KDeps: A Comprehensive AI Framework for Full-Stack Applications

Executive Summary

KDeps is an innovative, all-in-one, offline-ready AI framework designed to democratize the development of full-stack applications with integrated AI capabilities. Built with high-performance Golang and leveraging Apple's PKL (Package Language) for configuration, KDeps provides a low-code/no-code platform that enables developers and non-technical users alike to create sophisticated applications featuring integrated open-source Large Language Models (LLMs) for AI-powered APIs and workflows with minimal complexity.

The framework's core value proposition lies in its ability to package complete applications into portable, Dockerized containers that include integrated open-source LLMs as the AI engine. These LLMs power the application's AI capabilities through APIs and workflows, eliminating the traditional barriers to AI application development, including complex infrastructure setup, model deployment, and API integration challenges.

Introduction
Architecture Overview
Core Components
Key Features
Technical Implementation
Use Cases and Applications
Competitive Analysis
Future Roadmap
Conclusion

Introduction

About the name

“KDeps, short for ‘knowledge dependencies,’ is inspired by the principle that knowledge—whether from AI, machines, or humans—can be represented, organized, orchestrated, and interacted with through graph-based systems. The name grew out of my work on Kartographer, a lightweight graph library for organizing and interacting with information. KDeps builds on Kartographer’s foundation and serves as a RAG-first (Retrieval-Augmented Generation) AI agent framework.” — Joel Bryan Juliano, KDeps creator

Why Offline-First?

Organizations increasingly require AI systems that can operate securely, reliably, and predictably without cloud dependencies. Offline-first design provides:

Privacy and compliance: Data remains on-premises to satisfy PII, GDPR, HIPAA, and enterprise policies
Reliability: Applications continue functioning during outages or network degradation
Low latency: Local inference eliminates network round-trips for faster responses
Predictable cost: No per-token fees or vendor subscriptions; models run locally
Control and independence: Avoids vendor lock-in and enables reproducible deployments
Data residency: Meets jurisdictional requirements by processing data locally
Security: Reduces external attack surface by removing third-party AI API dependencies
Edge readiness: Processes data near its source for real-time use cases

KDeps enables offline-first operation by integrating open-source LLMs (via Ollama) and packaging complete applications—including models and runtimes—into Docker images. No external AI APIs are required in production.

Problem Statement

The current landscape of AI application development presents significant challenges:

Complexity: Building applications with integrated AI capabilities requires expertise in multiple domains including machine learning, infrastructure, API development, and frontend development
Infrastructure Overhead: Deploying and managing AI models as part of applications requires substantial infrastructure setup and maintenance
Vendor Lock-in: Many AI platforms create dependencies on proprietary services and APIs
Scalability Issues: Traditional application architectures struggle with scaling AI components and resource management
Development Velocity: The time-to-market for applications with AI features is often extended due to integration complexity

Solution Overview

KDeps addresses these challenges through a comprehensive framework that provides:

Declarative Configuration: Uses PKL (Package Language) for human-readable, type-safe configuration
Dockerized Deployment: Complete applications packaged as portable containers with integrated AI engines
Open-Source LLM Integration: Built-in support for Ollama and other open-source models that power application AI capabilities
Full-Stack Capabilities: Integrated API and web server functionality with AI-powered endpoints
Low-Code Development: Visual workflow design with minimal programming required for AI features
Offline-First Design: Complete offline operation with local LLM inference, no external API dependencies required

Current Capabilities

Offline AI Processing

KDeps is designed from the ground up for offline operation, providing complete AI capabilities without requiring external API calls:

Local LLM Inference: Integrated Ollama support enables full offline AI processing with open-source models
Self-Contained Applications: Dockerized applications include all necessary AI components and dependencies
No External Dependencies: Applications can run entirely offline without internet connectivity
Resource-Constrained Environments: Optimized for deployment in edge computing and IoT environments
Privacy-First: All AI processing happens locally, ensuring data privacy and security

Edge Computing Ready

KDeps applications are immediately deployable in edge computing scenarios:

Portable Containers: Self-contained Docker images work in any environment with Docker support
Minimal Resource Requirements: Optimized for resource-constrained edge devices
Local Model Management: Built-in model downloading and management for offline operation
Scalable Architecture: Can be deployed across distributed edge networks

Architecture Overview

High-Level Architecture

KDeps employs a modular, microservices-inspired architecture built around several key components:

┌─────────────────────────────────────────────────────────────┐
│                    KDeps Application                        │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐         │
│  │   API       │  │   Web       │  │   LLM       │         │
│  │  Server     │  │  Server     │  │  Engine     │         │
│  └─────────────┘  └─────────────┘  └─────────────┘         │
├─────────────────────────────────────────────────────────────┤
│                    Resource Resolver                        │
│              (Dependency Graph Engine)                      │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐         │
│  │   Python    │  │   HTTP      │  │   Exec      │         │
│  │  Runtime    │  │  Client     │  │  Runtime    │         │
│  └─────────────┘  └─────────────┘  └─────────────┘         │
├─────────────────────────────────────────────────────────────┤
│                    Docker Container                         │
│              (Ubuntu + Ollama + Anaconda)                   │
└─────────────────────────────────────────────────────────────┘

Core Design Principles

Declarative Configuration: All application logic is defined through PKL configuration files
Dependency Resolution: Resources are executed based on a directed acyclic graph (DAG)
Containerization: Complete applications are packaged as self-contained Docker images
Modularity: Resources can be composed, reused, and remixed across applications
Type Safety: PKL provides compile-time validation of configurations

Core Components

1. Workflow Engine

The workflow engine is the central orchestrator of KDeps applications. It manages:

Configuration Loading: Parses PKL workflow files and validates configurations
Resource Resolution: Determines execution order based on dependencies
State Management: Handles application state and memory operations
Error Handling: Provides comprehensive error reporting and recovery

Key Features:

Support for both API server and Lambda execution modes
Integrated CORS and security configurations
Multi-model LLM support
Custom package and repository management

2. Resource System

Resources are the fundamental building blocks of KDeps applications. Each resource represents a discrete unit of functionality:

Resource Types:

LLM Resources: Interface with language models for text generation and analysis
HTTP Resources: Make external API calls and handle web requests
Python Resources: Execute Python code in isolated Anaconda environments
Exec Resources: Run shell commands and system operations
Response Resources: Format and return API responses
Data Resources: Manage file operations and data persistence
Memory Resources: Persistent state management with SQLite backend
Session Resources: User session data and temporary state
Tool Resources: Script execution and tool management

Resource Characteristics:

Unique ActionID for identification
Dependency declarations for execution ordering
Validation and preflight checks with custom error handling
Conditional execution logic with skip conditions
Timeout and error handling
Asynchronous processing with goroutines
Base64 encoding support for binary data
Environment variable management
File upload and processing capabilities

3. Dependency Resolver

The dependency resolver implements a sophisticated graph-based execution engine powered by the Kartographer library:

Features:

DAG Construction: Builds dependency graphs from resource declarations
Topological Sorting: Determines optimal execution order
Cycle Detection: Prevents circular dependencies
Parallel Execution: Runs independent resources concurrently
Error Propagation: Handles failures gracefully across the dependency chain
Resource Isolation: Each resource ID can only be used once per workflow to prevent complex dependency issues
Sequential Execution: Resources execute in top-down queue manner for predictable behavior

Dependency Management:

pkl

// Example dependency chain
Requires {
    "llmResource"      // First: LLM processing
    "pythonResource"   // Second: Data transformation
    "httpResource"     // Third: External API call
}
// Target: responseResource (executed last)

Resource Reusability:

Resources can be remixed and reused across different workflows
External agents can be imported and referenced with versioning
Support for agent composition and modular design

4. Docker Integration

KDeps provides comprehensive Docker support for application packaging and deployment:

Capabilities:

Image Generation: Creates optimized Docker images with all dependencies including integrated AI engines
Model Management: Integrates Ollama for LLM model deployment as the application's AI engine
Environment Setup: Configures Ubuntu packages, Python environments, and system dependencies for AI-powered applications
Compose Integration: Generates docker-compose files for multi-service deployments with AI capabilities
Volume Management: Handles persistent storage for models and application data

Key Features

1. Low-Code/No-Code Development

KDeps enables rapid development of applications with integrated AI capabilities through declarative configuration:

pkl

// Example: Customer Support AI Agent
Name = "ticketResolutionAgent"
Description = "Automates customer support ticket resolution"
Version = "1.0.0"
TargetActionID = "responseResource"

Settings {
  APIServerMode = true
  APIServer {
    HostIP = "127.0.0.1"
    PortNum = 3000
    Routes {
      new { Path = "/api/v1/ticket"; Methods { "POST" } }
    }
  }
  AgentSettings {
    Models { "llama3.2:1b" }
    OllamaImageTag = "0.6.8"
  }
}

2. Multimodal AI Support

KDeps supports vision and multimodal LLMs as integrated AI engines for comprehensive application capabilities:

Image Processing: Analyze and extract information from images
Document Analysis: Process PDFs, images, and other document formats
Video Processing: Handle video content through frame extraction
Audio Integration: Process audio files and transcriptions

3. Tool Integration and Automation

KDeps enables integrated LLMs to automatically execute tools and scripts with sophisticated tool management:

pkl

Chat {
  Model = "llama3.2:1b"
  Prompt = "Generate a sales report based on database query results"
  Tools {
    new {
      Name = "query_sales_db"
      Script = "@(data.filepath('tools/1.0.0', 'query_sales.py'))"
      Description = "Queries the sales database for recent transactions"
      Parameters {
        ["date_range"] { Required = true; Type = "string" }
      }
    }
  }
}

Tool Management Features:

Automatic Tool Execution: Integrated LLMs can automatically invoke tools based on context
Tool History Tracking: All tool executions are logged and can be retrieved
Parameter Validation: Tools validate input parameters before execution
Output Caching: Tool results are cached to avoid redundant executions
Error Handling: Comprehensive error handling for tool execution failures
Script Isolation: Tools run in isolated environments for security

4. Structured Output Generation

KDeps ensures consistent, machine-readable responses from integrated LLMs:

pkl

Chat {
  Model = "llama3.2:1b"
  JSONResponse = true
  JSONResponseKeys { "summary"; "keywords"; "sentiment" }
  TimeoutDuration = 60.s
}

JSON Processing Features:

Type Hints: Support for type annotations (e.g., first_name_string, age_integer)
JSON Parsing: Built-in JSONParser and JSONParserMapping functions
JSON Rendering: JSONRenderDocument for converting PKL objects to JSON
Structured Validation: Automatic validation of JSON structure and types
Dynamic Mapping: Support for nested and dynamic JSON structures

5. Full-Stack Application Support

KDeps can serve both APIs and web applications:

API Server: RESTful API endpoints with validation and CORS support
Web Server: Static file serving and reverse proxy capabilities
Frontend Integration: Support for Streamlit, React, and other frontend frameworks
Session Management: Persistent state and user session handling

6. Resource Reusability and Remixing

KDeps promotes code reuse through a modular resource system with comprehensive agent composition:

7. Advanced Validation and Error Handling

KDeps provides sophisticated validation and error handling mechanisms:

pkl

// Preflight validations with custom error handling
PreflightCheck {
    Validations {
        "@(request.filecount())" > 0
        "@(request.data().query.length)" > 5
        "@(request.filetype('document'))" == "image/jpeg"
    }
    Error {
        Code = 422
        Message = "Invalid input: File required and query must be at least 5 characters"
    }
}

// Skip conditions for conditional execution
SkipCondition {
    "@(request.data().priority)" == "low"
}

Validation Features:

Preflight Checks: Validate conditions before resource execution
Custom Error Codes: Define specific HTTP status codes and messages
Skip Conditions: Conditionally bypass resource execution
Type Safety: PKL provides compile-time validation
Input Sanitization: Automatic sanitization of user inputs

8. State Management and Persistence

KDeps provides comprehensive state management capabilities:

pkl

// Memory operations for persistent state
Expr {
    "@(memory.setRecord('user_preferences', request.data().preferences))"
}

// Session management for temporary state
local sessionData = "@(session.getRecord('current_user'))"

// Tool execution history
local toolResult = "@(tool.run('script_name', 'parameters'))"

State Management Features:

Memory Resources: Persistent SQLite-based storage
Session Resources: Temporary user session data
Tool Resources: Script execution history and caching
Data Resources: File-based data management
Cross-Resource State: Shared state across resource boundaries

9. Expression Blocks and Side Effects

KDeps supports expression blocks for executing side effects and inline logic:

pkl

Expr {
    "@(memory.setRecord('status', 'active'))"
    "@(memory.setRecord('timestamp', request.data().timestamp))"
    "@(session.setRecord('user_id', request.data().user_id))"
}

Expression Block Features:

Side Effect Execution: Execute functions that modify state without returning values
Inline Scripting: Evaluate arbitrary PKL expressions for logic and assignments
Sequential Evaluation: Multiple expressions evaluated in sequence
State Updates: Direct manipulation of memory, session, and tool resources
Procedural Logic: Implement complex logic directly within configurations

10. Items Iteration and Sequential Processing

KDeps supports sophisticated iteration over multiple items with contextual awareness:

pkl

Items {
    "A long, long time ago"
    "I can still remember"
    "How that music used to make me smile"
    "And I knew if I had my chance"
}

run {
    SkipCondition {
        item.current() == "And I knew if I had my chance"
    }
    Chat {
        Model = "llama3.2:1b"
        Prompt = "Generate MTV scenario for: @(item.current())"
        Scenario {
            new { Role = "system"; Prompt = "Previous: @(item.prev())" }
            new { Role = "system"; Prompt = "Next: @(item.next())" }
        }
    }
}

Iteration Features:

Contextual Access: item.current(), item.prev(), item.next()
Conditional Processing: Skip specific items during iteration
Sequential Execution: Process items in defined order
Cross-Item Context: Access previous and next items for context
Resource Integration: Works with all resource types (LLM, HTTP, Python, etc.)

11. Promise Operator and Deferred Execution

KDeps uses a sophisticated promise system for deferred execution:

pkl

// Promise operator ensures deferred execution
local response = "@(llm.response('chatResource'))"
local filePath = "@(data.filepath('tools/1.0.0', 'script.py'))"

// Skip conditions with promises
SkipCondition {
    "@(request.data().priority)" == "low"
}

// Preflight validations with promises
PreflightCheck {
    Validations {
        "@(request.filecount())" > 0
        "@(request.filetype('document'))" == "image/jpeg"
    }
}

Promise System Features:

Deferred Execution: Functions execute only when needed
Template Processing: @() syntax for dynamic value resolution
Type Safety: PKL provides compile-time validation
Resource Integration: Seamless integration with all resource functions
Error Prevention: Prevents premature execution of dependent functions

12. Advanced Tool Management and Execution

KDeps provides sophisticated tool management with automatic execution and history tracking:

pkl

Chat {
    Model = "llama3.2:1b"
    Tools {
        new {
            Name = "database_query"
            Script = "@(data.filepath('tools/1.0.0', 'query_db.py'))"
            Description = "Execute database queries with parameters"
            Parameters {
                ["query"] { Required = true; Type = "string" }
                ["limit"] { Required = false; Type = "integer" }
            }
        }
    }
}

Tool Management Features:

Automatic Tool Discovery: LLMs automatically identify and use appropriate tools
Parameter Validation: Tools validate input parameters before execution
Execution History: All tool executions are logged and retrievable
Output Caching: Tool results are cached to avoid redundant executions
Error Handling: Comprehensive error handling for tool execution failures
Script Isolation: Tools run in isolated environments for security
Cross-Tool Communication: Tools can share data and results

13. Dynamic Import and Resource Management

KDeps automatically manages imports and resource dependencies:

pkl

// Automatic import resolution
import "package://schema.kdeps.com/core@0.2.30#/LLM.pkl" as llm
import "package://schema.kdeps.com/core@0.2.30#/HTTP.pkl" as client
import "package://schema.kdeps.com/core@0.2.30#/Python.pkl" as python

// Resource function access
local llmResponse = "@(llm.response('chatResource'))"
local httpData = "@(client.responseBody('apiResource'))"
local pythonOutput = "@(python.stdout('processResource'))"

Import Management Features:

Dynamic Import Resolution: Automatic import generation based on resource usage
Schema Version Management: Handles schema compatibility across versions
Resource File Organization: Automatic organization of resource output files
Dependency Tracking: Tracks and resolves resource dependencies
Cleanup Management: Automatic cleanup of temporary files and resources

14. Advanced LLM Chat Processing and Tool Chaining

KDeps provides sophisticated LLM processing with intelligent tool chaining and iteration management:

pkl

Chat {
    Model = "llama3.2:1b"
    Role = "assistant"
    Prompt = "Analyze this data and generate a comprehensive report"
    Scenario {
        new { Role = "system"; Prompt = "You are a data analyst expert" }
        new { Role = "system"; Prompt = "Previous analysis: @(llm.response('previousAnalysis'))" }
    }
    Tools {
        new {
            Name = "data_query"
            Script = "@(data.filepath('tools/1.0.0', 'query_data.py'))"
            Description = "Query the database for specific data"
            Parameters {
                ["table"] { Required = true; Type = "string" }
                ["columns"] { Required = false; Type = "array" }
            }
        }
    }
    JSONResponse = true
    JSONResponseKeys { "summary"; "insights"; "recommendations" }
    TimeoutDuration = 120.s
}

Advanced LLM Features:

Multi-Iteration Tool Execution: Up to 5 iterations for complex tool chains
Tool Call Deduplication: Prevents redundant tool executions
Contextual Message History: Maintains conversation context across iterations
Automatic Tool Discovery: LLMs automatically identify and use appropriate tools
Structured JSON Output: Enforced JSON responses with predefined keys
Role-Based Message Processing: Support for system, user, assistant, and tool roles
File Processing: Direct integration with uploaded files and documents
Timeout Management: Configurable timeouts with graceful degradation

15. Multimodal AI and File Processing

KDeps supports comprehensive multimodal AI capabilities:

pkl

Chat {
    Model = "llama3.2-vision"
    Prompt = "Analyze this image and extract key information"
    Files { "@(request.files()[0])" }
    JSONResponse = true
    JSONResponseKeys {
        "description_text"
        "style_text"
        "category_text"
        "objects_detected"
    }
}

Multimodal Features:

Vision Models: Support for LLaVA, Llama3.2-vision, and other vision models
File Upload Processing: Direct handling of uploaded images and documents
Binary Content Support: Native handling of binary file content
MIME Type Detection: Automatic file type detection and validation
Structured Image Analysis: JSON-structured output for image analysis
Cross-Modal Integration: Seamless integration between text and vision processing

16. Advanced Response Processing and Error Handling

KDeps provides sophisticated response processing with comprehensive error management:

pkl

APIResponse {
    Success = true
    Response {
        Data {
            "@(llm.response('chatResource'))"
            "@(python.stdout('processResource'))"
        }
    }
    Meta {
        RequestID = "@(request.ID())"
        Headers {
            ["Content-Type"] = "application/json"
            ["X-Processing-Time"] = "@(exec.stdout('timingResource'))"
        }
    }
    Errors {
        new {
            Code = 422
            Message = "Validation failed for input data"
        }
    }
}

Response Processing Features:

Dynamic JSON Generation: Automatic PKL to JSON conversion with type validation
Error Accumulation: Thread-safe error collection with action ID tracking
Response Metadata: Request ID, headers, and processing information
Fallback Mechanisms: SDK-first evaluation with CLI fallback
Memory Management: Automatic cleanup of request-specific error collections
Structured Error Handling: Comprehensive error codes and messages

17. Global Functions and Utilities

KDeps provides extensive global functions for enhanced resource capabilities:

pkl

// API Request Functions
local requestID = "@(request.ID())"
local clientIP = "@(request.IP())"
local requestData = "@(request.data())"
local fileCount = "@(request.filecount())"
local filePaths = "@(request.files())"

// Memory Operations
expr {
    "@(memory.setRecord('user_preferences', request.data().preferences))"
    "@(session.setRecord('current_user', request.data().user_id))"
}

// Skip Conditions
local skipAuth = "@(skip.ifFileExists('/tmp/bearer.txt'))"
SkipCondition { skipAuth == "true" }

// Document Processing
local jsonData = "@(document.JSONParser(request.data().json))"
local yamlOutput = "@(document.yamlRenderDocument(processedData))"

Global Function Categories:

API Request Functions: Request ID, IP, data, parameters, headers, files
Memory Operations: Persistent and session-based state management
Skip Conditions: File existence, folder checks, empty file detection
Document Processing: JSON, YAML, XML parsing and rendering
Item Iteration: Current, previous, and next item access
Tool Management: Manual tool execution and history tracking
Data Functions: File path resolution and data folder access

pkl

// Install external agents
// kdeps install conveyour_counting_ai-1.2.5.kdeps

// Reuse existing agents in workflow
Workflows { 
  "@conveyour_counting_ai:1.2.5"
  "@ticketResolutionAgent" 
}

// Reference specific resources with versioning
Requires {
  "llmResource"
  "@conveyour_counting_ai/countImageLLM:1.2.5"
  "@ticketResolutionAgent/llmResource:1.0.0"
}

// Access external agent outputs
local externalResult = "@(llm.response('@conveyour_counting_ai/countImageLLM:1.2.5'))"

Agent Composition Features:

Agent Installation: Install external agents with version management
Resource Referencing: Reference specific resources from external agents
Version Control: Specify exact versions or use latest available
Output Integration: Seamlessly integrate outputs from external agents
Dependency Resolution: Automatic resolution of cross-agent dependencies

Technical Implementation

1. Core Technology Stack

Backend:

Golang: High-performance, concurrent execution engine with goroutines
PKL (Package Language): Declarative configuration and validation with type safety
Gin: HTTP framework for API server implementation with middleware support
Afero: Virtual file system for testing and abstraction
LangChain: LLM integration and tool execution framework

AI/ML:

Ollama: Local LLM deployment and management with model caching
Anaconda: Python environment management with isolated execution
SQLite: Lightweight data persistence for state management

Infrastructure:

Docker: Containerization and deployment with multi-stage builds
Docker Compose: Multi-service orchestration with volume management
Kartographer: Custom graph library for dependency resolution

Development Tools:

Base64 Encoding: Secure data transmission and storage
UUID Generation: Unique identifier management
Timestamp Management: Resource execution timing and synchronization
JSON Processing: Advanced JSON validation, parsing, and repair utilities
Error Management: Thread-safe error accumulation and tracking systems

2. Performance Characteristics

Scalability:

Concurrent resource execution with goroutines
Efficient dependency resolution using DAG algorithms
Memory-optimized model loading with Ollama integration
Horizontal scaling through container orchestration
Asynchronous processing for non-blocking operations

Reliability:

Comprehensive error handling with custom error codes
Graceful degradation with fallback mechanisms
Automatic retry mechanisms with exponential backoff
Health check integration and monitoring
Database connection resilience with retry logic

Security:

Input validation and sanitization with PKL type safety
CORS configuration for cross-origin requests
Trusted proxy support for secure deployments
Isolated execution environments for Python and shell scripts
Base64 encoding for secure data transmission
SQLite-based state management with proper access controls
Request isolation with thread-safe error handling
File upload security with MIME type validation
Memory leak prevention with automatic cleanup

3. Development Workflow

Local Development:

Create project with kdeps new <project-name>
Configure workflow and resources with PKL
Test locally with kdeps run
Package with kdeps package
Install external agents with kdeps install

Deployment:

Build Docker image with kdeps build
Deploy to container orchestration platform
Configure environment variables and secrets
Monitor and scale as needed

Validation and Testing:

Preflight validations ensure resource readiness
Skip conditions for conditional execution
Comprehensive error handling and reporting
Resource dependency validation
Tool execution testing and validation
JSON validation and repair utilities
Error accumulation with action ID tracking

Import Management:

Dynamic import resolution for resource functions
Automatic PKL file generation and management
Schema version compatibility handling
Resource output file organization and cleanup
Global function integration and management

Use Cases and Applications

1. Customer Support Automation

Scenario: Automate customer ticket resolution with AI-powered responses

Implementation:

LLM resources for response generation
HTTP resources for CRM integration
Validation for ticket data
Structured output for consistent responses

2. Document Processing and Analysis

Scenario: Extract and analyze information from uploaded documents

Implementation:

Vision models for document processing
Python resources for data transformation
HTTP resources for external API calls
Memory resources for state persistence

3. Data Analytics and Reporting

Scenario: Generate automated reports from database queries

Implementation:

Python resources for data analysis
Tool integration for database queries
LLM resources for report generation
HTTP resources for report delivery

5. IoT and Sensor Data Processing

Scenario: Process and analyze IoT sensor data streams

Implementation:

HTTP resources for data ingestion
Python resources for data processing
LLM resources for anomaly detection
Real-time API endpoints

6. Multi-Agent Workflows

Scenario: Orchestrate complex workflows involving multiple specialized agents

Implementation:

Agent composition with external agent integration
Cross-agent resource dependencies
Shared state management through memory resources
Coordinated execution with dependency resolution
Tool chaining across multiple agents

7. Content Generation and Management

Scenario: Create and manage content with AI assistance

Implementation:

LLM resources for content generation
File upload and processing
Structured output for content formatting
API endpoints for content management
Memory resources for content state persistence

8. Advanced Analytics and Reporting

Scenario: Complex data analysis with AI-powered insights

Implementation:

Python resources for data processing and analysis
LLM resources for natural language insights
Tool integration for database queries and external APIs
Structured output for automated reporting
Memory resources for caching analysis results

9. Batch Processing and Iteration

Scenario: Process large datasets with sequential item handling

Implementation:

Items iteration for batch processing
Contextual processing with previous/next item awareness
Skip conditions for selective processing
Resource functions for data transformation
Structured output for batch results

10. Real-time Data Processing

Scenario: Process streaming data with immediate AI analysis

Implementation:

HTTP resources for data ingestion
LLM resources for real-time analysis
Memory resources for state persistence
Tool resources for data transformation
API endpoints for immediate responses

11. Advanced Data Processing and Analytics

Scenario: Complex data transformation and analysis workflows

Implementation:

Python resources for data processing and statistical analysis
LLM resources for natural language insights and interpretation
Tool integration for database operations and external APIs
Structured JSON output for automated reporting
Memory resources for caching intermediate results
Expression blocks for side effects and state management

13. Advanced API Development and Response Management

Scenario: Building sophisticated APIs with comprehensive response handling

Implementation:

Dynamic JSON generation with PKL to JSON conversion
Thread-safe error accumulation and tracking
Response metadata with request ID and headers
Fallback mechanisms with SDK-first evaluation
Memory management with automatic cleanup
Structured error handling with comprehensive codes
Global functions for enhanced request processing

Competitive Analysis

Comparison with Existing Solutions

Feature	KDeps	LangChain	Streamlit	Hugging Face
Deployment Model	Containerized	Library	Web App	Cloud/API
Configuration	Declarative (PKL)	Code-based	Code-based	API-based
LLM Integration	Local (Ollama)	Multiple	Limited	Cloud-based
Full-Stack	Yes	No	Frontend only	API only
Portability	High	Medium	Low	High
Learning Curve	Low	High	Medium	Medium

Competitive Advantages

Unified Platform: Single framework for complete AI application development
Local Deployment: No dependency on cloud services or external APIs
Declarative Configuration: Reduces complexity and improves maintainability
Containerization: Ensures consistent deployment across environments
Resource Reusability: Promotes code sharing and ecosystem development

Market Positioning

KDeps targets the intersection of several market segments:

AI Application Development: Simplifying AI application creation
DevOps and MLOps: Streamlining AI model deployment and management
Enterprise AI: Providing on-premises AI capabilities
Edge Computing: Enabling offline AI applications in resource-constrained environments with local model inference

Future Roadmap

Short-term Goals (6-12 months)

Enhanced Model Support
- Integration with additional LLM providers (OpenAI, Anthropic, etc.)
- Support for fine-tuned models and custom model training
- Model versioning and management with rollback capabilities
- Model performance monitoring and optimization
- Multimodal model support (vision, audio, text)
Improved Developer Experience
- Visual workflow editor with drag-and-drop interface
- Enhanced debugging and monitoring with real-time logs
- IDE integration and extensions for popular editors
- Interactive resource testing and validation tools
- Advanced tool management and debugging capabilities
Performance Optimizations
- Model caching and optimization for faster inference
- Parallel execution improvements with resource pooling
- Resource usage optimization and memory management
- Distributed execution across multiple containers
- Tool execution optimization and caching
Advanced AI Capabilities
- Multi-iteration tool chaining with intelligent deduplication
- Contextual message history management
- Advanced role-based message processing
- Structured JSON output with type hints
- File processing and multimodal integration
- Global functions and utility integration
- Advanced response processing and error handling

Medium-term Goals (1-2 years)

Enterprise Features
- Multi-tenant support with isolation
- Advanced security and compliance (SOC2, GDPR)
- Enterprise authentication and authorization (OAuth, SAML)
- Audit logging and compliance reporting
Scalability Enhancements
- Kubernetes integration with custom operators
- Auto-scaling capabilities based on demand
- Distributed execution across multiple nodes
- Load balancing and failover mechanisms
Ecosystem Development
- Resource marketplace with versioning
- Community-driven templates and examples
- Third-party integrations and plugins
- Developer SDKs and APIs
Advanced AI Features
- Multi-agent orchestration and coordination
- Federated learning support for distributed training
- Advanced reasoning and planning capabilities
- Custom model training and fine-tuning

Long-term Vision (2+ years)

AI-First Development Platform
- AI-assisted application development with code generation
- Automated optimization and tuning of workflows
- Intelligent resource composition and orchestration
- Natural language workflow definition
Advanced Edge AI Capabilities
- Enhanced mobile and IoT deployment with resource constraints
- Advanced offline AI processing with local model inference (already available)
- Edge-cloud synchronization and data management
- Distributed AI across edge networks
Advanced AI Features
- Multi-agent systems with emergent behaviors
- Federated learning support for privacy-preserving training
- Advanced reasoning capabilities with symbolic AI
- Autonomous agent development and training
Quantum Computing Integration
- Quantum-ready algorithms and workflows
- Hybrid classical-quantum processing
- Quantum machine learning capabilities
- Quantum-safe security protocols

Conclusion

KDeps represents a significant advancement in application development with integrated AI capabilities, addressing the fundamental challenges that have hindered widespread adoption of AI technologies. By providing a unified, containerized platform with declarative configuration and integrated open-source LLMs, KDeps enables developers and organizations to rapidly build and deploy sophisticated applications with AI-powered features without the traditional complexity and infrastructure overhead.

Key Value Propositions

Democratization of AI: Makes application development with integrated AI capabilities accessible to non-experts
Operational Efficiency: Reduces time-to-market and development costs for AI-powered applications
Vendor Independence: Eliminates lock-in to proprietary AI services through open-source LLM integration
Scalability: Provides enterprise-grade scalability and reliability for applications with AI features
Innovation Acceleration: Enables rapid prototyping and iteration of AI-powered applications

Strategic Impact

KDeps has the potential to transform how organizations approach AI adoption:

Reduced Barriers to Entry: Lower technical requirements for AI implementation
Increased Innovation: Faster experimentation and deployment cycles
Cost Optimization: Reduced infrastructure and development costs
Competitive Advantage: Faster time-to-market for AI-powered features

Call to Action

For organizations looking to accelerate their AI initiatives, KDeps provides a compelling solution that balances simplicity with power. The framework's open architecture, comprehensive feature set, and focus on developer experience make it an ideal choice for both startups and enterprises seeking to leverage AI capabilities effectively.

The future of AI application development is not just about more powerful models, but about making those models accessible and usable in real-world applications. KDeps represents a significant step toward that future, providing the tools and infrastructure needed to bring AI capabilities to every application and organization.

This whitepaper provides a comprehensive overview of the KDeps framework, its capabilities, and its potential impact on the AI application development landscape. For more information, visit the official documentation at kdeps.com or explore the open-source repository at github.com/kdeps/kdeps.

API Server Settings

Web Server Settings

Web Server Routes

AI Agent Settings

KDeps: A Comprehensive AI Framework for Full-Stack Applications ​

Executive Summary ​

Table of Contents ​

Introduction ​

About the name ​

Why Offline-First? ​

Problem Statement ​

Solution Overview ​

Current Capabilities ​

Offline AI Processing ​

Edge Computing Ready ​

Architecture Overview ​

High-Level Architecture ​

Core Design Principles ​

Core Components ​

1. Workflow Engine ​

2. Resource System ​

3. Dependency Resolver ​

4. Docker Integration ​

Key Features ​

1. Low-Code/No-Code Development ​

2. Multimodal AI Support ​

3. Tool Integration and Automation ​

4. Structured Output Generation ​

5. Full-Stack Application Support ​

6. Resource Reusability and Remixing ​

7. Advanced Validation and Error Handling ​

8. State Management and Persistence ​

9. Expression Blocks and Side Effects ​

10. Items Iteration and Sequential Processing ​

11. Promise Operator and Deferred Execution ​

12. Advanced Tool Management and Execution ​

13. Dynamic Import and Resource Management ​

14. Advanced LLM Chat Processing and Tool Chaining ​

15. Multimodal AI and File Processing ​

16. Advanced Response Processing and Error Handling ​

17. Global Functions and Utilities ​

Technical Implementation ​

1. Core Technology Stack ​

2. Performance Characteristics ​

3. Development Workflow ​

Use Cases and Applications ​

1. Customer Support Automation ​

2. Document Processing and Analysis ​

3. Data Analytics and Reporting ​

5. IoT and Sensor Data Processing ​

6. Multi-Agent Workflows ​

7. Content Generation and Management ​

8. Advanced Analytics and Reporting ​

9. Batch Processing and Iteration ​

10. Real-time Data Processing ​

11. Advanced Data Processing and Analytics ​

13. Advanced API Development and Response Management ​

Competitive Analysis ​

Comparison with Existing Solutions ​

Competitive Advantages ​

Market Positioning ​

Future Roadmap ​

Short-term Goals (6-12 months) ​

Medium-term Goals (1-2 years) ​

Long-term Vision (2+ years) ​

Conclusion ​

Key Value Propositions ​

Strategic Impact ​

Call to Action ​

KDeps: A Comprehensive AI Framework for Full-Stack Applications

Executive Summary

Table of Contents

Introduction

About the name

Why Offline-First?

Problem Statement

Solution Overview

Current Capabilities

Offline AI Processing

Edge Computing Ready

Architecture Overview

High-Level Architecture

Core Design Principles

Core Components

1. Workflow Engine

2. Resource System

3. Dependency Resolver

4. Docker Integration

Key Features

1. Low-Code/No-Code Development

2. Multimodal AI Support

3. Tool Integration and Automation

4. Structured Output Generation

5. Full-Stack Application Support

6. Resource Reusability and Remixing

7. Advanced Validation and Error Handling

8. State Management and Persistence

9. Expression Blocks and Side Effects

10. Items Iteration and Sequential Processing

11. Promise Operator and Deferred Execution

12. Advanced Tool Management and Execution

13. Dynamic Import and Resource Management

14. Advanced LLM Chat Processing and Tool Chaining

15. Multimodal AI and File Processing

16. Advanced Response Processing and Error Handling

17. Global Functions and Utilities

Technical Implementation

1. Core Technology Stack

2. Performance Characteristics

3. Development Workflow

Use Cases and Applications

1. Customer Support Automation

2. Document Processing and Analysis

3. Data Analytics and Reporting

5. IoT and Sensor Data Processing

6. Multi-Agent Workflows

7. Content Generation and Management

8. Advanced Analytics and Reporting

9. Batch Processing and Iteration

10. Real-time Data Processing

11. Advanced Data Processing and Analytics

13. Advanced API Development and Response Management

Competitive Analysis

Comparison with Existing Solutions

Competitive Advantages

Market Positioning

Future Roadmap

Short-term Goals (6-12 months)

Medium-term Goals (1-2 years)

Long-term Vision (2+ years)

Conclusion

Key Value Propositions

Strategic Impact

Call to Action