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Experimental Implementations

Based on our findings from studying Claude CLI, we've developed several experimental implementations to test our understanding and explore the architectural patterns we've discovered.

Overview​

These implementations are educational in nature and designed to:

  1. Test our hypotheses about Claude CLI's architecture
  2. Provide concrete examples of architectural patterns
  3. Serve as a learning resource for those studying LLM CLI tools
  4. Explore extensions to other LLM providers

Our implementations are not intended to replace Claude CLI but rather to learn from its design and explore similar patterns in different contexts.

Core Implementations​

Claude CLI Emulator​

Our CLI Emulator is a prototype that attempts to emulate Claude CLI's core functionality:

  • Command-Line Interface: Similar command structure and interaction model
  • Hybrid Architecture: Local processing with remote API calls
  • Semantic Chunking: Implementation of semantic chunking based on our findings
  • Differential Updates: Processing changes without reprocessing unchanged code
# Example code from our CLI Emulator
class ClaudeCliEmulator:
    def __init__(self, config):
        self.config = config
        self.semantic_chunker = SemanticChunker()
        self.context_manager = ContextManager()
        self.tool_manager = ToolManager()
        
    def process_query(self, query, codebase_path):
        # Determine relevant files
        relevant_files = self.semantic_chunker.find_relevant_files(query, codebase_path)
        
        # Prepare context
        context = self.context_manager.prepare_context(relevant_files)
        
        # Send to API
        response = self.api_client.send_query(query, context)
        
        # Process response
        return self.tool_manager.process_response(response)

Multi-Provider CLI​

Our Multi-Provider CLI extends Claude CLI patterns to other LLM providers:

  • LiteLLM Integration: Using LiteLLM to support multiple providers
  • Provider-Specific Optimizations: Adapting context for different models
  • Unified Interface: Consistent interface across providers
  • Performance Comparison: Tools for comparing provider performance

Semantic Chunker​

Our standalone Semantic Chunker implements the semantic chunking patterns we've observed:

  • AST-Based Analysis: Using abstract syntax trees for semantic understanding
  • Reference Tracking: Following imports and dependencies
  • Query-Driven Relevance: Prioritizing files based on query relevance
  • Token Optimization: Optimizing context for token efficiency

Experimental Tools​

In addition to our core implementations, we've developed several experimental tools:

Context Analyzer​

The Context Analyzer helps understand and optimize context:

  • Token Usage Analysis: Analyzing token usage in context
  • Relevance Scoring: Scoring context elements by relevance
  • Optimization Suggestions: Suggesting context optimizations

Differential Update Processor​

The Differential Update Processor explores efficient updates:

  • Change Detection: Detecting code changes efficiently
  • Context Integration: Integrating changes into existing context
  • Update Optimization: Optimizing updates for token efficiency

Session Manager​

The Session Manager explores session state management:

  • Context Persistence: Maintaining context across interactions
  • State Synchronization: Synchronizing local and remote state
  • Context Pruning: Pruning context to stay within token limits

Getting Started​

To explore our experimental implementations:

  1. Clone the repository: git clone https://github.com/your-github-username/sonnet-3.7-docs.git
  2. Navigate to the implementations directory: cd sonnet-3.7-docs/experiments/implementations
  3. Install dependencies: pip install -r requirements.txt
  4. Run the CLI emulator: python cli_emulator.py

Contributing​

We welcome contributions to our experimental implementations! See our contribution guidelines for details on how to contribute.

Disclaimer​

These implementations are for educational purposes only and are not endorsed by Anthropic. Claude CLI is a product of Anthropic, and our goal is to learn from its design, not to replace it.