Overview
Every AI framework looked different — LangGraph, CrewAI, MCP — but they all broke in the same place: the tools.
Inputs, outputs, retries, logging, tracing — everyone rebuilt the same plumbing.
I built Kernel.dev to stop that.
Kernel is a lightweight runtime that standardizes how tools run — not just how they’re called.
The Problem
The agent ecosystem was maturing fast but unevenly.
Each framework had its own way of defining a tool, passing arguments, handling failures, and returning results.
That fragmentation made it impossible to share tools between projects, or even reason about their behavior at scale.
When I built TractionX, I realized:
80% of the work was writing wrappers, not intelligence.
Debugging across toolchains was chaotic — logs lived everywhere and nowhere.
Portability was a myth; reuse was luck.
So I asked: What if tools ran like containers — predictable, observable, portable?
The Solution
Kernel.dev is a runtime for AI tools, built around three principles: reliability, portability, and traceability.
Define Once: Tools are defined declaratively via schema — Pydantic models describe inputs, outputs, and constraints. No framework lock-in. A
searchtool means the same thing everywhere.Run Anywhere: Kernel exposes a uniform interface compatible with LangGraph, CrewAI, and MCP. Whether a node runs locally, in Docker, or via HTTP — behavior stays identical.
See Everything: Tracing and metrics are baked in with OpenTelemetry and LangFuse. Every call, error, and retry has a unique correlation ID. Reliability becomes visible, not anecdotal.
The Developer Experience
A simple CLI handles most of the work: