feat: LangGraph viral YouTube Short generator — 4-agent pipeline with BMasterAI telemetry

[ellucas-creator]

New Example: langgraph-viral-youtube

Four specialist agents collaborate in a LangGraph StateGraph to produce a complete, production-ready YouTube Shorts package — fully instrumented with BMasterAI telemetry.

Architecture

trend_researcher → hook_writer → script_writer → title_and_tags
                                                        ↓
                                                  quality_gate
                                                   /         \
                                            approved       retry (≤2x)
                                               ↓                ↓
                                              END          hook_writer

Agents

Agent	Responsibility
trend_researcher	Tavily web search → viral angle + competitor hooks
hook_writer	≤12-word scroll-stopping opening line
script_writer	Full 45–60s Hook→Conflict→Build→Payoff→CTA script
title_and_tags	Viral title + 10 SEO tags + thumbnail concept
quality_gate	Validates package; triggers retry loop (max 2 iterations)

BMasterAI Integration

Every agent node logs structured telemetry:

bm.log_event(EventType.TASK_START, agent_id="hook-writer", metadata={...})
bm.log_event(EventType.LLM_CALL, ...)
bm.log_event(EventType.TASK_COMPLETE, ...)
bm.log_event(EventType.TASK_ERROR, ...)

Output: logs/agents.log + logs/agents.jsonl

Quick Start

pip install -r requirements.txt
cp .env.example .env   # ANTHROPIC_API_KEY + TAVILY_API_KEY
python main.py "AI agents taking over software engineering"

Why LangGraph?

• 38M+ monthly PyPI downloads — largest framework audience
• Framework-agnostic (complements the AWS-heavy existing examples)
• Conditional retry loop demonstrates LangGraph's strengths vs linear pipelines
• Creative content generation use case — different from research/technical examples

[claude]

Claude finished @ellucas-creator's task —— View job

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Code Review: LangGraph Viral YouTube Agent

Overall Assessment ✅

This is a well-structured implementation that effectively demonstrates LangGraph capabilities with BMasterAI telemetry integration. The code is clean, follows Python best practices, and provides a compelling creative content generation use case.

✅ Strengths

Architecture & Design

• Clean separation of concerns across agents.py, graph.py, state.py, and main.py
• Smart retry logic with quality gate validation
• TypedDict state management provides excellent type safety
• Well-designed agent specialization (trend research → hook → script → title/tags)

BMasterAI Integration

• Comprehensive telemetry coverage with structured logging to both .log and .jsonl
• Proper event types used throughout (TASK_START, LLM_CALL, TASK_COMPLETE, TASK_ERROR)
• Monitoring setup with get_monitor().start_monitoring() in agents.py:22

Code Quality

• Consistent error handling with try/catch blocks in all agent functions
• Clear function documentation and type hints
• Good use of environment variables for API keys
• Readable CLI interface with interactive prompts

⚠️ Issues & Recommendations

Security Concerns

• Medium Risk: API keys loaded globally without validation in agents.py:26. Consider adding:

  if not os.environ.get("TAVILY_API_KEY"):
      raise EnvironmentError("TAVILY_API_KEY required")

Error Handling Gaps

• agents.py:85-86: Dictionary parsing could fail with malformed LLM responses. Add validation:

  lines = {}
  for line in raw.splitlines():
      if ":" in line:
          key, value = line.split(":", 1)
          lines[key.strip()] = value.strip()

• No handling for Anthropic API rate limits or network timeouts

Performance Considerations

• Sequential agent execution in graph.py:47-50 - trend research and title/tags could potentially run in parallel since they don't depend on each other
• No LLM response caching for similar queries

Code Improvements

• agents.py:241-248: Magic numbers in quality gate validation (15 words, 200 chars, 5 tags) should be constants
• main.py:64: Hard-coded output filename could conflict in concurrent runs

🧪 Test Coverage

Major Gap: No unit tests provided. Recommend adding:

• Agent function testing with mock LLM responses
• State validation testing
• Error scenario coverage
• Integration tests for the full pipeline

📋 Missing Features

• No configuration file for customizing quality thresholds
• No support for different LLM models/providers
• Limited observability beyond logs (no metrics dashboard)

🔍 Dependencies

• All dependencies are well-scoped and use appropriate version ranges
• BMasterAI integration is correctly implemented per the library patterns

💡 Suggestions

1. Add input validation for topic length/content
2. Consider adding structured output validation for LLM responses
3. Implement configurable retry strategies
4. Add health checks for external APIs before starting pipeline

Final Verdict: Approve with Minor Fixes ✅

This is a solid implementation that effectively showcases LangGraph + BMasterAI integration. The identified issues are minor and don't prevent the example from functioning correctly. The code demonstrates best practices for multi-agent systems and provides excellent documentation.

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