What is an AI agent framework?

An AI agent framework is a code library that wires an LLM to a set of tools, memory, and an orchestration loop, so it can plan and act across multiple steps instead of answering once. LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, and Semantic Kernel are the names you meet first, and each expresses the same four pieces in its own style. A framework shapes how the agent reasons and leaves hosting, connectors, scheduling, and governance to you. Major is not a framework at all; it is the platform layer where an agent turns the repeatable work into a deterministic app with a managed database, scheduling, and audit attached.

What is the difference between an AI agent framework and an AI agent platform?

A framework is a code library for building agent logic, and a platform is the managed runtime that logic runs inside. The framework gives you the LLM loop, tool registry, memory, and control flow. The platform layer adds hosting, prebuilt connectors, role-based access, audit logs, scheduling, and human-in-the-loop approvals. You build with a framework and you operate and govern at the platform layer. Major sits in that second layer with a specific twist: rather than hosting the reasoning loop, an agent reasons once to build a deterministic app for the repeatable work, and the app is what runs afterward.

Which AI agent framework should I use?

Match the framework to the job. LangChain or LangGraph when you need broad integrations or an explicit state machine you can read and inspect. CrewAI when role-based multi-agent collaboration fits the work and readability matters. LlamaIndex when retrieval over your own data is the core problem. Semantic Kernel for .NET enterprise stacks. For production the choice matters less than it feels like it does, because most frameworks ship without policy-before-dispatch, durable scheduling, or audit evidence, which is the layer a platform like Major supplies underneath whichever library you pick.

Do I need an AI agent framework at all?

Not always. A framework is the right call when the reasoning loop is your product, or when you need to know by Friday whether the idea works, and LangGraph or CrewAI will get you a prototype faster than anything else. When the work is repeatable and has to run every day under someone else's name, the library is the smaller half of the job, because you still own hosting, state, credentials, scheduling, and audit. Major covers that half, with the agent building a deterministic app for the repeatable part. It is not a Python library for custom agent loops, so keep the framework for the parts that need one.

Can I use CrewAI and LangChain together?

Yes, and the common hybrid uses LangChain for tooling and retrieval with CrewAI for role-based orchestration, since they solve different parts of the problem. Start combined if it keeps each piece simple, then consolidate onto one framework when the integration overhead outweighs the convenience. Neither combination answers where the agent runs at 3am, who is allowed to trigger it, or how to prove what it did last Tuesday. Major can sit under either one and answer those three, since scoped credentials, role-based access, scheduling, and audit are properties of the platform rather than code you write.

AI Agent Frameworks: An Honest Map for Teams Choosing a Layer

AI agent frameworks like LangChain and CrewAI build agent logic. See what each one gives you, what you still own, and when the platform layer is the real choice.

Jason BaoJune 30, 2026Updated August 28, 2026

Mapping the AI agent frameworks landscape

The short answer

An AI agent framework is a code library that wires an LLM to a set of tools and an orchestration loop, so it can plan and act across multiple steps rather than answer once. LangChain, CrewAI, AutoGen, and LlamaIndex are the names you meet first. This map holds every one of them to the same test: what the library actually hands you, and what it leaves for you to build. A framework shapes how the agent reasons. It does not hand you connectors, governance, scheduling, or a managed place to run, and that gap is what this map is about. If you want a working prototype this week, a framework is the right answer and LangGraph or CrewAI will get you there faster than anything else here. If the thing has to run every day under someone else's name, the layer above the framework decides more than the library does. Frameworks build agent logic. Platforms run and govern it.

First decide the layer, then pick the framework

Framework choice is the second decision. The first is how much of the runtime you intend to own. Every library on this page gives you the same thing: scaffolding to orchestrate model calls. You still own the infrastructure it runs on, the state it keeps between runs, the credentials it holds, and the record of what it did. That bill arrives the week the prototype has to run on a schedule for someone who is not you.

Major sits at the other end of that trade, and it is a platform rather than another library to import. The agent reasons once to build a deterministic app for the repeatable work, then the app runs as code instead of re-reasoning on every execution. The platform holds the managed database and file storage, scoped credentials and role-based access through the credential proxy, scheduling, and audit at the point of action. You are not standing up Postgres, a secrets store, a cron runner, and a logging pipeline around a graph you wrote last month. Reason once. Run forever.

The honest limit: Major is more platform than a weekend demo needs, and it is not a Python library for arbitrary custom agent loops. If the reasoning loop itself is your product, or you only need to know by Friday whether the idea works, build it in a framework and skip ahead. Everything below maps the frameworks on their own terms.

What an AI agent framework actually is

Strip away the branding and every framework solves the same four problems. It runs an LLM in a loop, so the model can act, observe the result, and decide again. It maintains a tool registry, the set of functions the model is allowed to call. It carries memory or state between steps, so the agent does not start cold each turn. And it handles orchestration, the control flow that decides which step runs next. LangChain and its graph-based sibling LangGraph, CrewAI, AutoGen, LlamaIndex, and Microsoft's Semantic Kernel each express those four pieces in their own style, and newer entrants like the OpenAI Agents SDK and Smolagents trim the surface further. If you are still pinning down what an AI agent is at the concept level, start there; this piece assumes the loop and picks up at the code.

The differences show up in how you express that orchestration. CrewAI leans on roles, reading almost like an org chart of cooperating agents.

from crewai import Agent, Task, Crew

researcher = Agent(role="Researcher", goal="Find supporting facts", backstory="Analyst")
write_up = Task(description="Summarize the findings", agent=researcher)
crew = Crew(agents=[researcher], tasks=[write_up])
result = crew.kickoff()

LangGraph models the same work as a state machine, with explicit nodes and edges you can read and inspect.

from langgraph.graph import StateGraph, START, END

graph = StateGraph(AgentState)
graph.add_node("plan", plan_step)
graph.add_node("act", act_step)
graph.add_edge(START, "plan")
graph.add_edge("plan", "act")
graph.add_edge("act", END)
app = graph.compile()

Both are good at what they do. CrewAI's role-based ergonomics make multi-agent collaboration readable. LangGraph's explicit graph makes control flow auditable in code. Neither one tells you where the agent runs at 3am, who is allowed to trigger it, or how to prove what it did last Tuesday.

What an AI agent platform actually is

A platform layer sits above the framework and owns the parts a framework leaves to you. Managed hosting, so the agent runs without you operating a server. Prebuilt connectors to the systems the agent touches. Role-based access control and audit logs, so every action is attributable. Scheduling, so work runs on a cadence instead of only when a developer invokes it. And human-in-the-loop approvals for the steps that need a person. Agentforce, Copilot Studio, and watsonx Orchestrate live here, and so does Major, though Major sits at a distinct point in this layer: the enterprise platform where agents build the software they run on. Here governance is structural rather than bolted on, because the work lives in apps with permissions and enterprise AI governance rather than inside a prompt.

The differences that matter

A framework and a platform are not competitors. They answer different questions. The table holds the framework column to what a code library gives you out of the box, and the platform column to what a managed runtime adds. Where a framework says you can build that, the platform says it is already running.

Determinism
- Framework-only: Reasoning reruns on each invocation; you write the retries and guards
- Platform layer: Repeatable work compiles to an app that runs the same way every time
State persistence
- Framework-only: In-memory, or a database you stand up and maintain
- Platform layer: Managed database and storage attached to each app
Governance and audit
- Framework-only: You instrument logging and access control yourself
- Platform layer: Scoped credentials, RBAC, and audit at the point of action
Scheduling
- Framework-only: External cron or queue you operate
- Platform layer: Built-in scheduling per app
Connectors
- Framework-only: You write and maintain each integration
- Platform layer: Prebuilt, managed connectors
Deployment overhead
- Framework-only: You host, scale, and monitor the runtime
- Platform layer: Managed hosting, no separate infrastructure
Human approvals
- Framework-only: You build the approval routing and UI
- Platform layer: Human-in-the-loop approvals built in

The audit and logging row is where framework-only stacks hurt most in production. Watching a graph reason in a notebook is easy. Getting agent observability across scheduled, multi-step runs is the work teams underestimate. Here is the uncomfortable part: for production, framework choice matters less than the architecture around it. Most frameworks ship without policy-before-dispatch, durable scheduling, or audit evidence, so teams that pick on GitHub stars optimize the layer that matters least.

When you want each

The choice is rarely framework versus platform in the abstract. It comes down to where your team's effort should go.

Choose a platform when the work has to run on a schedule, across systems, under governance, and the people who depend on it are operators rather than engineers. That is where Major fits: the agent builds a deterministic app for the repeatable work, and the managed database, scoped credentials, role-based access, and audit trail come with it. The value lives in connectors, scheduling, and audit, not in a hand-built loop.
Choose a framework when agent logic is your core IP and you have the engineering and DevOps capacity to host, secure, and schedule it yourself. If the reasoning loop is the product, own it.
Choose both when a framework proves the concept and a platform operationalizes it. Prototype the agent in LangGraph or CrewAI, then move the repeatable parts onto managed infrastructure once it has to run every day. See how to build an AI agent for that path.

The Major take

A senior engineer can stand up a capable agent in LangChain or CrewAI in an afternoon. The cost shows up later. You rebuild connectors, scheduling, credential scoping, and audit every time the use case changes, and that glue is never the interesting part of the work. The framework was never the bottleneck; the runtime around it was.

Major takes a different cut at the problem. Instead of running the agent's reasoning on every execution, a predictable agent on Major builds a deterministic app for the repeatable work. The model reasons once to generate the app, then steps out, and the app runs as code with its state in a managed database and its own logs. Scoped credentials and role-based access are enforced through the credential proxy, so every tool call is attributable at the point of action and agent security is structural rather than something you instrument afterward. Scheduling and cross-system connectors come built in, so the app runs without a separate cron job or glue service. That is the layer a framework-only stack cannot reach: the deterministic app layer, where repeatable work runs the same way every time and the model carries less of the load on each run. Reason once. Run forever. And because every app an agent builds is reusable across the org, the work compounds instead of being rebuilt per use case.

Be clear about the boundary. Major is not a Python library for arbitrary custom agent loops, and it does not replace engineering judgment about what the agent should do. If the reasoning loop itself is your product, keep building it in a framework. Major is for teams that want connectors, governance, and scheduling without assembling that runtime by hand, and it can sit on top of the framework you already use rather than replacing LangChain or CrewAI.

Pick the framework you like. The part worth reconsidering is everything you end up building around it: the same connectors, the same scheduling, the same credential scoping and audit plumbing, rewritten for every new agent. Describe the workflow and Major ships the governed app that runs it, with scoped credentials, a managed database, and a schedule already wired in. Get started on Major and turn your framework prototype into a deterministic app you can govern.

Frequently asked questions

What is an AI agent framework?: An AI agent framework is a code library that wires an LLM to a set of tools, memory, and an orchestration loop, so it can plan and act across multiple steps instead of answering once. LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, and Semantic Kernel are the names you meet first, and each expresses the same four pieces in its own style. A framework shapes how the agent reasons and leaves hosting, connectors, scheduling, and governance to you. Major is not a framework at all; it is the platform layer where an agent turns the repeatable work into a deterministic app with a managed database, scheduling, and audit attached.
What is the difference between an AI agent framework and an AI agent platform?: A framework is a code library for building agent logic, and a platform is the managed runtime that logic runs inside. The framework gives you the LLM loop, tool registry, memory, and control flow. The platform layer adds hosting, prebuilt connectors, role-based access, audit logs, scheduling, and human-in-the-loop approvals. You build with a framework and you operate and govern at the platform layer. Major sits in that second layer with a specific twist: rather than hosting the reasoning loop, an agent reasons once to build a deterministic app for the repeatable work, and the app is what runs afterward.
Which AI agent framework should I use?: Match the framework to the job. LangChain or LangGraph when you need broad integrations or an explicit state machine you can read and inspect. CrewAI when role-based multi-agent collaboration fits the work and readability matters. LlamaIndex when retrieval over your own data is the core problem. Semantic Kernel for .NET enterprise stacks. For production the choice matters less than it feels like it does, because most frameworks ship without policy-before-dispatch, durable scheduling, or audit evidence, which is the layer a platform like Major supplies underneath whichever library you pick.
Do I need an AI agent framework at all?: Not always. A framework is the right call when the reasoning loop is your product, or when you need to know by Friday whether the idea works, and LangGraph or CrewAI will get you a prototype faster than anything else. When the work is repeatable and has to run every day under someone else's name, the library is the smaller half of the job, because you still own hosting, state, credentials, scheduling, and audit. Major covers that half, with the agent building a deterministic app for the repeatable part. It is not a Python library for custom agent loops, so keep the framework for the parts that need one.
Can I use CrewAI and LangChain together?: Yes, and the common hybrid uses LangChain for tooling and retrieval with CrewAI for role-based orchestration, since they solve different parts of the problem. Start combined if it keeps each piece simple, then consolidate onto one framework when the integration overhead outweighs the convenience. Neither combination answers where the agent runs at 3am, who is allowed to trigger it, or how to prove what it did last Tuesday. Major can sit under either one and answer those three, since scoped credentials, role-based access, scheduling, and audit are properties of the platform rather than code you write.