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and maintainable agent library","maintainers":[{"name":"subeshb1","email":"subeshb1@gmail.com"}],"readme":"# @agenite/agent\n\nA powerful and flexible TypeScript library for building AI agents with advanced tool integration and state management capabilities.\n\n## Features\n\n- 🛠️ Advanced tool integration - Seamlessly integrate custom tools and APIs with type safety\n- 🔄 Stateful conversations - Built-in state management with custom reducers\n- 🌊 Streaming support - Real-time streaming of agent responses and tool executions\n- 🎯 Execution context - Track and manage nested agent executions with context inheritance\n- 🔌 Provider agnostic - Support for multiple LLM providers (Ollama, Bedrock)\n- 🎨 Flexible architecture - Build simple to complex agent hierarchies with middleware support\n- 📊 Token usage tracking - Monitor and optimize token consumption across executions\n- 🔄 Step-based execution - Fine-grained control over agent execution flow\n\n## Installation\n\n```bash\nnpm install @agenite/agent\n```\n\n## Quick start\n\n```typescript\nimport { Agent } from '@agenite/agent';\nimport { OllamaProvider } from '@agenite/ollama';\n\n// Create a simple calculator tool\nconst calculatorTool = new Tool({\n  name: 'calculator',\n  description: 'Perform basic math operations',\n  execute: async ({ input }) => {\n    // Tool implementation\n    return { success: true, data: result.toString() };\n  },\n});\n\n// Initialize the agent\nconst agent = new Agent({\n  name: 'math-buddy',\n  provider: new OllamaProvider({ model: 'llama2' }),\n  tools: [calculatorTool],\n  instructions: 'You are a helpful math assistant.',\n});\n\n// Execute the agent\nconst result = await agent.execute({\n  messages: [{ role: 'user', content: [{ type: 'text', text: 'What is 1234 * 5678?' }] }],\n});\n```\n\n## Core concepts\n\n### Agent\n\nThe main class that orchestrates interactions between the LLM and tools. It handles:\n\n- Message processing and state management\n- Tool execution and result handling\n- Response streaming\n- Nested agent execution\n- Token usage tracking\n\n### Tools\n\nTools are functions that agents can use to perform specific tasks. Each tool has:\n\n- Name and description\n- Input schema with type safety\n- Execute function with context support\n- Error handling capabilities\n\n### Providers\n\nLLM providers that handle the actual language model interactions:\n\n- Ollama\n- Amazon Bedrock\n- Extensible for other providers\n\n### Steps\n\nThe agent execution is broken down into steps:\n\n- `llm-call` - Handles LLM interactions\n- `tool-call` - Manages tool execution\n- `tool-result` - Processes tool results\n- `agent-call` - Handles nested agent execution\n\n## Advanced usage\n\n### Stateful agent with custom reducer\n\n```typescript\nconst customReducer = {\n  messages: (newValue, previousValue) => [\n    ...(previousValue || []),\n    ...(newValue || []),\n  ],\n  runningTotal: (newValue, previousValue) =>\n    (previousValue || 0) + (newValue || 0),\n};\n\nconst agent = new Agent({\n  name: 'stateful-calculator',\n  provider,\n  tools: [calculatorTool],\n  stateReducer: customReducer,\n  initialState: {\n    runningTotal: 0,\n  },\n  instructions:\n    'You are a helpful math assistant that maintains a running total.',\n});\n```\n\n### Nested agents with delegation\n\n```typescript\n// Specialist agents\nconst calculatorAgent = new Agent({\n  name: 'calculator-specialist',\n  provider,\n  tools: [calculatorTool],\n  description: 'Specializes in mathematical calculations',\n});\n\nconst weatherAgent = new Agent({\n  name: 'weather-specialist',\n  provider,\n  tools: [weatherTool],\n  description: 'Provides weather information',\n});\n\n// Coordinator agent\nconst coordinatorAgent = new Agent({\n  name: 'coordinator',\n  provider,\n  agents: [calculatorAgent, weatherAgent],\n  instructions:\n    'Coordinate between specialist agents to solve complex problems.',\n});\n```\n\n### Streaming responses with middleware\n\n```typescript\nconst agent = new Agent({\n  name: 'streaming-agent',\n  provider,\n  tools: [calculatorTool],\n  middlewares: [\n    executionContextInjector(),\n    // Add custom middleware here\n  ],\n});\n\nconst iterator = agent.iterate({\n  messages: [{ role: 'user', content: [{ type: 'text', text: 'Your query here' }] }],\n  stream: true,\n});\n\nfor await (const chunk of iterator) {\n  switch (chunk.type) {\n    case 'agenite.llm-call.streaming':\n      console.log(chunk.content);\n      break;\n    case 'agenite.tool-call.params':\n      console.log('Using tool:', chunk.toolUseBlocks);\n      break;\n    case 'agenite.tool-result':\n      console.log('Tool result:', chunk.result);\n      break;\n  }\n}\n```\n\n## API reference\n\n### Agent constructor\n\n```typescript\nnew Agent({\n  name: string;\n  provider: LLMProvider;\n  tools?: Tool[];\n  instructions?: string;\n  description?: string;\n  agents?: Agent[];\n  stateReducer?: CustomStateReducer;\n  initialState?: Partial<StateFromReducer<CustomStateReducer>>;\n  steps?: Steps;\n  middlewares?: Middlewares;\n})\n```\n\n### Execute method\n\n```typescript\nexecute({\n  messages: BaseMessage[];\n  stream?: boolean;\n  context?: Record<string, unknown>;\n}): Promise<ExecutionResult>\n```\n\n### Iterate method\n\n```typescript\niterate({\n  messages: BaseMessage[];\n  stream?: boolean;\n  context?: Record<string, unknown>;\n}): AsyncIterator<StreamChunk>\n```\n\n## Examples\n\nCheck out the [examples](./examples) directory for more detailed examples:\n\n- `basic/` - Simple examples showing core functionality\n  - `simple-chat.ts` - Basic chat agent with calculator tool\n- `advanced/` - More complex examples\n  - `nested-agents.ts` - Agent composition and delegation\n  - `stateful-agent.ts` - Maintaining conversation state\n  - `streaming-agent.ts` - Real-time response streaming\n  - `multi-tool-agent.ts` - Using multiple tools\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n## License\n\nMIT\n","readmeFilename":"README.md"}