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Smith core","maintainers":[{"name":"synw","email":"synwx@protonmail.com"}],"readme":"[![npm version](https://img.shields.io/npm/v/@agent-smith/core)](https://www.npmjs.com/package/@agent-smith/core)\n\n# @agent-smith/core — Runtime Engine\n\nThe central runtime engine for the [Agent Smith toolkit](https://github.com/lynxai-team/agent-smith). Provides SQLite-backed configuration management, filesystem-based feature discovery, a unified tool execution engine, and Model Context Protocol (MCP) client integration.\n\n## ✨ Features\n\n- 🗄️ **SQLite Configuration** — 17-table database for features, backends, plugins, settings, and workspaces\n- 🔍 **Feature Discovery** — Scans directories for agents, actions, workflows, commands, adaptaters, and skills\n- ⚡ **Unified Tool Execution** — Execute actions (JS/Python/YAML), agents, workflows, and commands through a common interface\n- 🌐 **MCP Client** — Connect to external MCP servers and expose their tools as `ToolSpec` objects\n- 🔧 **Multi-Language Support** — JavaScript (ESM), Python (`python-shell`), and YAML/shell actions\n- 📡 **Reactive State** — Vue `ref`/`reactive` for cross-module state management\n- 🔄 **Backend Agnostic** — Multiple inference backends (OpenAI-compatible, local llamacpp) with per-agent selection\n\n## Documentation\n\n### For AI Agents\n- [Codebase Summary](.agents/documentation/codebase-summary.md) — Architecture, key files, and patterns for the core package\n- [Configuration](https://raw.githubusercontent.com/lynxai-team/agent-smith/refs/heads/main/docsite/public/doc/libraries/core/2.configuration.md) — Configuration file format, loading, and management\n- [Feature Discovery](https://raw.githubusercontent.com/lynxai-team/agent-smith/refs/heads/main/docsite/public/doc/libraries/core/3.feature-discovery.md) — Directory structure, discovery process, and SQLite registration\n- [Tool Execution](https://raw.githubusercontent.com/lynxai-team/agent-smith/refs/heads/main/docsite/public/doc/libraries/core/4.tool-execution.md) — Execution functions, multi-language support, and error handling\n- [MCP Client](https://raw.githubusercontent.com/lynxai-team/agent-smith/refs/heads/main/docsite/public/doc/libraries/core/5.mcp.md) — MCP server connection, tool extraction, and agent integration\n\n### For Humans\n- [Get Started](https://lynxai-team.github.io/agent-smith/libraries/core/get_started) — Installation and basic usage\n- [Configuration](https://lynxai-team.github.io/agent-smith/libraries/core/configuration) — Configuration file format, loading, and management\n- [Feature Discovery](https://lynxai-team.github.io/agent-smith/libraries/core/feature-discovery) — Directory structure, discovery process, and SQLite registration\n- [Tool Execution](https://lynxai-team.github.io/agent-smith/libraries/core/tool-execution) — Execution functions, multi-language support, and error handling\n- [MCP Client](https://lynxai-team.github.io/agent-smith/libraries/core/mcp) — MCP server connection, tool extraction, and agent integration\n\n## 📦 Installation\n\n```bash\nnpm install @agent-smith/core\n```\n\n**Dependencies:** `@agent-smith/types`, `better-sqlite3`, `@vue/reactivity`, `yaml`, `@modelcontextprotocol/sdk`, `python-shell`\n\n## 🚀 Quick Start\n\n```typescript\nimport { conf, db, executeAction, executeWorkflow, McpClient } from \"@agent-smith/core\";\n\n// Initialize the database and process configuration\nawait conf.updateConfCmd([\"conf\"]);\n\n// Discover and register features from configured paths\nawait conf.updateFeaturesCmd({});\n\n// Execute an action by name\nconst result = await executeAction(\"read\", [\"./file.txt\"], {});\n\n// Or run a workflow that chains multiple steps\nconst workflowResult = await executeWorkflow(\"my-workflow\", [\"arg1\"], {});\n```\n\n## 📖 Usage\n\n### Configuration Management\n\nLoad and manage configuration from YAML files:\n\n```typescript\nimport { conf } from \"@agent-smith/core\";\n\n// Process an existing config file\nconst { paths, pf, dd } = await conf.processConfPath(\"~/.config/agent-smith/config.yml\");\n// paths: Array<string> — all feature search paths\n// pf: string — prompt file path\n// dd: string — data directory path\n\n// Create a default config if none exists\nconst fp = conf.createConfigFileIfNotExists();\n\n// Get platform-specific config paths\nconst { confDir, dbPath } = conf.getConfigPath(\"agent-smith\", \"config.db\");\n```\n\n### Feature Discovery\n\nDiscover and register features from filesystem directories:\n\n```typescript\nimport { getFeatureSpec, conf } from \"@agent-smith/core\";\n\n// Re-scan all registered feature paths\nawait conf.updateFeaturesCmd({});\n\n// Look up a feature by name and type\nconst spec = getFeatureSpec(\"writer\", \"agent\");\nif (spec.found) {\n  console.log(spec.path);   // Full file path\n  console.log(spec.ext);    // File extension\n  console.log(spec.variables); // Variable definitions\n}\n```\n\n### Tool Execution\n\nExecute actions, workflows, and agents:\n\n```typescript\nimport { executeAction, executeWorkflow, executeAgent } from \"@agent-smith/core\";\n\n// Execute a JavaScript action\nconst result = await executeAction(\"read\", [\"./file.txt\"], {});\n\n// Execute a YAML action (shell command)\nconst grepResult = await executeAction(\"grep\", [\"error\"], {});\n\n// Execute a Python action\nconst pythonResult = await executeAction(\"analyze\", [\"data.csv\"], {});\n\n// Run a workflow chaining multiple steps\nconst pipelineResult = await executeWorkflow(\"data-pipeline\", [\"input.csv\"], {\n  verbose: true,\n  debug: false\n});\n\n// Execute an agent with inference\nconst agentResult = await executeAgent(\"writer\", [\"AI trends\"], {\n  model: \"llama3\",\n  temperature: 0.7\n});\n```\n\n### Agent Executor\n\nCreate a full agent execution context:\n\n```typescript\nimport { useAgentExecutor } from \"@agent-smith/core\";\n\nconst executor = await useAgentExecutor(\"researcher\", { prompt: \"Analyze the data\" }, {});\nconst result = await executor.execute();\n// Returns: InferenceResult\n```\n\nThe executor handles backend resolution, MCP server initialization, grammar compilation, and streaming.\n\n### Reactive State\n\nAccess and manage reactive state:\n\n```typescript\nimport { state } from \"@agent-smith/core\";\n\n// List all inference backends\nconst backends = state.listBackends();\n\n// Set the default backend\nawait state.setBackend(\"openai\");\n\n// Get per-agent inference settings\nconst settings = state.getAgentSettings();\n```\n\n### MCP Client\n\nConnect to external MCP servers:\n\n```typescript\nimport { McpClient } from \"@agent-smith/core\";\n\nconst client = new McpClient(\n  \"filesystem\",                    // server name\n  \"npx\",                           // command\n  [\"-y\", \"@modelcontextprotocol/server-filesystem\", \"/tmp\"], // args\n  null,                            // authorizedTools (null = all allowed)\n  null                             // askUserTools (null = no confirmation needed)\n);\n\n// Start the server connection\nawait client.start();\n\n// Extract tools as ToolSpec objects\nconst tools = await client.extractTools({ confirmToolUsage });\n\n// Stop the connection\nawait client.stop();\n```\n\n## 🔧 Complete Example\n\nA full working example demonstrating configuration, feature discovery, and execution:\n\n```typescript\nimport {\n  conf,\n  db,\n  state,\n  executeAction,\n  executeWorkflow,\n  useAgentExecutor,\n  getFeatureSpec,\n  McpClient\n} from \"@agent-smith/core\";\n\nasync function main() {\n  // Step 1: Initialize database and configuration\n  console.log(\"Initializing...\");\n  await conf.updateConfCmd([\"conf\"]);\n  \n  // Verify state readiness\n  if (!state.isStateReady()) {\n    throw new Error(\"Configuration not loaded\");\n  }\n\n  // Step 2: Discover features from all registered paths\n  console.log(\"Discovering features...\");\n  await conf.updateFeaturesCmd({});\n\n  // Step 3: Look up a feature\n  const agentSpec = getFeatureSpec(\"writer\", \"agent\");\n  if (!agentSpec.found) {\n    throw new Error(\"Writer agent not found\");\n  }\n  console.log(`Found agent at: ${agentSpec.path}`);\n\n  // Step 4: Execute an action\n  console.log(\"Executing read action...\");\n  const fileContent = await executeAction(\"read\", [\"./README.md\"], {});\n  console.log(`File content length: ${fileContent.length}`);\n\n  // Step 5: Run a workflow\n  console.log(\"Running workflow...\");\n  try {\n    const result = await executeWorkflow(\"my-pipeline\", [\"input.txt\"], {\n      verbose: true\n    });\n    console.log(\"Workflow completed:\", result);\n  } catch (e) {\n    console.error(\"Workflow failed:\", e.message);\n  }\n\n  // Step 6: Execute an agent\n  console.log(\"Running agent...\");\n  try {\n    const executor = await useAgentExecutor(\"writer\", { prompt: \"Write about AI\" }, {});\n    const inferenceResult = await executor.execute();\n    console.log(\"Agent response:\", inferenceResult.content);\n  } catch (e) {\n    console.error(\"Agent execution failed:\", e.message);\n  }\n\n  // Step 7: Use MCP client for external tools\n  console.log(\"Setting up MCP client...\");\n  const mcpClient = new McpClient(\n    \"filesystem\",\n    \"npx\",\n    [\"-y\", \"@modelcontextprotocol/server-filesystem\", \"/tmp\"]\n  );\n  \n  try {\n    await mcpClient.start();\n    const tools = await mcpClient.extractTools({ confirmToolUsage: async () => true });\n    console.log(`Loaded ${tools.length} MCP tools`);\n  } finally {\n    await mcpClient.stop();\n  }\n\n  console.log(\"Done!\");\n}\n\nmain().catch(console.error);\n```\n\n## 📋 API Reference\n\n### Named Exports\n\n| Export | Type | Description |\n|--------|------|-------------|\n| `db` | `object` | Database operations: `init`, `...readOps`, `...writeOps`, `db` (raw better-sqlite3) |\n| `fs` | `object` | Filesystem helpers: `openAgentSpec`, `readWorkflow` |\n| `conf` | `object` | Configuration management: `getConfigPath`, `processConfPath`, `updateConfigFile`, etc. |\n| `utils` | `object` | Utilities: `execute`, `runShellCmd`, `deleteFileIfExists`, `readAgent`, etc. |\n| `state` | `object` | Reactive state: `init`, `initState`, `setBackend`, `listBackends`, `agentSettings`, etc. |\n| `executeAction` | `function` | Execute an action by name |\n| `executeWorkflow` | `function` | Execute a workflow by name |\n| `executeAgent` | `function` | Execute an agent by name |\n| `useAgentExecutor` | `function` | Create an agent execution context |\n| `getFeatureSpec` | `function` | Look up a feature by name and type |\n| `extractToolDoc` | `function` | Extract tool specification from a feature file |\n| `McpClient` | `class` | MCP server client for external tool integration |\n\n### `executeAction(name, payload, options)`\n\nExecutes an action by name, resolving it from the SQLite database.\n\n| Parameter | Type | Description |\n|-----------|------|-------------|\n| `name` | `string` | Action name (matches registered feature) |\n| `payload` | `Array<string>` | Arguments passed to the action |\n| `options` | `object` | Execution options (`debug`, `verbose`) |\n\n**Returns:** `Promise<any>` — Action result\n\n**Dispatch by extension:**\n- `.js`: Dynamically imports and calls exported `action(args, options)`\n- `.yml`: Executes YAML-defined shell command with args\n- `.py`: Runs via `python-shell` package\n\n### `executeWorkflow(name, args, options)`\n\nExecutes a workflow as a sequence of steps (agent/action/adaptater/cmd).\n\n| Parameter | Type | Description |\n|-----------|------|-------------|\n| `name` | `string` | Workflow name |\n| `args` | `Array<string>` | Initial arguments for the first step |\n| `options` | `object` | Execution options (`verbose`, `debug`) |\n\n**Returns:** `Promise<any>` — Result of the final step\n\n### `executeAgent(name, args, options)`\n\nExecutes an agent by resolving its prompt and running inference.\n\n| Parameter | Type | Description |\n|-----------|------|-------------|\n| `name` | `string` | Agent name |\n| `args` | `Array<string>` | Arguments for prompt generation |\n| `options` | `object` | Inference options (`model`, `temperature`, etc.) |\n\n**Returns:** `Promise<InferenceResult>` — Agent inference result\n\n### `useAgentExecutor(name, payload, options)`\n\nCreates an agent execution context with full setup.\n\n| Parameter | Type | Description |\n|-----------|------|-------------|\n| `name` | `string` | Agent name |\n| `payload` | `object` | Payload including `prompt` and optional context |\n| `options` | `object` | Execution options |\n\n**Returns:** `Promise<{ execute: () => Promise<InferenceResult> }>`\n\n### `getFeatureSpec(name, type)`\n\nLooks up a feature in the SQLite database.\n\n| Parameter | Type | Description |\n|-----------|------|-------------|\n| `name` | `string` | Feature name |\n| `type` | `\"agent\" \\| \"action\" \\| \"workflow\" \\| \"adaptater\" \\| \"cmd\" \\| \"skill\"` | Feature type |\n\n**Returns:** `{ found: boolean; path?: string; ext?: string; variables?: Record<string, any> }`\n\n### `McpClient` Class\n\n| Constructor | `(servername: string, command: string, args: Array<string>, authorizedTools?: Array<string> \\| null, askUserTools?: Array<string> \\| null)` |\n|-------------|--------------------------------------------------------------------------------------------------------------------------|\n| `start()` | `async () => Promise<void>` — Connect to the MCP server via stdio transport |\n| `stop()` | `async () => Promise<void>` — Close the connection |\n| `extractTools(options)` | `async (options: { confirmToolUsage?: Function }) => Promise<Array<ToolSpec>>` — Extract tools as ToolSpec objects |\n\n## ⚠️ Important Notes\n\n- **Node.js Environment**: This package is designed for Node.js environments. It depends on `better-sqlite3` which requires native compilation.\n- **SQLite First Run**: The database is initialized automatically on first use with 17 tables. No manual migration needed.\n- **Feature Discovery Required**: Before executing features, you must call `conf.updateConfCmd()` and `conf.updateFeaturesCmd()` to discover and register them.\n- **Python Actions**: Require `python-shell` package and a working Python installation.\n- **MCP Servers**: External MCP servers are spawned as child processes. Ensure required binaries (e.g., `npx`) are available.\n- **Related Packages**: See `@agent-smith/types` for shared interfaces, `@agent-smith/agent` for the Agent class, `@agent-smith/cli` for CLI usage.\n\n## 📖 Documentation Links\n\n- [Full docsite](https://lynxai-team.github.io/agent-smith/) — Complete project documentation\n- [Architecture Overview](https://lynxai-team.github.io/agent-smith/architecture/overview) — System architecture and design principles\n- [Tool Abstraction](https://lynxai-team.github.io/agent-smith/architecture/tool-abstraction) — Unified `ToolSpec` interface\n\n## 📄 License\n\nMIT\n","readmeFilename":"README.md"}