kdeps/kdeps — AI agent framework in YAML — workflow pipelines + autonomous agent loop.

Description and use

**Brief Summary**
kdeps/kdeps is an open‑source AI‑agent framework written in Go that lets you describe workflow pipelines and autonomous agent loops in plain YAML. It supports building, deploying, and exporting the resulting agents as Docker containers, Kubernetes manifests, or even bootable ISO images, making it easy to move from prototype to production‑grade deployments.

**Value Proposition**

• **From prompts to repeatable pipelines** – kdeps turns ad‑hoc prompts and single‑tool calls into version‑controlled, reusable agent workflows, reducing duplication and onboarding time.
• **Multi‑agent orchestration** – The YAML DSL lets you coordinate several agents, inject tool‑use stages, and manage shared memory, enabling complex, end‑to‑end AI solutions without writing custom glue code.
• **Portable deployment** – With built‑in exporters for Docker, K8s, and ISO, the same definition can run locally, in the cloud, or on edge devices, simplifying CI/CD and environment consistency.

**Practical Adoption Path**

1. **Prototype** – Define a simple YAML pipeline (e.g., “fetch data → LLM summarise → store result”) and run it via the CLI to validate logic.
2. **Integrate** – Use the provided SDK or REST API to embed kdeps pipelines in existing services, or call the CLI from CI scripts.
3. **Standardise** – Create a library of reusable YAML modules (memory handling, tool wrappers, error‑recovery) that teams can share across projects.
4. **Deploy** – Export the pipeline as a Docker image or Helm chart for Kubernetes, or generate an ISO for edge/air‑gap installations.
5. **Monitor & Iterate** – Leverage the framework’s signalling hooks (API/SDK/CLI) to plug in observability, logging, and version control.

**Production Readiness**

• **Maturity** – Medium. The project is actively maintained (last commit 2026‑06‑23) and has modest community traction (34 stars, 4 forks).
• **Strengths** – Clear YAML DSL, multi‑target exporters, and Go’s static binary model give a solid base for reliability and performance.
• **Considerations** – Before production use, verify the license compatibility, perform a security audit of the Docker/K8s images it generates, and assess the maintainers’ capacity for long‑term support. Adding automated tests for your specific pipelines and monitoring the upstream repository for breaking changes will mitigate the remaining risks.

Problem

kdeps/kdeps helps turn isolated prompts and tools into repeatable agent workflows.

Outcome

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.

Workflow

• coordinate multi-agent workflows
• add tool-use pipelines
• standardize agent memory

Evidence

AI agent framework in YAML — workflow pipelines + autonomous agent loop. NVIDIA Inception member. Build, deploy, export as Docker/K8s/ISO.

Source