An AI agent framework gives developers building blocks for model calls, tools, state, and orchestration. A full-stack agent platform adds managed capabilities for running agents and handling infrastructure concerns such as identity, observability, evaluation, and scaling. Some products span both layers, so compare what you will actually use—not just the product category—against your workload, cloud, and team.
What is the difference between an agent framework and a platform?
A framework is primarily a set of programming abstractions and libraries: your application code uses them to define an agent, connect tools, manage state, or coordinate steps. You generally decide how and where to host that code and which operational services to assemble around it.
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A platform adds managed services for some of those operational jobs. Depending on the product, that can include runtime hosting, identity, integrations, monitoring, or evaluation. A platform may run agents built with different frameworks rather than requiring its own framework. Conversely, a framework can include workflows, hosting integrations, and other capabilities that blur a simple framework-versus-platform distinction.
Microsoft Agent Framework illustrates the overlap: its documentation covers agents, workflows, tools, state and memory, integrations, hosting, and security. AWS describes Bedrock AgentCore as a set of managed runtime and lifecycle services that can work with a choice of frameworks. These are different scopes of product, not proof that one architecture is right for every team.
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What the terms mean in practice
- Framework: helps you build and orchestrate agent behavior in code.
- Platform: offers managed services for running, connecting, securing, observing, or evaluating agent applications.
- Framework plus platform: a team can use a framework for behavior and choose separate hosting or operations services—or use a broader product that covers both.
Do you need an agent at all?
Use an agent when the task is open-ended enough to benefit from a model planning and using tools across changing conditions. If the work has known inputs, rules, and steps, a conventional function or explicit workflow is usually easier to control, test, and explain. Microsoft’s Agent Framework documentation puts the rule plainly: “If you can write a function to handle the task, do that instead of using an AI agent.”
Workflows and agents are not mutually exclusive. A workflow can make the important sequence and handoffs explicit while an agent handles a bounded decision or tool-using task within one step. This can preserve predictable control around the parts of an application that do not need autonomy.
How should you compare agent frameworks and platforms?
Start with the system you need to operate, not a feature-count contest. Answer these questions for the expected workload and deployment environment:
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| Decision axis | Questions to ask |
|---|---|
| Control and orchestration | Can you make execution paths, handoffs, and approval points explicit, or does the task benefit from more autonomous behavior? |
| State and recovery | How are conversation state, persistence, checkpoints, retries, and long-running tasks handled? |
| Developer fit | Does the framework fit your team’s language, SDK conventions, existing services, and skills? |
| Models and integrations | Which model providers, tools, and protocols are supported? Are any constraints material to your application? |
| Operations | Are hosting, scaling, tracing, evaluation, and debugging managed, or will your team assemble and operate them? |
| Security and data boundaries | How are identities, credentials, network access, data handling, and human approvals configured? |
| Economics | What is metered? What continues to cost money while idle? How do model, tool, networking, and runtime usage affect the bill? |
Set workload-specific requirements before shortlisting products: language, cloud environment, models, latency and concurrency needs, tool access, data and compliance boundaries, operational capacity, and expected usage. Then prototype the critical path and estimate its operating cost under realistic usage. The available comparisons do not establish a universal fastest, cheapest, most secure, or most reliable choice.
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Which frameworks are worth evaluating?
A June 6, 2026 guide from LangChain compares seven frameworks across developer experience during prototyping, production reliability, observability and debugging, ecosystem integrations, and pricing transparency. LangChain sells products in this category, so its recommendations are a vendor’s comparative view, not an independent ranking or like-for-like benchmark.
| Option | LangChain guide’s characterization | Consider evaluating it when… |
|---|---|---|
| LangChain | Useful for rapid prototyping | You want to explore an application quickly using its framework abstractions. |
| LangGraph | Useful for precise, stateful orchestration | You need explicit control over stateful execution paths. |
| CrewAI | Useful for quick role-based multi-agent prototypes | Your prototype is organized around role-based agents and their coordination. |
| Microsoft Agent Framework | A fit for Microsoft-stack teams | You are already evaluating Microsoft’s agent and workflow ecosystem. |
| LlamaIndex Workflows | A fit for document-heavy, event-driven pipelines | Your application centers on documents and event-driven processing. |
| Google ADK | A fit for GCP-oriented teams | Your team is building in a Google Cloud-oriented environment. |
| OpenAI Agents SDK | A fit for scoped assistants and delegation | Your application needs scoped assistant behavior and delegation. |
| Mastra | A fit for TypeScript teams | Your team wants a TypeScript-oriented option. |
These descriptions report the guide’s positioning; they do not establish that a framework will outperform alternatives on your workload. The guide does not provide a comparable benchmark that settles speed, output quality, or total cost across these options.
AWS also names Strands Agents as a framework that Bedrock AgentCore supports. Treat framework compatibility as one input: verify that the specific model, tools, runtime, and operational features your application needs are available in your chosen setup.
When does a managed agent platform make sense?
A managed platform may reduce the amount of infrastructure your team must assemble and operate. That can matter when you need managed runtime, identity integration, tool connectivity, observability, or evaluation. It does not remove the work of designing agent behavior, validating data flows, or configuring access controls.
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Microsoft describes Agent Framework as combining AutoGen abstractions with Semantic Kernel enterprise features and positions it as the successor to both. Its documentation covers individual agents that use language models to process inputs, call tools and MCP servers, and respond; it also describes a harness agent for longer tasks, graph-based workflows, and integrations. Microsoft documents migration guidance for teams moving from AutoGen or Semantic Kernel. Check the current documentation for the language, runtime, and provider details relevant to your deployment.
AWS Bedrock AgentCore
AWS describes AgentCore as a managed platform that can host agents made with custom frameworks or named options including CrewAI, LangGraph, LlamaIndex, Google ADK, OpenAI Agents SDK, and Strands Agents. The services AWS lists include Runtime, Memory, Gateway, Browser and Code Interpreter tools, Identity, Policy, Observability, and Evaluations. Its FAQ describes runtime choices that include serverless microVMs and managed EC2 instances. These are documented AWS capabilities, not independent performance guarantees, and availability or service details may change.
Before choosing a platform, map its services to your design. A listed capability is useful only if it fits your application’s configuration, security boundaries, and operating model.
How do you take an agent application to production?
- Define the task and boundaries. Specify what the agent may decide, which tools and data it may access, what it must not do, and when a person must approve or take over. Use a normal function or explicit workflow for steps that do not need model-driven autonomy.
- Choose the orchestration layer. Select a framework that fits your language and the control, state, and recovery behavior the task requires. Decide whether you will host it yourself or use a managed platform.
- Design the data and access path. Identify what each model, tool, server, and external service receives and returns. Apply least-necessary access, manage credentials and network access deliberately, and define any human approval points.
- Test the application’s failure cases. Exercise incorrect or unexpected tool output, unavailable dependencies, retries, long-running work, and cases where the agent should refuse or escalate. Evaluate the actual application rather than assuming framework or platform features guarantee safe behavior.
- Instrument and evaluate it. Decide how your team will inspect execution, diagnose failures, and assess behavior against representative tasks. Use built-in platform capabilities where appropriate, or assemble these functions separately.
- Estimate and monitor operating cost. Include model and tool use as well as runtime, networking, and idle time where applicable. Revisit the estimate when actual usage differs from the assumptions.
What security and reliability work remains yours?
Using a framework or managed platform does not by itself make an agent secure, compliant, or reliable. Microsoft says builders remain responsible for application-specific safeguards and testing, particularly when third-party systems are involved. Its guidance calls for reviewing what data is shared and received, retention and location, whether data crosses organizational Azure compliance or geographic boundaries, and what safeguards the particular application needs.
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Microsoft also notes that third-party servers, agents, code, and direct models outside Azure may have their own terms and costs. Review those dependencies as part of the application’s data-flow and operating review rather than assuming the framework governs their behavior.
AWS documents AgentCore capabilities including VPC connectivity, identity integration, and session isolation. Those are platform capabilities to configure and assess against your design; they are not a blanket guarantee of security or compliance. The application still needs appropriate access controls, data-flow review, testing, and safety measures.
How should you think about platform cost?
There is no complete, comparable price calculation across the frameworks and platforms discussed here. Framework use and hosting or operations services are separate choices: a team can use a framework without adopting an associated hosting or observability service, while a managed platform may charge for the services it uses.
AWS describes AgentCore billing as consumption-based and modular. Its FAQ says the serverless microVM runtime bills active CPU and memory; managed instances use underlying EC2 billing plus an AgentCore management fee. That pricing description does not mean AgentCore is always cheaper. Compare the charges that apply to your selected modules and workload, including model and tool usage, runtime activity, idle periods, networking, and security needs.
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For any option, ask what is metered, how usage changes with concurrency and task duration, what costs persist when idle, and whether required operational services are included or separate. Use your own expected workload assumptions; a product label alone cannot predict total cost.
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