Show HN: Kdeps – A New CLI Coding Agent That Runs on Open Weight Models

Kdeps, a new CLI coding agent that runs on open-weight models and is a member of the NVIDIA Inception program, allows developers to run AI workflows locally or deploy them as Docker, Kubernetes, or a single binary without an API key when using Ollama or llamafile. The tool supports multiple backends, multi-agent orchestration, and deterministic DAG pipelines defined in YAML, with features including error handling, resource wiring, and a registry for reusable packages.

Local AI agent Run kdeps and you are in an AI REPL. Use Ollama or llamafile for a fully offline, private coding agent - no API key, no cloud dependency. Run AI workflows locally. Or deploy them anywhere. Install kdeps, run kdeps , get an AI agent - no API key needed with Ollama or llamafile. Build your workflow in YAML. Deploy as Docker, Kubernetes, or a single binary when you're ready. Proud member of the NVIDIA Inception program. bash $ kdeps kdeps v2.x.x | Local agent mode Model: llama3.2 Ollama | Type /help for commands write a Go function that parses a CSV file Sure. Here's an idiomatic Go CSV parser... func ParseCSV r io.Reader string, error { reader := csv.NewReader r return reader.ReadAll } /model claude-opus-4-8 Switched to claude-opus-4-8 Anthropic 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. Run kdeps for an instant AI REPL - fully offline with llamafile or Ollama, no API key needed. Load workflows as tools: the LLM decides what to call and when. 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-agentbook Everything from first agent to production deployment, in one place. Build & Deploy Autonomous AI Agents and Agencies in YAML Your AI prototype works. Now ship it. Hands-on guide to deterministic pipelines, multi-agent orchestration, error handling, and vendor-agnostic deployment — the production challenges most AI frameworks leave to you. Read the book -