Small Models.
Big Impact.
An agent framework purpose-built for 1-7B parameter models. Template-based structured output, automatic JSON repair, and workflow visualization — all from a few lines of Python.
Native support for local and API-based models
Calculators, python execution, web search & MCP
Robust vector DB integrations for memory & RAG
Flawless structured output accuracy tests
Automatic repair and recovery handles corrupt outputs
Permissive license ready for enterprise use
Small models are cheap.
But they break.
Running 1-7B parameter models cuts your inference cost by 10-50×. But smaller models produce malformed JSON, miss schema fields, and hallucinate structures that crash your pipeline.
of 3B model responses fail JSON schema validation on first attempt — missing fields, wrong types, malformed syntax.
50× cheaper — if you can make them reliable.
The savings are real. The reliability gap is the only thing standing between you and production-grade small model workflows. Kern closes that gap.
Features
Everything you need for small model agents
Production-ready features optimized for models that run on consumer hardware — not 200B parameter clusters.
Examples
See it in action
From structured output to workflow visualization — Kern handles the hard parts.
1from kern import Agent2from kern.models.openai import OpenAIChat3from pydantic import BaseModel, Field45class MovieReview(BaseModel):6 title: str = Field(description="Movie title")7 rating: int = Field(description="Rating 1-10")8 summary: str = Field(description="Brief review")910agent = Agent(11 model=OpenAIChat(id="gpt-4o-mini"),12 output_schema=MovieReview,13)1415result = agent.run("Review The Matrix")16# Works reliably even with 2B parameter models!1718print(result.content)19# {20# "title": "The Matrix",21# "rating": 9,22# "summary": "A groundbreaking sci-fi film..."23# }Comparison
Why Kern?
Built on Agno with critical optimizations for models that run on consumer hardware.
| Feature | Kern | Agno | LangChain |
|---|---|---|---|
| Structured output for <7B models | |||
| Automatic JSON repair | |||
| Template-based output (no JSON Schema) | |||
| LaTeX protection in JSON | |||
| Workflow engine | |||
| Workflow visualization | |||
| Multi-model support | |||
| Agent teams | |||
| Tool ecosystem | |||
| Knowledge bases + Vector DB | |||
| Lightweight / minimal deps | |||
| Optimized for local models |
Engineering Blog
Latest from the Reliability Layer
Deep dives, playbook guides, and benchmark findings on making small models run reliably in production.
End-to-End Agent Infrastructure
Fully managed agent infrastructure — deployment, monitoring, scaling, and observability for agents powered by small models. Join the waitlist to be first in line.