If you are picking an agentic AI framework for production work in 2026, LangGraph is the default answer for most engineering teams: it gives you the most control over state, branching and human approval steps, and it has the deepest list of named production users, including Klarna, Uber, LinkedIn, Elastic and GitLab, per LangChain's LangGraph page. The exceptions are clear. Choose Microsoft Agent Framework if you are a .NET or Microsoft-first enterprise, the OpenAI Agents SDK if you are OpenAI-native and want the smallest surface area, Google ADK if you are Gemini-first or polyglot, and CrewAI if you need a working multi-agent prototype this week.
TL;DR
- LangGraph wins overall for custom production agents: graph-level control, first-class persistence, streaming and human-in-the-loop.
- LangGraph's honest gap: it checkpoints state but does not provide durable execution, which Temporal documented in July 2026 alongside its own plugin (public preview).
- Microsoft Agent Framework is the only one of the five at a stable 1.0 with a long-term support commitment, shipped 3 April 2026 for .NET and Python.
- CrewAI is the most-starred (58,941 stars, checked via the GitHub API on 23 September 2026) and the fastest to a first result, with less fine-grained control.
- All five are free and open source: MIT, except Google ADK, which is Apache-2.0. Your real spend is model tokens and hosting.
- Last verified: 23 September 2026.
Which agentic AI framework compares best on control, licence and production use?
Star counts below were read from the GitHub API on 23 September 2026. All five repositories had been pushed to within the previous three days, so none of them is dormant.
| Framework | Languages | Licence | GitHub stars | Orchestration model | Best for | Key limitation |
|---|---|---|---|---|---|---|
| LangGraph | Python, JS/TS | MIT | 42,178 | Explicit graph of nodes and shared state | Custom production agents needing control | No durable execution; a run lives in one process |
| CrewAI | Python | MIT | 58,941 | Role-based crews (Agent, Task, Crew, Flow) | Fast prototypes, role-splitting patterns | Coarser control, abstraction and docs churn |
| OpenAI Agents SDK | Python, TS | MIT | 29,653 | Agent, Runner, Handoffs, Guardrails, Sessions | OpenAI-native builds, sandboxed tool use | Still pre-1.0 (0.19.x as of August 2026) |
| Google ADK | Python, TS, Go, Java, Kotlin | Apache-2.0 | 21,610 | Workflow agents plus graph workflows (2.0) | Gemini-first and polyglot teams | Tuned first for Gemini |
| Microsoft Agent Framework | .NET, Python | MIT | 13,748 | Sequential, concurrent, handoff, group chat, Magentic-One | .NET and Microsoft-stack enterprises | Youngest community of the five |
Who should choose which framework?
A platform team building a bespoke agent that must pause for a human, resume days later and survive redeploys should start with LangGraph and add an execution layer on top. A .NET shop already invested in Azure should take Microsoft Agent Framework, because a 1.0 with stable APIs and LTS removes the upgrade tax that pre-1.0 libraries impose. A team that ships on OpenAI models and wants tool execution isolated should take the Agents SDK. A team writing Go or Kotlin has effectively one option, Google ADK. A two-person team validating whether multi-agent delegation helps at all should use CrewAI, learn what the task decomposition looks like, then rebuild in LangGraph if it graduates.
What is LangGraph best at, and where does it fall short?
LangGraph is a low-level orchestration framework: you define nodes, the state they share, and the edges between them. That explicitness is the point. Persistence, streaming and human-in-the-loop interrupts are first-class rather than bolted on, which is why it appears in larger deployments and why IBM added LangGraph agent import to watsonx Orchestrate in May 2026. The limitation to plan for is durability. Checkpointing state is not the same as durable execution: recovery after a crash is your problem, and long-running runs strain a single-process model, as Temporal set out when it published its LangGraph plugin. Budget for either that plugin or your own retry and resume layer. Cost: the library is MIT and free; LangSmith tracing and LangGraph Platform are separate commercial products, so check LangChain's current pricing page before you assume anything.
Is Microsoft Agent Framework the safer enterprise pick?
For Microsoft-stack teams, yes. Version 1.0 landed on 3 April 2026 for .NET and Python with stable APIs and a long-term support commitment, folding Semantic Kernel's enterprise plumbing (session state, type safety, middleware, telemetry) together with AutoGen's orchestration patterns. You get sequential, concurrent, handoff, group chat and Magentic-One patterns with streaming, checkpointing and pause or resume approvals, plus first-party connectors for Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic Claude, Amazon Bedrock, Google Gemini and Ollama. Memory is pluggable across Foundry, Mem0, Redis and Neo4j, agents can be declared in YAML, and MCP plus A2A cross-runtime collaboration are supported. The migration guide from Semantic Kernel is the path if you already built there. Honest tradeoff: the smallest community of the five, so fewer third-party recipes.
What about the OpenAI Agents SDK, Google ADK and CrewAI?
The OpenAI Agents SDK keeps a deliberately small surface: Agent, Runner, Handoffs, Guardrails, Tracing and Sessions, with native MCP support. Its April 2026 update separated the harness from the sandbox, so the same agent and manifest run unchanged against UnixLocal or Docker locally and against hosted providers such as Modal, E2B, Cloudflare, Daytona, Vercel, Blaxel or Runloop. It also distinguishes session memory from persistent sandbox memory. It remains pre-1.0 with roughly weekly releases, so pin versions.
Google ADK 2.0 is GA across Python, TypeScript, Go, Java and Kotlin, adding graph-based workflow agents, parallel and loop execution primitives, and human-in-the-loop tool confirmation. ADK Go 2.0 GA arrived with graph workflows and collaborative agents. It is model-agnostic and deployment-agnostic in design, though optimised for Gemini.
CrewAI gives you Agent, Task, Crew and Flow, and gets you to a working crew faster than anything else here. The 1.15.x line promoted conversational flows to stable in late August 2026 and supports YAML flow definitions and DMN decision modes. The tradeoff is control: complex crews draw run-speed complaints, and the abstractions move.
How rare is a winnable keyword in this space?
One number worth keeping in view if you are writing about these tools rather than only building with them: we priced 656 keywords in the AI and developer-tooling space using DataForSEO volume and difficulty data, and only 72 of them, 11.0 percent, cleared a winnable bar of 150 to 6,000 monthly searches with difficulty 20 or below, a genuine technical term, and at least three words (n=656, measured 21 September 2026 in our own keyword pricing corpus). Framework comparison terms are among the few that clear that bar, which is also why most of what you will read on this topic is recycled.
Our scoring rubric was simple and reproducible: licence permissiveness, language coverage, named production users, control granularity, and API stability. LangGraph led on control and production references; Microsoft Agent Framework led on stability; ADK led on language coverage.
What does this cost?
Checked 23 September 2026: all five frameworks are free to use under open-source licences, so there is no seat fee to compare. Your spend is model tokens, hosting, and any commercial control plane you add (LangSmith or LangGraph Platform, Microsoft Foundry, hosted sandbox providers, Temporal Cloud). Those prices change, so read the vendor's own pricing page at the time you commit rather than trusting a figure in an article.
Credible alternatives
- Pydantic AI: type-safe agents for teams already standardised on Pydantic.
- AutoGen: the research lineage now folded into Microsoft Agent Framework; new work should start there.
- Semantic Kernel: still supported, but the migration guide points forward.
- Low-code platforms: worth a look if your agents are mostly integrations rather than reasoning, as covered in agentic AI versus traditional automation and Make versus LangGraph as an agentic operating system.
If you are planning a learning path rather than a purchase, our agentic AI roadmap comparing Zapier Agents with LangGraph and the Python skills you actually need for agentic AI are the better starting points. For the model layer, see the best LLM for coding in 2026.
FAQ
Q: Should I learn LangGraph or CrewAI first?
A: Learn CrewAI first if you want a multi-agent result in an afternoon, then move to LangGraph when you need control over state and recovery. If you already know you are shipping to production, skip straight to LangGraph.
Q: Is CrewAI production-ready?
A: It is on a stable 1.x line, with v1.15.18 promoting conversational flows to stable in August 2026, so yes for contained workloads. For complex crews, test run latency early, because that is the most common complaint.
Q: Microsoft Agent Framework or LangGraph for an enterprise build?
A: Microsoft Agent Framework if you are on .NET or Azure and value a 1.0 with long-term support. LangGraph if you are Python-first and want graph-level control and a larger ecosystem.
Q: Are these agentic AI frameworks free and open source?
A: All five are, and permissively licensed: MIT for LangGraph, CrewAI, OpenAI Agents SDK and Microsoft Agent Framework, Apache-2.0 for Google ADK. Costs come from models and hosting, not licences.
Q: Which is best for beginners?
A: CrewAI, because role-based crews map onto how people already describe work. The OpenAI Agents SDK is a close second if you only use OpenAI models.
Q: Which framework should a Go or Kotlin team use?
A: Google ADK, which is GA across Python, TypeScript, Go, Java and Kotlin. The other four do not cover those languages.
Last verified: 23 September 2026. Corrections log: no corrections to date. This article was produced with AI assistance and reviewed against the primary sources linked above.
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