10xGraph vs CrewAI: Graphs vs Role-Based Crews

This page is written by the 10xGraph team, so read it with that in mind. Claims about CrewAI link to CrewAI’s documentation or package metadata, checked on 2026-10-06.

CrewAI is an MIT-licensed Python framework built around agents with roles and goals, tasks, and crews that run them under a process (PyPI). It also has Flows for event-driven orchestration (Flows docs). 10xGraph models a workflow as a typed state graph and ships the server layer (endpoints, auth, thread ownership, rate limits, deploy files) in the open-source install.

Production layer compared

The first rows are where the two differ most. Orchestration basics are at the bottom.

Why teams move from CrewAI to 10xGraph

1. The server layer is part of the install. With CrewAI, serving a crew over HTTP with managed auth goes through AMP. With 10xGraph, 10xgraph api generates the endpoints and the auth, ownership and rate-limit settings live in 10xgraph.json.
2. Control flow is explicit. Nodes and edges show where a run goes next. In CrewAI’s hierarchical process, a manager agent decides at runtime (processes docs), which suits prototypes and is harder to audit.
3. Side effects survive crashes. If a worker dies after a tool returns, 10xGraph reads its tool ledger on resume and skips tools that already ran. See replay-safe tools.
4. State is typed and inspectable. AgentState carries a list of typed Message objects you can log and migrate.

A two-agent flow in 10xGraph

A triage agent classifies a support ticket, then a resolver drafts the reply. The order is fixed by edges:

from tenxgraph.core.graph import Agent, StateGraph
from tenxgraph.core.state import AgentState
from tenxgraph.storage.checkpointer import InMemoryCheckpointer
from tenxgraph.utils import END

triage = Agent(
    model="google/gemini-2.5-flash",
    system_prompt=[{
        "role": "system",
        "content": "Classify the ticket as billing, shipping or other. Reply with the label and one sentence of reasoning.",
    }],
)

resolver = Agent(
    model="google/gemini-2.5-flash",
    system_prompt=[{
        "role": "system",
        "content": "Use the classification in context to draft a short, polite reply to the customer.",
    }],
)

graph = StateGraph(AgentState)
graph.add_node("TRIAGE", triage)
graph.add_node("RESOLVE", resolver)
graph.set_entry_point("TRIAGE")
graph.add_edge("TRIAGE", "RESOLVE")
graph.add_edge("RESOLVE", END)

app = graph.compile(checkpointer=InMemoryCheckpointer())

To add tools such as lookup_order(order_id: str) and refund_order(order_id: str, amount: float), give the agent a ToolNode. See add a tool. For the CrewAI equivalent, see CrewAI’s Agents and Processes docs.

Hierarchical and delegated patterns

CrewAI’s hierarchical process uses a manager agent that plans, delegates and reviews (processes docs). In 10xGraph, delegation is a router node plus handoff tools:

from tenxgraph.core.graph import Agent, ToolNode
from tenxgraph.prebuilt.tools import create_handoff_tool

router_tools = ToolNode([
    create_handoff_tool("billing", "Send the ticket to the billing specialist"),
    create_handoff_tool("shipping", "Send the ticket to the shipping specialist"),
])

router = Agent(
    model="gemini-2.5-flash",
    provider="google",
    system_prompt=[{"role": "system", "content": "Route the ticket to the right specialist."}],
    tool_node="ROUTER_TOOLS",
)

Add ROUTER, BILLING and SHIPPING nodes to a StateGraph and let the router hand off control. The full pattern is in the handoff how-to. When routing is deterministic, use a plain Python function as the router and skip the extra model call.

Serving as an API

pip install 10xgraph 10xgraph-api
10xgraph init --yes --template production --auth jwt --rate-limit redis
10xgraph api --host 0.0.0.0 --port 8000
10xgraph build --docker-compose --k8s

10xgraph.json points at your compiled graph:

{"agent": "graph.support:app", "auth": "jwt", "authorization": "ownership"}

You get POST /v1/graph/invoke, POST /v1/graph/stream (SSE), a WebSocket endpoint and thread state endpoints, with JWT checks and owner-only threads in front of them.

Migrating from CrewAI to 10xGraph

1. Each CrewAI Agent(role=..., goal=..., backstory=...) becomes a 10xGraph Agent(model=..., system_prompt=[{"role": "system", "content": "<role, goal, backstory>"}]).
2. Each Task becomes either a Message pushed into the graph state or a node that prepares the prompt for the next agent.
3. Process.sequential becomes a chain of add_edge calls. Process.hierarchical becomes a router node with handoff tools.
4. crew.kickoff(inputs=...) becomes app.invoke({"messages": [...]}, config={"thread_id": "..."}).
5. CrewAI memory maps onto a checkpointer, plus a vector retrieval tool if you need semantic recall.

Where CrewAI is the better choice

• Role-based prototypes. If you want a researcher, writer and editor defined in a few declarative objects, CrewAI’s model matches that directly.
• You want a managed platform. AMP offers a visual studio, tracing and hosted deployment, with a free Basic plan for small usage (pricing).
• You prefer roles and tasks to nodes and edges. That is a legitimate preference about how to think about the problem.
• You need Python 3.10 or 3.11. 10xGraph requires 3.12 or newer.
• Community size. CrewAI has a larger user base and more examples.

Weak spots of 10xGraph

• Smaller community and fewer integrations.
• Pre-1.0: pin versions and read changelogs before upgrading.
• Renamed from Agentflow, so the 10xGraph name has little search history yet.
• No visual studio. The playground is a test chat, not a no-code builder.
• Code-first only, and Python 3.12 or newer.

Sources

Verified on 2026-10-06.

• crewai on PyPI
• CrewAI pricing
• CrewAI AMP introduction
• CrewAI Flows (persistence)
• CrewAI Agents (max_rpm, max_execution_time)
• CrewAI Processes

Next steps