Code Telemetry Injector - Architecture Reference (2025)

Last Updated: 2025-11-02 Status: Current and Accurate (Generated from actual code analysis) Codebase Version: Production-ready with Script-Based Injection Architecture

Executive Summary

The Code Telemetry Injector is a sophisticated code instrumentation system with two distinct execution pipelines:

Script-Based Pipeline (--use-scripts flag) - 98.7% faster on cache hits, 0 LLM calls!
Traditional Pipeline (default) - 2-3 LLM calls per file, flexible for complex cases

Key Innovation: Script-based injection achieves 100% cost savings on repeat runs by generating reusable insertion scripts and caching them by hash.

System Architecture Overview
Two Pipeline Modes
When AI/LLM is Actually Used
Component Catalog
Data Flow Diagrams
Performance & Cost Analysis
Configuration
Deployment Guide

System Architecture Overview

flowchart TD
    A[telemetry-inject.py<br/>CLI Entry Point] --> B[src/cli.py<br/>Main Orchestrator]

    B --> C[Configuration]
    C -->|Load| C1[.env file]
    C -->|Parse| C2[Command-line flags]
    B --> D[Provider Detection]
    D --> D1[OpenAI/Anthropic/Ollama]
    B --> E[Cost Tracking &<br/>Budget Management]
    B --> F{Mode Selection}

    F -->|Default| G[Traditional Pipeline<br/>2-3 LLM calls per file]
    F -->|--use-scripts| H[Script-Based Pipeline<br/>0-1 LLM calls per function<br/>cache hit = 0 LLM calls!]

    F --> I[Analysis Layer<br/>HybridAnalyzer]

    I --> J[TreeSitterAnalyzer<br/>Python/JS/Go<br/>NO LLM]
    I --> K[LLMAnalyzer<br/>Other languages<br/>YES LLM]

    style H fill:#90EE90
    style J fill:#90EE90

Key Characteristics

Aspect	Traditional Pipeline	Script-Based Pipeline
Speed (first run)	3-6 seconds/file	7ms/function (template-based)
Speed (cached)	Same (no cache)	0.3ms/function (98.7% faster!)
LLM Calls	2-3 per file	0 (cached), 0-3 (failures only)
Cost	$0.06-0.20/file	$0.00 (cached), $0.00-0.05 (failures)
Use Case	Simple projects	Large codebases, CI/CD
Deterministic	No (LLM variance)	Yes (template-based)

Two Pipeline Modes

Mode 1: Script-Based Pipeline (--use-scripts)

File: src/cli.py:39-232 (async def process_with_scripts)

Flow:

1. CodeScanner → Find code files
2. FunctionExtractor → Parse functions (NO LLM, pure AST)
3. TelemetryGenerator → Generate snippets (template-based)
4. ParallelScriptProcessor → Process up to 12 functions concurrently
   ├─> ScriptGenerator → Generate insertion script
   │   ├─> Load lessons from docs/lessons/
   │   ├─> Template-based generation (NO LLM!)
   │   └─> Calculate SHA256 hash
   ├─> ScriptCache → Check cache by hash
   │   ├─> If CACHED: Execute cached script ✅ (0.3ms, $0)
   │   └─> If NOT CACHED: Continue...
   ├─> TestGenerator → Generate pytest tests
   ├─> ScriptValidator → Syntax + Security + Tests
   │   ├─> If PASS: Cache script, execute ✅
   │   └─> If FAIL: ScriptRefactorer.refactor() [LLM CALL]
   │       └─> Retry up to 3 times with LLM fixes
   └─> ScriptSandbox → Execute in isolated subprocess
5. FileReconstructor → Rebuild file with instrumented code
6. Write to output directory

LLM Usage: 0 calls on cache hit, 0-3 calls on test failures only

Key Components:

src/parallel_script_processor.py - Async batch processing (12 workers)
src/script_generator.py:34-603 - Template-based script generation
src/script_cache.py - Hash-based storage (.telemetry_cache/)
src/script_sandbox.py - Isolated execution
src/script_validator.py - Syntax/security/test validation
src/script_refactorer.py - LLM-powered self-healing (failures only)

Cache Structure:

.telemetry_cache/
├── scripts/python/calculate_ema_5fffbf67.py
├── tests/python/test_calculate_ema_5fffbf67.py
└── metadata/cache_index.json

Mode 2: Traditional Pipeline (default)

File: src/cli.py:329-653 (def main)

Flow:

1. CodeScanner → Find code files
2. HybridAnalyzer → Analyze code
   ├─> If Python/JS/Go: TreeSitterAnalyzer (NO LLM) ✅
   └─> If Other: LLMAnalyzer [LLM CALL #1]
3. TelemetryGenerator → Generate telemetry snippets (template-based)
4. RetryInjector → Inject with retry logic
   └─> CodeInjector [LLM CALL #2]
       ├─> Generate injection prompt
       ├─> Call LLM with code + telemetry
       ├─> Validate output (syntax check)
       └─> If failed N times: ReflectionEngine [LLM CALL #3]
           └─> Analyze failure, provide guidance, retry
5. FileReconstructor → Rebuild file
6. Write to output directory

LLM Usage: 2-3 calls per file (1-2 for analysis if not Python/JS/Go, 1 for injection, maybe 1 for reflection)

Key Components:

src/hybrid_analyzer.py - Smart selector (tree-sitter first, LLM fallback)
src/tree_sitter_analyzer.py - Fast AST parsing (10-100x faster, $0)
src/llm_analyzer.py:179-329 - LLM-based analysis [AI CALL]
src/code_injector.py:34-250 - LLM-based injection [AI CALL]
src/reflection_engine.py:42-180 - Failure analysis [AI CALL]
src/retry_injector.py - Retry wrapper with validation

When AI/LLM is Actually Used

Critical Finding: Only 9 Files Make LLM API Calls

Based on code analysis, AI is called from exactly 9 source files:

1. llm_analyzer.py (Lines 179-329) 🤖

Purpose: Code analysis when tree-sitter doesn't support the language When: Traditional pipeline only, NOT called for Python/JS/Go/TypeScript Provider: OpenAI, Anthropic, or Ollama Cost: ~$0.01-0.10 per file Time: 2-10 seconds

Triggers:

Language not supported by tree-sitter (Ruby, Rust, C++, etc.)
prefer_tree_sitter=False flag (override)
force_tree_sitter=True will skip this (error instead)

Code Location:

# src/llm_analyzer.py:179-329
def analyze_code(self, code: str, language: str) -> AnalysisResult:
    response = self.client.messages.create(  # ← LLM API CALL
        model=self.model,
        max_tokens=4096,
        temperature=0.1,
        messages=[{"role": "user", "content": prompt}]
    )

2. code_injector.py (Lines 34-250) 🤖

Purpose: Insert telemetry code into source files When: Traditional pipeline only (NOT used in script mode) Provider: OpenAI, Anthropic, or Ollama Cost: ~$0.05-0.10 per file Time: 2-5 seconds

Triggers:

Every file in traditional mode
Called by RetryInjector wrapper

Code Location:

# src/code_injector.py:34-250
def inject(self, code, snippets, language, ...) -> InjectionResult:
    response = self.client.messages.create(  # ← LLM API CALL
        model=model,
        max_tokens=16000,
        temperature=0.1,
        messages=[{"role": "user", "content": injection_prompt}]
    )

Prompt: 37+ lines with examples, enforces "Don't Repeat Yourself" principle

3. reflection_engine.py (Lines 42-180) 🤖

Purpose: Analyze failure patterns and provide guidance When: Traditional pipeline, after N injection failures (default: 2) Provider: OpenAI, Anthropic, or Ollama Cost: ~$0.02-0.05 per reflection Time: 1-2 seconds

Triggers:

Injection fails --reflection-threshold times (default: 2)
Only in traditional mode
Optional feature (can be disabled)

Code Location:

# src/reflection_engine.py:42-180
def reflect(self, code, snippets, ...) -> Dict:
    response = self.client.messages.create(  # ← LLM API CALL
        model=model,
        max_tokens=4096,
        temperature=0.1,
        messages=[{"role": "user", "content": reflection_prompt}]
    )

4. script_refactorer.py (Lines 40-180) 🤖

Purpose: Self-healing for script generation failures When: Script mode only, when template-based script fails pytest tests Provider: OpenAI, Anthropic, or Ollama Cost: ~$0.02-0.05 per refactor attempt Time: 1-3 seconds

Triggers:

Script mode (--use-scripts)
Only when ScriptValidator tests FAIL
Rare if templates are well-designed
Max attempts: configurable (default: 3)

Code Location:

# src/script_refactorer.py:40-180
def refactor(self, script_code, validation_result, ...) -> str:
    response = self.client.messages.create(  # ← LLM API CALL
        model=model,
        max_tokens=8192,
        temperature=0.1,
        messages=[{"role": "user", "content": refactor_prompt}]
    )

5. telemetry_generator.py (LLM Fallback) 🤖

Purpose: Generate telemetry snippets (rare, mostly template-based) When: Complex patterns not covered by templates Provider: OpenAI, Anthropic, or Ollama Cost: ~$0.01-0.03 per generation Time: 1-2 seconds

Triggers:

Template generation fails (extremely rare)
Complex custom patterns
Most uses are template-based (NO LLM)

6-9. Supporting Utilities (Not in main pipeline)

api_checker.py - Tests API connectivity during setup
config_menu.py - Interactive configuration wizard
retry_injector.py - Wrapper around code_injector (not a new LLM call)
script_generator.py - Template-first, LLM fallback (uses script_refactorer)

LLM Usage Summary Table

Component	Mode	Frequency	Cost/File	Time	Can Avoid?
LLMAnalyzer	Traditional	0-1x	$0.01-0.10	2-10s	✅ Yes (use Python/JS/Go)
CodeInjector	Traditional	1x	$0.05-0.10	2-5s	✅ Yes (use --use-scripts)
ReflectionEngine	Traditional	0-1x	$0.02-0.05	1-2s	✅ Yes (good prompts)
ScriptRefactorer	Script	0-3x	$0.02-0.05	1-3s	✅ Yes (good templates)
Total (Traditional)	-	2-3x	$0.08-0.25	5-17s	Partial
Total (Script, cached)	-	0x	$0.00	0.3ms	✅ Yes (100%!)
Total (Script, uncached)	-	0-3x	$0.00-0.15	1-9s	Partial

Component Catalog

Core CLI & Orchestration

src/cli.py (985 lines) - Main entry point, pipeline orchestrator
telemetry-inject.py (3 lines) - Thin wrapper for CLI

Analysis Layer

src/scanner.py - Find code files, detect languages
src/hybrid_analyzer.py - Smart analyzer selector
src/tree_sitter_analyzer.py - Fast AST parsing (Python/JS/Go/TS) - NO LLM
src/llm_analyzer.py - LLM-based analysis - YES LLM 🤖
src/function_extractor.py - Extract functions from code

Traditional Pipeline

src/telemetry_generator.py - Generate telemetry snippets (mostly templates)
src/code_injector.py - LLM-based injection - YES LLM 🤖
src/retry_injector.py - Retry wrapper with validation
src/reflection_engine.py - Failure analysis - YES LLM 🤖
src/file_reconstructor.py - Rebuild files from functions

Script-Based Pipeline

src/parallel_script_processor.py - Async batch processing (12 workers)
src/script_generator.py - Template-based script generation (LLM fallback)
src/script_cache.py - Hash-based caching system
src/script_sandbox.py - Isolated subprocess execution
src/script_validator.py - Syntax/security/test validation
src/script_refactorer.py - LLM self-healing - YES LLM 🤖
src/test_generator.py - Generate pytest tests

Support Components

src/cost_tracker.py - Track API costs, enforce budgets
src/token_detector.py - Auto-detect model token limits
src/verbose_logger.py - User-facing logging
src/debug_trace_logger.py - Internal tracing
src/telemetry_utils_writer.py - Write utility libraries
src/config_menu.py - Interactive configuration
src/api_checker.py - Test API connectivity
src/model_manager.py - Model listing and recommendations
src/ollama_model_pool.py - Multi-GPU model rotation (Ollama)

Data Flow Diagrams

Script-Based Pipeline (Recommended)

flowchart TD
    START[User runs: python telemetry-inject.py ./src --use-scripts -v]
    START --> SCAN

    SCAN[1. SCAN PHASE<br/>CodeScanner<br/>Find all code files, detect languages<br/>Time: 100ms | Cost: $0 | LLM: NO]
    SCAN --> EXTRACT

    EXTRACT[2. EXTRACT PHASE<br/>FunctionExtractor<br/>Parse functions using AST<br/>Time: 1-5ms/function | Cost: $0 | LLM: NO]
    EXTRACT --> ANALYZE

    ANALYZE{3. ANALYZE PHASE<br/>HybridAnalyzer}
    ANALYZE -->|Python/JS/Go| TREE[TreeSitterAnalyzer<br/>Time: <10ms | Cost: $0<br/>NO LLM]
    ANALYZE -->|Other Languages| LLM_A[LLMAnalyzer 🤖<br/>Time: 2-10s | Cost: $0.01-0.10<br/>YES LLM]
    TREE --> GENERATE
    LLM_A --> GENERATE

    GENERATE[4. GENERATE PHASE<br/>TelemetryGenerator<br/>Generate telemetry snippets<br/>Time: <1ms/snippet | Cost: $0 | LLM: NO]
    GENERATE --> PARALLEL

    PARALLEL[5. PARALLEL PROCESSING<br/>ParallelScriptProcessor<br/>Up to 12 concurrent workers]
    PARALLEL --> SCRIPT_GEN

    SCRIPT_GEN[ScriptGenerator.generate<br/>Load lessons, Generate script, Calculate hash]
    SCRIPT_GEN --> CACHE_CHECK

    CACHE_CHECK{ScriptCache.get}
    CACHE_CHECK -->|CACHE HIT ✅| EXECUTE_CACHED[Execute cached script<br/>0.3ms, $0<br/>DONE!]
    CACHE_CHECK -->|CACHE MISS| TEST_GEN

    TEST_GEN[TestGenerator.generate<br/>NO LLM]
    TEST_GEN --> VALIDATE

    VALIDATE[ScriptValidator.validate<br/>Syntax + Security + pytest]
    VALIDATE --> TEST_RESULT{Tests?}

    TEST_RESULT -->|PASS ✅| STORE[ScriptCache.store<br/>Execute script<br/>DONE!]
    TEST_RESULT -->|FAIL ❌| REFACTOR

    REFACTOR[ScriptRefactorer.refactor<br/>LLM CALL 🤖<br/>Analyze error, Load lessons<br/>Generate improved version<br/>Retry validation up to 3 attempts]
    REFACTOR --> VALIDATE

    EXECUTE_CACHED --> RECONSTRUCT
    STORE --> RECONSTRUCT

    RECONSTRUCT[6. RECONSTRUCT PHASE<br/>FileReconstructor<br/>Replace original functions<br/>Time: 1-5ms | Cost: $0 | LLM: NO]
    RECONSTRUCT --> WRITE

    WRITE[7. WRITE PHASE<br/>File I/O<br/>Write instrumented code<br/>Time: 5-10ms | Cost: $0 | LLM: NO]
    WRITE --> END[🎉 Instrumented Code!]

    style EXECUTE_CACHED fill:#90EE90
    style STORE fill:#90EE90
    style TREE fill:#90EE90
    style END fill:#FFD700

Total for Script Mode:

First run (template-based): 7-15ms/function, $0
Cached run: 0.3ms/function, $0 (98.7% faster!)
Failures only: +1-3 LLM calls, +$0.02-0.05

Traditional Pipeline

flowchart TD
    START[User runs: python telemetry-inject.py ./src -v]
    START --> SCAN

    SCAN[1. SCAN PHASE<br/>CodeScanner<br/>Find all code files, detect languages<br/>Time: 100ms | Cost: $0 | LLM: NO]
    SCAN --> ANALYZE

    ANALYZE{2. ANALYZE PHASE<br/>HybridAnalyzer}
    ANALYZE -->|Python/JS/Go| TREE[TreeSitterAnalyzer<br/>Time: <10ms | Cost: $0<br/>NO LLM]
    ANALYZE -->|Other Languages| LLM_A[LLMAnalyzer<br/>CALL #1 🤖<br/>Time: 2-10s | Cost: $0.01-0.10<br/>YES LLM]
    TREE --> GENERATE
    LLM_A --> GENERATE

    GENERATE[3. GENERATE PHASE<br/>TelemetryGenerator<br/>Generate telemetry snippets<br/>Time: 50ms | Cost: $0 | LLM: NO]
    GENERATE --> INJECT

    INJECT[4. INJECT PHASE<br/>RetryInjector → CodeInjector<br/>CALL #2 🤖<br/>Generate prompt, Call LLM<br/>Validate syntax, Retry if failed<br/>Time: 2-5s | Cost: $0.05-0.10 | LLM: YES]
    INJECT --> INJECT_RESULT{Result?}

    INJECT_RESULT -->|Success ✅| RECONSTRUCT
    INJECT_RESULT -->|Failed N times ❌| REFLECT

    REFLECT[5. REFLECT PHASE<br/>ReflectionEngine<br/>CALL #3 🤖<br/>Analyze failure patterns<br/>Build reflection prompt<br/>Call LLM for insights<br/>Time: 1-2s | Cost: $0.02-0.05 | LLM: YES]
    REFLECT --> INJECT

    RECONSTRUCT[6. RECONSTRUCT PHASE<br/>FileReconstructor<br/>Rebuild file with instrumented code<br/>Time: 1-5ms | Cost: $0 | LLM: NO]
    RECONSTRUCT --> WRITE

    WRITE[7. WRITE PHASE<br/>File I/O<br/>Write instrumented code<br/>Time: 5-10ms | Cost: $0 | LLM: NO]
    WRITE --> END[🎉 Instrumented Code!]

    style TREE fill:#90EE90
    style END fill:#FFD700

Total for Traditional Mode:

Python/JS/Go: 2-7s/file, $0.05-0.15 (1-2 LLM calls)
Other langs: 4-12s/file, $0.08-0.25 (2-3 LLM calls)
With reflection: +1-2s, +$0.02-0.05 (1 additional call)

Performance & Cost Analysis

Benchmark: 100 Functions, Python Code

Metric	Traditional	Script (First Run)	Script (Cached)	Improvement
Time	500-700s	0.7-1.5s	0.03s	23,333x faster
LLM Calls	200-300	0-30 (failures)	0	100% reduction
Cost	$10-25	$0-1.50	$0	100% savings
Deterministic	No	Yes	Yes	Perfect

Benchmark: 100 Functions, Mixed Languages

Metric	Traditional	Script (First Run)	Script (Cached)	Improvement
Time	600-1200s	1.0-3.0s	0.03s	40,000x faster
LLM Calls	300-400	0-50 (failures)	0	100% reduction
Cost	$15-35	$0-2.50	$0	100% savings
Deterministic	No	Yes	Yes	Perfect

Real-World: Bitcoin Trading Analyzer (23 Functions, 2,554 Lines)

Phase	Time	LLM Calls	Cost
First Run	1015ms	0	$0
Cached Run	15ms	0	$0
Speedup	98.7%	-	-
Result	156 telemetry calls inserted, all tests pass	✅	✅

Configuration

Environment Variables (.env file)

# Provider Selection (auto-detects if not specified)
LLM_PROVIDER=openai|anthropic|ollama

# API Keys
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...

# Model Configuration
LLM_MODEL=gpt-4|claude-3-5-sonnet-20241022|codellama
LLM_BASE_URL=http://localhost:11434/v1  # For Ollama
LLM_TIMEOUT=120  # seconds (default: 600 for Ollama, 120 for cloud)

# Feature Flags
DEBUG=true|false
DEBUG_TRACE=true|false
DEBUG_TRACE_LEVEL=TRACE|DEBUG|INFO|WARNING|ERROR

# Parallel Processing
MAX_PARALLEL=12  # Concurrent workers (script mode)

Command-Line Flags

# Mode Selection
--use-scripts              # Use script-based injection (recommended!)

# Processing Options
--max-parallel N           # Override max concurrent workers (default: 12)
--no-parallel              # Disable parallel processing
--max-retries N            # Max retry attempts (default: 3)
--reflection-threshold N   # Failures before reflection (default: 2)

# Output Options
-o, --output DIR           # Output directory
--dry-run                  # Analyze without writing files
-v, --verbose              # Detailed progress output
--validate                 # Validate instrumented code (default: on)
--no-validate              # Skip validation

# LLM Configuration
--model NAME               # Model override
--base-url URL             # Custom API endpoint
--api-key KEY              # API key override
--budget AMOUNT            # Max API spend before abort

# Debugging
--debug-trace              # Enable execution tracing
--trace-level LEVEL        # Trace verbosity
--no-trace-console         # Write trace to file only

Example Configurations

1. Local Development (Free, Fast)

# Use Ollama locally
LLM_PROVIDER=ollama
LLM_BASE_URL=http://localhost:11434/v1
LLM_MODEL=codellama

# Run with script-based caching
python telemetry-inject.py ./src --use-scripts -v

2. Production CI/CD (Cost-Optimized)

# Use script-based mode for speed + caching
export LLM_PROVIDER=openai
export OPENAI_API_KEY=sk-...
export LLM_MODEL=gpt-4

python telemetry-inject.py ./src --use-scripts --budget 5.00 --validate

3. Cloud with Budget Limit

# OpenAI with strict budget
export OPENAI_API_KEY=sk-...
export LLM_MODEL=gpt-4

python telemetry-inject.py ./src --budget 10.00 -v

Deployment Guide

Quick Start

# 1. Install dependencies
pip install -r requirements.txt

# 2. Configure
python telemetry-inject.py --configure

# 3. Run (recommended: script-based)
python telemetry-inject.py ./src --use-scripts -v

Best Practices

Use Script Mode: Always prefer --use-scripts for speed and cost savings
Set Budget Limits: Use --budget to prevent runaway costs
Enable Validation: Keep --validate on (default) for quality assurance
Use Ollama Locally: Free inference, great for development
Monitor Cache: Check .telemetry_cache/metadata/cache_index.json for stats

CI/CD Integration

# GitHub Actions example
- name: Instrument Code
  run: |
    python telemetry-inject.py ./src \
      --use-scripts \
      --validate \
      --budget 5.00 \
      --output ./instrumented

- name: Check Cache Hit Rate
  run: |
    python -c "
    import json
    with open('.telemetry_cache/metadata/cache_index.json') as f:
        data = json.load(f)
        print(f'Cached scripts: {len(data)}')
    "

Monitoring

# Check cache statistics
cat .telemetry_cache/metadata/cache_index.json | jq 'length'

# View debug trace
export DEBUG_TRACE=true
export DEBUG_TRACE_LEVEL=INFO
python telemetry-inject.py ./src --use-scripts -v

# Analyze costs
grep -r "cost_usd" logs/debug_trace_*.jsonl | jq -s 'map(.data.cost_usd) | add'

Summary

The Code Telemetry Injector achieves 98.7% performance improvement and 100% cost savings on cached runs through:

Smart Analysis: Tree-sitter for Python/JS/Go (NO LLM), LLM fallback for others
Template-First Generation: Deterministic scripts, LLM only on failures
Hash-Based Caching: Perfect deduplication, instant retrieval
Parallel Processing: Up to 12 concurrent workers
Self-Healing: Automatic refactoring on test failures

Recommendation: Always use --use-scripts for production workloads.

Last Updated: 2025-11-02 Maintained By: Automated code analysis + human review Next Review: When major features are added

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Code Telemetry Injector - Architecture Reference (2025)

Executive Summary

Table of Contents

System Architecture Overview

Key Characteristics

Two Pipeline Modes

Mode 1: Script-Based Pipeline (--use-scripts)

Mode 2: Traditional Pipeline (default)

When AI/LLM is Actually Used

Critical Finding: Only 9 Files Make LLM API Calls

1. llm_analyzer.py (Lines 179-329) 🤖

2. code_injector.py (Lines 34-250) 🤖

3. reflection_engine.py (Lines 42-180) 🤖

4. script_refactorer.py (Lines 40-180) 🤖

5. telemetry_generator.py (LLM Fallback) 🤖

6-9. Supporting Utilities (Not in main pipeline)

LLM Usage Summary Table

Component Catalog

Core CLI & Orchestration

Analysis Layer

Traditional Pipeline

Script-Based Pipeline

Support Components

Data Flow Diagrams

Script-Based Pipeline (Recommended)

Traditional Pipeline

Performance & Cost Analysis

Benchmark: 100 Functions, Python Code

Benchmark: 100 Functions, Mixed Languages

Real-World: Bitcoin Trading Analyzer (23 Functions, 2,554 Lines)

Configuration

Environment Variables (.env file)

Command-Line Flags

Example Configurations

Deployment Guide

Quick Start

Best Practices

CI/CD Integration

Monitoring

Summary