AI agent SDKs compared: choose your orchestration layer

Decide how much control flow you want to own

Agent SDKs differ most in who decides the next step. In some, the model chooses the next tool or hands the conversation to another agent, and the SDK runs that loop. In others, you write the graph of steps and the model fills in one node at a time. Most of these SDKs now support both styles, so ask which one a given SDK makes easy and which one it only permits.

The AI agent frameworks explainer compares LangGraph, CrewAI and AutoGen by orchestration model. This guide covers a different shortlist of SDKs a team might pick for a new project. The documented workflows in the table come from the linked primary documentation, checked October 24, 2026. The fit questions and the pilot are our editorial recommendations. The five options are not a complete inventory or a ranking, and this guide reports no measured performance.

Compare the primitives, not the demos

OpenAI Agents SDK: the documentation describes a deliberately small set of primitives: agents with instructions and tools, agents used as tools or handoffs for delegation, and guardrails that validate input and output. The SDK runs the tool-calling loop for you. The tracing page says tracing is on by default, can be disabled with an environment variable or per run, and is unavailable to organizations using OpenAI’s APIs under a Zero Data Retention policy. Check where those traces are sent before you run it against customer data.

Pydantic AI: the overview describes a Python agent framework built around type safety. You declare the output type and tool signatures, and the framework validates model responses against them. Models are swapped by name across providers, and instrumentation follows OpenTelemetry, so the traces are not tied to Pydantic’s own Logfire product. Pydantic Graph, for typed control flow, and Pydantic Evals ship as separate libraries.

Google ADK: adk.dev describes an open-source toolkit with SDKs for Python, TypeScript, Go, Java and Kotlin. Its graph workflows combine deterministic code with model reasoning, and the site describes building, debugging and deploying agents with the same toolkit. The Python repository says ADK is optimized for Gemini and the Google ecosystem but is model-agnostic and deployment-agnostic. Test the model you plan to use rather than assuming parity.

LangGraph: the LangGraph overview describes a low-level orchestration runtime for long-running, stateful agents. It lists durable execution, streaming, human-in-the-loop steps and persistence, and it can be used without LangChain. You write more of the control flow yourself, and you get explicit state you can inspect, checkpoint and resume.

Microsoft Agent Framework: the overview describes agents, a harness agent with built-in capabilities, graph-based workflows, model clients, sessions, middleware and MCP clients. The AutoGen repository now tells new users to start with Microsoft Agent Framework and points existing users to a migration guide. The same overview marks the Go SDK as a public preview with some features unavailable, so check the language you plan to use.

We left two names off the shortlist. CrewAI is covered in the frameworks explainer linked above. Inngest, a current presenting sponsor of the show, sells durable execution for agent workflows; we did not include it so the comparison carries no sponsor relationship.

Build one workflow twice

Suggested pilot, not a reported benchmark: pick a real workflow with at least one tool call, one decision and one step that waits on a person. Build it in two SDKs from the shortlist, using the same model, tools and test inputs.

Pilot step	What to check
The model returns malformed output	Whether the SDK validates it, retries it or passes it to your code unchanged
A tool fails halfway through a run	Whether the run can resume from saved state or has to start over
A person must approve one step	How the run pauses, where its state lives while it waits and how it resumes
You switch to a second model provider	How much code changes, and which features stop working
Two agents share one task	Whether delegation is a handoff, a tool call or a graph edge, and how the trace shows it
You read the trace of a failed run	Whether you can see each model call, tool call and decision without adding instrumentation

Count the code you wrote outside the SDK to make each step work. Glue code you have to maintain is a larger long-term cost than the first day of setup. For the trace step, the LLM observability tools comparison covers where those spans can go.

Plan for what the SDK will not own

None of these SDKs removes the need for evaluation, access control on tools or a plan for long-running state. Check the release cadence and the deprecation policy of each SDK you shortlist. AutoGen’s own repository now points new users elsewhere, which shows how quickly the field moves. Keep your tools, prompts and evaluation cases in code you own, so moving to another SDK means rewriting orchestration and not the whole application.

Where the interviews fit

LlamaIndex co-founder Jerry Liu described the value of a framework as core abstractions that make it easier to build certain things. Lyzr’s Siva Surendira argued that teams building something deep and highly complex should skip frameworks and build from scratch. Read both as the guests’ positions, not as findings about the SDKs above. Episode 21 was sponsored by Galileo while Conor worked there; later in that episode Conor mentions Galileo’s agent leaderboard.