Six Agent Frameworks Now Control Where Most Builders Ship Production Agents

Three independent framework comparisons dropped within seven days of each other, all reaching the same conclusion: the agent framework market has stopped expanding and started consolidating. Artiverse, KDnuggets, and Geek Source Codes each surveyed the landscape independently and converged on the same six names: LangGraph, CrewAI, OpenAI Agents SDK, Google ADK, PydanticAI, and AutoGen.

The convergence is the story. A year ago, every week brought a new framework claiming to solve agents. Now the field has calcified around a handful of options, each serving a distinct builder archetype.

The Six and Who They Serve

LangGraph (~36,000 GitHub stars) dominates stateful, production-grade orchestration. It models agent workflows as explicit state graphs with checkpointing, streaming, and human-in-the-loop approval gates. According to KDnuggets, it has “the steepest learning curve” but is “the most mature choice when one workflow needs cycles, branching, or an approval gate.” Teams building long-running agents that need audit trails, rollback, and deterministic control points cluster here.

CrewAI (~55,000 stars) holds the largest community by raw star count. Its model is straightforward: define agents with roles, give them tasks, organize them into crews. Artiverse calls it the easiest entry point for “developers who want clarity and teamwork in their agents.” It draws teams that decompose problems into specialist roles and want a working multi-agent system with minimal boilerplate.

OpenAI Agents SDK (~27,000 stars) occupies the clean, minimalist tier. It ships agents, tools, handoffs, guardrails, sessions, and tracing in a compact API surface. Geek Source Codes positions it for developers who want to start with one focused agent and add complexity only when justified. Despite the OpenAI branding, the SDK supports other model providers.

Google ADK (~20,000 stars) is the newcomer gaining ground fastest. It offers agent-as-workflow patterns, tool authentication, evaluation, callbacks, asynchronous execution, and MCP integrations. All three surveys flag it as the natural choice for teams already embedded in Google Cloud and Gemini. KDnuggets describes it as a “code-first toolkit” with session, memory, and multi-agent support.

PydanticAI (~18,000 stars) targets Python developers who want type safety and structured outputs. It brings dependency injection and validated tool inputs to agents, according to Artiverse. It is model-agnostic and free.

AutoGen (Microsoft Research) specializes in agents that talk to each other: group debates, consensus-building, sequential dialogues. Its niche is research applications and systems where multi-agent communication patterns are the core product requirement.

The Wrapper Era Ended

All three surveys make the same observation explicitly. As Geek Source Codes puts it: “A framework gives you the agent loop: tools, memory, state, retries, handoffs. That’s one layer. Getting an agent to run reliably in production raises harder questions the framework alone won’t answer.” The bar has moved from “can it call an LLM and use a tool?” to “can it checkpoint state, enforce approval gates, handle credential scoping, and track cost?”

Artiverse frames the shift similarly: frameworks “are no longer simple wrappers. They are complex platforms that support collaboration, long-running tasks, and rich integrations.”

The practical effect is that builder decisions now map to infrastructure commitments. Choosing LangGraph means betting on explicit state management. Choosing CrewAI means betting on role decomposition. Choosing Google ADK means accepting tighter coupling to Google Cloud. These are architectural decisions with migration costs, not trial-period experiments.

The Consolidation Signal

The fact that three independent surveys, published within a week using different methodologies, produced nearly identical top-six lists suggests the market has reached a stable equilibrium. New entrants face a cold start problem: the top frameworks have accumulated 18,000 to 55,000 stars, extensive documentation, and production deployment patterns that new projects cannot replicate quickly.

The exception may be vendor-backed entrants. Google ADK went from zero to 20,000 stars on the strength of Cloud and Gemini integration alone. If Apple, Amazon, or another platform vendor ships an agent framework tightly coupled to its infrastructure, the top six could become seven. But for independent open-source projects, the window for gaining mainstream builder adoption has likely closed.

For builders evaluating frameworks today, the decision matrix is clearer than it has been at any point in the agent era: match your deployment pattern to the framework that was built for it, and accept that switching costs will only increase from here.