Agent Frameworks Compared: LangGraph, CrewAI, AutoGen, OpenAI Agents SDK and Google ADK

An agent framework handles the plumbing: the loop of model calls, tool execution, memory, state, retries and handoffs between agents. Choosing one is a decision about control versus speed of development. Here is how the main options differ.

Tool names, versions and pricing in this market change quickly. This article reflects the landscape as of November 2026; verify current details with each vendor before making a purchasing decision.

The main options

• LangGraph (LangChain ecosystem) — models workflows as graphs with explicit state. High control, good for complex branching and human-in-the-loop steps. Steeper learning curve.
• CrewAI — role-based multi-agent design (researcher, writer, reviewer). Fast to prototype and easy to reason about. Less fine-grained control for complex production logic.
• Microsoft AutoGen — conversational multi-agent framework with strong research roots; agents collaborate through messages. Good for experimentation and code-execution scenarios.
• OpenAI Agents SDK — lightweight primitives: agents, tools, handoffs and guardrails. Minimal abstraction, tightly aligned with OpenAI models and tracing.
• Google Agent Development Kit (ADK) — code-first framework aligned with Gemini and Google Cloud deployment, with multi-agent composition built in.
• Semantic Kernel — Microsoft's SDK for embedding AI into .NET, Java and Python applications, strong in enterprise line-of-business software.

What to compare

• Control over state and flow — LangGraph leads; CrewAI and the OpenAI SDK trade control for simplicity.
• Model portability — LangGraph and CrewAI support many providers; OpenAI SDK and Google ADK are strongest on their home models.
• Observability — check tracing, evaluation and replay before production. Every serious framework now offers some form of this, but quality varies.
• Language support — Python dominates; Semantic Kernel covers .NET and Java teams.

Our recommendation pattern

For a first production agent with a clear workflow, a thin SDK and your own orchestration code often beats a heavy framework: fewer surprises, easier debugging. Reach for LangGraph when the workflow has many branches and approval steps. Use CrewAI or AutoGen for exploratory multi-agent research, and move the proven design into a controlled runtime before go-live.

See the AI Agents resource hub for more comparisons, or talk to our agent engineering team about the right stack for your workflow.