The agent framework landscape has consolidated considerably in early 2026, but “which framework should I use?” is still one of the most common questions in every AI engineering channel. The answer isn’t the same for everyone — it depends on your timeline, performance requirements, and production durability needs.

This guide synthesizes the latest benchmarks and a sharp community analysis from Lukasz Grochal on dev.to to give you a practical decision framework. We’ll compare the three dominant players — CrewAI, Microsoft Agent Framework RC, and LangGraph — across the dimensions that actually matter for real projects.

The Three Contenders in 2026

CrewAI

CrewAI is the fastest path from zero to a working agent demo. It has the most mature developer experience of the three, extensive documentation, and the widest collection of community examples. The trade-off: it carries roughly a 3x token overhead compared to more efficient alternatives.

That token overhead isn’t a dealbreaker for prototypes or lower-frequency tasks, but it compounds quickly at production scale. A workflow that feels affordable in development can become expensive when running hundreds or thousands of times daily.

Best for: Demos, prototypes, hackathons, and production workloads where token cost isn’t a primary constraint.

Microsoft Agent Framework RC (Release Candidate)

Microsoft’s Agent Framework RC is the technical performance leader right now. It unifies Semantic Kernel and AutoGen into a single production-ready SDK for both .NET and Python — and benchmarks are showing approximately 2.5x latency improvement over CrewAI on comparable workloads, with the highest quality scores of the three frameworks.

The catch: it’s still in Release Candidate, with General Availability expected in approximately 2 months. That means you can build on it today, but you’re accepting some migration risk between RC and GA, and some production-readiness features may still be in flux.

Best for: Teams who can wait ~2 months for GA, .NET shops, and performance-critical applications where latency and benchmark quality are primary selection criteria.

LangGraph

LangGraph is the stateful durability choice. Where CrewAI and Microsoft’s framework focus on agent capability, LangGraph’s architecture is built around state graph execution — workflows that can pause, resume, branch, and recover from failures in ways that the other frameworks don’t natively support.

If your agents need to handle long-running tasks, multi-turn interactions that span hours or days, or workflows that must survive process restarts and infrastructure failures, LangGraph’s stateful foundation is worth the steeper learning curve.

Best for: Production workflows requiring high durability, stateful multi-turn agents, and systems where workflow recovery from failures is non-negotiable.

The Decision Tree

Here’s the framework comparison distilled into a practical flowchart:

Do you need it working by Friday?
├── YES → Use CrewAI
│         Fast to demo, broad community support, absorb the token overhead
│
└── NO → Is production durability (stateful, fault-tolerant workflows) required?
          ├── YES → Use LangGraph
          │         Built for stateful production; best recovery characteristics
          │
          └── NO → Can you wait ~2 months for Microsoft Agent Framework GA?
                    ├── YES → Use Microsoft Agent Framework RC
                    │         Best latency, best benchmarks, .NET/Python unified SDK
                    │
                    └── NO → Use LangGraph (or CrewAI for simpler tasks)

The Grochal framing — “Need it by Friday? Use CrewAI. Can wait 2 months? MS Agent Framework.” — is a useful heuristic, but LangGraph is the hidden answer for teams who don’t fit either of those buckets cleanly.

Benchmark Summary

Framework Latency vs. Baseline Token Overhead Stateful GA Status
CrewAI Baseline ~3x Limited GA ✅
Microsoft Agent Framework RC 2.5x faster Lower No ~2 months
LangGraph Comparable to CrewAI Moderate Yes (native) GA ✅

Latency benchmarks from InfoQ’s technical analysis of Microsoft Agent Framework RC; token overhead from Grochal’s dev.to comparison.

What About OpenClaw?

If you’re reading this on subagentic.ai, you may already be in the OpenClaw ecosystem. OpenClaw isn’t an “agent framework” in the same sense — it’s a personal/professional agent platform that runs on top of model APIs with its own skill and memory system. The frameworks above are more relevant for teams building custom multi-agent pipelines from scratch.

That said, LangGraph’s durability model is worth studying even if you’re primarily an OpenClaw user — the patterns for state management, workflow recovery, and long-running task handling translate well to understanding how robust agentic systems should be designed.

Practical Migration Notes

Coming from Semantic Kernel or AutoGen to Microsoft Agent Framework RC?

The RC explicitly unifies these — migration is the intended path, and InfoQ’s technical deep-dive covers the architecture differences in detail. Expect some refactoring around agent definitions and tool registration, but the conceptual model is compatible.

Coming from CrewAI to LangGraph?

The conceptual shift is meaningful. CrewAI’s crew/agent/task model maps to LangGraph’s node/edge/state graph differently than you might expect. Budget time for rethinking your workflow graph structure rather than expecting a 1:1 translation.

Starting fresh?

The decision tree above is your starting point. When in doubt, CrewAI gets you to a working demo fastest, which helps you validate your architecture before committing to a production framework.

The Bottom Line

There’s no universally correct answer — but there is a correct answer for your situation:

  • Timeline pressure → CrewAI
  • Performance + .NET/Python → Microsoft Agent Framework RC (wait for GA or accept RC risk)
  • Stateful, durable production workflows → LangGraph

The good news: none of these choices are irreversible. The agent framework landscape is moving fast enough that the framework you start with for a proof of concept doesn’t have to be the one you run in production six months from now.

Sources

  1. Lukasz Grochal on dev.to — “Choosing an Agent Framework in 2026: A Data-Driven Decision Guide”
  2. InfoQ — Microsoft Agent Framework RC Technical Deep-Dive
  3. Confluent A2A Announcement — multi-framework interoperability context

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260226-0800

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