result-parser-agent 0.3.2

A deep agent for extracting metrics from raw result files using LangGraph and intelligent parsing

🎯 Results Parser Agent

A powerful, intelligent agent for extracting metrics from raw result files using LangGraph and AI-powered parsing. The agent automatically analyzes unstructured result files and extracts specific metrics into structured JSON output with high accuracy.

🚀 Features

• 🤖 AI-Powered Parsing: Uses advanced LLMs (OpenAI GPT-4o, GROQ, Anthropic, Google Gemini, Ollama) for intelligent metric extraction
• 📁 Flexible Input: Process single files or entire directories of result files
• 🎯 Pattern Recognition: Automatically detects and adapts to different file formats and structures
• ⚙️ Simple Configuration: Environment variable-based configuration with sensible defaults
• 📊 Structured Output: Direct output in Pydantic schemas for easy integration
• 🛠️ Professional CLI: Simple, intuitive command-line interface
• 🔧 Python API: Easy integration into existing Python applications
• 🔄 Error Recovery: Robust error handling and retry mechanisms

📦 Installation

Quick Install (Recommended)

pip install result-parser-agent

Development Install

# Clone the repository
git clone https://github.com/Infobellit-Solutions-Pvt-Ltd/result-parser-agent.git
cd result-parser-agent

# Install with uv (recommended)
curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync
uv pip install -e .

# Or install with pip
pip install -e .

📋 Configuration

Environment Variables

Create a .env file in your project directory:

# API Keys - Set only the one you need
OPENAI_API_KEY=your_openai_api_key_here
GROQ_API_KEY=your_groq_api_key_here
ANTHROPIC_API_KEY=your_anthropic_api_key_here
GOOGLE_API_KEY=your_google_api_key_here

# Optional: Override default LLM settings
LLM_PROVIDER=openai
LLM_MODEL=gpt-4o

🎯 Quick Start

1. Set up your API key

# For OpenAI (default - recommended)
export OPENAI_API_KEY="your-openai-api-key-here"

# For GROQ
export GROQ_API_KEY="your-groq-api-key-here"

# For Anthropic
export ANTHROPIC_API_KEY="your-anthropic-api-key-here"

# For Google Gemini
export GOOGLE_API_KEY="your-google-api-key-here"

2. Use the CLI

# Parse all files in a directory (uses default metrics)
result-parser ./benchmark_results

# Parse with specific metrics
result-parser ./benchmark_results --metrics "RPS,latency,throughput"

# Parse a single file
result-parser ./results.txt --metrics "accuracy,precision"

# Custom output file
result-parser ./results/ --output my_results.json

# Verbose output
result-parser ./results/ --verbose

# Show setup instructions
result-parser setup

3. Use the Python API

from result_parser_agent import ResultsParserAgent, settings
import os

# Set your API key
os.environ["OPENAI_API_KEY"] = "your-api-key-here"

# Get default configuration
config = settings

# Initialize agent
agent = ResultsParserAgent(config)

# Parse results (file or directory)
results = await agent.parse_results(
    input_path="./benchmark_results",  # or "./results.txt"
    metrics=["RPS", "latency", "throughput"]
)

# Output structured data
print(results.json(indent=2))