Superstep vs CrewAI and PydanticAI¶
This page is for teams that would rather write agents in code than draw them in
a visual studio. CrewAI and PydanticAI are code-first Python agent frameworks,
in the same category as LangGraph itself. Superstep is not a framework: it is a
studio on top of LangGraph. Its workflows compile to a LangGraph StateGraph
and export as LangGraph code, so the fair comparison is Superstep plus
LangGraph against CrewAI or PydanticAI. Facts about the other
products come from their own documentation and license files (listed under
Sources) as checked on 25 October 2026; check those pages before
you decide. For visual builders, see
Superstep vs n8n, Dify, Langflow and Flowise.
In one sentence each¶
| Product | What it is |
|---|---|
| Superstep + LangGraph | Build LangGraph workflows visually in Superstep (MIT), run them as services, and export them as LangGraph Python or LangGraph.js projects when you want to continue in code. |
| CrewAI | An open-source Python framework for multi-agent systems (Crews of role-playing agents inside event-driven Flows), with a commercial platform, CrewAI AMP, that adds deployment, tracing and the no-code Crew Studio. |
| PydanticAI | A typed Python agent framework from the Pydantic team, with Pydantic Graph, Pydantic Evals, durable execution integrations and the Logfire observability platform around it. |
Summary comparison¶
| Superstep + LangGraph | CrewAI | PydanticAI | |
|---|---|---|---|
| How you build | Draw the graph, or write LangGraph code; export moves from one to the other | Python code (Flows and Crews); no-code Crew Studio in AMP | Python code; YAML/JSON agent specs |
| Core model | Explicit graph: nodes, edges, typed state, checkpoints; an Agent node runs a model-chosen tool loop inside it, and can call other workflows (their own agents) as tools | Flows (state, events, branching) that call Crews of role-playing agents | A typed agent loop; Pydantic Graph for graph control flow |
| License | MIT (Superstep and LangGraph) | MIT (framework); AMP is commercial | MIT |
| Human in the loop | Human nodes, approval before an Agent node's tool calls, and LangGraph interrupts; approve in the studio, Slack, Telegram, WhatsApp, email or Teams | @human_feedback in Flows; resume endpoint in AMP |
Human approval of tool calls; long waits through durable engines |
| Durable runs and time travel | Checkpoints in the database (SQLite or Postgres): paused and running queue runs continue after a restart; fork a finished run from a checkpoint | @persist flow state, restore from a saved state |
Durable execution on Temporal, DBOS, Prefect and others; step persistence to save, resume and fork |
| Tests, evals, observability | Workflow tests, LangSmith evals, OpenTelemetry traces | AMP tracing; many third-party integrations | Pydantic Evals; OpenTelemetry-native, Logfire |
| Running it as a service | Built in: API tokens, webhooks, schedules, public API with Python and TypeScript SDKs | Your own service, or AMP deployments with a REST API | Your own service; AG-UI and Vercel AI event streams for front ends |
How Superstep relates to code-first frameworks¶
- Same category as LangGraph, not as Superstep. CrewAI and PydanticAI compete with LangGraph as the library your agent is written in. Superstep uses LangGraph as its runtime and adds an editor, run history, approvals, tests and an API around it.
- You can leave the canvas. Any Superstep workflow exports as a runnable LangGraph project (see Export to Python), and the LangGraph-compatible API lets LangGraph clients call workflows that stay in Superstep.
- An agent loop where the model should choose. The Agent node lets a model call the tools you list (MCP, HTTP, built-in functions, knowledge base search) until it answers, with a round limit and per-tool approval. An Agent can also call other workflows as tools, each with its own agent: a supervisor and its specialists, each built and tested on its own canvas (see Workflows as tools). There are no hand-offs between agents and no free conversation among them: for role-based crews or typed agent code, the frameworks below fit better.
- Code where you need it. Python and TypeScript Code nodes cover steps that are easier to write than to draw.
- Mixing is fine. A CrewAI or PydanticAI program can start a Superstep workflow through the public API and SDKs, and a Superstep HTTP or MCP node can call an agent you serve yourself.
CrewAI¶
CrewAI is an open-source Python framework (MIT) for multi-agent systems.
Flows define the structure: state, event-driven steps, branching and loops;
Crews are teams of role-playing agents that collaborate on a task inside a
Flow step. Flow state can be persisted with @persist, and @human_feedback
(CrewAI 1.8.0 and later) pauses a Flow for a person to approve, reject or
revise. Observability comes through CrewAI tracing on AMP and integrations such
as Langfuse, Arize Phoenix, MLflow, Opik and Datadog. The docs include a guide
for moving projects from LangGraph to CrewAI.
CrewAI AMP (Agent Management Platform) is the commercial side: deploy crews to managed infrastructure from GitHub, the CLI or Crew Studio (a no-code/low-code interface), call them through a REST API, stream events by webhook and monitor traces.
Where CrewAI is strong: role-based multi-agent collaboration with little code, and a managed path to production.
PydanticAI¶
PydanticAI is the Pydantic team's Python agent framework (MIT): typed agents, structured outputs validated by Pydantic, typed dependency injection and tools, and many model providers swappable by a string. Around it: Pydantic Graph for typed graph control flow, Pydantic Evals for grading outputs and tool-call trajectories, durable execution on engines such as Temporal, DBOS and Prefect (with human approval of tool calls), step persistence to save, resume and fork a run, and OpenTelemetry-native instrumentation. Pydantic Logfire is the company's observability platform (free tier available); any OpenTelemetry backend works instead.
Where PydanticAI is strong: type safety and testability for developers, wide model support, and production durability on an engine you already run.
When to choose a framework instead¶
- Choose CrewAI when your team writes Python and the problem suits autonomous, role-based agents collaborating, optionally with a managed platform.
- Choose PydanticAI when developers want a typed, code-first agent library with strong testing, evals and durable execution, and do not need a visual editor.
- Choose plain LangGraph when you want LangGraph in code without a studio; Superstep exports are a starting point.
- Choose Superstep when non-developers should be able to read and change the workflow, every branch and approval should be explicit and reviewable, you want runs, approvals, tests and an API without building them yourself, or you want a Vietnamese UI, while keeping a path to LangGraph code.