gnn-llm-analysis

name: gnn-llm-analysis description: GNN LLM-enhanced analysis and model interpretation. Use when generating natural language descriptions of GNN models, getting AI-assisted model explanations, or performing LLM-powered analysis of Active Inference specifications.

GNN LLM Analysis (Step 13)

Purpose

Provides LLM-enhanced analysis of GNN models including natural language interpretation, model summarization, structural analysis, and AI-assisted insights using multiple provider backends.

Key Commands

# Run LLM analysis
python src/13_llm.py --target-dir input/gnn_files --output-dir output --verbose

# As part of pipeline
python src/main.py --only-steps 13 --verbose

Provider Recovery Chain

The LLM module supports multiple providers with automatic recovery:

1. Ollama (local) — Preferred for privacy and speed
2. OpenAI — GPT-4/3.5 API
3. Anthropic / OpenRouter — Additional providers

API

from llm import (
    process_llm, LLMProcessor, LLMAnalyzer,
    analyze_gnn_file_with_llm, extract_variables, extract_connections,
    perform_semantic_analysis, generate_model_insights,
    generate_documentation, generate_llm_summary,
    get_available_providers, get_module_info
)

# Process LLM step (used by pipeline)
process_llm(target_dir, output_dir, verbose=True)

# Use the LLMProcessor class
processor = LLMProcessor()
result = processor.analyze(gnn_content)
description = processor.generate_description(gnn_content)

# Use the LLMAnalyzer class
analyzer = LLMAnalyzer()
insights = analyzer.analyze_content(gnn_content)

# Analyze a file with LLM
result = await analyze_gnn_file_with_llm(content)

# Extract model components
variables = extract_variables(content)
connections = extract_connections(content)

# Check available providers
providers = get_available_providers()  # e.g., ['ollama', 'openai']

Key Exports

• LLMProcessor — class with analyze(), analyze_model(), generate_description()
• LLMAnalyzer — class with analyze_content(), extract_insights()
• analyze_gnn_file_with_llm — async full analysis
• extract_variables, extract_connections, extract_sections — component extraction
• generate_model_insights, generate_documentation, generate_llm_summary
• UnifiedLLMProcessor, AnalysisType, ProviderType — advanced provider API

Dependencies

# LLM PyPI packages are core dependencies (uv sync)
uv sync

# LLM client packages are core dependencies; use uv sync.
# Install Ollama CLI separately for local inference: https://ollama.com

Output

• LLM analysis reports in output/13_llm_output/
• Natural language model summaries
• AI-generated insights and recommendations

MCP Tools

This module registers tools with the GNN MCP server (see mcp.py):

• analyze_gnn_with_llm
• generate_llm_documentation
• get_llm_module_info
• get_llm_providers
• process_llm

References

• AGENTS.md — Module documentation
• README.md — Usage guide
• SPEC.md — Module specification
• providers/ — Provider-specific implementations

Documentation

• README: Module Overview
• AGENTS: Agentic Workflows
• SPEC: Architectural Specification
• SKILL: Capability API