🐝 hive-tools - Multi-Model Consensus Platform

Why trust one AI when you can trust them all?

Eliminate AI hallucinations through our revolutionary 4-stage consensus pipeline. hive-tools combines multiple AI models to deliver production-ready, trustworthy responses for mission-critical applications.

✨ What You Get

🆓 Free Tier (No Credit Card Required)

• 5 daily conversations - Perfect for trying our consensus technology
• Single-model AI queries - Access to OpenAI, Anthropic, Google, and more
• IDE integration - Works with VS Code, Cursor, Windsurf
• Provider management - Configure and test multiple AI providers

🚀 Premium Features (Start $5/month)

• 7-day FREE trial with unlimited conversations
• 4-stage consensus pipeline - Generator → Refiner → Validator → Curator
• 323+ models from 55+ providers with intelligent selection
• Advanced analytics - Cost tracking, performance monitoring
• Team collaboration - Shared usage pools and admin controls

🎯 Start Your Free Trial →

🚀 Quick Start

Installation Options

Option 1: NPM (Recommended)

npm install -g @hivetechs/hive-ai

Option 2: Clone & Build

git clone https://github.com/hivetechs-collective/hive.ai.git
cd hive.ai
npm install && npm run build

Option 3: GitHub Release (Manual)

# Download binary for your platform from:
# https://github.com/hivetechs-collective/hive.ai/releases

🔄 Staying Updated

Why Keep Updated?

• ✅ Latest AI models from 323+ providers
• ✅ Security patches and bug fixes
• ✅ New features and consensus improvements
• ✅ Performance optimizations
• ✅ Compatibility with latest IDEs

Check Your Current Version

# Check your current version
hive --version

# Or get detailed version info
npm list -g @hivetechs/hive-ai

Automatic Update System (NEW!)

hive-tools now includes an intelligent auto-update system:

Check for Updates:

hive update-check    # Check for package updates
hive update-status   # Show detailed update status  

Configure Notifications:

hive update-configure  # Set update preferences

Features:

• 🔔 Smart Notifications - Get notified when updates are available
• 📅 Scheduled Checks - Weekly background checks (configurable)
• 🎯 Update Types - Major, minor, and patch update filtering
• 🛡️ Non-Intrusive - Silent background operation

Manual Update Commands

Update to Latest Beta Version:

npm install -g @hivetechs/hive-ai@beta

Update to Latest Stable Version:

npm update -g @hivetechs/hive-ai

Get Beta Features (Optional):

npm install -g @hivetechs/hive-ai@beta

When to Update

Update immediately when:

• 🚨 Security updates are released
• 🐛 You encounter known bugs
• 🆕 New AI models are added
• ⚙️ IDE compatibility issues arise

Check for updates:

• 📅 Weekly: For active development projects
• 📅 Monthly: For production environments
• 📅 As needed: When new features are announced

Update Process

1. Backup your configuration (optional - but recommended):

   # Your settings are in ~/.hive-ai/ and are preserved during updates
   ls ~/.hive-ai/

2. Update the package:

   npm update -g @hivetechs/hive-ai

3. Verify the update:

   hive --version

4. Test basic functionality:

   hive configure --help

💡 Pro Tip: Your license keys, provider configurations, and conversation history are automatically preserved during updates.

Troubleshooting Updates

If update fails:

# Clear npm cache and retry
npm cache clean --force
npm install -g @hivetechs/hive-ai@latest

If permissions error on macOS/Linux:

# Use sudo (not recommended long-term)
sudo npm update -g @hivetechs/hive-ai

# Better: Fix npm permissions permanently
npm config set prefix ~/.npm-global
export PATH=~/.npm-global/bin:$PATH

Reset to clean state if needed:

# Uninstall and reinstall
npm uninstall -g @hivetechs/hive-ai
npm install -g @hivetechs/hive-ai

Release Notes & What's New

Stay informed about new features and changes:

• 📢 Release announcements: hivetechs.io/changelog
• 📧 Email updates: Subscribe at hivetechs.io/updates
• 🐙 GitHub releases: github.com/hivetechs-collective/hive.ai/releases

First Steps

🚨 IMPORTANT: Get Your License First

Before using hive-tools, you need a license (free, trial, or premium):

1. Get FREE License: Visit hivetechs.io/pricing
2. Start FREE Trial: 7 days unlimited at hivetechs.io/pricing
3. Configure License:

   hive-ai configure --license YOUR_LICENSE_KEY

Then Start Using:

# Setup wizard
hive-ai setup wizard

# Or configure your own API keys
hive-ai provider configure openai YOUR_OPENAI_KEY

No license = No functionality. All features require a valid license key from hivetechs.io.

The onboarding assistant will guide you through:

1. License key validation
2. IDE configuration (VS Code, Cursor, or Windsurf)
3. Adding one AI provider for immediate use
4. Testing your first consensus query

📋 Subscription Plans & Conversation Limits

Visit hivetechs.io/pricing to upgrade your plan.

After completing the onboarding process, you'll be ready to use hive-tools in your IDE by typing @hive-tools. followed by a command.

🚀 Transformative Features

🧠 4-Stage Consensus Pipeline

hive-tools's core innovation is its unique 4-stage consensus pipeline that transforms user queries into exceptionally high-quality responses:

1. Generator Stage (GPT-3.5-Turbo) - Creates comprehensive initial responses with broad topic coverage
2. Refiner Stage (GPT-4-Turbo) - Enhances clarity, corrects inaccuracies, and improves structure
3. Validator Stage (GPT-4-Turbo) - Verifies factual accuracy and performs critical reasoning checks
4. Curator Stage (GPT-3.5-Turbo) - Delivers polished, well-formatted responses with consistent tone

🔄 Thematic Knowledge Retrieval System

Unlike standard AI assistants, hive-tools features an advanced thematic knowledge retrieval system that:

• Automatically maintains conversation continuity across related topics
• Identifies thematic relationships between seemingly disparate queries
• Builds a comprehensive knowledge graph from user interactions
• Provides context-aware responses without requiring explicit conversation references

🧩 Technical Domain Expertise

hive-tools excels at specialized technical domains with deep understanding of:

• Software Engineering & Programming Languages
• Machine Learning & AI Systems
• Database Technologies & Data Science
• Cloud Infrastructure & DevOps
• Security & Authentication
• Web Development & Modern Frameworks

📚 Persistent Contextual Memory

Our SQLite-based persistent storage ensures:

• Long-term memory across sessions
• Automatic context retrieval for related questions
• Progressive knowledge building from user interactions
• Intelligent response adaptation based on conversation history

🚀 Getting Started

1. Environment Configuration

hive-tools requires OpenAI API access for its multi-model consensus pipeline. Configure your environment by creating a file at src/env/keys.ts:

export const OPENAI_API_KEY = "your_key_here";

⚠️ Security Note: For production environments, we recommend using environment variables or a secure secrets management solution.

2. Installation

Install all dependencies to power the advanced consensus pipeline and thematic knowledge retrieval system:

npm install
# or
yarn install

3. Build the Server

Compile the TypeScript implementation of our multi-stage consensus pipeline:

npm run build

4. Database Initialization

The SQLite database for persistent conversation memory will be automatically initialized on first startup. The system stores:

• Conversation history with timestamped entries
• Semantic topic embeddings for thematic retrieval
• Conversation metadata for context persistence

5. Using the CLI Tool

hive-tools offers a powerful CLI tool for easy interaction:

# Start the clean interactive CLI (recommended)
npm run cli

# Or use the colorful CLI with syntax highlighting
npm run cli:color

# Run a direct query (quiet mode is the default, showing only the result)
npm run cli -- consensus "What is the capital of France?"

# Enable verbose mode for debugging or detailed output
npm run cli -- --debug consensus "What is the capital of France?"

You can also install the CLI globally for easier access:

# Install the CLI globally
npm link

# Now you can use the 'hive' command directly
hive consensus "What is the capital of France?"

# Or simply type your query directly - any unrecognized input is treated as a consensus query
hive "What is the capital of France?"

For detailed CLI documentation, see CLI.md.

Once the CLI starts, you'll see the hive> prompt. From there, you can run commands directly:

hive> list_providers
hive> configure_provider OpenAI sk-your-api-key-here
hive> test_providers
hive> configure_pipeline_interactive my_pipeline

🚀 Shorthand Commands

hive-tools supports intuitive shorthand commands for all tools, making interactions faster and more natural. These shortcuts work both in the CLI and when interacting with AI agents through your IDE.

Main Tools

• Consensus: consensus, cons
  • Example: consensus what is quantum computing?
• Capture: capture, cap
  • Example: capture My insights about React | React is a JavaScript library for building user interfaces | code_analysis | react,javascript

Profile Management

• List Profiles: list_profiles, profiles, lp
  • Example: profiles
• Get Profile: get_profile, profile, gp
  • Example: profile default
• Update Profile: update_profile, up
  • Example: update_profile default | {"temperature": 0.7}

Provider Configuration

• List Providers: list_providers, providers, lprov
  • Example: providers
• Configure Provider: configure_provider, config_provider, cp
  • Example: configure_provider OpenAI sk-abc123 https://api.openai.com
• Test Providers: test_providers, test, tp
  • Example: test_providers or test OpenAI:gpt-4

Pipeline Configuration

• List Pipeline Profiles: list_pipeline_profiles, pipelines, lpp
  • Example: pipelines
• Configure Pipeline: configure_pipeline, config_pipeline, cpp
  • Example: configure_pipeline default OpenAI:gpt-4:0.7 Anthropic:claude-3-opus:0.5 Gemini:gemini-pro:0.3
• Set Default Profile: set_default_profile, default_profile, sdp
  • Example: set_default_profile high_quality

Model Registry Management

• Update Model Registry: update_model_registry, update_registry, umr
  • Example: update_model_registry
• Add Custom Model: add_custom_model, add_model, acm
  • Example: add_custom_model OpenAI gpt-4-turbo-preview GENERAL
• List Models: list_models, models, lm
  • Example: list_models OpenAI

This interactive mode makes it much easier to work with hive-tools, as you don't need to prefix commands with npm run cli --. We recommend using this interactive mode for all your hive-tools configuration and usage.

CLI Options

• Standard CLI (npm run cli): Current CLI implementation with enhanced functionality
• Colorful CLI (npm run cli:color): CLI with syntax highlighting for better readability
• Legacy CLI (npm run cli:old): Original CLI implementation (for backward compatibility)

Note: The colorful CLI enhances the terminal experience with color-coded commands, parameters, and output messages, making it easier to read and navigate the CLI interface.

6. Configure MCP in Your IDE

hive-tools integrates seamlessly with modern IDEs like Windsurf, Cursor, VS Code, and Zed. The easiest way to configure is using our automatic configuration script:

npm run configure-ide

This script automatically generates all necessary configuration files for supported IDEs:

• Windsurf: Configures ~/.codeium/windsurf/mcp_config.json
• Cursor: Configures ~/.cursor/mcp.json
• VS Code: Configures .vscode/settings.json
• Zed: Configures .zed/mcp_config.json

For detailed IDE-specific instructions, see the IDE Configuration Guide.

You can also manually configure your IDE using this configuration:

{
  "mcpServers": {
    "hive-tools": {
      "command": "node",
      "args": [
        "/path/to/your/hive.ai/dist/index.js"
      ]
    }
  }
}

📘 Pro Tip: Replace /path/to/your/hive.ai/ with the actual path to your hive-tools installation.

After configuration, you'll see the "hive-tools Consensus" tool available in your IDE. This single tool provides access to our entire multi-model pipeline and thematic knowledge system.

For detailed integration guides, visit our documentation portal.

💪️ Available Tools

hive-tools provides a comprehensive suite of 11 tools through the MCP server, organized into five categories.

💡 CLI Usage Tip: While examples below use the full hive-tools.command format for IDE integration, you can also use our interactive CLI by running npm run cli once and then typing commands directly at the hive> prompt without any prefix.

1️⃣ Core Consensus Tools

consensus

Use the hive-tools multi-model consensus pipeline to generate high-quality responses.

hive-tools.consensus: What are the tradeoffs between microservices and monoliths?

Optional parameters:

• profile_id: Specify a pipeline profile (default: the default profile)
• conversation_id: Continue a specific conversation thread

capture

Capture insights, code analyses, and other valuable content into the hive-tools knowledge base.

hive-tools.capture: 
  title: "React Component Best Practices"
  content_type: "best_practice"
  content: "1. Use functional components with hooks instead of class components..." 
  tags: ["react", "frontend", "components"]

Parameters:

• title: Title for the captured content
• content_type: Type of content ("code_analysis", "architecture_insight", "design_pattern", "best_practice", "general")
• content: The main content to capture
• tags: (Optional) Array of tags to categorize the content

2️⃣ Provider Configuration Tools

list_providers

List all configured LLM providers with masked API keys.

hive-tools.list_providers

configure_provider

Configure an LLM provider with an API key and optional base URL.

hive-tools.configure_provider: OpenAI sk-your-api-key-here

hive-tools.configure_provider: OpenAI sk-your-api-key-here https://custom-endpoint.com

Format: PROVIDER_NAME API_KEY [BASE_URL]

test_providers

Test configured LLM providers to verify API keys and connectivity.

hive-tools.test_providers

Test a specific provider with optional model:

hive-tools.test_providers: OpenAI:gpt-4

3️⃣ Pipeline Configuration Tools

list_pipeline_profiles

List all configured pipeline profiles.

hive-tools.list_pipeline_profiles

configure_pipeline

Configure a pipeline profile with models for each stage. The order of models is extremely important!

hive-tools.configure_pipeline: profile_name GENERATOR REFINER VALIDATOR [CURATOR]

Where:

• profile_name: A simple name you choose for this configuration (e.g., "standard", "fast", "premium")
• GENERATOR: The first model that creates initial content (position 1)
• REFINER: The second model that improves the content (position 2)
• VALIDATOR: The third model that checks facts (position 3)
• CURATOR: The fourth model that formats the final output (position 4, optional)

Each stage uses this format: PROVIDER_NAME:MODEL_NAME[:TEMPERATURE]

Example:

hive-tools.configure_pipeline: standard OpenAI:gpt-3.5-turbo:0.7 OpenAI:gpt-4:0.0 Anthropic:claude-3-haiku:0.0

This creates a pipeline named "standard" where:

• OpenAI's GPT-3.5 is the Generator (first position)
• OpenAI's GPT-4 is the Refiner (second position)
• Anthropic's Claude is the Validator (third position)
• Claude is also used as the Curator since none was specified

configure_pipeline_interactive

Configure a pipeline profile interactively with guided model selection.

hive-tools.configure_pipeline_interactive: my_interactive_pipeline

This interactive tool:

1. Lists Available Providers: Shows all configured providers (Anthropic, Grok, Gemini, etc.)
2. Displays Available Models: For each provider, shows available models with descriptions
3. Guides Through Each Stage: Walks you through configuring each pipeline stage:
   • Generator (creates initial content)
   • Refiner (improves the content)
   • Validator (checks for accuracy)
   • Curator (adds final polish)
4. Sets Default Temperatures: Suggests appropriate temperature settings for each stage

Benefits of Interactive Configuration:

• No need to memorize model names
• Ensures provider compatibility
• Suggests appropriate temperature values
• Reduces configuration errors
• Shows model descriptions to help with selection

Example Interactive Session:

Configuring pipeline profile: my_interactive_pipeline

=== Configuring Generator stage ===

Available providers:
1. Anthropic
2. Gemini
3. Grok

Using provider: Anthropic

Available models:
claude-3-haiku-20240307 - Fast and efficient model for routine tasks
claude-3-sonnet-20240229 - Balanced model for most use cases
claude-3-opus-20240229 - Most capable model for complex tasks

Selected model: claude-3-haiku-20240307 (temperature: 0.7)

[Similar process repeats for Refiner, Validator, and Curator stages]

✅ Pipeline profile "my_interactive_pipeline" configured successfully!

set_default_profile

Set a pipeline profile as the default for consensus operations.

hive-tools.set_default_profile: standard

4️⃣ Profile Management Tools

list_profiles

Lists available provider profiles in the hive-tools system.

hive-tools.list_profiles

get_profile

Gets details of a specific provider profile.

hive-tools.get_profile: default

update_profile

Updates a provider profile configuration.

hive-tools.update_profile: 
  profile_name: "default"
  profile_data: "{ ... profile configuration ... }"

🔍 Configuration Guide for Beginners

Setting Up Your AI Pipeline: Step-by-Step

💡 Success Tip: Think of this as setting up a team of AI assistants, each with a specific job. By the end, you'll have your own customized AI team ready to work together!

Think of the consensus pipeline as an assembly line with four stations, each handled by an AI model you choose. Here's how to set up your own custom pipeline in simple steps:

Step 1: Start the CLI Tool

Begin by launching the interactive CLI:

npm run cli

Wait for the hive> prompt to appear, then proceed with the following steps directly at this prompt.

Step 2: Configure Your Providers (Required First Step)

Important: You must configure your providers with API keys before creating any pipelines. This is a required first step:

hive> configure_provider OpenAI your-openai-key-here
hive> configure_provider Anthropic your-anthropic-key-here
hive> configure_provider Gemini your-gemini-key-here
hive> configure_provider Grok your-grok-key-here

The system will automatically detect the appropriate base URLs for each provider.

Step 3: Test Your Connections

Make sure all your providers are working:

hive> test_providers

Step 4: Create a Pipeline with Interactive Configuration (Recommended)

The recommended way to create a pipeline is using our interactive configuration tool, which guides you through selecting the right providers and models:

hive> configure_pipeline_interactive my_custom_pipeline

This interactive tool will:

1. Show you all available providers you've configured
2. List compatible models for each provider with descriptions
3. Allow you to select the best model for each pipeline stage
4. Suggest appropriate temperature settings based on each stage's purpose
5. Let you decide whether to include an optional Curator stage
6. Ask if you want to set this as your default pipeline

Alternative: Manual Pipeline Configuration

If you prefer, you can also manually configure pipelines for different needs:

# Premium pipeline with top models
hive> configure_pipeline premium Anthropic:claude-3-opus:0.7 OpenAI:gpt-4:0.3 OpenAI:gpt-4:0.1 Anthropic:claude-3-sonnet:0.5

# Balanced pipeline with mixed providers
hive> configure_pipeline balanced OpenAI:gpt-4:0.7 Anthropic:claude-3-sonnet:0.3 Gemini:gemini-pro:0.1 OpenAI:gpt-3.5-turbo:0.5

# Budget-friendly pipeline
hive> configure_pipeline basic OpenAI:gpt-3.5-turbo:0.7 OpenAI:gpt-3.5-turbo:0.0 OpenAI:gpt-3.5-turbo:0.0

Each pipeline has four positions (the last one is optional):

1. Generator: Creates the first draft (like a writer)
2. Refiner: Improves the draft (like an editor)
3. Validator: Checks facts and accuracy (like a fact-checker)
4. Curator: Formats the final response (like a publisher)

The numbers after model names (like 0.7 or 0.0) control creativity - higher means more creative, lower means more consistent.

Step 5: Choose Your Default Pipeline

Select which pipeline to use when you don't specify one:

hive> set_default_profile premium

Step 6: Using Your AI Pipeline

Once configured, you can use your pipeline directly from the CLI:

# Ask a question using your default pipeline
hive> consensus What's the best way to learn JavaScript?

# Use a specific pipeline for this question only
hive> consensus How do quantum computers work? --profile premium

# Save insights to the knowledge base
hive> capture "JavaScript Best Practices" best_practice "Always use const and let instead of var..." javascript,frontend

IDE Integration

In your IDE, you'll use the full tool name format:

hive-tools.consensus: What's the best way to learn JavaScript?

hive-tools.consensus: 
  prompt: "How do quantum computers work?"
  profile_id: "premium"

Interactive Pipeline Configuration Experience

The configure_pipeline_interactive command provides a truly interactive experience that walks you through each step of creating a pipeline profile. Here's what to expect:

=== Configuring Generator stage ===

Available providers:
1. Gemini
2. OpenAI
3. Anthropic
4. Grok

Select provider number: 3

Selected provider: Anthropic

Available models:
1. claude-3-opus-20240229 - Most powerful Claude model for complex tasks
2. claude-3-sonnet-20240229 - Balanced model for most tasks
3. claude-3-haiku-20240307 - Fastest and most cost-effective Claude model

Select model number: 1

Enter temperature (0.0-1.0) [default: 0.7]: 0.7

Selected model: claude-3-opus-20240229 (temperature: 0.7)

This approach ensures you select compatible models and configure your pipeline correctly without needing to memorize model names or provider compatibility.

Quick Tips for Beginners

• Always configure providers first before trying to create pipelines
• The order of models in a pipeline is critical - position determines function!
• You can mix and match different providers in the same pipeline
• For important questions, use your best models at each stage
• For quick answers, use faster/cheaper models
• When in doubt, use list_pipeline_profiles to see your configurations
• After creating a pipeline interactively, you can fine-tune it with the standard configure_pipeline command

🌡️ Understanding Temperature Settings

Temperature is a crucial parameter that controls the randomness and creativity of AI model outputs. Understanding how to set temperature appropriately can significantly impact the quality and consistency of responses from your consensus pipeline.

What is Temperature?

Temperature is a hyperparameter that affects how the AI model selects the next token in a sequence. It's typically set between 0.0 and 1.0, with some models supporting higher values:

• Low temperature (0.0-0.3): More deterministic, focused, and consistent responses
• Medium temperature (0.4-0.7): Balanced creativity and coherence
• High temperature (0.8-1.0+): More random, creative, and diverse responses

Temperature Effects by Value

Recommended Temperature Settings by Pipeline Stage

Each stage of the consensus pipeline benefits from different temperature settings:

1. Generator Stage: 0.7-0.8

   • Rationale: Higher temperature encourages broader exploration of ideas and more comprehensive initial responses
   • Goal: Generate diverse content that covers multiple aspects of the query

2. Refiner Stage: 0.4-0.6

   • Rationale: Moderate temperature balances creativity with structure improvement
   • Goal: Enhance the content while maintaining coherence and adding valuable details

3. Validator Stage: 0.0-0.3

   • Rationale: Low temperature ensures consistent fact-checking and error detection
   • Goal: Critically evaluate content for accuracy with minimal randomness

4. Curator Stage: 0.3-0.5

   • Rationale: Moderate-low temperature provides consistent formatting while allowing some flexibility
   • Goal: Polish and format content with reliable, predictable results

Temperature Strategies for Different Use Cases

Technical Documentation

• Generator: 0.5-0.6 (balanced initial draft)
• Refiner: 0.3-0.4 (focused improvements)
• Validator: 0.0-0.1 (strict accuracy checking)
• Curator: 0.2-0.3 (consistent formatting)

Creative Content

• Generator: 0.8-0.9 (highly creative initial ideas)
• Refiner: 0.6-0.7 (creative but more structured improvements)
• Validator: 0.2-0.3 (fact checking while preserving style)
• Curator: 0.4-0.5 (stylistic formatting)

Balanced General-Purpose

• Generator: 0.7 (moderately creative)
• Refiner: 0.5 (balanced improvements)
• Validator: 0.2 (focused fact checking)
• Curator: 0.4 (balanced formatting)

Fine-Tuning Temperature

When setting temperature in your pipeline configuration, consider these tips:

• Start with defaults: Begin with our recommended settings and adjust based on results
• Iterative refinement: Test different temperatures and compare outputs
• Consider query complexity: More complex queries often benefit from lower temperatures
• Balance across stages: If one stage uses high temperature, consider lower temperatures in other stages
• Monitor consistency: Higher temperatures increase variability between runs

When configuring a pipeline with specific temperatures:

hive> configure_pipeline custom_pipeline OpenAI:gpt-3.5-turbo:0.7 OpenAI:gpt-4-turbo:0.5 Anthropic:claude-3-haiku:0.2 OpenAI:gpt-3.5-turbo:0.4

This creates a pipeline with carefully balanced temperature settings across all four stages.

💡 Recommended User Flow

For the best experience with hive-tools, we recommend following this workflow:

1. Start the CLI: Run npm run cli to launch the interactive CLI
2. Configure Providers: Set up your API keys with configure_provider (required first step)
3. Test Connections: Verify your providers work with test_providers
4. Create Pipeline Interactively: Use configure_pipeline_interactive to create a pipeline
5. Set as Default: Make your new pipeline the default if desired
6. Use the Pipeline: Start using the consensus command with your configured pipeline

This workflow ensures you have a properly configured system with the right models for each stage of the pipeline.

🧰️ Using hive-tools Consensus

hive-tools revolutionizes the way you interact with AI assistants. Here's how to leverage its unique capabilities:

Contextual Conversations

Start asking technical questions and watch as hive-tools maintains context across related topics:

• "What's the difference between REST and GraphQL?"
• Later: "How would I implement authentication in each approach?"

hive-tools automatically connects these related queries without you needing to reference the previous conversation.

Multi-Stage Processing

Every query passes through our comprehensive 4-stage pipeline:

1. Generator creates comprehensive initial responses
2. Refiner enhances clarity and structure
3. Validator verifies factual accuracy
4. Curator delivers polished, well-formatted results

Technical Domain Expertise

Test hive-tools's deep technical knowledge with complex questions spanning multiple domains:

• Complex software architecture decisions
• Machine learning implementation details
• Database optimization strategies
• System design considerations

💡 Tip: For optimal results, ask follow-up questions on related topics to leverage hive-tools's thematic knowledge retrieval system.

📊 Architecture Overview

src/
├── tools/
│   └── hiveai/
│       ├── consensus.ts             # 4-stage consensus pipeline implementation
│       ├── provider-config.ts       # Provider configuration tools
│       ├── pipeline-config.ts       # Pipeline profile management
│       └── conversation-memory.ts   # In-memory conversation tracking
├── storage/
│   ├── database.ts                  # SQLite database management
│   ├── contextManager.ts            # Persistent context management
│   ├── knowledgeRetrieval.ts        # Thematic relationship detection
│   ├── topicTagging.ts              # Technical domain topic extraction
│   ├── userManager.ts               # User identification and management
│   └── cloudSync.ts                 # Cloud synchronization for user data
├── cloudflare/
│   ├── worker.js                    # Cloudflare Workers API implementation
│   ├── schema.sql                   # D1 database schema
│   └── wrangler.toml                # Cloudflare Workers configuration
├── env/
│   └── keys.ts                      # Environment configuration
└── index.ts                         # MCP server entry point

🔐 User Identification System

hive-tools includes a comprehensive user identification system that enables subscription management and usage tracking:

Features

• Local-First Architecture: User data is stored locally for privacy and offline resilience
• Multi-Device Support: Users can register multiple devices under a single account
• Adaptive Verification: Optimizes cloud API calls based on subscription tier
• Usage Tracking: Monitors conversation counts with tier-based limits
• Cloud Synchronization: Syncs usage data across devices via Cloudflare Workers

Components

1. Client-Side Implementation

   • userManager.ts: Handles user creation, device registration, and usage tracking
   • cloudSync.ts: Manages communication with Cloudflare Workers API

2. Cloudflare Workers Backend

   • API endpoints for user verification, usage synchronization, and checkout
   • D1 database for storing user data in the cloud
   • Integration with Lemon Squeezy for subscription management

Deployment

Before deploying the Cloudflare Workers backend, you'll need to set up a Cloudflare account. See our Cloudflare Setup Guide for detailed instructions.

Once you have a Cloudflare account, you can deploy using our deployment script:

# Navigate to the cloudflare directory
cd src/cloudflare

# Run the deployment script
node deploy.js

The script will guide you through:

• Logging in to Cloudflare
• Creating a D1 database
• Setting up API keys
• Deploying the worker

For more details on the user identification system, see the Monetization Strategy document.

📊 Consensus Pipeline Analysis

hive-tools includes comprehensive analysis tools to help you understand and optimize the consensus pipeline. These tools analyze the SQLite database to provide insights into model performance, content transformations, and pipeline efficiency.

Available Reports and Tools

• Consensus Pipeline Report - Detailed statistical analysis of the pipeline
• Model Contribution Analysis - How different models contribute at each stage
• Interactive Pipeline Visualization - Visual exploration of pipeline data
• Visualization Guide - Guide to using the interactive visualization
• Use Case Guide - Practical applications and optimization strategies

Key Insights

Our analysis has revealed several important insights about the consensus pipeline:

1. Pipeline Structure: The 4-stage pipeline (Generator → Refiner → Validator → Curator) shows a pattern of content expansion followed by refinement.

2. Model Performance:

   • Generator: gpt-3.5-turbo is most commonly used
   • Refiner: gpt-4-turbo and grok-3-beta provide significant content expansion
   • Validator: gpt-4-turbo, grok-1, and gemini-2.0-flash make important corrections
   • Curator: gpt-3.5-turbo and claude-3-haiku provide final formatting

3. Efficient Combinations:

   • Fastest: gemini-pro → gpt-4 → grok-1 → gemini-pro (5.96s avg)
   • Most common: gpt-3.5-turbo → gpt-4-turbo → gpt-4-turbo → gpt-3.5-turbo (57.00s avg)

For more detailed analysis and optimization recommendations, see the reports directory.

Generating Your Own Analysis

You can generate updated reports with the latest data using these scripts:

# Generate comprehensive pipeline report
node consensus-pipeline-report.js

# Analyze model contributions
node model-contribution-analysis.js

# Create interactive visualization
node consensus-pipeline-visualization.js

These tools help you understand how the consensus pipeline works and how to optimize it for your specific needs.

📬 Contact Us

We'd love to hear from you! Reach out to us with any questions, feedback, or partnership opportunities:

• General Inquiries: hello@hivetechs.io
• Technical Support: support@hivetechs.io
• Information Requests: info@hivetechs.io
• Phone: (813) 400-0871

Business Address

HiveTechs Collective LLC
7901 4th St N STE 300
St. Petersburg, FL 33702

🌐 Learn More

Visit hivetechs.io to learn more about our revolutionary approach to AI consensus and context-aware conversations.

📜 License & Legal Notice

⚠️ PROPRIETARY SOFTWARE - COMMERCIAL LICENSING REQUIRED

hive-tools contains proprietary algorithms, trade secrets, and intellectual property owned exclusively by HiveTechs Collective LLC.

🚫 Unauthorized Use Strictly Prohibited

Without explicit commercial licensing, the following are PROHIBITED:

• Commercial use in any form or capacity
• Reverse engineering or extracting proprietary algorithms
• Creating derivative works or competing products
• Using multi-model consensus methodologies in other products
• Training AI models on proprietary outputs or methodologies

✅ Permitted Uses (Non-Commercial Only)

• Personal projects and individual learning
• Academic research with proper attribution
• Educational use by students and researchers
• 30-day commercial evaluation period

💼 Commercial Licensing Required

For any commercial use, enterprise deployment, or production environment:

• Licensing Contact: licensing@hivetechs.io
• Pricing Plans: https://hivetechs.io/pricing
• Enterprise Sales: enterprise@hivetechs.io

⚖️ Legal Protection

This software is protected by:

• U.S. and international copyright law
• Patent applications and trade secret protections
• Proprietary consensus algorithms and AI methodologies
• Advanced multi-model optimization techniques

🔒 Enforcement

Unauthorized commercial use will result in:

• Immediate legal action for damages and injunctive relief
• Recovery of attorney fees and litigation costs
• Potential criminal prosecution under applicable law

For complete license terms, see the LICENSE file.

📞 Contact

• Legal/Licensing: legal@hivetechs.io
• Technical Support: support@hivetechs.io
• General Inquiries: hello@hivetechs.io
• Website: https://hivetechs.io

Copyright © 2025 HiveTechs Collective LLC. All rights reserved.
hive-tools Professional Software License v2.0 (Enhanced Protection)