LangGraph is a strong first candidate when you need explicit control over state and workflow; CrewAI fits role-based delegation organized around agents, tasks, crews, and Flows; and Microsoft Agent Framework (MAF) is worth evaluating when its Python and .NET support, Microsoft integrations, or documented orchestration patterns suit your stack. There is no universal winner: choose by the workflow you need to operate, not by a feature checklist alone.
How the three frameworks organize agent work
LangGraph: define the workflow as a graph
LangGraph is a low-level runtime for building long-running, stateful agents. You define the state and control flow, combining deterministic steps with model-driven decisions in a graph. Its Python overview describes persistence, memory, resuming after failures, and human review. LangGraph can be used without LangChain, though LangChain components can be used with it. See the LangGraph Python overview.
As an Amazon Associate I earn from qualifying purchases.
CrewAI: organize work around roles, tasks, and crews
CrewAI expresses work through role-defined agents and tasks grouped into crews. A crew can use a sequential, hierarchical, or hybrid process. When an application needs explicit state and execution order, Flows coordinate the work; the versioned v1.15.23 quickstart recommends Flows as the structure for production applications. The documentation describes Flow state, persistence, and resuming long-running workflows. See the CrewAI documentation and CrewAI v1.15.23 quickstart.
Free tools Windows power users keep installed
One-click scans. No signup required.
MAF: use named orchestration patterns across Python and .NET
Microsoft Agent Framework supports Python and .NET. Its repository documents graph-based sequential, concurrent, handoff, and group-collaboration patterns, along with checkpointing, streaming, human-in-the-loop workflows, and time-travel. Microsoft Learn provides a documentation index with Python samples and provider and integration material. MAF is not described as Azure-only: its repository names a range of providers. See the Microsoft Agent Framework repository and Microsoft Learn Agent Framework documentation.
#1 Best Overall
Compare them by the decisions your application must make
| Decision | LangGraph | CrewAI | Microsoft Agent Framework |
|---|---|---|---|
| Core model | Low-level graph and runtime; the application defines state and control flow. | Role-defined agents and tasks grouped into crews; Flows add explicit state and ordering. | Agents with graph-based sequential, concurrent, handoff, and group-collaboration patterns. |
| State and resumption | Documentation describes persistence, resuming after failures, short- and long-term memory, and long-running stateful workflows. | Documentation describes Flow state, persistence, and resuming long-running workflows. | Repository documents checkpoints and time-travel; Microsoft Learn indexes memory, persistence, checkpoint, and resume material. |
| Human oversight | Inspect and modify agent state at any point. | Human-in-the-loop triggers and human review workflows are documented. | Human-in-the-loop workflow support is documented. |
| Language context | This comparison concerns the Python documentation; LangGraph can run without LangChain. | This comparison concerns the Python documentation and quickstart. | Python and .NET are both supported. |
| Operational ecosystem | Documentation points to LangSmith for tracing and evaluation and describes a deployment ecosystem. | Documentation describes tracing and integrations; the quickstart shows AMP deployment. | Repository lists built-in OpenTelemetry integration and Foundry hosting options. |
This is a comparison of documented capabilities, not a feature-parity guarantee or performance ranking. The pages differ in scope and currency, and the vendor-authored LangChain framework comparison guide presents product positioning rather than an independent benchmark. Check the current API, provider adapter, deployment model, licensing, security posture, and support conditions for the specific versions and workload you intend to use.
Which Python agent framework should you choose?
Start with LangGraph for explicit workflow control
Consider LangGraph when you need visible state, branching or loops, deterministic steps alongside model decisions, durable resumption, or checkpoints for human review. Its low-level design gives the application team control, but also leaves more orchestration decisions to that team. The official overview recommends learning model and tool components first.
Start with CrewAI for role-based delegation
Consider CrewAI when work maps naturally to role-defined delegation and bounded tasks, and the team prefers a crews-and-Flows mental model. For longer-running applications, put state and execution order in a Flow. Check provider behavior and tool-call validation in your own project; documentation describing capabilities does not establish reliability for your workload.
Start with MAF for Python/.NET or Microsoft-stack needs
Consider MAF when Python/.NET interoperability, Microsoft integrations, or its documented workflow patterns fit your organization. Verify provider-specific behavior and hosting constraints for the deployment you plan; provider availability alone does not establish that every provider behaves the same way.
Do you need an agent framework at all?
For a small, single-turn request or a stateless tool loop, first decide whether multi-agent orchestration adds value. The documented capabilities of these frameworks do not mean every LLM application requires one.
Evaluate shortlisted frameworks on the same task
Documentation can help narrow the options, but it cannot tell you which implementation will work best for your application. Run the same representative task against each shortlisted framework, holding the model, tools, prompt, and success criteria constant. This is a practical evaluation method, not a benchmark result.
- Define success before testing. Specify what a correct result looks like, which tool actions are allowed, and when human approval is mandatory.
- Exercise the real workflow. Include the branching, delegation, or handoff your application actually needs rather than testing only a happy path.
- Record comparable outcomes. Track completion quality, tool-call correctness, retries, recovery after interruption, approval pauses, trace readability, latency, cost, and implementation and maintenance effort.
- Protect external systems. Make side effects idempotent and validate outputs before allowing an agent to change external data or trigger consequential actions.
- Verify operational fit. Check provider adapters, deployment constraints, tracing and evaluation needs, licensing, security posture, and support against the current versions you plan to run.
Separate framework capabilities from hosted services
A framework and the services around it are distinct choices. LangSmith is an optional observability and evaluation service referenced by LangChain’s comparison guide; CrewAI’s versioned quickstart documents deploying Flows to AMP. Their inclusion in the ecosystem does not make either hosted service necessary to use the corresponding framework. Check current service terms and deployment requirements directly before adopting them.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
