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It enables sophisticated tool usage and cognitive capabilities through MCP integrations.\n\n## Features\n\n- 🤖 Autonomous agent core with tool orchestration\n- 🔗 Automatic MCP server discovery and connection\n- 🧠 Context-aware memory integration\n- 🎭 Custom persona system for agent personality\n- ⚡ Multi-model support (OpenAI, Anthropic, etc.)\n- 🔄 Flexible architecture - works with or without HabilisServer\n- 📝 Integrated memory operations for context management\n\n## Installation\n\n```bash\n# Using bun:\nbun add @elite-agents/machina-habilis  # Updated package name\n```\n\n## Quick Start\n\n1. **Create an Agent**:\n\n```typescript\nimport { MachinaAgent, HabilisServer } from '@elite-agents/machina-habilis';\nimport { generateKeypairRawBytes } from '@elite-agents/oldowan';\n\nconst memoryServer = 'http://localhost:3000';\n// Generate a keypair for signing\nconst keypairBytes = await generateKeypairRawBytes();\nconst keypairBase64 = Buffer.from(keypairBytes).toString('base64');\n\nconst habilisServer = new HabilisServer(memoryServer);\nawait habilisServer.init([memoryServer]); // Updated initialization pattern\n\nconst agent = new MachinaAgent({\n  // Using MachinaAgent directly\n  persona: {\n    name: 'Research Assistant',\n    bio: ['Knowledge-focused', 'Detail-oriented', 'Curious'],\n  },\n  llm: {\n    provider: 'openai',\n    name: 'gpt-4',\n    apiKey: 'sk-your-openai-key',\n    endpoint: 'https://api.openai.com/v1',\n  },\n  keypairBase64,\n  abilities: [],\n  habilisServer,\n});\n```\n\n2. **Use Your Agent**:\n\n```typescript\nconst response = await agent.message(\n  \"What's the latest progress in AI alignment?\",\n  { channelId: 'research-discussions' },\n);\n\nconsole.log(response.output);\n```\n\n3. **Standalone Agent (Without HabilisServer)**:\n\n```typescript\nimport { MachinaAgent } from '@elite-agents/machina-habilis';\nimport { generateKeypairRawBytes } from '@elite-agents/oldowan';\n\n// Generate a keypair for signing\nconst keypairBytes2 = await generateKeypairRawBytes();\nconst keypairBase64_2 = Buffer.from(keypairBytes2).toString('base64');\n\n// Create agent with direct ability map\nconst agent = new MachinaAgent({\n  persona: {\n    name: 'Task Assistant',\n    bio: ['Efficient', 'Precise', 'Helpful'],\n  },\n  abilities: [\n    {\n      id: 'web_search',\n      name: 'web_search',\n      description: 'Search the web for information',\n      inputSchema: { query: 'string' },\n    },\n  ],\n  llm: {\n    provider: 'anthropic',\n    name: 'claude-3-opus',\n    apiKey: 'sk-your-anthropic-key',\n  },\n  keypairBase64: keypairBase64_2,\n});\n\n// Agent will handle tool calls directly\nconst response = await agent.message(\"What's trending today?\");\n```\n\n## Memory System Components\n\nThe package includes a robust memory system through the `mnemon.ts` module, which provides both server and client implementations for memory services:\n\n### MnemonServer\n\nThe `MnemonServer` class creates a Hono-based HTTP server that handles memory operations:\n\n```typescript\nimport { MnemonServer } from '@elite-agents/machina-habilis';\n\n// Create a memory service with custom recall and create functions\nconst memoryServer = new MnemonServer({\n  recallMemory: async (lifecycle) => {\n    // Custom memory recall logic\n    return { ...lifecycle, context: ['Retrieved memory'] };\n  },\n  createMemory: async (lifecycle) => {\n    // Custom memory creation logic\n    return lifecycle;\n  }\n});\n\n// Start the server with Bun\nBun.serve({\n  ...memoryServer.app,\n  port: 3000\n});\n```\n\n### MnemonClient\n\nThe `MnemonClient` class provides a client implementation to connect to a MnemonServer:\n\n```typescript\nimport { MnemonClient } from '@elite-agents/machina-habilis';\n\n// Connect to a memory server\nconst memoryClient = new MnemonClient('http://localhost:3000');\n\n// Use the client to recall or create memories\nconst memory = await memoryClient.recallMemory(messageLifecycle);\n```\n\nThe memory system integrates seamlessly with the `MachinaAgent` through the `recallContext` and `addKnowledge` methods.\n\n## Key Concepts\n\n### Agent Architecture\n\n**HabilisServer** - The foundational service layer that:\n\n- Manages connections to MCP servers\n- Discovers and catalogs available tools\n- Handles cross-server communication\n- Provides shared memory/cache services\n\nA Habilis Server can be thought of as the central repository for a web service that aggregates agent abilities and personas.\nYou may create an API wrapper around it to provide a simple MCP tool and agent personality listing service.\nIt also acts as a caching tool for MCP on the backend. You can imagine a cache ttl that is used to determine whether the entire list needs to be refreshed.\n\n**MachinaAgent** - The user-facing agent instance that:\n\n- Processes incoming messages through LLM pipelines\n- Maintains conversation state and context\n- Orchestrates tool usage via HabilisServer or directly\n- Manages persona-specific behavior\n- Handles cryptographic identity and signatures\n- Supports memory operations for context management\n\nA MachinaAgent can be used in the front-end or backend. Its main focus is to create encapsulation for an agent with abilities.\n\n### Relationship Flow\n\n1. `HabilisServer` initializes first, connecting to MCP services (if using HabilisServer)\n2. `MachinaAgent` is instantiated with a configured HabilisServer or with direct abilities\n3. Agent uses server's tool registry or internal abilities during message processing\n4. Server handles low-level tool execution and memory operations when available\n5. Agent focuses on LLM interaction and conversation management\n\n### Agent Core\n\n- Maintains conversation state and context\n- Orchestrates tool usage across MCP servers or directly\n- Handles memory storage and retrieval with methods:\n  - `recallContext`: Retrieve contextual information\n  - `addKnowledge`: Store new information\n\n### Tool Integration\n\n- Discovers tools from connected MCP servers or uses direct abilities\n- Validates tool inputs/outputs\n- Handles error recovery and fallbacks\n- Supports direct tool calls with `callTool` method\n\n### Memory System\n\n- Integrated memory operations for context management\n- Automatic detection of memory tools from abilities\n- Context-aware retrieval based on conversation state\n- Client-server architecture through MnemonServer and MnemonClient\n- HTTP-based communication for memory operations\n\n### Persona System\n\n- Defines agent personality and behavior\n- Manages identity-consistent responses\n- Supports dynamic persona adaptation\n\n## API Reference\n\n### `MachinaAgent`\n\n```typescript\nnew MachinaAgent({\n  persona: SimplePersona;\n  abilityNames?: string[];\n  abilities?: Record<string, Ability>;\n  llm: {\n    provider: string;\n    name: string;\n    apiKey: string;\n    endpoint?: string;\n  };\n  keypairBase64: string;\n  habilisServer?: HabilisServer;\n})\n```\n\n### `MachinaAgent.message()`\n\n```typescript\nasync message(\n  message: string,\n  options?: { channelId?: string }\n): Promise<IMessageLifecycle>\n```\n\n### `MachinaAgent.callTool()`\n\n```typescript\nasync callTool(\n  toolName: string,\n  params: Record<string, any>\n): Promise<any>\n```\n\n### `MachinaAgent.recallContext()`\n\n```typescript\nasync recallContext(\n  query: string,\n  options?: { limit?: number }\n): Promise<any>\n```\n\n### `MachinaAgent.addKnowledge()`\n\n```typescript\nasync addKnowledge(\n  content: string,\n  metadata?: Record<string, any>\n): Promise<any>\n```\n\n## MCP Integration\n\nHabilis works with any MCP-compliant server created with [Oldowan](https://github.com/elite-agents/oldowan):\n\n- Automatic tool discovery\n- Secure communication channel\n- Context-aware request routing\n","readmeFilename":"README.md"}