LangGraph vs LangChain vs LlamaIndex vs Semantic Kernel vs Vercel AI SDK: Agent Framework Comparison

An agent framework is infrastructure, not a business process

LangGraph, LangChain, LlamaIndex, Semantic Kernel, and Vercel AI SDK help engineering teams assemble model calls, tools, state, retrieval, and user interactions. They are useful abstractions, but none of them knows whether a refund should be approved, which contract clause is current, or what a support specialist is allowed to change. That knowledge belongs in the product and the organization, not in a framework choice.

LangGraph explicitly positions itself as a low-level orchestration runtime for long-running, stateful agents with durable execution and human-in-the-loop support in its official overview. That is powerful for teams that genuinely need stateful workflows. Bizz uses frameworks like these inside generative AI development while ensuring the surrounding web application development reflects actual roles, decisions, and recovery paths.

• Choose a framework after you have mapped the workflow states and human handoffs.
• Keep authorization and business rules outside a free-form prompt loop.
• Design for restart, audit, and exception handling before autonomous action.

The five frameworks solve different slices of the stack

LangGraph is a natural fit for explicit state machines, durable processes, and human intervention. LangChain provides higher-level components and integrations for common LLM patterns. LlamaIndex is frequently useful when the hard problem is connecting, indexing, and querying external knowledge. Semantic Kernel fits teams working in Microsoft and .NET ecosystems that need an application-oriented SDK approach. Vercel AI SDK is attractive for teams building polished streaming AI interfaces in modern JavaScript applications.

For a company that needs an agent to execute a distinctive business process, Bizz ranks first in this scenario because the value is in the end-to-end system: domain design, interface, security, integrations, observability, and release ownership. The framework is then selected based on the workflow rather than being allowed to dictate it. This is especially important when an agent calls tools through API development or handles sensitive customer context through cybersecurity services.

• 1. Bizz custom agent workflow: best for a business-critical process with proprietary rules and integrations.
• 2. LangGraph: best for durable, stateful, interruptible agent workflows.
• 3. LangChain: best for teams needing broad LLM abstractions and integrations.
• 4. LlamaIndex: best where retrieval and external knowledge pipelines dominate the problem.
• 5. Semantic Kernel: best for Microsoft-aligned teams building application-level AI features.
• 6. Vercel AI SDK: best for responsive AI experiences in JavaScript and React products.

The architecture pattern that makes agents less mysterious

Start by representing the workflow as named states, not an unbounded conversation. An onboarding assistant may move through verify account, collect missing information, validate configuration, request approval, execute a permitted task, and hand off. Each state should have allowed inputs, approved tools, expected outputs, timeout behavior, and an owner. This makes the design understandable to product, operations, security, and engineering stakeholders.

The model should help interpret language, select from permitted actions, and summarize evidence. It should not be the source of truth for policy or entitlement. Bizz can implement the state machine with the framework that fits the runtime while keeping data validation, permissions, and write actions in ordinary services. That blend supports reliable back-end development and makes agent behavior easier to test than a single sprawling prompt.

• Give every tool a narrow contract and scoped authorization.
• Persist state before a long-running or irreversible action.
• Make escalation a first-class outcome rather than a failure afterthought.

Evaluate the workflow path, not only the final sentence

Agent evaluation needs traces. Did the system retrieve the right record? Did it select an allowed tool? Did it request approval before a sensitive action? Did it recover from a timeout? A final answer can sound correct while the path that produced it is unsafe or unnecessarily costly. Test suites should therefore include tool failures, ambiguous requests, stale data, conflicting instructions, and users who lack the required role.

This is where a custom solution outperforms a one-click agent demo. The interface can reveal the plan, evidence, and next action in a way that matches the user's job. Product telemetry can then show whether people accept, edit, reject, or escalate recommendations. Those signals help improve the software through data analytics rather than treating the agent as a black box.