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It provides a low-code solution to streamline the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.\n\n## Installation\n\n```bash\nnpm install praisonai\n```\n\n## Development Setup\n\n1. Clone the repository:\n```bash\ngit clone https://github.com/MervinPraison/PraisonAI.git\ncd src/praisonai-ts\n```\n\n2. Install dependencies:\n```bash\nnpm install\n```\n\n3. Build the package:\n```bash\nnpm run build\n```\n\n## Usage\n\nHere are examples of different ways to use PraisonAI:\n\n### 1. Single Agent Example\n\n```typescript\nimport { Agent, AgentTeam } from 'praisonai';\n\nasync function main() {\n    // Create a simple agent (no task specified)\n    const agent = new Agent({\n        name: \"BiologyExpert\",\n        instructions: \"Explain the process of photosynthesis in detail.\",\n        verbose: true\n    });\n\n    // Run the agent with AgentTeam\n    const team = new AgentTeam({\n        agents: [agent],\n        tasks: [\"Explain the process of photosynthesis in detail.\"],\n        verbose: true\n    });\n\n    try {\n        console.log('Starting single agent example...');\n        const results = await team.start();\n        console.log('\\nFinal Results:', results);\n    } catch (error) {\n        console.error('Error:', error);\n    }\n}\n\nmain();\n```\n\n### 2. Multi-Agent Example\n\n```typescript\nimport { Agent, AgentTeam } from 'praisonai';\n\nasync function main() {\n    // Create multiple agents with different roles\n    const researchAgent = new Agent({\n        name: \"ResearchAgent\",\n        instructions: \"Research and provide detailed information about renewable energy sources.\",\n        verbose: true\n    });\n\n    const summaryAgent = new Agent({\n        name: \"SummaryAgent\",\n        instructions: \"Create a concise summary of the research findings about renewable energy sources. Use {previous_result} as input.\",\n        verbose: true\n    });\n\n    const recommendationAgent = new Agent({\n        name: \"RecommendationAgent\",\n        instructions: \"Based on the summary in {previous_result}, provide specific recommendations for implementing renewable energy solutions.\",\n        verbose: true\n    });\n\n    // Run the agents in sequence with AgentTeam\n    const team = new AgentTeam({\n        agents: [researchAgent, summaryAgent, recommendationAgent],\n        tasks: [\n            \"Research and analyze current renewable energy technologies and their implementation.\",\n            \"Summarize the key findings from the research.\",\n            \"Provide actionable recommendations based on the summary.\"\n        ],\n        verbose: true\n    });\n\n    try {\n        console.log('Starting multi-agent example...');\n        const results = await team.start();\n        console.log('\\nFinal Results:', results);\n    } catch (error) {\n        console.error('Error:', error);\n    }\n}\n\nmain();\n```\n\n### 3. Task-Based Agent Example\n\n```typescript\nimport { Agent, Task, AgentTeam } from 'praisonai';\n\nasync function main() {\n    // Create agents first\n    const dietAgent = new Agent({\n        name: \"DietAgent\",\n        role: \"Nutrition Expert\",\n        goal: \"Create healthy and delicious recipes\",\n        backstory: \"You are a certified nutritionist with years of experience in creating balanced meal plans.\",\n        verbose: true,  // Enable streaming output\n        instructions: `You are a professional chef and nutritionist. Create 5 healthy food recipes that are both nutritious and delicious.\nEach recipe should include:\n1. Recipe name\n2. List of ingredients with quantities\n3. Step-by-step cooking instructions\n4. Nutritional information\n5. Health benefits\n\nFormat your response in markdown.`\n    });\n\n    const blogAgent = new Agent({\n        name: \"BlogAgent\",\n        role: \"Food Blogger\",\n        goal: \"Write engaging blog posts about food and recipes\",\n        backstory: \"You are a successful food blogger known for your ability to make recipes sound delicious and approachable.\",\n        verbose: true,  // Enable streaming output\n        instructions: `You are a food and health blogger. Write an engaging blog post about the provided recipes.\nThe blog post should:\n1. Have an engaging title\n2. Include an introduction about healthy eating`\n    });\n\n    // Create tasks\n    const createRecipesTask = new Task({\n        name: \"Create Recipes\",\n        description: \"Create 5 healthy and delicious recipes\",\n        agent: dietAgent\n    });\n\n    const writeBlogTask = new Task({\n        name: \"Write Blog\",\n        description: \"Write a blog post about the recipes\",\n        agent: blogAgent,\n        dependencies: [createRecipesTask]  // This task depends on the recipes being created first\n    });\n\n    // Run the tasks with AgentTeam\n    const team = new AgentTeam({\n        tasks: [createRecipesTask, writeBlogTask],\n        verbose: true\n    });\n\n    try {\n        console.log('Starting task-based example...');\n        const results = await team.start();\n        console.log('\\nFinal Results:', results);\n    } catch (error) {\n        console.error('Error:', error);\n    }\n}\n\nmain();\n```\n\n### Running the Examples\n\n1. First, set up your environment variables:\n```bash\nexport OPENAI_API_KEY='your-api-key'\n```\n\n2. Create a new TypeScript file (e.g., `example.ts`) with any of the above examples.\n\n3. Run the example:\n```bash\nnpx ts-node example.ts\n```\n\nFor more examples, check out the `examples/concepts/` directory in the repository.\n\n## Package Structure\n\n```\nsrc/\n├── agent/         # Agent-related interfaces and implementations\n├── agents/        # Multi-agent system management\n├── knowledge/     # Knowledge base and management\n├── llm/          # Language Model interfaces\n├── memory/       # Memory management systems\n├── process/      # Process management\n├── task/         # Task management\n└── tools/        # Various utility tools\n    ├── arxivTools.ts\n    └── ... (other tools)\n```\n\n## Contributing\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add some amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## License\n\nMIT License - see the LICENSE file for details\n\n## Testing\n\n### Manual Testing\n\n```bash\nexport OPENAI_API_KEY='your-api-key'\nnpx ts-node tests/development/simple/single-agent.ts\nnpx ts-node tests/development/simple/multi-agent.ts\nnpx ts-node tests/development/simple/multi-agents-simple.js\n```\n\n## Examples Testing\n\n```bash\nexport OPENAI_API_KEY='your-api-key'\nnpx ts-node examples/simple/single-agent.ts\nnpx ts-node examples/simple/multi-agent.ts\n```\n\n### Automated Testing (WIP)\n\n```bash\nnpm run test\n```\n\n","readmeFilename":"README.md"}