Stop Debating Frameworks. Build a Portability Layer. (2026)

Stop Debating Frameworks. Build a Portability Layer.

The Framework You Pick Today Won't Exist in 18 Months

A client called me in January. They'd spent 5 months building a multi-agent sales system on AutoGen. Then Microsoft rolled AutoGen into the new Microsoft Agent Framework and published a migration guide that basically said: rewrite everything.

Two engineers. Six weeks. That's what it cost them to move.

I've seen this happen three times now. LangChain to LangGraph. AutoGen to Microsoft Agent Framework. CrewAI to custom. Every time, the team acts surprised. Every time, the rewrite takes longer than anyone expected.

Md. Bazlur Rahman Likhon — an AI engineer who's shipped 23 production agent systems — wrote about choosing between LangGraph, CrewAI, and AutoGen for a $2.3M Saudi fintech contract. His whole post is about picking the right one. I respect the work. But I think he's asking the wrong question.

The right question isn't "which framework?" It's "how do I make frameworks disposable?"

That's what an agent portability layer does.

What an Agent Portability Layer Actually Is

An agent portability layer is a thin abstraction between your business logic and whatever framework runs it. It has three parts. Each one is boring on purpose.

1. Tool Schemas (MCP + JSON Schema)

Model Context Protocol (MCP) gives you a standard way to define what your tools do. It works at the protocol layer — not tied to LangChain, not tied to OpenAI. MCP does runtime tool discovery across any AI provider. OpenAI Function Calling locks you into OpenAI. LangChain Tools lock you into LangChain.

Write your tool definitions once in MCP with JSON Schema contracts. Every framework can read them. When you swap frameworks, your tools don't change.

2. Trace Format (OpenTelemetry)

When an agent breaks at 2 AM, you need to know what it did, what it called, and where it went wrong. If your traces are in a LangSmith-specific format, you can't take them anywhere.

OpenTelemetry is the answer. It's vendor-neutral. It works with Datadog, Grafana, Jaeger — whatever you're already running.

Agent harness engineering matters. Context management, error recovery, state persistence. You can't do any of that well without portable traces.

3. Eval Harness (promptfoo, TruLens, or Ragas)

Your tests shouldn't know what framework they're testing. Write eval suites that check inputs and outputs against expected behavior. Tools like promptfoo let you run the same test battery against any backend.

When we build agents at StoryPros, our eval harness runs before and after every framework change. If the scores hold, we ship. If they don't, we fix. The framework is irrelevant.

The Lock-In Tax Is Real — and It Compounds

Here's the math nobody does upfront.

Without a portability layer: A framework migration takes 2 senior engineers about 4-6 weeks. At $150/hour, that's $48,000–$72,000 per migration. Add the opportunity cost of whatever those engineers aren't building.

With a portability layer: The same migration takes 3–5 days. You swap the adapter. Run your eval harness. Ship.

That 2-week investment to build the portability layer pays for itself the first time you have to move. And you will have to move.

Microsoft just forced every AutoGen user to migrate. LangChain went through a major architecture shift with LangGraph. CrewAI has pivoted twice.

There's a hidden cost too. A single agent task costs $5–$8 in API fees. When you're debugging a framework migration with no portable traces, you're burning tokens on every test run. I've watched teams spend $2,000+ in API costs just figuring out why a migrated agent started hallucinating.

The APEX-Agents benchmark tested frontier models on real professional tasks. Best pass@1 rate? 24%. The failures weren't knowledge gaps. They were orchestration problems — exactly the kind of thing a good eval harness catches before it hits production.

How We Build This at StoryPros

I'm not writing theory. We've built 100+ AI automations. Here's what we actually do.

Step 1: Every tool gets an MCP definition with a JSON Schema contract. Takes about 30 minutes per tool. We use mcp-cli to test them in isolation before they go anywhere near a framework.

Step 2: All agent runs emit OpenTelemetry traces. We tag every tool call, every LLM request, every decision branch. When something breaks, we know exactly where.

Step 3: We write eval suites in promptfoo that test the agent's behavior, not its internals. "Given this prospect profile, does the agent book a meeting or disqualify?" The eval doesn't care if CrewAI or LangGraph is running underneath.

Step 4: The framework itself is a thin adapter. Right now we use n8n for most orchestration. If something better shows up next quarter, we swap the adapter and move on.

Sergey Bolshchikov at Sweep.io described the same pattern. His team uses LangChain and LangGraph, but designed their architecture to be framework-agnostic. The ideas work regardless of what's running underneath.

That's the whole point. Frameworks are the thing you're least sure about. Build your confidence in the layers you control.

The 90-Day Payoff

Here's what a 90-day timeline looks like if you start today.

Weeks 1–2: Define MCP tool schemas for your existing agents. Set up OpenTelemetry tracing. Pick an eval tool (we like promptfoo — it's open source and fast).

Weeks 3–4: Write eval suites for your top 3 agent workflows. Run baseline scores against your current framework.

Weeks 5–8: Operate normally. Your traces are building a dataset. Your evals are catching regressions you didn't know existed.

Weeks 9–12: When a framework change comes — and it will — you swap the adapter instead of rewriting the system. 3–5 days instead of 6 weeks.

Vercel cut 80% of their agent's tools (from 15 down to 2) and accuracy jumped from 80% to 100%. The portability layer forces that same discipline. When you have to define every tool in a standard schema, you start asking "do we actually need this?" The answer is usually no.

Most AI projects fail because they start with technology instead of a business problem. AI agent framework lock-in is a technology problem disguised as a technology choice. The agent portability layer is how you stop playing that game.

StoryPros builds AI agents that book meetings, run campaigns, and process tickets — and every one of them sits on top of a portability layer. Because I've done enough rewrites to know better.

FAQ

How do I avoid vendor/framework lock-in when building AI agents?

Build an agent portability layer: standardized MCP tool schemas, OpenTelemetry traces, and a framework-independent eval harness. This lets you treat frameworks like CrewAI, LangGraph, and AutoGen as swappable adapters. When a framework changes, the migration takes days instead of weeks.

What is an agent portability layer and why does my team need one?

An agent portability layer is a thin abstraction between your business logic and whatever AI framework runs it. It includes tool schemas (MCP + JSON Schema), a portable trace format (OpenTelemetry), and an eval harness (promptfoo or similar). Without one, every framework migration costs 2 engineers 4–6 weeks — roughly $48,000–$72,000. With one, the same move takes 3–5 days.

How can I migrate agents between LangChain, CrewAI, and AutoGen without rewriting skills?

Define all your tools using MCP tool definitions with JSON Schema contracts. These definitions are framework-neutral — any framework can read them. Your tool logic stays the same. Only the adapter layer that connects tools to the framework needs to change.

What standards exist for portable agent tooling?

MCP (Model Context Protocol) by Anthropic is the leading open standard for tool definitions. It supports runtime discovery across any AI provider. JSON Schema handles tool contracts. OpenTelemetry covers traces and is supported by Datadog, Grafana, and Jaeger. For evals, promptfoo, TruLens, and Ragas all support framework-independent testing. These four pieces together form the minimum portable core.

What does framework portability cost to set up?

About 2 weeks of engineering time for a team running 3–5 agent workflows. That covers MCP schema definitions, OpenTelemetry tracing setup, and a baseline eval harness. The first framework migration you avoid saves $48,000–$72,000 in rewrite costs. Most teams see the payoff within 90 days.