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There is no single best open-source agent framework. Choose based on how much control you need over execution, whether your work is document-heavy or multi-agent, the protocols your systems must support, and who will operate the service. LangGraph is a strong fit for explicit, stateful workflows; CrewAI emphasizes role-based collaboration; LlamaIndex Workflows suits event-driven, data-intensive applications; Google ADK is a code-first option for modular workflows; and Microsoft Agent Framework brings together the AutoGen and Semantic Kernel lineages with stated support for several interoperability and production capabilities.
What an agent framework does
An agent framework supplies the software structure around an AI model so an application can do more than send a prompt and return a response. It helps define when a model is called, which tools it may use, what state it can access, how work is handed between steps or agents, and how execution is observed and resumed.
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- Orchestration: The sequence, graph, event flow, or delegation rules that determine what runs next.
- Tools and data: The functions, APIs, and sources an agent can invoke or consult.
- State and memory: The information retained across steps or runs, and the ability to recover execution after interruption.
- Operations: Tracing, evaluation, approval gates, deployment, and maintenance of the running system.
These are separate concerns. Supporting multiple agents does not automatically mean a framework provides durable state or production monitoring. Likewise, a protocol for connecting to tools does not decide how an application should plan or recover a workflow.
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How the leading frameworks differ
The practical distinction is the framework’s organizing model. The descriptions below reflect the projects’ stated positioning and the AWS framework comparison; they are not results of a common performance test.
| Framework | Organizing model and suitable work | Deployment and ecosystem notes |
|---|---|---|
| LangGraph / LangChain | LangGraph is an agent runtime for complex agents and stateful workflows. It is the control-oriented choice when explicit workflow structure is important. | Open-source MIT ecosystem. AWS rates LangChain/LangGraph strongest for workflow complexity and lists platform-or-DIY deployment. |
| CrewAI | Role-based crews, roles, and tasks make multi-agent collaboration the central mental model. | Open-source MIT. AWS rates its multi-agent support strong and lists DIY deployment. |
| LlamaIndex Workflows | Workflow framework positioned for document-centric, event-driven, data-intensive systems. | Open-source MIT. AWS lists LlamaIndex as platform-or-DIY deployment. |
| Google ADK | Code-first and modular, with a fit for teams building workflows around a GCP-oriented stack. | Open-source Apache-2.0. Specific deployment modes are not stated in the cited comparison material. |
| Microsoft Agent Framework | Microsoft describes it as an open-source engine combining AutoGen research patterns with Semantic Kernel enterprise capabilities. | Stated capabilities include MCP, A2A, OpenAPI, pluggable memory, observability, approvals, CI/CD, and long-running durability. A deployment classification is not stated in the cited AWS matrix. |
“Not stated” in the table means the cited material does not establish that comparison point; it should not be read as proof that a capability is absent. In particular, framework documentation and release states can change, so verify a capability against the version you intend to deploy.
LangGraph: explicit control over complex workflows
LangGraph is the clearest fit when execution should be represented as a controlled, stateful workflow rather than left to an open-ended group of agents. This is useful when a process has branches, conditional decisions, or steps that must be inspectable. Its positioning is not limited to multi-agent applications: complex single-agent systems can also benefit from explicit orchestration.
AWS’s comparison rates LangChain/LangGraph highest for autonomous workflow complexity and model/API breadth, and gives it platform-or-DIY deployment. Those are qualitative ratings in that comparison, not a measured lead in speed, cost, or reliability. The MIT-licensed ecosystem can be a fit for teams seeking an open-source foundation, but the team still needs to decide how to persist state, operate the runtime, and manage monitoring.
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CrewAI: roles and collaboration as the starting point
CrewAI organizes work around crews, defined roles, and tasks. That model can be intuitive when an application naturally divides into responsibilities—for example, one agent gathers information and another reviews or synthesizes it. AWS describes its multi-agent support as strong and lists DIY deployment.
The role-based abstraction is a design choice, not a guarantee that adding agents improves results. Each additional agent can introduce more handoffs and more execution paths to inspect. Use a crew when delegation is part of the actual problem; do not select it solely because the application is described as “agentic.” CrewAI is MIT-licensed, and AWS characterizes its learning curve as moderate.
LlamaIndex Workflows: data-heavy and event-driven applications
LlamaIndex Workflows is positioned for document-centric and data-intensive systems, especially where events trigger workflow steps. That makes it worth considering when the application’s core challenge is processing or responding to information from documents and connected data sources, rather than coordinating a large team of autonomous agents.
It is MIT-licensed. AWS includes LlamaIndex among the options that can be deployed through a platform or DIY approach. That classification does not tell you which hosting arrangement is best for a particular workload; the operator, persistence model, and integration requirements still determine the practical choice.
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Google ADK: code-first modularity
Google describes ADK as a code-first, modular, open-source agent framework under Apache-2.0. It is a natural candidate for teams that want to define agent behavior in code and are already oriented toward GCP. A GCP fit is a reason to evaluate it, not proof that the framework requires Google’s platform or that every component is locked to it.
The cited comparison material does not provide the same deployment classification or learning-curve rating for ADK as it does for some other frameworks. Treat those as questions to validate with the project’s current documentation and a small deployment exercise, rather than infer answers from the license or platform orientation.
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Microsoft Agent Framework: a successor direction, not just another name for AutoGen
Microsoft positions Agent Framework as an open-source engine that combines AutoGen’s research patterns and Semantic Kernel’s enterprise capabilities. Its stated feature set includes MCP, A2A, and OpenAPI interoperability, pluggable memory, observability, approval mechanisms, CI/CD, and durability for long-running workflows.
For a current comparison, AutoGen and Semantic Kernel are best treated as lineage and context for this unified successor direction, rather than as unrelated peers to the new framework. If an existing application uses either predecessor, check migration and compatibility details for the specific project version; the stated successor direction does not establish that every existing component transfers unchanged.
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How MCP and A2A fit together
MCP and A2A address different connections. The A2A Protocol project summarizes the distinction this way: “MCP is for agent-to-tool communication … A2A is for agent-to-agent communication.” MCP is relevant when an agent needs a standard way to reach tools or data. A2A is relevant when independently built agents need to communicate and collaborate.
MCP connects an agent to tools and data
Think of MCP as the tool-and-context connection layer. It does not, by itself, select an agent framework, define the application’s workflow, or make a tool call safe. The application still needs to decide which tools are available, what permissions they have, how inputs are validated, and what happens if a call fails. Microsoft lists MCP among the interoperability capabilities of Agent Framework.
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The A2A Protocol project defines A2A as an open standard for communication and collaboration between AI agents. Its documentation describes it as a communication layer, not an agent development kit, and says it can connect agents built with LangGraph, CrewAI, Semantic Kernel, or custom solutions. This distinction matters: A2A does not replace the framework that runs each agent.
The A2A project records that Google originally developed the protocol and donated it to the Linux Foundation. It is maintained by a technical steering committee with representatives from AWS, Cisco, Google, IBM Research, Microsoft, Salesforce, SAP, and ServiceNow. That governance context may matter to organizations assessing how an open standard is maintained; it does not guarantee that every framework integration has identical maturity or behavior.
Choosing whether you need either protocol
- Use a tool/data integration approach such as MCP when the main boundary is between an agent and an external capability or source.
- Consider A2A when separate agents—possibly built with different frameworks—must coordinate across an explicit agent-to-agent boundary.
- Do not add either protocol just to label an application interoperable. Confirm that the target tools or agents implement the protocol and test authentication, error handling, and version compatibility in your deployment.
Production deployment: decide who owns the hard parts
“Deployable” is not the same as production-ready for your organization. AWS’s comparison lists platform-or-DIY deployment for LangChain/LangGraph and LlamaIndex, and DIY deployment for CrewAI and AutoGen. These are broad deployment categories, not guarantees about a particular managed service, operational burden, or service-level outcome. For the other framework details, validate the current options directly rather than assuming that a framework’s cloud orientation determines where it can run.
Before committing, answer the operational questions that influence reliability and cost more than a framework label:
- Runtime ownership: Who deploys, scales, patches, and monitors the process that runs agents—the application team, a platform team, or a managed platform?
- State persistence: Where are conversation state, workflow state, and any checkpoints stored? What is retained, for how long, and under whose access controls?
- Recovery: If a model call, tool, worker, or network connection fails, can the workflow resume from a known point, or must it start again? Which steps are safe to repeat?
- Human approvals: Can execution pause for review and continue afterward? Define who can approve, what information they see, and what happens when approval is denied or delayed.
- Tracing and evaluation: Where are prompts, tool calls, decisions, errors, and outputs recorded? Can operators trace a run without exposing sensitive data, and how will behavior be evaluated after changes?
- Self-hosting and platform dependence: Can the application run in the environment your organization requires? Identify framework-specific services, model APIs, and data stores before estimating how difficult a later migration would be.
These checks are especially important for long-running or approval-sensitive work. A framework may expose a useful primitive, but the application still needs a persistence design, access policy, failure policy, and an operating team. Microsoft lists approvals and long-running durability as Agent Framework capabilities; verify how those features behave in the release you plan to use and how they fit your deployment.
A practical selection process
Use the workflow and your organization’s constraints to narrow the shortlist. AWS specifically recommends considering organizational fit, team expertise, existing infrastructure, maintenance, deployment, monitoring, and enterprise requirements. The questions below turn those factors into a decision sequence.
- Describe the workflow before choosing a framework. Sketch the triggers, model calls, tools, branches, handoffs, approval points, and failure paths. If the workflow is highly structured and needs explicit control, start by evaluating LangGraph. If the work naturally divides into named collaborative roles, evaluate CrewAI.
- Identify the dominant data shape. If the central task is document-heavy, event-driven, and data-intensive, include LlamaIndex Workflows. If you want a modular, code-first approach and your team is GCP-oriented, include Google ADK.
- Map interoperability boundaries. List the tools and data sources agents must call, then identify any other agents they must coordinate with. Evaluate MCP for the former and A2A for the latter; check actual implementation support at both ends.
- Set operational requirements before a proof of concept. Specify where state must live, what must survive a restart, whether approval gates are mandatory, where traces may be stored, and whether self-hosting is required. Eliminate options that cannot meet a firm requirement.
- Test one representative workflow end to end. Include a realistic tool call, an interruption or failure, an approval if relevant, and a trace review. The goal is to expose integration and operational gaps, not to claim a universal framework winner from a small test.
- Review maintenance and portability. Assess team familiarity, release and dependency management, hosting responsibilities, the cost of operating observability, and the effort to replace framework-specific integrations. An open-source license does not remove these ongoing obligations.
Quick-fit decision matrix
| If your main need is… | Start by evaluating… | Why this is a fit |
|---|---|---|
| Explicit control of a complex, stateful workflow | LangGraph | Its stated focus is complex agents and stateful workflows; AWS rates LangChain/LangGraph strongest for workflow complexity. |
| Role-based multi-agent task collaboration | CrewAI | Crews, roles, and tasks are its central orchestration model; AWS rates multi-agent support strong. |
| Document-heavy, event-driven data workflows | LlamaIndex Workflows | Its positioning directly targets document-centric, event-driven, data-intensive systems. |
| Code-first modular workflows in a GCP-oriented team | Google ADK | Google describes ADK as code-first and modular, with a fit for GCP-native teams. |
| A unified direction for AutoGen and Semantic Kernel capabilities | Microsoft Agent Framework | Microsoft describes it as combining those lineages and lists interoperability, observability, approvals, and durability capabilities. |
| Connecting agents built on different frameworks | A2A alongside the chosen framework | A2A is a communication layer for agent-to-agent collaboration, not a replacement development framework. |
The matrix is a shortlist, not a ranking. License, deployment model, and stated feature set can help eliminate mismatches, but they do not answer whether a framework meets your security, latency, cost, or reliability targets. The sources summarized here offer capability comparisons and qualitative guidance, not a shared benchmark across frameworks; performance claims therefore need workload-specific testing.
What to verify before adopting a framework
Framework capabilities, integration coverage, and project status can evolve. LangChain published “The best AI agent frameworks in 2026” on June 6, 2026; the current comparison should be read in that dated context rather than as a timeless ranking. Before implementation, verify the release documentation and licensing for the versions you will actually use.
Quick Recap
- Confirm the language and runtime version your application needs, along with the framework’s supported integrations.
- Check how state, checkpoints, retries, and long-running execution work in the selected release; do not equate a feature name with your recovery requirements.
- Run a deployment proof of concept in the intended environment, including monitoring and a failure/restart scenario.
- Confirm protocol support on both sides of each connection. A framework’s stated MCP or A2A support does not establish that a particular server, peer agent, or feature is compatible.
- Review the project’s license and dependency obligations with the team responsible for adoption.
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