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ZenML for ML pipelines, Kitaru for AI agents. See how each holds up against the other tools you might be considering.
MLOps · ZenML
e2e platforms
orchestrators
orchestrators
e2e platforms
e2e platforms
e2e platforms
orchestrators
orchestrators
e2e platforms
e2e platforms
data model versioning
orchestrators
modeling
orchestrators
model serving
orchestrators
data annotators
llm observability
genai frameworks
e2e platforms
experiment trackers
orchestrators
model serving
e2e platforms
e2e platforms
Agents · Kitaru
Keep the SDK. Add durable execution, artifact lineage, and cloud-native deployment underneath.
Durable execution for Python agents, with agent-native primitives such as llm(), wait(), save()/load(), and cloud stacks.
Durable execution, shaped around Python agents. LLM calls, waits, and artifacts in the box.
Keep your harness freedom. Add durable execution underneath whatever your teams pick.
Pause, resume, and replay any Python agent harness, including a CrewAI crew, on your own cloud.
Build the agent with Pydantic AI. Run it reliably with Kitaru.
Build the agent with the OpenAI SDK. Run it durably with Kitaru, via a first-class adapter.
Durable execution for Python agents, with agent-native primitives such as llm(), wait(), save()/load(), and cloud stacks.
The self-hosted Python runtime layer underneath your existing agent harness, with primitives for `llm()`, `wait()`, memory, and artifact lineage.
The self-hosted Python runtime layer for agents, against a broader orchestration platform for tasks, workflows, queues, and parallel workloads.
