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Costa"},"license":"ISC","homepage":"https://github.com/ryzxxn/zentis#readme","keywords":["mcp","model-context-protocol","ai","agent","llm","zentis"],"repository":{"type":"git","url":"git+https://github.com/ryzxxn/zentis.git"},"description":"A high-level agentic framework for Model Context Protocol (MCP) with memory and LLM integration.","maintainers":[{"name":"eltoncosta","email":"elton02costa@gmail.com"}],"readme":"# Zentis\n\nZentis is a high-performance Node.js framework for orchestrating **Model Context Protocol (MCP)** agents. It provides a robust, zero-identity reasoning layer with persistent multi-turn memory, multi-agent parallelization, and JIT tool discovery.\n\n## 🚀 Key Features\n\n- **Multi-Server MCP Orchestration**: Seamless connection to multiple MCP servers via SSE or HTTP.\n- **Dynamic Discovery (JIT Injection)**: Optimizes context window by hiding tool schemas until they are explicitly searched and \"unlocked.\"\n- **Swarm Intelligence (Parallel Agents)**: Spawn concurrent agent instances and automatically aggregate their findings into a unified response.\n- **Context Slicing for \"Huge Data\"**: Automatically summarizes massive datasets for the LLM while passing the full raw data to your frontend.\n- **Process Mapping & Audit**: Every internal turn (Planner, Tool, Aggregate) is tracked in an execution graph.\n- **Hybrid Storage**: Support for SQLite and PostgreSQL with strict session isolation.\n\n---\n\n## 📦 Installation\n\n```bash\nnpm install zentis\n```\n\n### Peer Dependencies\n```bash\nnpm install better-sqlite3 # for SQLite\nnpm install pg             # for PostgreSQL\n```\n\n---\n\n## 🛠️ Usage Guide\n\n### 1. Initialization\n\n```typescript\nimport { ZentisAgent } from 'zentis';\n\nconst agent = new ZentisAgent({\n  llm: { apiKey: '...', model: 'gemini-3.5-flash' },\n  mcp: [\n    { name: 'Analytics', url: 'https://api.example.com/sse' },\n    { name: 'Inventory', url: 'http://localhost:3001/mcp' }\n  ],\n  storage: { \n    type: 'sqlite', \n    userId: 'user_1', \n    sessionId: 'session_A' \n  },\n  planner: true,   // Pre-plans tool sequences\n  discovery: true  // Just-in-Time tool schema injection\n});\n\nawait agent.waitReady();\n```\n\n---\n\n### 2. Standard Querying\n\n```typescript\nconst response = await agent.query(\"Analyze sales data\", {\n  onStep: (step) => console.log(`[${step.type}] ${step.message}`),\n  extraArgs: { auth_token: '...' } // Passed to tools, hidden from LLM\n});\n\n// Full Response Structure\nconsole.log(response.text);           // Aggregated answer\nconsole.log(response.results);        // ALL raw data results keyed by ID\nconsole.log(response.interactions);   // Audit log of all calls\nconsole.log(response.processes);      // Execution graph\n```\n\n---\n\n### 3. Parallel Agents (Swarm Mode)\n\nWait for multiple agent processes to solve sub-tasks concurrently.\n\n```typescript\nconst res = await agent.parallel([\n  \"Get current price of BTC\",\n  \"Summarize crypto news from today\",\n  \"Calculate RSI for top 10 coins\"\n]);\n\n// res.text contains the final synthesis of all three agents\n// res.subResponses contains the raw results from each individual agent\nconsole.log(res.subResponses[0].text); // \"BTC is at $65,000...\"\n```\n\n---\n\n### 4. Handling Huge Datasets (Context Slicing)\n\nWhen a tool returns >5 objects, Zentis hides the data from the LLM and provides a **Result ID** (e.g., `res_1_list_all_employees`).\n\n- **LLM sees**: `[DATA_REFERENCE:res_1_list_all_employees] (Metadata only)`\n- **You see**: `response.results.res_1_list_all_employees` contains the **full raw data**.\n- **UI Components**: Zentis recursively scans UI props and **automatically resolves** IDs into full datasets.\n\n---\n\n### 5. UI Component Registry\n\nDefine which components your LLM can \"trigger.\"\n\n```typescript\nimport { ZentisUI } from 'zentis';\nconst ui = new ZentisUI();\n\nui.register({\n  name: 'Table',\n  description: 'Dynamic data table.',\n  props: {\n    title: { type: 'string', description: 'Table title' },\n    data: { type: 'data_reference', description: 'Link to a tool result ID' },\n    fullWidth: { type: 'boolean', description: 'Enable wide mode' }\n  }\n});\n\nconst agent = new ZentisAgent({ ui, ... });\n```\n\n---\n\n### 6. Process Tracking & Reliability\n\n- **Planner**: A dedicated turn to lay out a tool plan, improving accuracy for complex multi-step tasks.\n- **Recursion Guard**: Automatically blocks identical tool calls with same arguments to prevent infinite loops.\n- **Execution Graph**: Every turn is recorded:\n  ```typescript\n  response.processes.forEach(p => console.log(`${p.label} took ${p.duration}ms`));\n  ```\n\n---\n\n## 🎨 Response Data Contract\n\n```typescript\ninterface AgentResponse {\n  text: string;                 // Conversational text\n  components: UIComponent[];    // Auto-resolved UI components\n  results: Record<string, any>; // Keyed raw data (res_N_toolName)\n  interactions: ToolInteraction[]; // Full audit trail\n  processes: ProcessNode[];     // Execution graph\n  subResponses?: AgentResponse[]; // (Optional) Individual agent results\n}\n```\n\n## 🛡️ License\nISC\n","readmeFilename":"README.md"}