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Decision guide

Framework vs. Model-Native: Which Approach Fits Your Project?

You can build agents with a model-agnostic framework such as LangGraph, CrewAI or the Microsoft Agent Framework, or use the model providers' own agent tools, such as the Claude Agent SDK or OpenAI Codex. The right path depends on your requirements. The decision matrix helps you sort them out.

Your requirements

Enter your project requirements to get a recommendation

Requirements
Setup time

Predefined scenarios

Framework or model-native?

Hybrid recommended

Framework-based

LangGraph, CrewAI, AutoGen

Score1/10

Reasons

  • Short tasks work well with frameworks

Strengths

  • Orchestrate multiple models
  • Cut costs with cheaper models
  • Open source available
  • Large community

Model-native

Claude Agent SDK, OpenAI Agents SDK, Google ADK

Score3/10

Reasons

  • A single LLM is enough
  • Budget allows premium models

Strengths

  • Long, multi-step tasks
  • Little setup
  • Full model capabilities
  • Intervene during execution

Hybrid approach

Your requirements are balanced. A hybrid approach combines both sides:

  • A framework to orchestrate multiple models and optimize costs
  • Model-native tools for critical, long-running tasks
  • Choose per use case
Personal recommendationDetailed comparison

What does framework-based mean?

Frameworks such as LangGraph, CrewAI, the Microsoft Agent Framework (successor to AutoGen) or the OpenAI Agents SDK give you building blocks to define agents, tools and workflows yourself. You choose models from different providers, define state, handoffs and approvals, and run everything on your own infrastructure.

Advantages: Free choice of models, cost control through cheaper models for sub-steps, mostly open source, full control over workflows and data, testable like regular code.

Disadvantages: More effort to build and maintain, a learning curve, and you have to handle tools, sandboxing and context management yourself.

What does model-native mean?

Model-native refers to the model providers' own agent tools, such as Claude Code and the Claude Agent SDK from Anthropic, or OpenAI Codex. They ship with a tuned agent loop, built-in tools (files, terminal, web search), subagents and context management. You configure more than you program workflows.

Advantages: Very fast start, agent loop and tools are tuned to the provider's own model, good results on long tasks involving code and files, ongoing improvements from the provider.

Disadvantages: Lock-in to one model provider, less detailed control over the workflow, costs depend on the provider's pricing model, limited options for your own approval and review steps.

When does a combination pay off?

A combination makes sense if you:

  • need fixed workflows with approvals, but individual steps are very demanding
  • want to handle standard tasks cheaply and hand only difficult steps to a specialized agent
  • want to use different providers depending on the task
  • want to extend an existing framework workflow step by step with the providers' agent tools

Example: A LangGraph workflow handles ticket intake, approval and deployment. The actual code change is made by an agent built on the Claude Agent SDK or Codex, and the workflow checks its result and submits it for approval.

Author: Multi-Agent Navigator editorial team

Last reviewed: