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AI for Developers

Kdeps — AI for Software Development

Build and deploy portable AI agents and workflows using simple YAML files, from local development to production.

Run AI agents locally without an API key using Ollama, then deploy as Docker, Kubernetes, or a single binary.

Best forAI for Developers
DifferentiatorReplaces glue code with a single YAML file for local development and production deployment.
ProofApache 2.0 open-source project; NVIDIA Inception program member.
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Free Plan
Free plan verified · free AI tools for Developers
7.6 Zekai
YAML-based AI Orchestration
AI for Software Development
Ease of Use
7.2
Accuracy
8.0
Value
8.8
Time Saving
6.4
YAML-basedLocal LLMsMulti-AgentVendor AgnosticOpen Source
📊 Independently reviewed and scored. How we score →
✍️ Reviewed by Zekai Editorial Team · Updated August 7, 2026
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Zekai Verdict

What is it?
Kdeps is an open-source tool for software developers to define, run, and deploy AI workflows and agents using YAML.
Best for
Developers who need to quickly build, test, and deploy portable, production-ready AI workflows without being locked…
Not ideal for
YAML-centric approach may be less flexible than pure code for complex logic.
Price
Free plan
Zekai Score
7.6/10
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Hand-scored by Zekai

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⚡ Quick answer

For AI for Software Development, Kdeps is an excellent choice for building and deploying portable AI workflows. It allows developers to define complex agent orchestration in simple YAML, run it locally without API keys using Ollama, and then deploy the same configuration as a Docker container or Kubernetes service, drastically reducing glue code and speeding up the path to production.

CategoryAI Workflow Orchestration
Best ForDeploying portable AI agents defined in YAML.
Price FromFree
FreeYes (Open Source)
DifferentiatorReplaces glue code with a single YAML file for local development and production deployment.
ProofApache 2.0 open-source project; NVIDIA Inception program member.
Rating7.6/10
📖 About Kdeps
How It Works

Your workflow, automated

1
Write Workflow in YAML
Declare resources like models, APIs, and shell commands in a `workflow.yaml` file, wiring them together without writing any glue code.
2
Run Locally
Use the `kdeps run` command to instantly start your workflow as a local API server, using offline models for fast, private iteration.
3
Deploy to Production
Export your validated workflow as a Docker image, Kubernetes manifest, or single binary for consistent deployment to any environment.
Ready to automate your workflow with Kdeps?
Try Kdeps →
Real Impact

Before & After

❌ Before

Writing complex Python glue code, Dockerfiles, and CI scripts to deploy AI prototypes.

Days of manual scripting
✅ After

Defining entire AI pipelines in a single YAML file that deploys anywhere.

Minutes to deploy
Prompt Templates

Try it with these prompts

Copy any prompt and paste it directly into the tool.

Generate Go CSV Parser

Write a Go function that parses a CSV file. The function should accept an io.Reader and return a slice of string slices representing the CSV data, along with an error if parsing fails.

Summarize URL Content

Summarize the content of a given URL. Make an HTTP POST request to the kdeps API endpoint with the URL in the request body. The response should contain the summarized content.

Create AI Agent Workflow

Scaffold a kdeps workflow for an AI agent. Define resources like chat, HTTP, Python, and SQL, and specify their dependencies using the requires: keyword in YAML format.

AI Prompts

Prompts for Developers

Prompt 01 Sample Prompt for AI App Prototyping
Generate a functional prototype for a mobile app called 'PlantPal'. It's for new plant owners. Key Screens: - **Onboarding:** A simple 3-step intro to the app's features. - **Login/Sign-up:** Email and password authentication. - **My Plants…
Prompt 02 Generate a Function Docstring
Analyze the following TypeScript function. Write a complete TSDoc comment for it. Explain what the function does, describe each parameter (including its type and purpose), and describe the return value. [[Paste your code here]]
Prompt 03 React Component Refactor with Context
I need to refactor a React component located at `src/components/OldProfile.tsx`. **Goal:** Create a new component `src/components/ProfileHeader.tsx` by extracting the header logic from `OldProfile.tsx`. **Context:** - The new `ProfileHead…
See all 29 AI prompts for Developers →
Social Proof

Trusted by professionals

Ease of Use
7.2
Accuracy
8.0
Value
8.8
Time Saving
6.4

"Kdeps is a game-changer for our MLOps. The ability to define an entire agent in YAML and get a Docker image out is exactly the simplicity we needed. It cut our deployment time for new AI services by at least 70%."

Mark S., DevOps Engineer · June 2026

"The local-first approach with Ollama is brilliant. I can build and test complex agent workflows completely offline on my laptop, which is faster and keeps our data private. No more waiting for cloud instances or burning through API credits during dev."

Jenna P., AI Developer · May 2026

"It's incredibly powerful for standard pipelines, but the pure YAML approach can feel a bit rigid for highly custom logic that would be trivial in Python. If your workflow fits their model it's perfect, but be prepared to work within its declarative structure."

Carlos R., Senior Software Engineer · June 2026

"As a startup, not being locked into a single LLM provider is critical. With Kdeps, we can switch from OpenAI to Groq by changing one line in a config. This flexibility is invaluable for managing costs and performance."

Anika L., Founder · May 2026
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Connects With

Kdeps integrations

Ollama llamafile OpenAI Anthropic Groq LM Studio Docker Kubernetes Any OpenAI-compatible endpoint
Setup complexity: Intermediate
Kdeps is an open-source tool for software developers to define, run, and deploy AI workflows and agents using YAML. It enables local, offline development with models via Ollama or llamafile and supports deploying the same configuration to Docker, Kubernetes, or as a single binary.
Comparison

Best Kdeps alternatives

Kdeps vs. LangChain: The primary difference is paradigm. LangChain is a code-first framework, offering immense flexibility for developers who want to define AI chains and agents programmatically in Python or TypeScript. This is ideal for complex, bespoke logic. In contrast, Kdeps is a declarative, YAML-first tool. Choose Kdeps when your priority is speed, portability, and operational simplicity. Its YAML approach drastically reduces boilerplate for common patterns and ensures a clean separation between logic and configuration, making it faster to go from local prototype to a standardized Docker or Kubernetes deployment.

The decision

Is Kdeps worth it?

Return on investment
Since Kdeps is free, the ROI is immediate, saving developers days of scripting glue code and manual deployment configuration for each AI project.
Built for
Software developers, DevOps engineers, and AI engineers building and deploying AI-powered applications and services.
Effort to adopt
Intermediate
Compliance
Compliance posture not publicly documented — verify with vendor.
Who It's For

Why Software Development choose this tool

🎯
Built for
Developers who need to quickly build, test, and deploy portable, production-ready AI workflows without being locked into a specific LLM provider or writing extensive glue code.
In-Depth Overview
For developers, the journey from a working AI prototype to a production-ready, scalable application is filled with boilerplate code, configuration drift, and deployment complexity. Kdeps directly addresses this by replacing custom Python scripts and manual wiring with a declarative YAML file. This approach radically simplifies the creation of AI workflows, allowing you to define resources, dependencies, and error handling in a structured, version-controllable format. The standout benefit is its 'write once, deploy anywhere' philosophy. You can build and test an agent entirely on your local machine, using free, private models via Ollama or llamafile without needing an API key. Once validated, the exact same `workflow.yaml` can be bundled into a Docker container, Kubernetes manifest, or even a single self-contained binary for production. This eliminates environment-specific rewrites. Furthermore, Kdeps is backend-agnostic, letting you switch between OpenAI, Anthropic, Groq, or any other compatible endpoint through a simple config change, preventing vendor lock-in. Proof of its robust design includes its status as an Apache 2.0 open-source project and its membership in the NVIDIA Inception program.

Key Use Cases

🤖
Prototype and Deploy a RAG API
AI Engineer
Define a Retrieval-Augmented Generation (RAG) pipeline in a single `workflow.yaml` file. Test it entirely on your local machine with Ollama, then deploy the exact same file as a production-ready Docker container.
From local prototype to deployed API in hours, not days.
✓ Pros
YAML-based configuration drastically reduces boilerplate glue code.
Supports fully offline, private AI agents using local models.
'Write once, deploy anywhere' portability across Docker, Kubernetes, and binaries.
Backend-agnostic design prevents vendor lock-in with LLMs.
Open-source with an Apache 2.0 license, ensuring transparency and control.
· Cons
YAML-centric approach may be less flexible than pure code for complex logic.
As a newer tool, the component registry is still growing.
Requires familiarity with command-line tools and concepts like Docker/Kubernetes for deployment.
Lacks a graphical user interface for building workflows.
⚡ Editorial Verdict

Kdeps excels at simplifying the path from AI prototype to production by replacing complex glue code with declarative YAML. Its local-first, backend-agnostic approach is a major strength for rapid iteration and cost control. The main trade-off is its reliance on YAML, which may feel restrictive for developers who require the granular control of a programmatic framework for highly complex, non-standard logic.

See all 441 AI tools for Developers → Browse the AI Directory →
Questions & Answers

Frequently asked questions

Is Kdeps free to use?

+
Yes, Kdeps is a free and open-source tool released under the Apache 2.0 License. You can install and use it without any cost.

Do I need an API key to get started with Kdeps?

+
No. You can run Kdeps in a fully offline, private mode using local LLMs through Ollama or llamafile, which does not require any API keys or cloud dependency.

How do I deploy a Kdeps workflow to production?

+
Kdeps is built for portability. You can export your `workflow.yaml` as a Docker image, Kubernetes manifests, a bootable ISO, or a self-contained single binary for deployment to any environment.

What is the best tool for deploying AI agents built with local LLMs?

+
Kdeps is an excellent tool for deploying agents built with local LLMs. It integrates seamlessly with Ollama and llamafile, allowing you to develop and test privately, then package the same workflow for production deployment as a Docker container or other formats.

How can I orchestrate multiple AI agents without writing a lot of Python code?

+
Kdeps allows you to orchestrate multiple agents declaratively using YAML. You can define one agent that calls another via the `agent:` resource type, composing them like functions without writing complex orchestration scripts.

Which tool lets me define AI workflows in YAML and deploy to Kubernetes?

+
Kdeps is designed for this exact use case. You define your entire AI workflow in a `workflow.yaml` file and can then use the `kdeps` command-line tool to generate standard Kubernetes manifests for easy deployment.

Last reviewed:

Plans & Pricing

Kdeps pricing and plans

Free
$0
Get Started Free

Prices and features are updated regularly but can change at any time — always confirm on the official website. Some links on this page are affiliate links.

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Free guide

Take it with you

Getting Started: From YAML to Production API with Kdeps
  • **What is Kdeps?** Declarative AI Orchestration Explained.
  • **Setup:** Configuring Your Local Environment with Ollama.
  • **Core Concepts:** Resources, Workflows, Agents, and Agencies.
  • **Anatomy of a `workflow.yaml` file:** Understanding the key components.
  • **Component Registry:** Adding pre-built capabilities like web scraping.
+4 more steps inside the guide
Send me the full guide

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About Kdeps

Full Description

Kdeps is an open-source tool for software developers to define, run, and deploy AI workflows and agents using YAML. It enables local, offline development with models via Ollama or llamafile and supports deploying the same configuration to Docker, Kubernetes, or as a single binary.

Editorial Verdict

Kdeps excels at simplifying the path from AI prototype to production by replacing complex glue code with declarative YAML. Its local-first, backend-agnostic approach is a major strength for rapid iteration and cost control. The main trade-off is its reliance on YAML, which may feel restrictive for developers who require the granular control of a programmatic framework for highly complex, non-standard logic.

Last reviewed:
Disclaimer
Zekai is an independent AI tools directory. We are not affiliated with, endorsed by, or officially connected to Kdeps unless clearly stated. All product names, logos, and brands are the property of their respective owners and are used for identification purposes only. The information on this page — including pricing, features, and availability — is general information, may have changed since our last review, and is not professional advice. Zekai Scores and verdicts are our editorial opinion. Some outbound links are affiliate links that may earn us a commission at no extra cost to you. Spotted outdated or incorrect information? Request a correction →
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