Build and deploy AI agents in YAML.
Workflow pipelines and autonomous agents in YAML. Export as Docker, Kubernetes, ISO, or a single binary. Works with Ollama, OpenAI, Anthropic, and any OpenAI-compatible backend.
apiVersion: kdeps.io/v1
kind: Workflow
metadata:
name: summarizer
version: "1.0.0"
targetActionId: summarize
settings:
apiServer:
portNum: 16395
routes:
- path: /summarize
methods: [POST]getting started
Three steps from idea to running AI API.
Declare resources -- chat, HTTP, Python, SQL, shell. Wire them with requires:. No glue code.
kdeps run workflow.yaml starts the API server. kdeps serve starts the autonomous agent loop.
POST to your route, get back structured JSON. Export as Docker, Kubernetes, ISO, or a single binary.
platform agnostic
Switch backends in config. No code changes, no lock-in.
run modes
Workflows, agents, and agencies — all from the same YAML.
Resources run in DAG order defined by requires:. Every request takes the same path. Predictable, auditable, ships to production.
The LLM decides which resources to call and in what order. Every resource auto-registers as a tool. No wiring required.
One agent calls another via the agent: resource type. Compose agents like functions — each runs independently, results flow back.
why kdeps
No Python scripts, no wiring, no boilerplate.
| Traditional approach | kdeps |
|---|---|
| Python script + Flask + OpenAI SDK + retry logic | One workflow.yaml file |
| Manual dependency wiring | requires: in YAML |
| Write error handling by hand | onError: block (continue / retry / fail) |
| Write Dockerfile + CI pipeline for deployment | kdeps bundle build --tag then docker push |
| Glue code between services | Resources pass data via output() |
| Manual polling loop for bots | input.sources: [bot] in workflow config |
| Chaining agents by hand | agent: resource — one agent calls another declaratively |
registry
Install pre-built packages from the registry. Publish your own.
Reusable capability extensions. Install with one command, invoke with component: and typed inputs.
Complete DAG pipelines packaged as .kdeps archives. Drop them into an agency or run them standalone.
Multi-agent orchestration bundles packaged as .kagency archives. One entry point, many agents.
examples
Real patterns from the examples directory. Every one is a working workflow.
POST a JSON body, run a DAG pipeline, get structured JSON back. The default pattern for workflow mode.
workflow modePoll for messages, run a multi-step pipeline, reply with LLM responses. Two resources: llm and reply.
workflow modeRead from stdin, call an LLM, write to stdout. One-shot. Perfect for cron jobs and CI pipelines.
workflow modeIndex documents locally, search with keywords, feed results into an LLM prompt. Fully on-prem.
workflow modeOrchestrate multiple agents. One summarises, another translates — each an independent workflow.
multi-agent