AI Agent Frameworks: How to Choose the Right Stack

Which AI Agent Framework Should You Choose?

Framework selection starts with the workflow, not the framework. Before comparing libraries, write down what your system must control: the state schema, the branching conditions, the tool calls, the human approval points, the tracing requirements, and the deployment target. That list of requirements is the selection criteria. A framework that matches seven of eight requirements is a better choice than one that is more popular but matches four.

Framework capabilities change between releases. A feature that was missing six months ago may be native today; a feature that was native may be deprecated or restructured. The comparison matrix below reflects capabilities at the time of writing. Before committing to a framework, verify current official documentation for the features that matter most to your system. Treat any framework comparison — including this one — as a starting point, not a final verdict.

Avoid permanent statements like “Framework X is always best.” The right framework depends on the system you are building. A team building a durable, multi-step workflow with checkpoint recovery has different needs from a team building a single-agent chatbot with two tools. Match the framework to the system, not the system to the framework.

Evaluate Frameworks by Engineering Requirement

The matrix gives an overview. The sections below break down each engineering requirement in detail so you can evaluate frameworks against the specific needs of your system.

State and Durable Execution

State is the working data of your agent — progress, intermediate results, context, and execution position. A framework's state model determines how state is represented, updated, and persisted. LangGraph uses a typed state object that flows through graph nodes and is checkpointed after each step, enabling durable execution and resume-after- failure. Other frameworks may use session objects, conversation context, or agent-local state with varying levels of persistence.

Durable execution means the agent can resume from the last checkpoint after a crash, timeout, or deployment restart. If your system runs long workflows — minutes or hours — durable execution is not optional. Check whether the framework provides checkpointing natively, requires an external store, or leaves persistence entirely to you. A framework that checkpoints natively saves significant engineering effort for long-running agents.

For short-lived agents that complete in a single request-response cycle, in-memory state may suffice. The cost of durable execution — checkpoint storage, serialization overhead, recovery logic — is justified only when the agent runs long enough that failure mid-loop is a real risk.

Routing, Handoffs and Multi-Agent Coordination

Routing determines which step runs next. In graph frameworks like LangGraph, routing is explicit: conditional edges connect nodes, and you define the conditions in code. In role-oriented frameworks like CrewAI, routing is implicit: the process type (sequential, hierarchical) determines task assignment. In SDK-style frameworks, handoffs between agents define the flow.

Multi-agent coordination adds complexity. Each handoff is a potential failure point: state must transfer correctly, the receiving agent must understand the context, and the overall trajectory must remain evaluable. Frameworks that provide native multi-agent support reduce boilerplate but may hide coordination details you need to debug.

Evaluate the routing model against your workflow complexity. If your workflow has conditional branches, loops, and parallel paths, explicit graph edges give you control. If your workflow is a linear sequence of specialist tasks, role-based assignment is simpler. Match the abstraction to the workflow shape.

Tool Calling and MCP Integration

Every framework provides some mechanism for tool calling — defining tool schemas, executing tool invocations, and returning results to the model. The differences are in how tools are defined, how arguments are validated, and how results are formatted. Some frameworks use decorator-based tool definitions; others use class-based or function-based schemas.

The Model Context Protocol (MCP) standardizes tool connections. If your system needs to connect to many external tools, or if you want tools to be portable across frameworks, MCP support matters. Check whether the framework has native MCP client support, requires an adapter, or needs you to integrate an MCP client yourself. MCP support is evolving across frameworks — verify current documentation.

For systems with a small number of stable tools, framework-specific tool definitions are fine. For systems with many tools, cross-service integration, or a need for tool portability, MCP reduces coupling between your tools and your framework.

Human-in-the-Loop Control

Human approval gates pause the execution loop before high-impact actions. The framework's approval mechanism determines how the pause works, how state is preserved during the pause, and how the agent resumes after approval or rejection. LangGraph provides native interrupt/resume with checkpoint persistence — the agent pauses at a defined node, state is checkpointed, and the agent resumes from that checkpoint after the human decision.

Other frameworks may support approval via hooks, callbacks, or external orchestration. If human approval is a core requirement — not an afterthought — evaluate how the framework handles the pause-resume cycle, whether state is durable during the pause, and whether the approval interface can show the human what the agent intends to do, not just a yes/no prompt.

If your system has no high-impact actions, human approval may be unnecessary. Do not add approval gates because the framework supports them — add them because the system requires them.

Tracing, Evaluation and Debugging

Tracing records every step of the agent's execution: model calls, tool invocations, state changes, and routing decisions. Evaluation uses traces to assess quality — both the final outcome and the trajectory that produced it. A framework's tracing integration determines how much of this you get for free and how much you build yourself.

LangGraph integrates with LangSmith for tracing and evaluation. OpenAI Agents SDK integrates with the OpenAI platform. Google ADK integrates with Google Cloud observability. Microsoft Agent Framework integrates with Azure AI observability. If you are already in one of these ecosystems, the native integration reduces setup effort. If you need provider-independent tracing, check whether the framework supports OpenTelemetry or custom tracing hooks.

For production systems, tracing is not optional. You cannot debug, evaluate, or audit an agent you cannot observe. Evaluate the framework's tracing story before committing — not after deployment.

Deployment and Provider Constraints

Frameworks often have provider preferences. OpenAI Agents SDK is designed for OpenAI models and infrastructure. Google ADK integrates with Google Cloud and Vertex AI. Microsoft Agent Framework integrates with Azure OpenAI. LangGraph and CrewAI are more provider-agnostic, supporting multiple LLM providers through adapters or LiteLLM. Plain application code gives you full provider control — you manage the client.

Provider flexibility matters when you need to switch models for cost, performance, or compliance reasons. If your organization standardizes on one cloud provider, a framework that integrates natively with that provider reduces integration effort. If you need to switch providers or use multiple providers, a provider-agnostic framework or plain code is the better choice.

Deployment target also matters. Some frameworks are designed for serverless or containerized deployment; others assume long-running processes. Check whether the framework's checkpoint store, tracing backend, and execution model fit your deployment environment. Verify current documentation for deployment guidance, as cloud platform support evolves.

CrewAI for Role-Oriented Agent Collaboration

CrewAI models an agent system as a crew of role-playing agents, each with a defined role, goal, and backstory. Tasks are assigned to agents, and the crew executes them according to a process — sequential or hierarchical. The abstraction is closer to a team of specialists than a state machine. You define who does what, and the framework manages task assignment and inter-agent communication.

Use CrewAI when your system decomposes naturally into specialist roles. A content pipeline with a researcher, a writer, and an editor maps cleanly to a crew. The role-task abstraction reduces the boilerplate of defining individual agents, handoffs, and message passing. CrewAI also supports tool integration and multiple LLM providers via LiteLLM.

Avoid CrewAI when you need fine-grained control over the execution graph. If your workflow has conditional branches at the node level, loops with specific entry and exit conditions, or checkpoint-based recovery, the role-task abstraction is less precise than explicit graph edges. You can work around this, but you lose the simplicity that makes CrewAI attractive in the first place. Verify current documentation for new process types and control features.

Google Agent Development Kit for Agent Development and Google Ecosystem Integration

Google's Agent Development Kit (ADK) provides tools for building agents that integrate with Google Cloud and Vertex AI. The ADK includes agent definitions, session- based state management, tool integration, and orchestration flows. The kit is designed for the Google ecosystem — Vertex AI models, Google Cloud observability, and Google Cloud deployment.

The ADK suits systems that already run on Google Cloud or that need Vertex AI model access. Session-based state management handles conversation and agent state, and the orchestration flows provide multi-agent patterns. The integration with Google Cloud observability reduces the effort of setting up tracing and evaluation if you are already in that ecosystem.

The ADK is evolving. Capabilities, patterns, and provider support may change between releases. Verify current Google ADK documentation for the latest features, multi-agent patterns, and provider flexibility before committing. If you are not in the Google ecosystem, evaluate whether the integration benefits outweigh the provider coupling.

When You Do Not Need an Agent Framework

Not every agent system needs a framework. If you have a single agent, a small number of tools, simple state that fits in memory, and no need for framework-provided tracing, evaluation, or checkpointing, plain application code is the better choice. You write a loop: assemble context, call the model, execute tool calls, check the result, repeat until done. A few dozen lines of code.

A framework adds a dependency, an abstraction layer, and a learning curve. If the abstraction does not reduce your code or solve a problem you actually have, it is cost without benefit. The control loop for a single-agent, few-tool system is straightforward to write, test, and debug without framework abstractions. You also get full control over state, tracing, and tool definitions — because you build them.

The decision is not permanent. You can start with plain code and adopt a framework when complexity grows — when you add a second agent, when state needs to be durable, when branching becomes conditional, or when tracing requirements exceed what you can build quickly. Starting simple and adding abstraction when justified is cheaper than starting with a framework and working around its constraints.

Existing Framework Comparisons

For deeper head-to-head comparisons of specific frameworks and protocols, these pages provide detailed analysis:

• CrewAI vs AutoGen vs LangGraph — a side-by-side comparison of three multi-agent frameworks, covering abstraction model, state management, and use-case fit.
• LangChain vs LangGraph — how LangChain's tool and retrieval integrations relate to LangGraph's graph orchestration, and when to use each.
• MCP vs ACP — a comparison of the Model Context Protocol and Agent Communication Protocol for tool integration and agent-to-agent communication.