As companies race to operationalize AI, one question keeps coming up: Which agent frameworks are actually being adopted in production? Recent survey data shows a clear consolidation around a few dominant players, with OpenAI and Google leading the pack, while frameworks like LangChain, LangGraph, and CrewAI continue carving out important, but more specialized, niches. The results reveal a rapidly maturing ecosystem where organizations are prioritizing stability, orchestration reliability, and integration depth over experimentation. Below is a breakdown of each framework’s role, strengths, and tradeoffs.
OpenAI Agents SDK (51%)
Summary: A first-party, production-ready framework tightly integrated with OpenAI models, tools, and memory.
Pros: Deep integration with OpenAI’s platform; strongest reliability and tooling support; easy to scale into production.
Cons: Less flexible for multi-model or hybrid-cloud deployments; ecosystem is still evolving quickly.
Google Agent Development Kit (ADK) (40%)
Summary: Google’s modular agent toolkit designed to build flexible, cross-platform workflows powered by Gemini.
Pros: Highly extensible; strong cross-service interoperability; great for Google Cloud environments.
Cons: Best functionality is tied to Google’s models; adoption is still catching up to OpenAI’s momentum.
LangChain (24%)
Summary: A popular open-source framework for chaining LLM calls, retrieval steps, and tools into custom workflows.
Pros: Massive ecosystem; excellent flexibility; strong for rapid prototyping and experimentation.
Cons: Can become overly complex; orchestration reliability lags behind newer agent frameworks.
LangGraph (16%)
Summary: A stateful, graph-based orchestration engine that enables deterministic, multi-step and multi-agent systems.
Pros: Robust state management; ideal for complex, branching workflows; more production-oriented than LangChain alone.
Cons: Requires deeper engineering expertise; still evolving with frequent updates.
CrewAI (15%)
Summary: A multi-agent collaboration framework where agents with defined roles coordinate as a “crew” to solve tasks.
Pros: Easy to set up multi-agent teamwork; widely used for structured task automation.
Cons: Not optimized for enterprise-scale reliability; limited controls for long-running orchestration.
PydanticAI (10%)
Summary: A structured-output-driven framework that uses Pydantic schemas to ensure validated, type-safe LLM responses.
Pros: Guarantees clean, structured outputs; great for data workflows where correctness is critical.
Cons: Not a full agent system; best used as a complement to other orchestration frameworks.
Temporal (7%)
Summary: A durable workflow engine used to provide reliability, retries, state, and long-running orchestration for agent systems.
Pros: Industry-leading workflow durability; perfect for enterprise automation and failure-resistant AI pipelines.
Cons: Not an agent framework itself; adds operational overhead and requires dedicated expertise.