Claude CLI Emulator

Our Claude CLI Emulator is an experimental implementation that attempts to replicate key aspects of Claude CLI's architecture and behavior. This implementation serves as a testbed for our hypotheses about how Claude CLI works.

Overview

The Claude CLI Emulator is designed to:

1. Implement the core architectural patterns we've observed in Claude CLI
2. Provide a concrete example of how these patterns can be implemented
3. Serve as a platform for further experimentation and learning

It is not intended to replace Claude CLI but rather to learn from its design and explore similar patterns in a controlled environment.

Architecture

The emulator follows the hybrid architecture pattern we've observed in Claude CLI:

┌─────────────────────────┐
│   Command-Line Interface│
└───────────┬─────────────┘
            │
┌───────────▼─────────────┐
│    Local Processing     │
│  ┌─────────────────────┐│
│  │  Semantic Chunker   ││
│  └─────────────────────┘│
│  ┌─────────────────────┐│
│  │  Context Manager    ││
│  └─────────────────────┘│
│  ┌─────────────────────┐│
│  │  Tool Manager       ││
│  └─────────────────────┘│
└───────────┬─────────────┘
            │
┌───────────▼─────────────┐
│  Remote API Integration │
└───────────┬─────────────┘
            │
┌───────────▼─────────────┐
│  Response Processing    │
└─────────────────────────┘

Key Components

1. Command-Line Interface

The CLI component handles user interaction:

class CommandLineInterface:
    def __init__(self, processor):
        self.processor = processor
        
    def run(self):
        while True:
            query = input("claude> ")
            if query.lower() == "exit":
                break
                
            response = self.processor.process_query(query)
            print(response)

2. Semantic Chunker

The Semantic Chunker implements our findings on Claude CLI's chunking behavior:

class SemanticChunker:
    def find_relevant_files(self, query, codebase_path):
        # Identify keywords in the query
        keywords = self._extract_keywords(query)
        
        # Find files containing those keywords
        relevant_files = self._search_files(keywords, codebase_path)
        
        # Score files by relevance
        scored_files = self._score_files(relevant_files, keywords)
        
        # Select top files within token budget
        return self._select_files(scored_files)
    
    def chunk_file(self, file_path):
        # Parse file into AST
        ast = self._parse_file(file_path)
        
        # Identify semantic units (functions, classes)
        semantic_units = self._identify_semantic_units(ast)
        
        # Generate chunks that preserve semantic units
        return self._generate_chunks(semantic_units)

3. Context Manager

The Context Manager handles context preparation and management:

class ContextManager:
    def __init__(self):
        self.context_cache = {}
        
    def prepare_context(self, relevant_files):
        context = []
        
        for file_path in relevant_files:
            # Check if file is already in cache
            if file_path in self.context_cache:
                chunks = self.context_cache[file_path]
            else:
                # Process file and add to cache
                chunks = semantic_chunker.chunk_file(file_path)
                self.context_cache[file_path] = chunks
                
            # Add chunks to context
            context.extend(chunks)
            
        # Optimize context for token efficiency
        return self._optimize_context(context)
    
    def update_context(self, file_path, content):
        # Update cache with new content
        chunks = semantic_chunker.chunk_content(content)
        self.context_cache[file_path] = chunks

4. Tool Manager

The Tool Manager handles tool selection and execution:

class ToolManager:
    def __init__(self):
        self.tools = {
            "file_read": FileReadTool(),
            "file_write": FileWriteTool(),
            "search": SearchTool(),
            "git": GitTool()
        }
        
    def process_response(self, response):
        # Extract tool calls from response
        tool_calls = self._extract_tool_calls(response)
        
        # Execute each tool call
        results = {}
        for tool_name, args in tool_calls:
            if tool_name in self.tools:
                results[tool_name] = self.tools[tool_name].execute(args)
                
        # Generate final response with tool results
        return self._generate_response(response, results)

5. API Client

The API Client handles communication with the Claude API:

class ApiClient:
    def __init__(self, api_key):
        self.api_key = api_key
        
    def send_query(self, query, context):
        # Prepare request with context
        request = self._prepare_request(query, context)
        
        # Send request to API
        response = self._send_request(request)
        
        # Process and return response
        return self._process_response(response)

Implementation Details

Semantic Chunking Implementation

Our semantic chunking implementation uses these strategies:

1. AST-Based Analysis: Python's ast module to parse code into abstract syntax trees
2. Reference Tracking: Following imports and references to build dependency graphs
3. Query-Driven Relevance: Using TF-IDF and other scoring methods to prioritize files
4. Token Efficiency: Normalizing whitespace and removing non-essential comments

Context Management Implementation

Our context management implementation includes:

1. Context Caching: Caching processed files to avoid redundant processing
2. Differential Updates: Processing only changed files rather than the entire codebase
3. Context Prioritization: Prioritizing context based on relevance to the query
4. Token Budget Management: Ensuring context stays within model token limits

Tool Implementation

Our tool implementation includes these tools:

1. File Tools: Reading and writing files
2. Search Tools: Searching codebases for keywords and patterns
3. Git Tools: Interacting with git repositories
4. Utility Tools: Miscellaneous utility functions

Running the Emulator

To run the Claude CLI Emulator:

# Clone the repository
git clone https://github.com/your-github-username/sonnet-3.7-docs.git
cd sonnet-3.7-docs/experiments/implementations

# Install dependencies
pip install -r requirements.txt

# Set API key
export CLAUDE_API_KEY=your_api_key_here

# Run the emulator
python cli_emulator.py

# With a specific codebase
python cli_emulator.py --codebase=/path/to/codebase

Current Limitations

Our emulator has these limitations compared to Claude CLI:

1. Limited Tool Support: Fewer built-in tools than Claude CLI
2. Simpler Chunking: Less sophisticated semantic chunking
3. No Extended Thinking: No equivalent to extended thinking mode
4. API Dependency: Requires API access for all operations

Future Work

Planned improvements to the emulator include:

1. Improved Semantic Chunking: More sophisticated AST analysis
2. Better Reference Tracking: More accurate dependency tracking
3. Local-First Processing: More operations processed locally
4. Extended Thinking Emulation: Emulation of extended thinking capabilities

Learning from the Implementation

Building this emulator has provided valuable insights into Claude CLI's architecture:

1. Architectural Complexity: Claude CLI's architecture is sophisticated and well-designed
2. Balance of Concerns: The balance between local and remote processing is critical
3. Token Efficiency: Token efficiency is a major design consideration
4. Tool Integration: Tool integration is central to the architecture

These insights have informed our understanding of how to design effective AI-assisted programming tools.