How to Choose an Agent Framework
There is no single best agent framework — there is the right pattern and runtime for your task, data, budget and security posture. This guide gives you a decision process: describe the work, decide whether you need self-improvement, weigh cost and determinism, check the security constraints, and only then map to a pattern like ReAct or Reflexion and a runtime like LangGraph, CrewAI or Google ADK.
In plain words
Choosing an agent framework means picking a reasoning pattern and a runtime to run it on — and PlayCISO has a free, browser-based agent pattern picker that works it through with you. You start from the task type (a single prompt, a ReAct tool-use loop, a Reflexion critique-and-retry loop, or a self-improving DSPy / GEPA / ADAS optimizer), decide whether you have the eval data to self-improve, then let cost, determinism and security needs select the runtime.
The pattern comes from the work; the runtime — LangGraph for explicit stateful control, CrewAI for role-based autonomy, Google ADK for a Gemini-centric stack — comes from how much control, spend and nondeterminism you can live with. The simplest pattern that clears your quality bar wins.
Step by step
- 1
Define the task type first, not the framework
Before you compare LangGraph and CrewAI, describe the work in plain terms: is it a bounded transformation (extract, classify, rewrite) that a single well-prompted call handles, a multi-step task that needs tool use and reasoning in a loop (ReAct), or something open-ended that benefits from critique and retry (Reflexion)? The pattern follows from the task. Picking a runtime before you know the pattern is how teams end up with a multi-agent graph for a job one prompt would have done.
- 2
Decide whether you need self-improvement and eval data
Some patterns optimize the system itself rather than just running it. DSPy compiles prompts and few-shot examples against a metric; GEPA evolves prompts with reflective, Pareto-aware search; ADAS searches over agent designs. All of them need a reliable evaluation signal — a labelled set or a programmatic metric — to improve against. If you cannot score outputs objectively, these buy you little. If you can, they turn "prompt engineering by hand" into a measurable optimization and are worth the setup.
- 3
Weigh cost and latency tolerance honestly
Every added agent turn, reflection pass or search iteration multiplies tokens and wall-clock time. A single prompt is cheapest and fastest; ReAct adds a few tool-call round trips; Reflexion and multi-agent debate can be several times the cost for a quality gain that may or may not matter to your use case. Decide up front how much latency a user will tolerate and what a run is allowed to cost, then let that cap rule out patterns you cannot afford to run at your volume.
- 4
Choose single-agent or multi-agent deliberately
Multi-agent setups (planner plus workers, or specialized roles) help when the work genuinely decomposes into parallel or distinct-expertise subtasks, and when you can define clean hand-offs. They also add coordination overhead, more failure modes and harder debugging. Default to a single capable agent with good tools; reach for multi-agent only when a concrete subtask boundary or parallelism justifies it, not because the diagram looks impressive.
- 5
Nail down your determinism and control needs
Ask how much of the flow must be fixed versus model-decided. If regulators, auditors or your own reliability bar require predictable paths, you want an explicit, inspectable control flow — a state graph where you own the transitions — over an agent that decides everything at runtime. LangGraph leans toward that explicit, stateful control; role-based frameworks like CrewAI favor higher-level autonomy. Match the framework to how much nondeterminism you can live with.
- 6
Check the security and egress constraints
Agents run tools, call out to the network and spend money, so the framework’s security posture is part of the choice, not an afterthought. Compare how each runtime lets you sandbox tool execution, restrict outbound egress, cap spend and token use per run, and audit what the agent did. Frameworks differ sharply here — some give first-class guardrails and others leave it to you. If an agent can reach the internet or move money, the one with real spend caps, egress controls and sandboxing wins even if another is nicer to prototype in.
- 7
Map to a pattern plus runtime and prototype
Now combine the answers: task type gives the pattern (single-prompt, ReAct, Reflexion, or a self-improving DSPy/GEPA/ADAS loop), and your determinism, cost and security answers give the runtime (LangGraph for explicit stateful control, CrewAI for role-based autonomy, Google ADK for a Gemini-and-Vertex-centric stack). Build the smallest end-to-end prototype on that choice, run it against real inputs, and measure cost, latency and quality before you commit — the cheapest pattern that clears your bar wins.
Answer a few questions about your task, data, cost tolerance and security posture, and get a recommended pattern and runtime with the trade-offs spelled out — in your browser.
Related
The interactive decision tool this guide walks you through, end to end.
Harness comparatorCompare agent harnesses and optimizers side by side on the dimensions that matter.
GEPA, DSPy, ADAS and Reflexion comparedA deeper look at the self-improving patterns and when each one earns its cost.
Agent orchestrator security comparisonHow LangGraph, CrewAI and others handle sandboxing, egress and spend controls.
Frequently asked questions
LangGraph vs CrewAI — which should I use?
They optimize for different things. LangGraph models your agent as an explicit, stateful graph where you own the transitions, which suits workflows that need determinism, inspectable control flow and careful state management. CrewAI is role-based and higher-level: you define agents with roles and let them collaborate with less wiring, which is faster to prototype when you want autonomy over tight control. Choose LangGraph when predictability and auditability matter most, CrewAI when you want role-based collaboration with less plumbing. Neither is a framework-agnostic winner — it depends on how much control versus convenience your task needs.
Do I even need an agent framework?
Often not. If the task is a bounded transformation — extract fields, classify, summarize, rewrite — a single well-prompted model call is cheaper, faster and far easier to debug than any agent loop. Reach for a framework when the work genuinely needs multi-step tool use, reasoning over intermediate results, retries driven by self-critique, or coordination between specialized roles. Starting with the simplest thing that works and adding structure only when you hit a real limit is almost always the right order.
What is the difference between an agent pattern and a runtime?
A pattern is the shape of the reasoning — single-prompt, ReAct (reason and act in a tool-use loop), Reflexion (act, critique, retry), or a self-improving optimizer like DSPy, GEPA or ADAS. A runtime is the framework you build it on — LangGraph, CrewAI, Google ADK and so on. The pattern should come from the task; the runtime from your determinism, cost and security needs. Different runtimes can implement the same pattern, so decide the pattern first and treat the runtime as how you execute it.
When are DSPy, GEPA or ADAS worth it?
When you can score outputs objectively and you expect to run the system enough that improving it pays off. DSPy compiles prompts and examples against a metric, GEPA evolves prompts with reflective Pareto-aware search, and ADAS searches over agent designs — all of them need a reliable evaluation signal to optimize against. If you have a labelled set or a programmatic metric, they turn manual prompt tuning into a measurable optimization. If you cannot evaluate quality automatically, they have little to work with and the setup cost is hard to justify.
How do security and egress concerns change the choice?
They can override everything else. An agent that executes tools, reaches the network or spends money is a new attack surface, so how a runtime lets you sandbox execution, restrict outbound egress, cap spend and tokens per run, and audit actions becomes a primary selection criterion. Frameworks differ sharply here — some ship first-class guardrails, others leave it to you. If the agent can touch the internet or move money, prefer the runtime with real spend caps, egress controls and sandboxing even if another is more pleasant to prototype in.
What is Harness² and where does it fit?
Harness² is research from Google Cloud AI Research and Northwestern University (arXiv 2610.14224) on optimizing the agent harness itself rather than just the prompt or the model. It is relevant to the self-improvement step of this decision: if you are choosing between GEPA, DSPy, ADAS and Reflexion-style approaches, it is part of the literature on when and how harness-level optimization helps. Treat it as background for deciding whether a self-improving loop is worth it, not as a runtime you build your production agent on.