Agent Frameworks Comparison: What Actually Differentiates Them

Agent frameworks converge quickly on the same mechanics, so comparing them on features misses what matters. This guide covers the real differentiators, drawing on FISTA Solutions' AI agents production work.

What should the comparison cover?

Six dimensions, most of which are not in feature lists.

Dimension	What to look for	Why it matters
Trajectory observability	Every step, with reasoning	Debugging and evaluation
Permission enforcement	Hooks before tool execution	Safety in production
Loop control	Ability to inspect and intervene	Understanding failures
Testability	Fixed scenarios, mocked tools	Evaluation is impossible without it
Streaming and cancellation	Propagates to the provider	Cost and experience
Portability	How much logic is framework-shaped	Cost of leaving

Why is observability the first criterion?

Because an agent you cannot see is an agent you cannot operate.

You need every step logged — the reasoning, the tool selected, the arguments, the result — with a correlation identifier linking a whole task. Frameworks differ substantially in whether this is built in or something you add.

A framework that emits structured trajectory events integrates with your existing observability; one that logs prose does not. That difference shows up the first time you investigate a production problem. See agent trace analysis pipeline.

What should permission enforcement look like?

A hook that runs before every tool execution and can refuse.

That is where authorisation belongs — outside the model, in code, checking this agent, acting for this user, may invoke this tool with these arguments. A framework providing that hook makes the control natural; one without it means building around the framework.

Also check whether limits and approval gates are supported natively, since those are the other two production requirements. See agent permission review checklist.

Why does loop control matter?

Because production debugging requires knowing exactly what happened, and abstractions hide it.

A framework that manages the loop invisibly is pleasant until something goes wrong, at which point you need to see the actual sequence of calls and decisions. Frameworks vary in how much they expose.

The pattern that ages well is a framework that handles the mechanics while leaving the control flow inspectable and interruptible. Magic that cannot be examined becomes a liability.

How do you assess testability?

Try to write the test you would need for evaluation.

Run an agent against a fixed input with deterministic mocked tools, capture the trajectory, and assert on the sequence of decisions. If that requires fighting the framework, your evaluation suite will be painful to build and therefore will not get built.

This single exercise during evaluation predicts more about your experience than any feature list. See how to build an agent evaluation harness.

What creates lock-in?

Logic expressed in framework-specific shapes.

Tool definitions, prompts, and business logic written as plain functions and strings are portable. The same content expressed through framework-specific decorators, graph structures, and configuration formats is not.

Keep your business logic in ordinary code the framework calls, rather than inside framework constructs. That single discipline makes any future migration a rewiring rather than a rewrite. See the consolidation of AI tooling.

When is no framework right?

When the agent is simple and you value control.

A loop calling a model, dispatching tool calls, and appending results is perhaps a hundred lines. Writing it directly gives complete visibility, no dependency, and no abstraction to fight.

Frameworks earn their place as you need the operational surfaces — approval workflows, trajectory storage, permission hooks, retry handling — which is real work you would otherwise build.

How do you run your own comparison?

Build the same small agent in each candidate, with your real tools, and then try to debug a deliberately broken trajectory. That exercise reveals observability and control quality immediately.

Also write one evaluation test in each. The framework where that is straightforward is the one where your evaluation suite will actually exist.

What does switching cost later?

Proportional to how much logic lives inside framework constructs. Business logic in plain functions moves easily; graph definitions and framework-specific tool declarations do not.

The ecosystem is young and consolidating, so assume you may switch and keep the boundary clean from the start.

What do people get wrong here?

Comparing feature lists. Ignoring testability until evaluation is needed. Expressing business logic in framework constructs. Choosing on ecosystem size rather than on operational fit. And adopting a framework for an agent simple enough not to need one.

What about the protocol layer?

It is separate and worth having regardless of framework. Standardised tool access means your tools work with any framework and any model, which decouples two decisions that would otherwise be one.

A framework that supports the protocol natively is preferable, because it means your integration work outlives your framework choice. See why enterprises are standardizing on MCP.

Which should you choose?

Compare on trajectory observability, permission hooks, testability, and portability rather than on features. Keep business logic in ordinary code the framework calls. For simple agents, consider writing the loop yourself and adding a framework only when the operational surfaces justify it.

What should you do first?

Write one evaluation test in each candidate framework. The one where that is easy is the one where your evaluation suite will exist a year from now.

How FISTA Solutions helps

FISTA Solutions builds and operates production AI systems through AI agents, AI enablement, and forward deployed engineering: frameworks assessed on observability and testability rather than features, with business logic kept in ordinary code so the choice stays reversible, decisions documented with their reasoning, and handover that leaves your team able to maintain what was delivered. The record is 150+ projects for 50+ companies across 12+ countries.

To run this comparison against your own workload, message FISTA on WhatsApp, or read AI agent production readiness checklist.