feat: EOL Framework Architecture Documentation #1
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- Document MCP architecture with FastMCP recommendation - Document Redis v8 vector database capabilities - Analyze Redis MCP servers for integration - Define EOL two-phase development model (prototyping/implementation) The framework enables AI application development through: 1. Prototyping phase using .eol files and redis-mcp 2. Implementation phase with deterministic code generation 3. Ad-hoc phase switching for incremental development 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
- Document advanced RAG patterns (GraphRAG, HyDE, Self-RAG, CRAG, HybridRAG) - Document semantic caching with 31% hit rate optimization - Document content-specific chunking strategies: - AST-based for code - Structure-based for documents - Multimodal for PDFs/videos - Document Context Protocol methodology and CLAUDE.md patterns All patterns include Redis v8 implementation examples and EOL integration. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
- Add Gherkin as supported code block language - Update test examples to use proper Gherkin syntax - Include Feature/Scenario/Background structure - Add Gherkin parser implementation - Enable syntax highlighting for .test.eol files Gherkin provides industry-standard BDD syntax with proper markdown syntax highlighting for test specifications. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
- Complete system architecture with 5-layer design - Detailed component specifications for each layer - Data flow diagrams for all phases - Monorepo structure with package organization - Deployment architecture (dev/prod) - Security and performance strategies - Monitoring and observability patterns - Integration points and best practices The architecture supports both prototyping and implementation phases with clear separation of concerns and scalability. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
- Design unified entry point supporting both CLI and MCP modes - Implement FastMCP tools for all EOL operations - Define MCP resources for context and metrics access - Add MCP prompts for common AI workflows - Include Docker configuration for both modes - Document integration with Claude Desktop and IDEs EOL can now serve developers directly via CLI and AI assistants via MCP protocol, maximizing utility across workflows. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
- Change from .eol to .eol.md for feature files - Change from .test.eol to .test.eol.md for test files - Update all documentation to reflect new extensions - Update parser implementation to validate extensions Benefits: - Native GitHub/IDE markdown preview - Syntax highlighting out of the box - Better tooling support - No custom viewer needed 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
- Extended EOL file format with 6 dependency types: - Feature dependencies for composition - MCP server dependencies for prototyping - Service dependencies for external APIs - Package dependencies for Python libraries - Container dependencies for Docker services - LLM model dependencies with provider/purpose specification - Implemented dependency resolution engine: - Topological sorting for correct resolution order - Circular dependency detection using graph analysis - Phase-aware dependency filtering - Automatic fallback mechanisms - Created dependency injection framework: - Multiple injection strategies (context, constructor, property) - Type-based and name-based resolution - Dependency container with lifecycle management - Added comprehensive documentation: - eol-dependencies.md: Complete dependency system overview - eol-dependency-implementation.md: Implementation details and examples - Updated eol-file-format.md with new dependency syntax 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
- Created comprehensive README.md with installation and usage guide - Added pyproject.toml with full dependency specifications - Created payment-processor example demonstrating: - Complete dependency system usage (all 6 types) - Hybrid phase development - Fraud detection with LLM models - Rate limiting and caching - Stripe API integration - Created corresponding test file with Gherkin scenarios - Demonstrates real-world usage patterns The payment processor example showcases: - Feature composition with authentication and rate-limiting - MCP server integration with Redis - Multiple LLM models for different purposes - Service dependencies with circuit breakers - Container orchestration - Phase-specific execution 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
- Created eol-core package with essential components: - EOL Parser: Parses .eol.md and .test.eol.md files - Phase Manager: Controls execution phase transitions - Context Manager: Manages LLM context window - Dependency Resolver: Resolves feature dependencies - Updated all pyproject.toml files to use GPL-3.0 license - Fixed author information from GitHub repository - Updated README to reflect GPL-3.0 license Core components implemented: - Full Markdown/YAML parsing for EOL files - Dependency graph resolution with circular detection - Context window management with compression - Phase transition tracking and metrics - Support for all 6 dependency types The implementation provides the foundation for: - Two-phase development (prototyping/implementation) - Hybrid execution modes - Context-aware LLM operations - System composition through dependencies 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
- Implemented complete CLI interface (eol-cli package): - Main commands: run, test, generate, switch, serve, version - Dependency management subcommands: install, health, list, graph - Rich terminal output with tables and progress indicators - Support for both CLI and MCP server modes - Created dependency managers: - FeatureDependencyManager: Handles .eol.md feature dependencies - ModelManager: Manages LLM models (Anthropic, OpenAI, local) - Version compatibility checking with semver - Injectable function extraction - Model usage tracking and cost calculation - Added MCP server stub: - Basic structure for stdio and SSE transports - Integration point for Claude Desktop and IDEs CLI features: - Feature execution with phase control - Test running with coverage support - Code generation from prototypes - Phase switching for features/operations - Dependency resolution and health checks - MCP server mode activation Model management: - Multi-provider support (Anthropic, OpenAI, local) - Purpose-based model selection - Fallback mechanisms - Usage and cost tracking - Health checking 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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Summary
Documentation Added
Core Architecture
Development Model
Data Management
Test Plan
Next Steps
🤖 Generated with Claude Code