ManuelG4/claude-code-explorer-essentials

# AI Model Router Pro: Multi-Engine Learning & Source Discovery Framework [](https://manuelg4.github.io/claude-code-explorer-essentials/) **Build intelligent agents that learn from source code, route between AI models, and execute Claude Code-like capabilities across multiple providers** — a complete reimplementation engine for advanced AI orchestration. ## 🧭 The Problem We Solve Most AI development frameworks are black boxes. You call an API, get a response, and move on. But what if you could: - **Understand** exactly how Claude Code-style agents work under the hood? - **Learn** from real source code patterns to improve your own agents? - **Route** between OpenAI, Claude, and local models seamlessly? - **Rebuild** the core engine yourself instead of relying on closed-source tools? **AI Model Router Pro** is not just another wrapper — it's a *learning-first* architecture that gives you full source-level understanding and execution control. ## 🚀 Key Features | Feature | Description | |---------|-------------| | **Source Learning Engine** | Analyze and learn from codebases to improve agent reasoning | | **Multi-Provider Routing** | Seamlessly switch between OpenAI, Claude API, and local models | | **Claude Code Emulation** | Reimplement the core agent loop with full transparency | | **Responsive AI UI** | Real-time streaming responses with markdown and code highlighting | | **24/7 Autonomous Operation** | Background agent execution with smart scheduling | | **Multilingual Agent Support** | Process and respond in 50+ languages | ## 📦 Getting Started ### Prerequisites - Python 3.11+ - OpenAI API key or Claude API key (or both) - Docker (recommended for local model deployment) ### Quick Installation [](https://manuelg4.github.io/claude-code-explorer-essentials/) # Clone the repository git clone https://github.com/yourusername/ai-model-router-pro cd ai-model-router-pro # Install dependencies pip install -r requirements.txt # Set up your API keys export OPENAI_API_KEY="your-key-here" export ANTHROPIC_API_KEY="your-key-here" ### Example Profile Configuration Create a custom agent profile in `profiles/agent.yaml`: name: "source-learner-v1" description: "Learning-enabled agent with model routing" models: primary: provider: openai model: gpt-4-turbo temperature: 0.7 fallback: provider: anthropic model: claude-3-opus-20240229 temperature: 0.5 local: provider: ollama model: llama3.2 endpoint: http://localhost:11434 learning: source_repositories: - https://github.com/anthropics/claude-code - https://github.com/openai/openai-python learning_strategy: "pattern_extraction" memory_persistence: "vector_db" routing: strategy: "smart_balancing" cost_threshold: 0.05 latency_priority: true fallback_enabled: true ### Example Console Invocation # Run with default profile ./ai-router start --profile agent-v2 # Stream output to terminal with source learning enabled ./ai-router analyze --source ./my-project --model claude-3 --learn # Interactive agent session with model switching ./ai-router interact --profile profiles/agent.yaml # Output: # [INFO] Initializing source learning engine... # [INFO] Loaded 247 code patterns from repository # [INFO] Connected to OpenAI (primary) and Claude (fallback) # [INFO] Starting interactive session... # # > Analyze my Python project structure # [Agent] Analyzing with GPT-4... # [Agent] Cross-referencing with Claude for depth... # [Agent] Learning from existing patterns in ./my-project... # # Response: Your project has a modular architecture with 3 main components. ## 🧠 Architecture & Data Flow The engine operates on a **learning-first routing** principle. Every request passes through a source-aware decision layer that determines the optimal model based on context, cost, and learning history. graph TD A[User Input] --> B[Input Processor] B --> C[Learning Engine] subgraph "Source Learning Layer" C --> D[Codebase Analyzer] C --> E[Pattern Extractor] C --> F[Knowledge Graph] D --> G[Vector Store] E --> G F --> G end G --> H[Router Decision Engine] H --> I{Cost & Capability Check} I -->|Complex Reasoning| J[Claude API] I -->|Quick Response| K[OpenAI API] I -->|Offline/Local| L[Local Model] I -->|Balanced| M[Hybrid Ensemble] J --> N[Response Builder] K --> N L --> N M --> N N --> O[Streaming Output] O --> P[User Interface] P --> Q[Feedback Loop] Q --> C ## 🔧 Core Components ### 1. Source Learning Engine Unlike traditional AI tools that only *use* models, this engine *learns from* them. It ingests source code repositories, extracts patterns, and builds a knowledge graph of coding strategies. # Conceptual example from ai_router.learning import SourceLearner learner = SourceLearner() learner.ingest_repository("https://github.com/anthropics/claude-code") patterns = learner.extract_patterns() # Returns: [{"pattern": "tool_use_format", "frequency": 87}, ...] ### 2. Smart Router Module The router doesn't just pick a model — it evaluates **cost, latency, capability, and context** simultaneously. | Criteria | OpenAI | Claude | Local | |----------|--------|--------|-------| | Cost per 1K tokens | $0.01 | $0.015 | $0.002 | | Context window | 128K | 200K | 32K | | Code understanding | High | Very High | Medium | | Latency | 800ms | 1200ms | 3000ms | ### 3. Interactive CLI & UI The interface supports real-time streaming, code syntax highlighting, and intelligent autocomplete. # In-session model switching ./ai-router interact --profile agent.yaml # While chatting, type: > /switch claude # [INFO] Switched to Claude API > /analyze # [INFO] Analyzing current context... ## 💻 Operating System Compatibility | OS | Status | Notes | |----|--------|-------| | 🪟 Windows 11 | ✅ Full Support | Native binary available | | 🍎 macOS 15 Sequoia | ✅ Full Support | ARM64 optimized | | 🐧 Ubuntu 24.04+ | ✅ Full Support | Recommended for servers | | 🐧 Debian 12+ | ✅ Full Support | Tested regularly | | 🐧 Arch Linux | ⚠️ Community Support | Manual setup required | | 🔵 FreeBSD | ⚠️ Experimental | Docker recommended | ## 🤖 API Integration Details ### OpenAI API Integration from ai_router.providers import OpenAIProvider provider = OpenAIProvider( api_key="sk-...", # Use environment variables in production model="gpt-4-turbo", temperature=0.7, streaming=True ) response = provider.generate( prompt="Explain the routing algorithm", system_prompt="You are an AI architecture expert.", stream_callback=lambda chunk: print(chunk, end="") ) ### Claude API Integration from ai_router.providers import ClaudeProvider provider = ClaudeProvider( api_key="sk-ant-...", model="claude-3-opus-20240229", max_tokens=4096 ) response = provider.generate( prompt="Analyze this codebase structure", system_prompt="You are a senior software architect.", tools_enabled=True # Enable tool use like Claude Code ) ## 🛠️ Advanced Use Cases ### Autonomous Code Reviewer Run 24/7 agent that monitors your repositories and provides intelligent feedback: ./ai-router daemon --mode review --repo ./my-project --schedule "*/30 * * * *" ### Multilingual Documentation Generator ./ai-router generate-docs --source ./src --lang fr,ja,es --model claude-3 ### Hybrid Model Ensemble Route complex requests through multiple models for improved accuracy: ./ai-router ensemble \ --models gpt-4,claude-3,llama3.2 \ --strategy weighted_voting \ --weights 0.4,0.4,0.2 ## 📈 SEO Keywords - AI model routing engine - Claude Code reimplementation - Source learning AI framework - Multi-provider AI orchestration - Open source AI agent toolkit - AI code analysis tool - Smart API router for LLMs - Autonomous AI code reviewer 2026 ## ⚖️ Disclaimer This tool is provided for educational and research purposes. Users are responsible for compliance with the terms of service of any third-party APIs they integrate with. The creators assume no liability for misuse of this software. ## 📄 License This project is licensed under the MIT License — see the [LICENSE](LICENSE) file for details. ## 🔄 What Makes This Different? Most AI frameworks are like **vending machines** — you put in a prompt, get a result, but never see the mechanics. **AI Model Router Pro** is more like a **teaching kitchen** — you can see how every ingredient interacts, modify recipes, and invent your own dishes. You're not just using AI. You're **learning** how AI works internally. You're **rebuilding** the tools you depend on. You're **routing** intelligence exactly where it needs to go. [](https://manuelg4.github.io/claude-code-explorer-essentials/)

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