6 Open-Source Projects for Serious AI Infrastructure

If you are building agents that need to run somewhere other than a notebook, these six open-source projects are worth knowing.

Each one covers a different failure domain.

You can adopt most of them independently, and together they form a useful reference architecture for a distributed AI product.

Let’s dive in.

Start With the Failure Boundaries

Here is how the responsibilities split across the six projects, along with the practical question each one answers:

• Temporal: durable workflow execution. Can this run resume after a failure?
• Dapr: service invocation and building blocks. How do services communicate consistently?
• NATS: messaging and event distribution. How do independent workers exchange work?
• Langfuse: LLM-specific tracing and evaluation. Which model step failed or cost too much?
• OpenTelemetry Collector: a vendor-neutral telemetry pipeline. Where did the end-to-end latency go?
• K3s: lightweight Kubernetes. Where do services and workers run?

The overlap is intentional.

• Dapr supports pub/sub and workflows.
• NATS supports request/reply.
• Langfuse builds on OpenTelemetry concepts.