Introduction ​

KDeps is a framework for building, configuring, and deploying AI agent workflows through simple YAML configuration. It packages everything needed for RAG and AI agents, eliminating the complexity of building self-hosted APIs with LLMs.

Key Highlights ​

YAML-First Configuration ​

Build AI agents using simple, self-contained YAML configuration blocks. No complex programming required - just define your resources and let KDeps handle the orchestration.

apiVersion: kdeps.io/v1
kind: Workflow
metadata:
  name: my-agent
  version: "1.0.0"
  targetActionId: responseResource
settings:
  apiServerMode: true
  apiServer:
    portNum: 3000
    routes:
      - path: /api/v1/chat
        methods: [POST]

Local-First Execution ​

Run workflows instantly on your local machine with sub-second startup time. Docker is optional and only needed for deployment.

# Run locally (instant startup)
kdeps run workflow.yaml

# Hot reload for development
kdeps run workflow.yaml --dev

Unified API ​

Access data from any source with just two functions: get() and set(). No more memorizing 15+ different function names.

# All of these work with get():
query: get('q')                    # Query parameter
auth: get('Authorization')         # Header
data: get('llmResource')           # Resource output
user: get('user_name', 'session')  # Session storage

Multi-LLM Support ​

Use any LLM backend - local or cloud. Mix and match different models in the same workflow.

Enterprise-Ready Features ​

• Session persistence with SQLite or in-memory storage
• Connection pooling for databases
• Retry logic with exponential backoff
• Response caching with TTL
• CORS configuration for web applications
• WebServer mode for static files and reverse proxying

Quick Start ​

# Install KDeps (Mac/Linux)
curl -LsSf https://raw.githubusercontent.com/kdeps/kdeps/main/install.sh | sh

# Or via Homebrew (Mac)
brew install kdeps/tap/kdeps

# Create a new agent interactively
kdeps new my-agent

Example: Simple Chatbot ​

workflow.yaml

apiVersion: kdeps.io/v1
kind: Workflow
metadata:
  name: chatbot
  version: "1.0.0"
  targetActionId: responseResource
settings:
  apiServerMode: true
  apiServer:
    portNum: 3000
    routes:
      - path: /api/v1/chat
        methods: [POST]
  agentSettings:
    models:
      - llama3.2:1b

resources/llm.yaml

apiVersion: kdeps.io/v1
kind: Resource
metadata:
  actionId: llmResource
  name: LLM Chat
run:
  chat:
    model: llama3.2:1b
    prompt: "{{ get('q') }}"
    jsonResponse: true
    jsonResponseKeys:
      - answer

resources/response.yaml

apiVersion: kdeps.io/v1
kind: Resource
metadata:
  actionId: responseResource
  requires:
    - llmResource
run:
  apiResponse:
    success: true
    response:
      data: get('llmResource')

Test it:

kdeps run workflow.yaml
curl -X POST http://localhost:3000/api/v1/chat -d '{"q": "What is AI?"}'

Documentation ​

Getting Started ​

• Installation - Install KDeps on your system
• Quickstart - Build your first AI agent

Configuration ​

• Workflow - Workflow configuration reference
• Session & Storage - Session persistence and storage
• CORS - Cross-origin resource sharing
• Advanced - Imports, request object, agent settings

Resources ​

• Overview - Resource types and common configuration
• LLM (Chat) - Language model integration
• LLM Backends - All supported LLM backends
• HTTP Client - External API calls
• SQL - Database queries
• Python - Python script execution
• Exec - Shell command execution
• API Response - Response formatting

Concepts ​

• Unified API - get(), set(), file(), info()
• Expression Helpers - json(), safe(), debug(), default()
• Expressions - Expression syntax
• Expression Functions Reference - Complete function reference
• Advanced Expressions - Advanced expression features
• Request Object - HTTP request data and file access
• Input Object - Property-based request body access
• Tools - LLM function calling
• Items Iteration - Batch processing with item object
• Validation - Input validation and control flow
• Error Handling - onError with retries and fallbacks
• Route Restrictions - HTTP method and route filtering

Deployment ​

• Docker - Build and deploy Docker images
• WebServer Mode - Serve frontends and proxy apps

Tutorials ​

• Building a Chatbot
• File Upload Processing
• Multi-Database Workflow

Why KDeps v2? ​

Community ​

• GitHub: github.com/kdeps/kdeps
• Issues: Report bugs and request features
• Examples: Browse example workflows