/reflective-engineer

Primary LanguageTypeScript

Reflective Engineer

A powerful LangChain-based development environment for building, testing, and deploying AI agents. Featuring advanced prompting techniques, sophisticated memory systems, and a comprehensive template library for creating intelligent, context-aware applications.

🚀 Try the Live Demo

Advanced Capabilities

Prompting Systems

  • Chain of Thought: Implement step-by-step reasoning in agent responses
  • Tree of Thoughts: Enable multi-path exploration for complex problem-solving
  • Zero-Shot Learning: Create agents that can handle new tasks without prior examples
  • Few-Shot Learning: Provide minimal examples for task adaptation
  • Structured Output: Generate responses in specific formats (JSON, XML, etc.)
  • Constitutional AI: Implement ethical constraints and behavioral guidelines
  • Prompt Chaining: Create sophisticated workflows by connecting multiple prompts
  • Context Window Management: Optimize token usage and maintain conversation context

Mathematical Frameworks

  • Set Theory: Model complex relationships and hierarchies
  • Category Theory: Define abstract transformations and mappings
  • Abstract Algebra: Structure group operations and symmetries
  • Topology: Explore continuous transformations and invariants
  • Complex Analysis: Handle multi-dimensional relationships

Agent Templates

  • Autonomous Agents: Self-directed agents with independent decision-making
  • Hierarchical Agents: Multi-level agent systems with command structures
  • Team Chat Agents: Collaborative agents working together
  • Supervisor Agents: Oversight and coordination of agent teams
  • XML Agents: Structured output generation with schema validation
  • Router Agents: Intelligent task distribution and workflow management

Memory Systems

  • Buffer Memory: Recent interaction storage
  • Conversation Memory: Full dialogue history management
  • Entity Memory: Track and update entity information
  • Summary Memory: Compressed historical context
  • Time-Weighted Memory: Temporal relevance-based storage
  • Vector Memory: Semantic similarity-based retrieval

Chain Templates

  • Sequential Chains: Multi-step processing pipelines
  • Router Chains: Dynamic workflow management
  • API Chains: External service integration
  • SQL Chains: Database interaction and query generation
  • Retrieval QA: Document-based question answering
  • OpenAI Function Chains: Structured function calling

Development Tools

Agent Development

  • Visual Template Editor: Customize agent behavior and responses
  • Live Preview: Real-time testing and iteration
  • Memory Visualization: Inspect and debug memory states
  • Chain Debugging: Step-through execution of chain operations
  • Performance Monitoring: Track token usage and response times

Testing & Deployment

  • Automated Testing: Verify agent behavior and responses
  • Environment Management: Dev/staging/prod configurations
  • Version Control: Track template and agent changes
  • Deployment Options: Local, cloud, and containerized deployment

Quick Start

  1. Clone & Install
git clone https://github.com/ruvnet/reflective-engineer.git
cd reflective-engineer
npm install
  1. Configure Environment
cp sample.env .env
# Edit .env with your API keys and configuration
  1. Start Development Server
npm run dev
  1. Build for Production
npm run build
npm run preview

Usage Guide

Creating an Agent

  1. Navigate to the Templates page
  2. Choose a template type (e.g., Autonomous Agent, Team Chat)
  3. Customize the configuration
  4. Test the agent using the built-in tools
  5. Deploy to your environment

Template Customization

  1. Open the Template Editor
  2. Modify the template structure
  3. Add custom functionality
  4. Save and export your changes

Memory Integration

  1. Select a memory system
  2. Configure memory parameters
  3. Test with sample conversations
  4. Monitor memory usage and performance

Architecture

Core Components

  • Template Engine: Manages and processes agent templates
  • Memory Systems: Handles various types of agent memory
  • Chain Manager: Coordinates different chain types
  • Deployment System: Handles agent deployment and scaling

Integration Points

  • LangChain
  • OpenAI
  • Vector Stores
  • Custom Tools

Security

Features

  • Secure API key management
  • Environment-based configuration
  • Input validation and sanitization
  • Rate limiting and usage monitoring

Best Practices

  • Use environment variables for sensitive data
  • Regularly rotate API keys
  • Monitor agent activities
  • Implement proper access controls

Contributing

We welcome contributions! Please follow these steps:

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Add tests if applicable
  5. Submit a pull request

See CONTRIBUTING.md for detailed guidelines.

Documentation

  • Templates: Detailed documentation for each template type
  • API Reference: Complete API documentation
  • Examples: Sample implementations and use cases
  • Tutorials: Step-by-step guides for common tasks

Support

  • GitHub Issues: Bug reports and feature requests
  • Documentation: In-app documentation
  • Community: Discussions and knowledge sharing

License

MIT License - see LICENSE file for details

Acknowledgments

  • LangChain for the foundational framework
  • OpenAI for language models
  • shadcn/ui for UI components
  • Tailwind CSS for styling
  • Vite for build system