⚒️ LLM-Forge

A unified response parser and SDK generator for LLM APIs across multiple programming languages

LLM-Forge provides a production-ready, type-safe way to parse and normalize responses from multiple LLM providers (OpenAI, Anthropic, Cohere, Google AI, Mistral, and more) with support for generating client libraries in 6 languages: TypeScript, Python, Rust, Go, Java, and C#.

Why LLM-Forge?

• 🔄 Unified API - One interface for 12+ LLM providers
• 🚀 Ultra-Fast - 136K-454K ops/sec parsing performance
• 🛡️ Type-Safe - Full TypeScript inference and validation
• 🧪 Battle-Tested - 666 tests, 93.77% coverage
• 📦 Zero Dependencies - Lightweight, production-ready
• 🌐 Multi-Language - Generate SDKs in 6 languages

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📋 Table of Contents

• Features
• Status
• Quick Start
• Use Cases
• Supported Providers
• Code Generation
• Performance
• Testing
• Security
• Documentation
• Development
• Contributing
• Roadmap
• Support

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✨ Features

Provider Support (12 Providers)

• ✅ Multi-Provider Parsing: Unified response format for 12 LLM providers
• ✅ Auto-Detection: Automatically detect provider from response structure
• ✅ Streaming Support: Real-time streaming chunk parsing
• ✅ Type-Safe: Full TypeScript type inference and safety
• ✅ Production Ready: 93.77% test coverage, 666 passing tests
• ✅ High Performance: 136K-454K ops/sec parsing, 1-10M ops/sec detection

Code Generation (6 Languages)

• ✅ TypeScript: Full type inference, decorators, async/await
• ✅ Python: Type hints, Pydantic models, async support
• ✅ Rust: Serde, strong typing, Result<T,E>
• ✅ Java: Record classes, Jackson, CompletableFuture
• ✅ C#: Record types, System.Text.Json, async streams
• ✅ Go: Struct tags, JSON marshaling, context support

Enterprise Features

• ✅ CI/CD Pipeline: 7 GitHub Actions workflows for automation
• ✅ Security Scanning: Multi-layer security with CodeQL, npm audit, OSSF
• ✅ Performance Monitoring: Automated benchmarking and regression detection
• ✅ Automated Releases: npm and GitHub Packages publishing
• ✅ Comprehensive Documentation: Production guides and API docs

📊 Status

Production Ready ✅

Test Coverage:  93.77% ✅
Tests Passing:  666/666 (100%) ✅
Benchmarks:     27 performance tests ✅
CI/CD:          7 automated workflows ✅
Documentation:  Complete ✅

🚀 Quick Start

Installation

# Using npm
npm install @llm-dev-ops/llm-forge

# Using yarn
yarn add @llm-dev-ops/llm-forge

# Using pnpm
pnpm add @llm-dev-ops/llm-forge

Basic Usage - Response Parsing

import { parseResponse } from '@llm-dev-ops/llm-forge';

// Parse any LLM provider response - automatically detects the provider
const response = await fetch('https://api.openai.com/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${process.env.OPENAI_API_KEY}`,
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: 'gpt-4',
    messages: [{ role: 'user', content: 'Hello, world!' }]
  })
});

const data = await response.json();
const parsed = await parseResponse(data);

if (parsed.success) {
  console.log(parsed.response.messages[0].content);
  // Output: "Hello! How can I help you today?"

  console.log(`Provider: ${parsed.response.provider}`);
  // Output: "Provider: openai"

  console.log(`Model: ${parsed.response.model.id}`);
  // Output: "Model: gpt-4"

  console.log(`Tokens: ${parsed.response.usage.totalTokens}`);
  // Output: "Tokens: 25"
}

Real-World Example - Multi-Provider Chat

import { parseResponse } from '@llm-dev-ops/llm-forge';

async function chat(provider: 'openai' | 'anthropic' | 'cohere', message: string) {
  // Use any provider's API - LLM-Forge normalizes the response
  const endpoints = {
    openai: 'https://api.openai.com/v1/chat/completions',
    anthropic: 'https://api.anthropic.com/v1/messages',
    cohere: 'https://api.cohere.ai/v1/chat'
  };

  const response = await fetch(endpoints[provider], {
    method: 'POST',
    headers: { /* provider-specific headers */ },
    body: JSON.stringify({ /* provider-specific payload */ })
  });

  const data = await response.json();
  const parsed = await parseResponse(data);

  if (parsed.success) {
    return {
      text: parsed.response.messages[0].content,
      tokens: parsed.response.usage.totalTokens,
      cost: calculateCost(parsed.response.usage, parsed.response.model.id)
    };
  }

  throw new Error(parsed.error.message);
}

// Works seamlessly with any provider
const openAIResult = await chat('openai', 'Explain quantum computing');
const claudeResult = await chat('anthropic', 'Explain quantum computing');
const cohereResult = await chat('cohere', 'Explain quantum computing');

Auto-Detection

import { parseResponse } from '@llm-dev-ops/llm-forge';

// Automatically detects provider from response structure
const openAIResponse = await parseResponse(openAIData);    // Detects OpenAI
const anthropicResponse = await parseResponse(claudeData); // Detects Anthropic
const cohereResponse = await parseResponse(cohereData);    // Detects Cohere

💡 Use Cases

1. Multi-Provider Chatbots

Build chatbots that can seamlessly switch between different LLM providers based on cost, performance, or availability.

2. LLM Abstraction Layer

Create a unified interface for your application that works with any LLM provider, making it easy to switch providers or A/B test different models.

3. Cost Optimization

Parse usage data from multiple providers to track and optimize token usage and costs across your organization.

4. SDK Generation

Generate type-safe client libraries in multiple programming languages from OpenAPI specifications, accelerating development across teams.

5. LLM Gateway/Proxy

Build an LLM gateway that routes requests to different providers, normalizes responses, and provides unified monitoring and analytics.

6. Testing & Development

Easily switch between providers during development and testing without changing your application code.

Provider-Specific Parsing

import { OpenAIProvider, AnthropicProvider } from '@llm-dev-ops/llm-forge';

const openai = new OpenAIProvider();
const result = await openai.parse(openAIResponse);

const anthropic = new AnthropicProvider();
const claudeResult = await anthropic.parse(anthropicResponse);

Streaming Support

import { OpenAIProvider } from '@llm-dev-ops/llm-forge';

const provider = new OpenAIProvider();

// Parse streaming chunks
for await (const chunk of streamingResponse) {
  const parsed = await provider.parseStream(chunk);
  if (parsed.success) {
    process.stdout.write(parsed.response.messages[0].content);
  }
}

🎯 Supported Providers

Provider	Status	Detection	Parsing	Streaming
OpenAI	✅ Complete	✅	✅	✅
Anthropic	✅ Complete	✅	✅	✅
Google AI	✅ Complete	✅	✅	✅
Cohere	✅ Complete	✅	✅	✅
Mistral	✅ Complete	✅	✅	✅
Azure OpenAI	✅ Complete	✅	✅	✅
Hugging Face	✅ Complete	✅	✅	⚠️ Limited
Replicate	✅ Complete	✅	✅	⚠️ Limited
Together AI	✅ Complete	✅	✅	⚠️ Limited
Perplexity	✅ Complete	✅	✅	✅
OpenRouter	✅ Complete	✅	✅	✅
Custom	✅ Complete	✅	✅	⚠️ Provider-dependent

🔧 Code Generation

Generate TypeScript Client

import { generateTypeScript } from '@llm-dev-ops/llm-forge';

const schema = {
  name: 'ChatCompletion',
  properties: {
    messages: { type: 'array', items: { type: 'Message' } },
    model: { type: 'string' }
  }
};

const code = await generateTypeScript(schema);
console.log(code);

Supported Languages

Language	Status	Package Manager	Type Safety	Async Support
TypeScript	✅ Complete	npm	Full	async/await
Python	✅ Complete	pip	Type hints	async/await
Rust	✅ Complete	cargo	Strong	tokio
Java	✅ Complete	Maven/Gradle	Strong	CompletableFuture
C#	✅ Complete	NuGet	Strong	async/await
Go	✅ Complete	go modules	Static	goroutines

📈 Performance

Benchmarks (ops/sec)

Provider Detection:

• OpenAI: 9.7M ops/sec
• Anthropic: 9.4M ops/sec
• Cohere: 8.7M ops/sec
• Mistral: 6.7M ops/sec
• Google AI: 5.5M ops/sec

Response Parsing:

• Mistral: 454K ops/sec (fastest)
• OpenAI: 422K ops/sec
• Anthropic: 368K ops/sec
• Cohere: 313K ops/sec
• Google AI: 137K ops/sec

Streaming:

• OpenAI: 504K chunks/sec
• Anthropic: 485K chunks/sec

Benchmarked on Node.js 20 with Vitest bench suite (27 benchmarks)

🏗️ Architecture

LLM-Forge uses a layered architecture:

┌─────────────────────────────────────────────────────┐
│  Provider Responses (OpenAI, Anthropic, etc.)       │
└────────────────┬────────────────────────────────────┘
                 │
┌────────────────▼────────────────────────────────────┐
│  Provider Detection & Auto-detection                │
└────────────────┬────────────────────────────────────┘
                 │
┌────────────────▼────────────────────────────────────┐
│  Unified Response Parsing                           │
│  - Message extraction                               │
│  - Metadata normalization                           │
│  - Token usage tracking                             │
│  - Error handling                                   │
└────────────────┬────────────────────────────────────┘
                 │
┌────────────────▼────────────────────────────────────┐
│  Code Generation (6 languages)                      │
│  - Type generation                                  │
│  - Client generation                                │
│  - Serialization                                    │
└─────────────────────────────────────────────────────┘

See docs/ARCHITECTURE.md for detailed architecture documentation.

🧪 Testing

Test Coverage

Overall Coverage:    93.77%
Providers Coverage:  92.68%
Generators Coverage: 98.17%
Parsers Coverage:    98.04%
Core Coverage:       97.73%

Total Tests: 666 passing
Test Files:  23 files
Duration:    ~10 seconds

Run Tests

# Run all tests
npm test

# Run with coverage
npm run test:coverage

# Run benchmarks
npm run bench

# Run specific test file
npm test tests/providers/integration.test.ts

🔒 Security

LLM-Forge implements multiple security layers:

• ✅ Daily Security Scans: Automated vulnerability detection
• ✅ CodeQL Analysis: Static security analysis
• ✅ Secret Detection: TruffleHog scanning
• ✅ License Compliance: Automated license checking
• ✅ Dependency Updates: Dependabot automation
• ✅ OSSF Scorecard: Security best practices validation

See docs/CI_CD_PIPELINE.md for security documentation.

🔄 CI/CD Pipeline

7 automated workflows ensure quality:

1. PR Validation - Quality gates for pull requests
2. Continuous Integration - Multi-OS testing (Ubuntu, macOS, Windows)
3. Security Scanning - Multi-layer security analysis
4. Performance Monitoring - Benchmark tracking and regression detection
5. Release & Publish - Automated npm publishing
6. Dependabot Auto-Merge - Safe dependency updates
7. Stale Management - Issue/PR lifecycle management

See .github/README.md for workflow documentation.

📚 Documentation

User Guides

• Production Readiness - Deployment guide
• CI/CD Pipeline - Pipeline documentation
• Architecture - System architecture

Implementation

• Provider System - Provider implementation
• Template Engine - Code generation templates
• Language Strategy - Multi-language support

Reference

• Implementation Summary - Complete implementation details
• DevOps Deliverables - CI/CD and deployment details
• Multi-Language Success - Code generation implementation

🛠️ Development

Prerequisites

• Node.js >= 20.0.0
• npm >= 10.0.0
• TypeScript >= 5.3.3

Setup

# Clone repository
git clone https://github.com/globalbusinessadvisors/llm-forge.git
cd llm-forge

# Install dependencies
npm install

# Run tests
npm test

# Build project
npm run build

# Run development mode
npm run dev

# Run benchmarks
npm run bench

# Run all quality checks
npm run quality

CLI Usage

LLM-Forge includes a command-line interface for generating SDKs:

# Run CLI in development
npm run cli -- --help

# Generate SDK from OpenAPI spec
npm run cli -- generate --input openapi.json --output ./sdk --language typescript

# After installation (globally or in project)
npx llm-forge generate --input openapi.json --language python

Project Structure

llm-forge/
├── src/
│   ├── core/           # Template engine and type system
│   ├── generators/     # Language-specific code generators
│   ├── parsers/        # OpenAPI and Anthropic parsers
│   ├── providers/      # Provider-specific parsers (12 providers)
│   ├── schema/         # Schema validation
│   └── types/          # TypeScript type definitions
├── tests/
│   ├── core/           # Core functionality tests
│   ├── generators/     # Code generator tests
│   ├── parsers/        # Parser tests
│   ├── providers/      # Provider tests (integration, benchmarks)
│   └── schema/         # Schema validation tests
├── docs/               # Comprehensive documentation
├── examples/           # Example usage
├── scripts/            # Build and utility scripts
└── .github/
    ├── workflows/      # 7 CI/CD workflows
    └── dependabot.yml  # Dependency automation

Available Scripts

npm test              # Run all tests
npm run test:coverage # Run tests with coverage report
npm run bench         # Run performance benchmarks
npm run type-check    # TypeScript type checking
npm run lint          # ESLint code linting
npm run format        # Prettier code formatting
npm run build         # Build package
npm run clean         # Clean build artifacts
npm run quality       # Run all quality checks

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes with tests
4. Run quality checks (npm run quality)
5. Commit your changes (git commit -m 'feat: add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

All PRs must pass:

• ✅ TypeScript type checking
• ✅ ESLint linting
• ✅ Prettier formatting
• ✅ All 666 tests
• ✅ 93%+ code coverage
• ✅ Security scans

See docs/CI_CD_PIPELINE.md for detailed contribution guidelines.

📦 Publishing

npm

npm install @llm-dev-ops/llm-forge

GitHub Packages

npm install @llm-dev-ops/llm-forge

🗺️ Roadmap

✅ Phase 1: Foundation (Complete)

• ✅ Provider response parsing (12 providers)
• ✅ Unified response format
• ✅ Auto-detection system
• ✅ Streaming support

✅ Phase 2: Code Generation (Complete)

• ✅ TypeScript generator
• ✅ Python generator
• ✅ Rust generator
• ✅ Java generator
• ✅ C# generator
• ✅ Go generator

✅ Phase 3: Production Ready (Complete)

• ✅ Comprehensive testing (666 tests)
• ✅ 93.77% code coverage
• ✅ Performance benchmarking
• ✅ CI/CD pipeline (7 workflows)
• ✅ Security scanning
• ✅ Complete documentation

🔮 Phase 4: Future Enhancements (Planned)

• CLI tool for SDK generation
• Plugin system for custom providers
• Cost tracking and analytics
• Advanced observability
• Custom provider templates
• GraphQL support

📄 License

Apache License 2.0 - see LICENSE for details.

🙏 Acknowledgments

Built with enterprise-grade quality using:

• Testing: Vitest
• CI/CD: GitHub Actions
• Security: CodeQL, TruffleHug, OSSF Scorecard
• Coverage: Codecov
• Type Safety: TypeScript

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📊 Project Metrics

Lines of Code:       ~15,000
Test Coverage:       93.77%
Tests:              666 passing
Benchmarks:         27 performance tests
Providers:          12 supported
Languages:          6 code generators
CI/CD Workflows:    7 automated
Documentation:      35+ comprehensive docs
Performance:        136K-454K ops/sec parsing
Security:           Multi-layer scanning

🆘 Support

• Documentation: docs/
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• CI/CD Status: GitHub Actions
• npm Package: npmjs.com
• Coverage: Codecov

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Status: ✅ Production Ready | License: Apache 2.0 | Version: 1.0.0

Made with ❤️ by the LLM-Dev-Ops Team

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