timestamp: [2025-10-15 00:11]
system: kernel.init()
status: experiment → product

Every agent framework looks different, LangGraph, CrewAI, MCP.
But when you peel back the layers, they all depend on the same foundation: tools.

Search something. Scrape a site. Parse a file. Call an API. Store results.
That’s where every agent begins, and where most of them break.

01 / The Problem

Everyone keeps rebuilding the same plumbing:

• Schema definitions for inputs and outputs

• Wrappers for every API call

• Logging, retries, caching logic

• Manual glue between frameworks

Even worse, every tool’s response format is different, some return JSON, others markdown, others chaos.
You spend hours writing adapters, not logic.
Something that works perfectly in one stack breaks the moment you switch frameworks.

There’s no single way to define, run, or reuse a tool between projects.
Every team reinvents the same runtime, privately, slightly differently, every time.

That was the moment I realized: agents don’t need another framework, they need a runtime.

02 / Why Now

Agents are leaving notebooks and entering production.
But every team still fights the same three demons:
reliability, consistency, and reuse.

• LangGraph connects nodes, but doesn’t make each node reliable.

• MCP calls tools, but doesn’t define how tools behave.

• CrewAI wraps everything, but the logic still leaks.

It’s the same fragmentation Docker once solved for code, we just don’t have that for tools yet.

That’s the gap Kernel fills.

Just like Docker standardized how code runs,
Kernel standardizes how tools run.

03 / The Solution

Kernel is a small runtime that makes AI tools:

• Reliable by default, validation, retries, and tracing built-in

• Portable across frameworks, LangGraph, CrewAI, MCP, same interface

• Runnable anywhere, local dev, container, or managed API

Define your tool once, and Kernel handles schema, execution, and monitoring.

npx kernel create-tool search.v1

The command generates a standard scaffold:

• Auto-generated schema with Pydantic

• Built-in logging and LangFuse-compatible traces

• Retry + rate limiting baked in

• Ready for any agent graph immediately

You can run it locally, publish it to a registry, or expose it as an MCP endpoint.
Every Kernel tool behaves consistently: same I/O shape, same observability, same resilience.

04 / The Vision

We’re not building another framework.
Frameworks come and go.
Tools stay.

Kernel focuses on the runtime for tools, not the orchestration for agents.

We imagine a world where:

• Any agent can call any tool, no adapter needed.

• Developers share tools that actually work everywhere.

• Enterprises trust that tools behave identically across clouds.

Kernel is that missing middle layer, the Open Container standard for AI tools.

Write once.
Run anywhere.
That’s the whole point.

conclusion: runtime > framework
status: SCOPING
next commit: kernel.publish()

End of Blog

Agents evolve. Frameworks compete.
But tools, tools persist.