な値を入力します。\n\n```env\n# --- Starknet configuration (mandatory) ---\nSTARKNET_PUBLIC_ADDRESS=\"YOUR_STARKNET_PUBLIC_ADDRESS\"\nSTARKNET_PRIVATE_KEY=\"YOUR_STARKNET_PRIVATE_KEY\"\nSTARKNET_RPC_URL=\"YOUR_STARKNET_RPC_URL\"\n\n# --- AI Model API Keys (mandatory) ---\n# Add the API keys for the specific AI providers you use in config/models/default.models.json\n# The agent will automatically load the correct key based on the provider name.\n\n# Example for OpenAI:\nOPENAI_API_KEY=\"YOUR_OPENAI_API_KEY\" # (e.g., sk-...)\n\n# Example for Anthropic:\nANTHROPIC_API_KEY=\"YOUR_ANTHROPIC_API_KEY\" # (e.g., sk-ant-...)\n\n# Example for Google Gemini:\nGEMINI_API_KEY=\"YOUR_GEMINI_API_KEY\"\n\n# Example for DeepSeek:\nDEEPSEEK_API_KEY=\"YOUR_DEEPSEEK_API_KEY\"\n\n# Note: You do not need an API key if using a local Ollama model.\n\n# --- General Agent Configuration (mandatory) ---\nSERVER_API_KEY=\"YOUR_SERVER_API_KEY\" # A secret key for your agent server API\nSERVER_PORT=\"3001\"\n\n# --- PostgreSQL Database Configuration (mandatory) ---\nPOSTGRES_USER=\"admin\"\nPOSTGRES_PASSWORD=\"admin\"\nPOSTGRES_ROOT_DB=\"postgres\" # Database used to create/manage the application database\nPOSTGRES_HOST=\"localhost\"\nPOSTGRES_PORT=\"5454\"\n\n# --- LangSmith Tracing (Optional) ---\n# Set LANGSMITH_TRACING=true to enable tracing\nLANGSMITH_TRACING=false\nLANGSMITH_ENDPOINT=\"https://api.smith.langchain.com\"\nLANGSMITH_API_KEY=\"YOUR_LANGSMITH_API_KEY\" # (Only needed if LANGSMITH_TRACING=true)\nLANGSMITH_PROJECT=\"Snak\" # (Optional project name for LangSmith)\n\n# --- Node Environment ---\nNODE_ENV=\"development\" # \"development\" or \"production\"\n```\n\n2. AIモデルの設定（オプション）： `config/models/default.models.json`ファイルは、様々なタスク（ `fast` 、 `smart` 、 `cheap` ）で使用されるデフォルトのAIモデルを定義します。このファイルをカスタマイズすることも、新しいモデル設定（例： `my_models.json` ）を作成してエージェント実行時に指定することもできます。ファイル構造については`config/models/example.models.json`をご覧ください。\n\n エージェントは、モデル構成の`provider`フィールドを使用して、 `.env`ファイルからロードする API キーを決定します (たとえば、 `provider`が`openai`の場合、 `OPENAI_API_KEY`ロードします)。\n\n3. `config/agents/`ディレクトリにエージェント構成ファイル (例: `default.agent.json`または`my_agent.json` ) を作成します。\n\n```json\n{\n \"name\": \"Your Agent name\",\n \"group\": \"Your Agent group\",\n \"description\": \"Your AI Agent Description\",\n \"lore\": [\"Some lore of your AI Agent 1\", \"Some lore of your AI Agent 1\"],\n \"objectives\": [\n \"first objective that your AI Agent need to follow\",\n \"second objective that your AI Agent need to follow\"\n ],\n \"knowledge\": [\n \"first knowledge of your AI Agent\",\n \"second knowledge of your AI Agent\"\n ],\n \"interval\": \"Your agent interval beetween each transaction of the Agent in ms,\",\n \"chatId\": \"Your Agent Chat-id for isolating memory\",\n \"maxIterations\": \"The number of iterations your agent will execute before stopping\",\n \"mode\": \"The mode of your agent, can be interactive, autonomous or hybrid\",\n \"memory\": {\n \"enabled\": \"true or false to enable or disable memory\",\n \"shortTermMemorySize\": \"The number of messages your agent will remember\"\n },\n \"plugins\": [\"Your first plugin\", \"Your second plugin\"],\n \"mcpServers\": {\n \"nxp_server_example\": {\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@npm_package_example/npx_server_example\"],\n \"env\": {\n \"API_KEY\": \"YOUR_API_KEY\"\n }\n },\n \"local_server_example\": {\n \"command\": \"node\",\n \"args\": [\"node /path/to/local_server/dist/index.js\"]\n }\n }\n}\n```\n\n[snakagent](https://www.snakagent.com/create-agent)のツールを使用して、独自のエージェント構成を簡単に作成できます。\n\n## 使用法\n\n### プロンプトモード\n\nプロンプトを実行します:\n\n```bash\n# start with the default.agent.json\npnpm run start\n\n# start with your custom configuration\npnpm run start --agent=\"name_of_your_config.json\" --models=\"name_of_your_config.json\"\n```\n\n### サーバーモード\n\nサーバーを実行します:\n\n```bash\n# start with the default.agent.json\npnpm run start:server\n\n# start with your custom configuration\npnpm run start:server --agent=\"name_of_your_config.json\" --models=\"name_of_your_config.json\"\n```\n\n#### 利用可能なモード\n\n|インタラクティブモード|自律モード|\n|---|---|---|\n|プロンプトモード|✅|✅|\n|サーバーモード|✅|✅|\n\n### プロジェクトにSnakを実装する\n\n1. snakパッケージをインストールする\n\n```bash\n#using npm\nnpm install @snakagent\n\n# using pnpm\npnpm add @snakagent\n```\n\n2. エージェントインスタンスを作成する\n\n```typescript\nimport { SnakAgent } from 'starknet-agent-kit';\n\nconst agent = new SnakAgent({\n provider: new RpcProvider({ nodeUrl: process.env.STARKNET_RPC_URL }),\n accountPrivateKey: process.env.STARKNET_PRIVATE_KEY,\n accountPublicKey: process.env.STARKNET_PUBLIC_ADDRESS,\n aiModel: process.env.AI_MODEL,\n aiProvider: process.env.AI_PROVIDER,\n aiProviderApiKey: process.env.AI_PROVIDER_API_KEY,\n signature: 'key',\n agentMode: 'interactive',\n agentconfig: y,\n});\n\nconst response = await agent.execute(\"What's my ETH balance?\");\n```\n\n## アクション\n\nアクションの詳細については、[こちらのドキュメントセクション](https://docs.kasar.io/agent-actions)をご覧ください。キットの包括的なインターフェースは、利用可能なすべてのプラグインとそのアクションを簡単にナビゲートできるカタログを提供し、プラグインの発見と使用を容易にします。\n\nエージェントにアクションを追加するには、[こちらの](https://docs.kasar.io/add-agent-actions)ステップバイステップガイドに従ってください。\n\n## 貢献\n\n貢献を歓迎します！お気軽にプルリクエストを送信してください。\n\n## ライセンス\n\nMIT ライセンス - 詳細については LICENSE ファイルを参照してください。\n\n***\n\n詳細なドキュメントについては、 [docs.kasar.io](https://docs.kasar.io)をご覧ください。","Japanese","ja-JP",{"_27":59,"_31":63,"_36":64},{"_29":60,"_31":61,"_33":62},"Starknet을 기반으로 하는 강력하고 안전한 AI 에이전트를 구축하세요.","뱀","Starknet 기반의 강력하고 안전한 AI 에이전트를 개발하는 에이전트 엔진입니다. NPM 패키지와 즉시 사용 가능한 백엔드로 제공됩니다.\n\n## 빠른 시작\n\n### 필수 조건\n\n* Starknet 지갑 (추천: [Argent X](https://www.argent.xyz/argent-x) )\n* AI 제공업체 API 키(Anthropic/OpenAI/Google Gemini/Ollama)\n* Node.js와 pnpm이 설치되었습니다.\n\n### 설치\n\n지엑스피1\n\n### 구성\n\n1. `.env.example` 을 복사하여 `.env` 파일을 만듭니다.\n\n```bash\ncp .env.example .env\n```\n\n그런 다음 `.env` 파일에 필요한 값을 입력합니다.\n\n```env\n# --- Starknet configuration (mandatory) ---\nSTARKNET_PUBLIC_ADDRESS=\"YOUR_STARKNET_PUBLIC_ADDRESS\"\nSTARKNET_PRIVATE_KEY=\"YOUR_STARKNET_PRIVATE_KEY\"\nSTARKNET_RPC_URL=\"YOUR_STARKNET_RPC_URL\"\n\n# --- AI Model API Keys (mandatory) ---\n# Add the API keys for the specific AI providers you use in config/models/default.models.json\n# The agent will automatically load the correct key based on the provider name.\n\n# Example for OpenAI:\nOPENAI_API_KEY=\"YOUR_OPENAI_API_KEY\" # (e.g., sk-...)\n\n# Example for Anthropic:\nANTHROPIC_API_KEY=\"YOUR_ANTHROPIC_API_KEY\" # (e.g., sk-ant-...)\n\n# Example for Google Gemini:\nGEMINI_API_KEY=\"YOUR_GEMINI_API_KEY\"\n\n# Example for DeepSeek:\nDEEPSEEK_API_KEY=\"YOUR_DEEPSEEK_API_KEY\"\n\n# Note: You do not need an API key if using a local Ollama model.\n\n# --- General Agent Configuration (mandatory) ---\nSERVER_API_KEY=\"YOUR_SERVER_API_KEY\" # A secret key for your agent server API\nSERVER_PORT=\"3001\"\n\n# --- PostgreSQL Database Configuration (mandatory) ---\nPOSTGRES_USER=\"admin\"\nPOSTGRES_PASSWORD=\"admin\"\nPOSTGRES_ROOT_DB=\"postgres\" # Database used to create/manage the application database\nPOSTGRES_HOST=\"localhost\"\nPOSTGRES_PORT=\"5454\"\n\n# --- LangSmith Tracing (Optional) ---\n# Set LANGSMITH_TRACING=true to enable tracing\nLANGSMITH_TRACING=false\nLANGSMITH_ENDPOINT=\"https://api.smith.langchain.com\"\nLANGSMITH_API_KEY=\"YOUR_LANGSMITH_API_KEY\" # (Only needed if LANGSMITH_TRACING=true)\nLANGSMITH_PROJECT=\"Snak\" # (Optional project name for LangSmith)\n\n# --- Node Environment ---\nNODE_ENV=\"development\" # \"development\" or \"production\"\n```\n\n2. AI 모델 구성(선택 사항): `config/models/default.models.json` 파일은 다양한 작업( `fast` , `smart` , `cheap` )에 사용되는 기본 AI 모델을 정의합니다. 이 파일을 사용자 지정하거나 새 모델 구성(예: `my_models.json` )을 생성하여 에이전트 실행 시 지정할 수 있습니다. 자세한 구조는 `config/models/example.models.json` 파일을 참조하세요.\n\n 에이전트는 모델 구성에서 `provider` 필드를 사용하여 `.env` 파일에서 로드할 API 키를 결정합니다(예: `provider` 가 `openai` 인 경우 `OPENAI_API_KEY` 로드합니다).\n\n3. `config/agents/` 디렉토리에 에이전트 구성 파일(예: `default.agent.json` 또는 `my_agent.json` )을 만듭니다.\n\n```json\n{\n \"name\": \"Your Agent name\",\n \"group\": \"Your Agent group\",\n \"description\": \"Your AI Agent Description\",\n \"lore\": [\"Some lore of your AI Agent 1\", \"Some lore of your AI Agent 1\"],\n \"objectives\": [\n \"first objective that your AI Agent need to follow\",\n \"second objective that your AI Agent need to follow\"\n ],\n \"knowledge\": [\n \"first knowledge of your AI Agent\",\n \"second knowledge of your AI Agent\"\n ],\n \"interval\": \"Your agent interval beetween each transaction of the Agent in ms,\",\n \"chatId\": \"Your Agent Chat-id for isolating memory\",\n \"maxIterations\": \"The number of iterations your agent will execute before stopping\",\n \"mode\": \"The mode of your agent, can be interactive, autonomous or hybrid\",\n \"memory\": {\n \"enabled\": \"true or false to enable or disable memory\",\n \"shortTermMemorySize\": \"The number of messages your agent will remember\"\n },\n \"plugins\": [\"Your first plugin\", \"Your second plugin\"],\n \"mcpServers\": {\n \"nxp_server_example\": {\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@npm_package_example/npx_server_example\"],\n \"env\": {\n \"API_KEY\": \"YOUR_API_KEY\"\n }\n },\n \"local_server_example\": {\n \"command\": \"node\",\n \"args\": [\"node /path/to/local_server/dist/index.js\"]\n }\n }\n}\n```\n\n[snakagent](https://www.snakagent.com/create-agent) 의 도구를 사용하여 간단히 자신의 에이전트 구성을 만들 수 있습니다.\n\n## 용법\n\n### 프롬프트 모드\n\n프롬프트를 실행합니다:\n\n```bash\n# start with the default.agent.json\npnpm run start\n\n# start with your custom configuration\npnpm run start --agent=\"name_of_your_config.json\" --models=\"name_of_your_config.json\"\n```\n\n### 서버 모드\n\n서버를 실행합니다 :\n\n```bash\n# start with the default.agent.json\npnpm run start:server\n\n# start with your custom configuration\npnpm run start:server --agent=\"name_of_your_config.json\" --models=\"name_of_your_config.json\"\n```\n\n#### 사용 가능한 모드\n\n|대화형 모드|자율 모드|\n|---|---|---|\n|프롬프트 모드|✅|✅|\n|서버 모드|✅|✅|\n\n### 프로젝트에 Snak을 구현하세요\n\n1. snak 패키지 설치\n\n```bash\n#using npm\nnpm install @snakagent\n\n# using pnpm\npnpm add @snakagent\n```\n\n2. 에이전트 인스턴스를 생성하세요\n\n```typescript\nimport { SnakAgent } from 'starknet-agent-kit';\n\nconst agent = new SnakAgent({\n provider: new RpcProvider({ nodeUrl: process.env.STARKNET_RPC_URL }),\n accountPrivateKey: process.env.STARKNET_PRIVATE_KEY,\n accountPublicKey: process.env.STARKNET_PUBLIC_ADDRESS,\n aiModel: process.env.AI_MODEL,\n aiProvider: process.env.AI_PROVIDER,\n aiProviderApiKey: process.env.AI_PROVIDER_API_KEY,\n signature: 'key',\n agentMode: 'interactive',\n agentconfig: y,\n});\n\nconst response = await agent.execute(\"What's my ETH balance?\");\n```\n\n## 행위\n\n액션에 대한 자세한 내용은 [이 문서 섹션을](https://docs.kasar.io/agent-actions) 참조하세요. 키트의 포괄적인 인터페이스는 사용 가능한 모든 플러그인과 액션을 쉽게 탐색할 수 있는 카탈로그를 제공하여 검색 및 사용을 더욱 간편하게 만들어 줍니다.\n\n에이전트에 작업을 추가하려면 [여기의](https://docs.kasar.io/add-agent-actions) 단계별 가이드를 쉽게 따르세요.\n\n## 기여하다\n\n기여를 환영합니다! 풀 리퀘스트를 제출해 주세요.\n\n## 특허\n\nMIT 라이센스 - 자세한 내용은 LICENSE 파일을 참조하세요.\n\n***\n\n자세한 내용은 [docs.kasar.io를](https://docs.kasar.io) 방문하세요.","Korean","ko-KR",{"_27":66,"_31":70,"_36":71},{"_29":67,"_31":68,"_33":69},"Создавайте мощные и безопасные ИИ-агенты на базе Starknet.","Змея","Agent Engine для создания мощных и безопасных агентов ИИ на базе Starknet. Доступно как пакет NPM и готовый к использованию бэкэнд.\n\n## Быстрый старт\n\n### Предпосылки\n\n* Кошелек Starknet (рекомендуется: [Argent X](https://www.argent.xyz/argent-x) )\n* Ключ API поставщика ИИ (Anthropic/OpenAI/Google Gemini/Ollama)\n* Node.js и pnpm установлены\n\n### Установка\n\n```bash\ngit clone https://github.com/kasarlabs/snak.git\ncd snak\npnpm install\n```\n\n### Конфигурация\n\n1. Создайте файл `.env` , скопировав `.env.example` :\n\n```bash\ncp .env.example .env\n```\n\nЗатем заполните необходимые значения в вашем файле `.env` :\n\n```env\n# --- Starknet configuration (mandatory) ---\nSTARKNET_PUBLIC_ADDRESS=\"YOUR_STARKNET_PUBLIC_ADDRESS\"\nSTARKNET_PRIVATE_KEY=\"YOUR_STARKNET_PRIVATE_KEY\"\nSTARKNET_RPC_URL=\"YOUR_STARKNET_RPC_URL\"\n\n# --- AI Model API Keys (mandatory) ---\n# Add the API keys for the specific AI providers you use in config/models/default.models.json\n# The agent will automatically load the correct key based on the provider name.\n\n# Example for OpenAI:\nOPENAI_API_KEY=\"YOUR_OPENAI_API_KEY\" # (e.g., sk-...)\n\n# Example for Anthropic:\nANTHROPIC_API_KEY=\"YOUR_ANTHROPIC_API_KEY\" # (e.g., sk-ant-...)\n\n# Example for Google Gemini:\nGEMINI_API_KEY=\"YOUR_GEMINI_API_KEY\"\n\n# Example for DeepSeek:\nDEEPSEEK_API_KEY=\"YOUR_DEEPSEEK_API_KEY\"\n\n# Note: You do not need an API key if using a local Ollama model.\n\n# --- General Agent Configuration (mandatory) ---\nSERVER_API_KEY=\"YOUR_SERVER_API_KEY\" # A secret key for your agent server API\nSERVER_PORT=\"3001\"\n\n# --- PostgreSQL Database Configuration (mandatory) ---\nPOSTGRES_USER=\"admin\"\nPOSTGRES_PASSWORD=\"admin\"\nPOSTGRES_ROOT_DB=\"postgres\" # Database used to create/manage the application database\nPOSTGRES_HOST=\"localhost\"\nPOSTGRES_PORT=\"5454\"\n\n# --- LangSmith Tracing (Optional) ---\n# Set LANGSMITH_TRACING=true to enable tracing\nLANGSMITH_TRACING=false\nLANGSMITH_ENDPOINT=\"https://api.smith.langchain.com\"\nLANGSMITH_API_KEY=\"YOUR_LANGSMITH_API_KEY\" # (Only needed if LANGSMITH_TRACING=true)\nLANGSMITH_PROJECT=\"Snak\" # (Optional project name for LangSmith)\n\n# --- Node Environment ---\nNODE_ENV=\"development\" # \"development\" or \"production\"\n```\n\n2. Настройка моделей ИИ (необязательно): Файл `config/models/default.models.json` определяет модели ИИ по умолчанию, используемые для различных задач ( `fast` , `smart` , `cheap` ). Вы можете настроить этот файл или создать новые конфигурации моделей (например, `my_models.json` ) и указать их при запуске агента. Структуру см. в `config/models/example.models.json` .\n\n Агент использует поле `provider` в конфигурации модели, чтобы определить, какой ключ API следует загрузить из файла `.env` (например, если `provider` является `openai` , он загружает `OPENAI_API_KEY` ).\n\n3. Создайте файл конфигурации агента (например, `default.agent.json` или `my_agent.json` ) в каталоге `config/agents/` :\n\n```json\n{\n \"name\": \"Your Agent name\",\n \"group\": \"Your Agent group\",\n \"description\": \"Your AI Agent Description\",\n \"lore\": [\"Some lore of your AI Agent 1\", \"Some lore of your AI Agent 1\"],\n \"objectives\": [\n \"first objective that your AI Agent need to follow\",\n \"second objective that your AI Agent need to follow\"\n ],\n \"knowledge\": [\n \"first knowledge of your AI Agent\",\n \"second knowledge of your AI Agent\"\n ],\n \"interval\": \"Your agent interval beetween each transaction of the Agent in ms,\",\n \"chatId\": \"Your Agent Chat-id for isolating memory\",\n \"maxIterations\": \"The number of iterations your agent will execute before stopping\",\n \"mode\": \"The mode of your agent, can be interactive, autonomous or hybrid\",\n \"memory\": {\n \"enabled\": \"true or false to enable or disable memory\",\n \"shortTermMemorySize\": \"The number of messages your agent will remember\"\n },\n \"plugins\": [\"Your first plugin\", \"Your second plugin\"],\n \"mcpServers\": {\n \"nxp_server_example\": {\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@npm_package_example/npx_server_example\"],\n \"env\": {\n \"API_KEY\": \"YOUR_API_KEY\"\n }\n },\n \"local_server_example\": {\n \"command\": \"node\",\n \"args\": [\"node /path/to/local_server/dist/index.js\"]\n }\n }\n}\n```\n\nВы можете просто создать свою собственную конфигурацию агента, используя наш инструмент на [snakagent](https://www.snakagent.com/create-agent)\n\n## Использование\n\n### Режим подсказки\n\nЗапустите команду:\n\n```bash\n# start with the default.agent.json\npnpm run start\n\n# start with your custom configuration\npnpm run start --agent=\"name_of_your_config.json\" --models=\"name_of_your_config.json\"\n```\n\n### Режим сервера\n\nЗапустите сервер:\n\n```bash\n# start with the default.agent.json\npnpm run start:server\n\n# start with your custom configuration\npnpm run start:server --agent=\"name_of_your_config.json\" --models=\"name_of_your_config.json\"\n```\n\n#### Доступные режимы\n\n|Интерактивный режим|Автономный режим|\n|---|---|---|\n|Режим подсказки|✅|✅|\n|Режим сервера|✅|✅|\n\n### Внедрите Snak в свой проект\n\n1. Установить пакет snak\n\n```bash\n#using npm\nnpm install @snakagent\n\n# using pnpm\npnpm add @snakagent\n```\n\n2. Создайте свой экземпляр агента\n\n```typescript\nimport { SnakAgent } from 'starknet-agent-kit';\n\nconst agent = new SnakAgent({\n provider: new RpcProvider({ nodeUrl: process.env.STARKNET_RPC_URL }),\n accountPrivateKey: process.env.STARKNET_PRIVATE_KEY,\n accountPublicKey: process.env.STARKNET_PUBLIC_ADDRESS,\n aiModel: process.env.AI_MODEL,\n aiProvider: process.env.AI_PROVIDER,\n aiProviderApiKey: process.env.AI_PROVIDER_API_KEY,\n signature: 'key',\n agentMode: 'interactive',\n agentconfig: y,\n});\n\nconst response = await agent.execute(\"What's my ETH balance?\");\n```\n\n## Действия\n\nЧтобы узнать больше о действиях, вы можете прочитать [этот раздел документа](https://docs.kasar.io/agent-actions) . Комплексный интерфейс в Kit предоставит простой в навигации каталог всех доступных плагинов и их действий, что упрощает обнаружение и использование.\n\nЧтобы добавить действия в своего агента, вы можете легко следовать пошаговому руководству [здесь.](https://docs.kasar.io/add-agent-actions)\n\n## Внося вклад\n\nВклады приветствуются! Не стесняйтесь отправлять запрос на извлечение.\n\n## Лицензия\n\nЛицензия MIT — подробности см. в файле LICENSE.\n\n***\n\nПодробную документацию можно найти на [docs.kasar.io](https://docs.kasar.io)","Russian","ru-RU",{"_27":73,"_31":77,"_36":78},{"_29":74,"_31":75,"_33":76},"构建由 Starknet 提供支持的强大且安全的 AI 代理。","蛇","一个由 Starknet 驱动的代理引擎，用于创建强大且安全的 AI 代理。它既可以作为 NPM 包使用，也可以作为即用型后端使用。\n\n## 快速入门\n\n### 先决条件\n\n* Starknet 钱包（推荐： [Argent X](https://www.argent.xyz/argent-x) ）\n* AI 提供商 API 密钥（Anthropic/OpenAI/Google Gemini/Ollama）\n* 已安装 Node.js 和 pnpm\n\n### 安装\n\n```bash\ngit clone https://github.com/kasarlabs/snak.git\ncd snak\npnpm install\n```\n\n### 配置\n\n1. 通过复制`.env.example`创建`.env`文件：\n\n```bash\ncp .env.example .env\n```\n\n然后，在`.env`文件中填写必要的值：\n\n```env\n# --- Starknet configuration (mandatory) ---\nSTARKNET_PUBLIC_ADDRESS=\"YOUR_STARKNET_PUBLIC_ADDRESS\"\nSTARKNET_PRIVATE_KEY=\"YOUR_STARKNET_PRIVATE_KEY\"\nSTARKNET_RPC_URL=\"YOUR_STARKNET_RPC_URL\"\n\n# --- AI Model API Keys (mandatory) ---\n# Add the API keys for the specific AI providers you use in config/models/default.models.json\n# The agent will automatically load the correct key based on the provider name.\n\n# Example for OpenAI:\nOPENAI_API_KEY=\"YOUR_OPENAI_API_KEY\" # (e.g., sk-...)\n\n# Example for Anthropic:\nANTHROPIC_API_KEY=\"YOUR_ANTHROPIC_API_KEY\" # (e.g., sk-ant-...)\n\n# Example for Google Gemini:\nGEMINI_API_KEY=\"YOUR_GEMINI_API_KEY\"\n\n# Example for DeepSeek:\nDEEPSEEK_API_KEY=\"YOUR_DEEPSEEK_API_KEY\"\n\n# Note: You do not need an API key if using a local Ollama model.\n\n# --- General Agent Configuration (mandatory) ---\nSERVER_API_KEY=\"YOUR_SERVER_API_KEY\" # A secret key for your agent server API\nSERVER_PORT=\"3001\"\n\n# --- PostgreSQL Database Configuration (mandatory) ---\nPOSTGRES_USER=\"admin\"\nPOSTGRES_PASSWORD=\"admin\"\nPOSTGRES_ROOT_DB=\"postgres\" # Database used to create/manage the application database\nPOSTGRES_HOST=\"localhost\"\nPOSTGRES_PORT=\"5454\"\n\n# --- LangSmith Tracing (Optional) ---\n# Set LANGSMITH_TRACING=true to enable tracing\nLANGSMITH_TRACING=false\nLANGSMITH_ENDPOINT=\"https://api.smith.langchain.com\"\nLANGSMITH_API_KEY=\"YOUR_LANGSMITH_API_KEY\" # (Only needed if LANGSMITH_TRACING=true)\nLANGSMITH_PROJECT=\"Snak\" # (Optional project name for LangSmith)\n\n# --- Node Environment ---\nNODE_ENV=\"development\" # \"development\" or \"production\"\n```\n\n2. 配置 AI 模型（可选）： `config/models/default.models.json`文件定义了用于不同任务（ `fast` 、 `smart` 、 `cheap` ）的默认 AI 模型。您可以自定义此文件，或创建新的模型配置（例如`my_models.json` ），并在运行代理时指定它们。请参阅`config/models/example.models.json`了解其结构。\n\n 代理使用模型配置中的`provider`程序字段来确定从`.env`文件加载哪个 API 密钥（例如，如果`provider`是`openai` ，它会加载`OPENAI_API_KEY` ）。\n\n3. 在`config/agents/`目录中创建代理配置文件（例如`default.agent.json`或`my_agent.json` ）：\n\n```json\n{\n \"name\": \"Your Agent name\",\n \"group\": \"Your Agent group\",\n \"description\": \"Your AI Agent Description\",\n \"lore\": [\"Some lore of your AI Agent 1\", \"Some lore of your AI Agent 1\"],\n \"objectives\": [\n \"first objective that your AI Agent need to follow\",\n \"second objective that your AI Agent need to follow\"\n ],\n \"knowledge\": [\n \"first knowledge of your AI Agent\",\n \"second knowledge of your AI Agent\"\n ],\n \"interval\": \"Your agent interval beetween each transaction of the Agent in ms,\",\n \"chatId\": \"Your Agent Chat-id for isolating memory\",\n \"maxIterations\": \"The number of iterations your agent will execute before stopping\",\n \"mode\": \"The mode of your agent, can be interactive, autonomous or hybrid\",\n \"memory\": {\n \"enabled\": \"true or false to enable or disable memory\",\n \"shortTermMemorySize\": \"The number of messages your agent will remember\"\n },\n \"plugins\": [\"Your first plugin\", \"Your second plugin\"],\n \"mcpServers\": {\n \"nxp_server_example\": {\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@npm_package_example/npx_server_example\"],\n \"env\": {\n \"API_KEY\": \"YOUR_API_KEY\"\n }\n },\n \"local_server_example\": {\n \"command\": \"node\",\n \"args\": [\"node /path/to/local_server/dist/index.js\"]\n }\n }\n}\n```\n\n您可以使用[snakagent](https://www.snakagent.com/create-agent)上的工具轻松创建您自己的代理配置\n\n## 用法\n\n### 提示模式\n\n运行提示：\n\n```bash\n# start with the default.agent.json\npnpm run start\n\n# start with your custom configuration\npnpm run start --agent=\"name_of_your_config.json\" --models=\"name_of_your_config.json\"\n```\n\n### 服务器模式\n\n运行服务器：\n\n```bash\n# start with the default.agent.json\npnpm run start:server\n\n# start with your custom configuration\npnpm run start:server --agent=\"name_of_your_config.json\" --models=\"name_of_your_config.json\"\n```\n\n#### 可用模式\n\n|交互模式|自主模式|\n|---|---|---|\n|提示模式|✅|✅|\n|服务器模式|✅|✅|\n\n### 在你的项目中实现 Snak\n\n1. 安装 snak 包\n\n```bash\n#using npm\nnpm install @snakagent\n\n# using pnpm\npnpm add @snakagent\n```\n\n2. 创建代理实例\n\n```typescript\nimport { SnakAgent } from 'starknet-agent-kit';\n\nconst agent = new SnakAgent({\n provider: new RpcProvider({ nodeUrl: process.env.STARKNET_RPC_URL }),\n accountPrivateKey: process.env.STARKNET_PRIVATE_KEY,\n accountPublicKey: process.env.STARKNET_PUBLIC_ADDRESS,\n aiModel: process.env.AI_MODEL,\n aiProvider: process.env.AI_PROVIDER,\n aiProviderApiKey: process.env.AI_PROVIDER_API_KEY,\n signature: 'key',\n agentMode: 'interactive',\n agentconfig: y,\n});\n\nconst response = await agent.execute(\"What's my ETH balance?\");\n```\n\n## 行动\n\n要了解有关操作的更多信息，您可以阅读[此文档部分](https://docs.kasar.io/agent-actions)。套件中全面的界面将提供所有可用插件及其操作的易于浏览的目录，使发现和使用更加简单。\n\n要向代理添加操作，您可以轻松按照[此处的](https://docs.kasar.io/add-agent-actions)分步指南进行操作\n\n## 贡献\n\n欢迎贡献代码！欢迎提交 Pull 请求。\n\n## 执照\n\nMIT 许可证 - 有关详细信息，请参阅 LICENSE 文件。\n\n***\n\n有关详细文档，请访问[docs.kasar.io](https://docs.kasar.io)","Chinese","zh-CN","githubUser","locale",{"_82":83,"_13":84,"_91":40,"_92":40,"_16":93,"_94":95,"_31":41,"_142":143,"_150":-5,"_151":152,"_226":227,"_232":-5,"_102":208,"_233":212,"_105":234,"_235":236},"addedAt","2025-03-27T13:40:07.389237Z",[85,86,87,88,89,90],"author:claimed","category:developer-tools","category:cloud-platforms","hosting:hybrid","language:typescript","license_grade:a","descriptionMarkdown","descriptionPlainText",3983,"integrations",[96,107,114,121,128,135],{"_97":98,"_29":104,"_105":106},"brand",{"_99":100,"_31":101,"_102":103},"logoSvgPath","M20.317 4.3698a19.7913 19.7913 0 00-4.8851-1.5152.0741.0741 0 00-.0785.0371c-.211.3753-.4447.8648-.6083 1.2495-1.8447-.2762-3.68-.2762-5.4868 0-.1636-.3933-.4058-.8742-.6177-1.2495a.077.077 0 00-.0785-.037 19.7363 19.7363 0 00-4.8852 1.515.0699.0699 0 00-.0321.0277C.5334 9.0458-.319 13.5799.0992 18.0578a.0824.0824 0 00.0312.0561c2.0528 1.5076 4.0413 2.4228 5.9929 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\n\n

\n\n $\"NPM$ \n\n\n $\"License\"$ \n\n\n $\"GitHub$ \n\n\n $\"Node$ \n\n

MCP directory API