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Overview How It Works Before & After Reviews Integrations Why This Tool FAQ Pricing Top 10 Get Alerts News
AI for Software Development

Kdeps

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 forDeploying portable AI agents defined in YAML.
DifferentiatorReplaces glue code with a single YAML file for local development and production deployment.
ProofApache 2.0 open-source project; NVIDIA Inception program member.
Try Kdeps
Free Plan
9.1 Zekai
YAML-based AI Orchestration
AI for Software Development
Ease of Use
8.5
Accuracy
9.2
Value
9.6
Time Saving
9.1
YAML-basedLocal LLMsMulti-AgentVendor AgnosticOpen Source
📊 Independently tested and scored. How we score →
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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.
Rating4.6/5
📖 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
Social Proof

Trusted by professionals

Ease of Use
8.5
Accuracy
9.2
Value
9.6
Time Saving
9.1

"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
Kdeps+ professionals are already using this tool.
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Connects With

Works with your existing stack

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.
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 Software Development AI tools → 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

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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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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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